You have been tasked to become an expert and master the current 
“AbstractCore” project on abstracting the complexity to talk with any
provider and model. It is in particular developed to support open source
LLMs. I have a question for you : should abstractcore live on the same
server as certain providers (eg hf, mlx, ollama, lmstudio, vllm) or should
it live independently and be a passthrough to distant servers ? what would
be the SOTA best practices here and why ? and who handle the scalability
and/or the routing of multiple queries ? or even say, a queue of queries ?
who orchestrate that ? I have designed abstractcore, but tonight, i am
asking myself this question and i would like to have your opinion. Before
answering, you must look at the code, its documentations, the llmx*txt too,
to provide an informed opinion. Do not guess. Ultrathink

∴ Thinking…

  The user is asking a fundamental architectural question about AbstractCore's 
  deployment topology and scalability patterns. They want to understand:

  1. Should AbstractCore live on the same server as providers (HF, MLX, Ollama, 
  LMStudio, vLLM) or independently?
  2. What are SOTA best practices?
  3. Who handles scalability and routing of multiple queries?
  4. Who orchestrates query queuing?

  This is a strategic architecture question that requires me to:
  1. First, explore the codebase to understand AbstractCore's current 
  architecture
  2. Look at documentation (llms.txt, llms-full.txt, README, architecture docs)
  3. Understand how providers work
  4. Research SOTA patterns for LLM infrastructure

  Let me start by exploring the codebase to understand the current architecture.

⏺ I'll thoroughly investigate the AbstractCore codebase, documentation, and
  architecture before providing an informed opinion on this important
  architectural question.

⏺ Explore(Explore AbstractCore architecture)
  ⎿  Prompt:                                                               
       Explore the AbstractCore codebase to understand its current
       architecture, deployment patterns, and how it interacts with
       providers. Focus on:

       1. How the server component works (abstractcore/server/)
       2. How providers are initialized and communicate
       (abstractcore/providers/)
       3. The factory pattern and provider abstraction
       4. Any existing scaling, routing, or queuing mechanisms
       5. Configuration and deployment patterns

       Be thorough - read key files to understand the architecture. Look
       for:
       - Server implementation (app.py)
       - Provider base class and key providers
       - Factory patterns
       - Any load balancing or routing logic
       - Configuration management

       Provide a comprehensive summary of the current architecture.
  ⎿  > Explore the AbstractCore codebase to understand its current architecture, 
     deployment patterns, and how it interacts with providers. Focus on:

     1. How the server component works (abstractcore/server/)
     2. How providers are initialized and communicate (abstractcore/providers/)
     3. The factory pattern and provider abstraction
     4. Any existing scaling, routing, or queuing mechanisms
     5. Configuration and deployment patterns

     Be thorough - read key files to understand the architecture. Look for:
     - Server implementation (app.py)
     - Provider base class and key providers
     - Factory patterns
     - Any load balancing or routing logic
     - Configuration management

     Provide a comprehensive summary of the current architecture. 
  ⎿  Search(pattern: "abstractcore/server/**/*.py")

  ⎿  Search(pattern: "abstractcore/providers/**/*.py")

  ⎿  Search(pattern: "abstractcore/core/**/*.py")

  ⎿  Search(pattern: "abstractcore/config/**/*.py")

  ⎿    ⎿  Found 7 files                                                    
          /Users/albou/projects/abstractcore/abstractcore/core/__init__.py
          /Users/albou/projects/abstractcore/abstractcore/core/enums.py
          /Users/albou/projects/abstractcore/abstractcore/core/factory.py
          /Users/albou/projects/abstractcore/abstractcore/core/interface.py
          /Users/albou/projects/abstractcore/abstractcore/core/types.py
          /Users/albou/projects/abstractcore/abstractcore/core/retry.py
          /Users/albou/projects/abstractcore/abstractcore/core/session.py
  ⎿    ⎿  Found 13 files                                                        
          /Users/albou/projects/abstractcore/abstractcore/providers/huggingface_
          provider.py
          /Users/albou/projects/abstractcore/abstractcore/providers/mlx_provider
          .py
          /Users/albou/projects/abstractcore/abstractcore/providers/model_capabi
          lities.py
          /Users/albou/projects/abstractcore/abstractcore/providers/base.py
          /Users/albou/projects/abstractcore/abstractcore/providers/anthropic_pr
          ovider.py
          /Users/albou/projects/abstractcore/abstractcore/providers/lmstudio_pro
          vider.py
          /Users/albou/projects/abstractcore/abstractcore/providers/ollama_provi
          der.py
          /Users/albou/projects/abstractcore/abstractcore/providers/openai_provi
          der.py
          /Users/albou/projects/abstractcore/abstractcore/providers/streaming.py
          /Users/albou/projects/abstractcore/abstractcore/providers/__init__.py
          /Users/albou/projects/abstractcore/abstractcore/providers/openai_compa
          tible_provider.py
          /Users/albou/projects/abstractcore/abstractcore/providers/registry.py
          /Users/albou/projects/abstractcore/abstractcore/providers/vllm_provide
          r.py
  ⎿    ⎿  Found 4 files                                                         
          /Users/albou/projects/abstractcore/abstractcore/config/main.py
          /Users/albou/projects/abstractcore/abstractcore/config/vision_config.p
          y
          /Users/albou/projects/abstractcore/abstractcore/config/__init__.py
          /Users/albou/projects/abstractcore/abstractcore/config/manager.py
  ⎿    ⎿  Found 2 files                                                     
          /Users/albou/projects/abstractcore/abstractcore/server/__init__.py
          /Users/albou/projects/abstractcore/abstractcore/server/app.py
  ⎿  Read(abstractcore/server/app.py)

  ⎿  Read(abstractcore/providers/base.py)

  ⎿  Read(abstractcore/core/factory.py)

  ⎿  Read(abstractcore/providers/registry.py)

  ⎿  Read 73 lines

  ⎿  Read 506 lines

  ⎿  Error: File content (25331 tokens) exceeds maximum allowed tokens 
     (25000). Please use offset and limit parameters to read specific 
     portions of the file, or use the GrepTool to search for specific 
     content.
  ⎿  Read 1559 lines

  ⎿  Read(abstractcore/server/app.py · lines 1-800)

  ⎿  Read(abstractcore/config/manager.py)

  ⎿  Read 498 lines

  ⎿  Read 800 lines

  ⎿  Read(abstractcore/server/app.py · lines 800-1599)

  ⎿  Read(abstractcore/providers/ollama_provider.py · lines 1-200)

  ⎿  Read 200 lines

  ⎿  Read 800 lines

  ⎿  Read(abstractcore/server/app.py · lines 1900-2499)

  ⎿  Read(abstractcore/core/retry.py)

  ⎿  Read(abstractcore/core/session.py · lines 1-200)

  ⎿  Read 512 lines

  ⎿  Read 373 lines

  ⎿  Read 200 lines

  ⎿  Search(pattern: "(load.?balanc|queue|pool|scal|worker|concurren)",
           path: "/Users/albou/projects/abstractcore", glob: "*.py",
           output_mode: "content", head_limit: 50)
  ⎿  Search(pattern: "docs/backlog/**/*.md")

  ⎿    ⎿  Found 50 lines                                                        
          abstractcore/processing/basic_judge.py:676:SCORING RUBRIC (1-5 scale):
          abstractcore/processing/basic_deepsearch.py:20:from concurrent.futures
           import ThreadPoolExecutor, as_completed
          abstractcore/processing/basic_deepsearch.py:647:        with
          ThreadPoolExecutor(max_workers=self.max_parallel_searches) as
          executor:
          abstractcore/processing/basic_deepsearch.py:2090:- importance: 1-10
          scale (10 = critical for answering original query)
          abstractcore/processing/basic_intent.py:499:- Likelihood of deception
          (0-1 scale)
          abstractcore/providers/base.py:1529:            # Run sync generate in
           thread pool (fallback)
          abstractcore/providers/base.py:1547:        # Get sync generator in
          thread pool
          abstractcore/utils/vlm_token_calculator.py:69:
          'scaling_factor': 0.5,
          abstractcore/utils/vlm_token_calculator.py:74:
          'scaling_factor': 0.7,
          abstractcore/utils/vlm_token_calculator.py:276:                scale =
           max_res / max(width, height)
          abstractcore/utils/vlm_token_calculator.py:277:                width =
           int(width * scale)
          abstractcore/utils/vlm_token_calculator.py:278:                height
          = int(height * scale)
          abstractcore/utils/vlm_token_calculator.py:280:                scale =
           min_res / min(width, height)
          abstractcore/utils/vlm_token_calculator.py:281:                width =
           int(width * scale)
          abstractcore/utils/vlm_token_calculator.py:282:                height
          = int(height * scale)
          abstractcore/utils/vlm_token_calculator.py:313:
          min_scale_factor = float('inf')
          abstractcore/utils/vlm_token_calculator.py:316:            scale_w =
          res_width / width
          abstractcore/utils/vlm_token_calculator.py:317:            scale_h =
          res_height / height
          abstractcore/utils/vlm_token_calculator.py:318:
          scale_factor = max(scale_w, scale_h)  # Scale to fit
          abstractcore/utils/vlm_token_calculator.py:320:            if
          scale_factor < min_scale_factor:
          abstractcore/utils/vlm_token_calculator.py:321:
          min_scale_factor = scale_factor
          abstractcore/utils/vlm_token_calculator.py:337:
          'scale_factor': min_scale_factor,
          abstractcore/utils/vlm_token_calculator.py:357:            scale =
          min(max_dim / width, max_dim / height)
          abstractcore/utils/vlm_token_calculator.py:358:            width =
          int(width * scale)
          abstractcore/utils/vlm_token_calculator.py:359:            height =
          int(height * scale)
          abstractcore/utils/vlm_token_calculator.py:364:            scale =
          short_side_target / min(width, height)
          abstractcore/utils/vlm_token_calculator.py:365:            width =
          int(width * scale)
          abstractcore/utils/vlm_token_calculator.py:366:            height =
          int(height * scale)
          abstractcore/utils/vlm_token_calculator.py:400:            scale =
          min(max_dim / width, max_dim / height)
          abstractcore/utils/vlm_token_calculator.py:401:            width =
          int(width * scale)
          abstractcore/utils/vlm_token_calculator.py:402:            height =
          int(height * scale)
          abstractcore/utils/vlm_token_calculator.py:476:            # Scale by
          efficiency factor
          abstractcore/utils/vlm_token_calculator.py:477:            tokens =
          int(total_patches * config['scaling_factor'])
          abstractcore/utils/vlm_token_calculator.py:485:
          'scaling_factor': config['scaling_factor'],
          abstractcore/utils/vlm_token_calculator.py:493:        scaling_factor
          = math.sqrt(actual_pixels / standard_pixels)
          abstractcore/utils/vlm_token_calculator.py:495:        tokens =
          int(base_tokens * scaling_factor * config['scaling_factor'])
          abstractcore/utils/vlm_token_calculator.py:501:
          'scaling_factor': config['scaling_factor'],
          abstractcore/utils/vlm_token_calculator.py:502:
          'pixel_scaling': scaling_factor
          abstractcore/compression/config.py:118:
          max_concurrent_compressions: int = 2
          abstractcore/compression/pil_text_renderer.py:723:        """Calculate
           the required image size for multi-column layout with DPI scaling."""
          abstractcore/compression/pil_text_renderer.py:725:            return
          (config.page_width, self._scale_dimension(100, config))  # Minimum
          size
          abstractcore/compression/pil_text_renderer.py:727:        # Calculate
          width (use page width - no scaling needed as it's already in points)
          abstractcore/compression/pil_text_renderer.py:730:        # Calculate
          height based on the tallest column with DPI scaling
          abstractcore/compression/pil_text_renderer.py:731:
          scaled_line_height = self._scale_dimension(config.line_height, config)
          abstractcore/compression/pil_text_renderer.py:732:
          scaled_line_spacing = int(scaled_line_height * 1.3)  # 30% more space
          between lines
          abstractcore/compression/pil_text_renderer.py:733:
          scaled_margin_y = self._scale_dimension(config.margin_y, config)
          abstractcore/compression/pil_text_renderer.py:734:
          scaled_padding = self._scale_dimension(20, config)
          abstractcore/compression/pil_text_renderer.py:737:        total_height
           = max_lines_in_column * scaled_line_spacing + 2 * scaled_margin_y +
          scaled_padding
          abstractcore/compression/pil_text_renderer.py:740:        min_height =
           self._scale_dimension(100, config)
          abstractcore/compression/pil_text_renderer.py:745:
               f"scaled_line_height={scaled_line_height},
          scaled_line_spacing={scaled_line_spacing}, height={height}")
  ⎿    ⎿  Found 26 files                                                        
          /Users/albou/projects/abstractcore/docs/backlog/abstract-processing.md
          /Users/albou/projects/abstractcore/docs/backlog/completed/media-next.m
          d
          /Users/albou/projects/abstractcore/docs/backlog/completed/media-roadma
          p.md
          /Users/albou/projects/abstractcore/docs/backlog/multi-provider-request
          s.md
          /Users/albou/projects/abstractcore/docs/backlog/completed/server.md
          /Users/albou/projects/abstractcore/docs/backlog/completed/vision-fallb
          ack.md
          /Users/albou/projects/abstractcore/docs/backlog/completed/feature_requ
          est_interaction_tracing.md
          /Users/albou/projects/abstractcore/docs/backlog/completed/001-document
          ation-consistency.md
          /Users/albou/projects/abstractcore/docs/backlog/003-connection-pool-op
          timization.md
          /Users/albou/projects/abstractcore/docs/backlog/006-architecture-decis
          ion-records.md
          /Users/albou/projects/abstractcore/docs/backlog/007-secrets-management
          .md
          /Users/albou/projects/abstractcore/docs/backlog/008-mcp-integration.md
          /Users/albou/projects/abstractcore/docs/backlog/async-mlx-hf.md
          /Users/albou/projects/abstractcore/docs/backlog/batching.md
          /Users/albou/projects/abstractcore/docs/backlog/completed/002-async-aw
          ait-support.md
          /Users/albou/projects/abstractcore/docs/backlog/completed/004-structur
          ed-logging.md
          /Users/albou/projects/abstractcore/docs/backlog/completed/005-error-me
          ssages.md
          /Users/albou/projects/abstractcore/docs/backlog/completed/009-base-url
          -openai-anthropic.md
          /Users/albou/projects/abstractcore/docs/backlog/completed/010-model-do
          wnload-api.md
          /Users/albou/projects/abstractcore/docs/backlog/completed/abstractcore
          -custom-base-url-feature-request.md
          /Users/albou/projects/abstractcore/docs/backlog/completed/structured_l
          ogging_audit_report.md
          /Users/albou/projects/abstractcore/docs/backlog/base-class-openai-comp
          atible.md
          /Users/albou/projects/abstractcore/docs/backlog/completed/011-provider
          -request-vllm.md
          /Users/albou/projects/abstractcore/docs/backlog/completed/011-vllm-pro
          vider-implementation.md
          /Users/albou/projects/abstractcore/docs/backlog/completed/openai-compa
          tible-server-integration-v2.6.5.md
          /Users/albou/projects/abstractcore/docs/backlog/completed/v2.6.5-dynam
          ic-base-url.md
  ⎿  Read(docs/backlog/003-connection-pool-optimization.md)

  ⎿  Read(docs/backlog/multi-provider-requests.md)

  ⎿  Read(docs/backlog/batching.md)

  ⎿  Read 577 lines

  ⎿  Read 473 lines

  ⎿  Read 600 lines

  ⎿  Response:
       Now I have a comprehensive understanding of the AbstractCore
       architecture. Let me provide the summary:

       ---
       AbstractCore Architecture Summary

       Overview

       AbstractCore is a lightweight, provider-agnostic LLM framework
       designed for building AI applications with minimal complexity. The
       architecture follows clean software engineering principles including:
       - Provider Abstraction Pattern: Single interface for multiple LLM
       providers
       - Factory Pattern: Centralized provider instantiation via
       create_llm()
       - Registry Pattern: Centralized provider discovery and metadata
       - Circuit Breaker Pattern: Fault tolerance with automatic recovery

       ---
       1. Server Component (abstractcore/server/app.py)

       Technology: FastAPI with uvicorn

       Key Features:
       - OpenAI-Compatible API: Endpoints mirroring OpenAI's API format
       - Multi-Provider Support: Routes requests to any registered provider
       - Media Processing: Supports images, PDFs, documents (up to 10MB per
       file, 32MB total)
       - Tool Call Syntax Conversion: Universal syntax rewriting (OpenAI,
       Codex, Qwen3, LLaMA3)
       - Streaming: SSE streaming for real-time responses
       - CORS Middleware: Full cross-origin support

       Main Endpoints:
       | Endpoint                  | Description
                        |
       |---------------------------|----------------------------------------
       -----------------|
       | GET /v1/models            | List models (filterable by provider,
       input/output type) |
       | POST /v1/chat/completions | Main chat completion endpoint
                        |
       | POST /v1/embeddings       | Text embeddings
                        |
       | POST /v1/responses        | OpenAI Responses API compatibility
                        |
       | GET /providers            | List all registered providers with
       metadata             |
       | GET /health               | Health check
                        |

       Request Processing Flow:
       1. Parse model string (e.g., openai/gpt-4 -> provider: openai, model:
        gpt-4)
       2. Process media content from messages (base64, URLs, local paths)
       3. Create LLM instance via create_llm(provider, model, **kwargs)
       4. Detect target format for tool call syntax
       5. Generate response (streaming or non-streaming)
       6. Convert response to OpenAI-compatible format
       7. Clean up temporary files

       ---
       2. Provider Architecture (abstractcore/providers/)

       Base Class: BaseProvider(AbstractCoreInterface, ABC)

       Registered Providers (8 total):
       | Provider          | Type    | Authentication | Key Features
                              |
       |-------------------|---------|----------------|---------------------
       -----------------------|
       | openai            | API     | Required       | Native tools,
       streaming, structured output |
       | anthropic         | API     | Required       | Native tools,
       streaming                    |
       | ollama            | Local   | None           | Native structured
       output, embeddings       |
       | lmstudio          | Local   | None           | OpenAI-compatible
       format                   |
       | mlx               | Local   | None           | Apple Silicon
       optimized                    |
       | huggingface       | Local   | Optional       | GGUF + Transformers
       support                |
       | vllm              | Local   | Optional       | GPU inference,
       guided decoding, Multi-LoRA |
       | openai-compatible | Generic | Optional       | For any
       OpenAI-compatible endpoint         |

       BaseProvider Features:
       - Telemetry Integration: Generation tracking, tool call tracking
       - Event Emission: Global event bus for observability
       - Retry Logic: Exponential backoff with jitter, circuit breaker
       - Interaction Tracing: Ring buffer for observability (opt-in)
       - Media Processing: Automatic handling of images, documents
       - Token Management: Unified parameter handling across providers
       - Async Support: Native async for network providers (6-7.5x
       performance for batch)

       Provider Communication Pattern:
       User Code -> create_llm() -> Registry -> Provider Instance -> HTTP 
       Client -> Provider API
                                                     |
                                                     v
                                               
       BaseProvider._generate_internal()
                                                     |
                                                     v
                                               Provider-specific 
       implementation

       ---
       3. Factory Pattern (abstractcore/core/factory.py)

       Entry Point: create_llm(provider: str, model: Optional[str] = None, 
       **kwargs)

       Flow:
       1. Auto-detect provider from model name (e.g., mlx-community/... ->
       MLX)
       2. Delegate to centralized registry: create_provider(provider, model,
        **kwargs)
       3. Registry handles:
         - Provider class lazy loading
         - Runtime configuration injection
         - Default model assignment
         - Error handling with helpful messages

       ---
       4. Provider Registry (abstractcore/providers/registry.py)

       Singleton Pattern: ProviderRegistry with global instance

       Key Functions:
       - get_provider_registry(): Get global instance
       - list_available_providers(): List all provider names
       - get_provider_info(name): Get metadata for provider
       - get_all_providers_with_models(): Full discovery with model lists
       - create_provider(name, model, **kwargs): Instance creation

       Provider Discovery:
       - Centralized source of truth for all providers
       - Lazy loading of provider classes
       - Model enumeration via list_available_models()
       - Health status via get_provider_status()

       ---
       5. Retry and Circuit Breaker (abstractcore/core/retry.py)

       RetryConfig (default values):
       - max_attempts: 3
       - initial_delay: 1.0s
       - max_delay: 60s
       - exponential_base: 2.0
       - use_jitter: True (full jitter per AWS recommendations)

       Circuit Breaker States:
       - CLOSED: Normal operation
       - OPEN: Failing, reject calls
       - HALF_OPEN: Testing recovery

       Error Classification:
       | Error Type   | Retry? | Notes              |
       |--------------|--------|--------------------|
       | Rate Limit   | Yes    | With backoff       |
       | Timeout      | Yes    | With backoff       |
       | Network      | Yes    | With backoff       |
       | Validation   | Yes    | Up to max_attempts |
       | API Error    | Once   | Single retry       |
       | Auth/Invalid | Never  | Immediate failure  |

       ---
       6. Configuration Management (abstractcore/config/manager.py)

       Configuration Layers:
       1. Persisted Config (~/.abstractcore/config/abstractcore.json)
         - Vision settings, embeddings, API keys, timeouts, cache paths
         - App-specific defaults (CLI, summarizer, extractor, judge)
       2. Runtime Config (_provider_config dict)
         - Programmatic provider configuration
         - Not persisted, lives for application lifetime

       Priority Chain (for base_url example):
       1. Constructor parameter (highest)
       2. Runtime configuration (configure_provider())
       3. Environment variable
       4. Default value (lowest)

       ---
       7. Existing Scaling/Routing/Queuing Mechanisms

       Current State: Minimal - No advanced scaling patterns yet

       What Exists:
       1. Circuit Breaker: Per-provider failure isolation
       2. Retry with Backoff: Exponential backoff with jitter
       3. Async Support: Native async for 4 network providers (6-7.5x
       concurrent performance)
       4. ThreadPoolExecutor: Used in BasicDeepSearch for parallel searches

       Backlog Items (Not Yet Implemented):
       | Feature                      | Status   | Effort  |
       |------------------------------|----------|---------|
       | Connection Pool Optimization | Proposed | 8-16h   |
       | Multi-Provider Requests      | Proposed | 4 weeks |
       | Batch Generation API         | Proposed | 10-15h  |

       ---
       8. Key Architectural Patterns

       1. Provider Abstraction:
       # All providers implement same interface
       class OllamaProvider(BaseProvider):
           def _generate_internal(self, prompt, messages, system_prompt, 
       tools, media, stream, **kwargs):
               # Provider-specific implementation

       2. Event System:
       from abstractcore.events import emit_global, EventType
       emit_global(EventType.GENERATION_COMPLETED, event_data,
       source="ProviderName")

       3. Unified Token Management:
       # BaseProvider handles token limits across providers
       self._prepare_generation_kwargs(**kwargs)
       self._get_provider_max_tokens_param(kwargs)

       4. Lazy Loading:
       - Provider classes loaded only when needed
       - Async HTTP clients created on first use
       - Reduces startup time and memory

       ---
       9. Deployment Patterns

       Server Mode:
       python -m abstractcore.server.app --host 0.0.0.0 --port 8000
       # Or with debug: --debug

       Library Mode:
       from abstractcore import create_llm
       llm = create_llm("ollama", model="qwen3:4b")
       response = llm.generate("Hello")

       Environment Variables:
       - OLLAMA_BASE_URL, OLLAMA_HOST: Ollama server URL
       - LMSTUDIO_BASE_URL: LMStudio server URL
       - OPENAI_BASE_URL, OPENAI_API_KEY: OpenAI configuration
       - ANTHROPIC_BASE_URL, ANTHROPIC_API_KEY: Anthropic configuration
       - VLLM_BASE_URL, VLLM_API_KEY: vLLM configuration

       ---
       10. What's NOT Present (Gaps)

       1. No Load Balancing: Single provider per request
       2. No Request Queuing: Direct pass-through to providers
       3. No Auto-Scaling: Fixed server capacity
       4. No Connection Pooling: Each provider instance creates own HTTP
       client
       5. No Rate Limiting: No server-side throttling
       6. No Multi-Provider Orchestration: No parallel/fallback strategies
       built-in
       7. No Batch Processing API: Sequential processing only

       ---
       Summary

       AbstractCore is a clean, well-architected LLM framework focused on:
       - Simplicity: Single interface for all providers
       - Reliability: Circuit breaker, retry logic, graceful degradation
       - Observability: Events, tracing, structured logging
       - Extensibility: Easy to add new providers via registry

       The architecture is production-ready for single-request scenarios but
        lacks advanced scaling patterns like load balancing, request
       queuing, multi-provider orchestration, and connection pooling. These
       are documented in the backlog but not yet implemented.










  ⎿  Done (20 tool uses · 157.3k tokens · 1m 32s)

⏺ Read(llms.txt)
  ⎿  Read 757 lines

⏺ Read(llms-full.txt)
  ⎿  Error: File content (33919 tokens) exceeds maximum allowed tokens (25000). 
     Please use offset and limit parameters to read specific portions of the 
     file, or use the GrepTool to search for specific content.

∴ Thinking…

  The llms-full.txt is too large. Let me read the relevant portions - especially
   any architecture or deployment related sections. Let me also look at the 
  server implementation and deployment patterns more closely.

⏺ Let me get more specific information about deployment patterns and
  architecture:

⏺ Search(pattern:
        "deploy|scale|queue|routing|load.?balanc|orchestrat|kubernetes|docker",
        path: "/Users/albou/projects/abstractcore", output_mode: "content")
  ⎿  Found 652 lines                                                       
     llms.txt:21:- **Environment Variable Support** (v2.6.1):
     `OLLAMA_BASE_URL`, `OLLAMA_HOST`, and `LMSTUDIO_BASE_URL` for remote
     servers, Docker deployments, and provider discovery
     llms.txt:22:- **Programmatic Provider Configuration** (v2.6.2):
     Runtime `configure_provider()` API for web UIs, Docker startup,
     testing, and multi-tenant deployments without environment variables
     llms.txt:25:- **Dynamic Base URL** (v2.6.5): POST `base_url` parameter
      for runtime endpoint routing without env vars
     llms.txt:35:- **[Structured Output](../../structured-output.md)**:
     Native vs prompted strategies, schema validation, production
     deployment
     llms.txt:460:# - Production deployment guidelines
     llms.txt:565:# Use cases: multi-tenant routing, A/B testing, dynamic
     load balancing
     CHANGELOG.md:75:  - **Use Cases**: Production GPU deployments,
     multi-GPU tensor parallelism, specialized AI agents with LoRA adapters
     CHANGELOG.md:86:  - **Use Cases**: llama.cpp local servers,
     text-generation-webui deployments, OpenAI-compatible proxies, custom
     endpoints
     CHANGELOG.md:96:  - OOM troubleshooting based on real deployment
     experience
     CHANGELOG.md:102:### Deployment Experience
     CHANGELOG.md:103:- Validated on **4x NVIDIA L4 GPUs** (23GB VRAM each,
      Scaleway Paris)
     CHANGELOG.md:158:    - Docker startup scripts: Read from custom env
     vars and configure programmatically
     CHANGELOG.md:160:    - Multi-tenant deployments: Configure different
     base URLs per tenant
     CHANGELOG.md:170:- **llms-full.txt**: Added comprehensive section with
      Web UI, Docker, testing, and multi-tenant examples
     CHANGELOG.md:189:    - Docker/Kubernetes deployments with custom
     networking
     CHANGELOG.md:190:    - Non-standard ports for multi-instance
     deployments (e.g., `:11435`, `:1235`)
     CHANGELOG.md:196:  - **Feature Request**: Submitted by Digital Article
      team for computational notebook deployment
     CHANGELOG.md:221:  - **Use Cases**: Docker deployments, automated
     setup, web UIs with SSE streaming, batch downloads
     CHANGELOG.md:400:  - **Expanded Mappings**: Added support for
     programming languages (.py, .js, .r, .R, .rs, .go, .jl, etc.),
     notebooks (.ipynb, .rmd), config files (.yaml, .toml, .ini), web files
      (.css, .vue, .svelte), build scripts (.sh, .dockerfile), and more
     CHANGELOG.md:727:    - `abstractcore.media.utils` (image_scaler
     utilities)
     CHANGELOG.md:731:  - **Credit**: Bug discovered and reported during
     production deployment testing
     CHANGELOG.md:1358:- **Model Discovery**: Heuristic-based filtering and
      provider-specific routing
     CHANGELOG.md:1448:#### GitHub Pages Deployment
     CHANGELOG.md:1452:- Structured deployment ready for custom domain
     configuration
     CHANGELOG.md:1481:- **Technical**: Modified `_generate_internal()`
     method to use `use_chat_format = tools is not None or messages is not
     None or True` for proper endpoint routing
     CHANGELOG.md:1735:  - **Flexible output formats**: JSON, plain text,
     YAML with structured scoring (1-5 scale)
     abstractcore/tools/README.md:68:├── handler.py               #
     Orchestration layer (UniversalToolHandler)
     abstractcore/tools/README.md:417:### 6. handler.py - Execution
     Orchestration
     abstractcore/embeddings/README.md:12:- Batch processing optimization
     for large-scale operations
     abstractcore/embeddings/README.md:883:**Increase cache size for
     large-scale applications:**
     abstractcore/embeddings/README.md:928:### Matrix Operations for Scale
     abstractcore/embeddings/README.md:930:Use matrix operations for
     large-scale similarity computations:
     abstractcore/embeddings/README.md:966:# - Large-scale indexing:
     truncated dimensions
     abstractcore/embeddings/README.md:973:- Best for offline/local
     deployments
     abstractcore/core/README.md:335:Central retry orchestration with smart
      error classification:
     abstractcore/core/README.md:853:-
     [`compression/`](../../../abstractcore/compression/README.md) - Compression orchestration
     abstractcore/config/README.md:887:4. **DO** use offline-first
     configuration for local deployments:
     abstractcore/processing/basic_judge.py:676:SCORING RUBRIC (1-5 scale):
     abstractcore/processing/README.md:27:| **BasicJudge** | Text | Scored
     assessment | 9 criteria, 1-5 scale, chain-of-thought |
     abstractcore/processing/basic_deepsearch.py:2090:- importance: 1-10
     scale (10 = critical for answering original query)
     abstractcore/processing/basic_intent.py:499:- Likelihood of deception
     (0-1 scale)
     abstractcore/providers/openai_compatible_provider.py:11:- Custom
     deployments and proxies
     abstractcore/providers/openai_compatible_provider.py:44:    - Custom
     deployments and proxies
     abstractcore/providers/README.md:13:| Privacy/offline | HuggingFace,
     MLX | Complete data control, air-gapped deployments |
     abstractcore/__init__.py:57:    from .compression import GlyphConfig,
     CompressionOrchestrator
     abstractcore/__init__.py:83:    __all__.extend(['GlyphConfig',
     'CompressionOrchestrator'])
     abstractcore/server/README.md:52:- **How do I deploy to production?**
     → See [Deployment](../../server.md#deployment)
     abstractcore/server/README.md:818:## Deployment
     abstractcore/server/README.md:850:### Production Deployment
     abstractcore/server/README.md:865:**Docker Deployment**:
     abstractcore/server/README.md:866:```dockerfile
     abstractcore/server/README.md:885:docker build -t abstractcore-server
     .
     abstractcore/server/README.md:886:docker run -p 8000:8000 \
     abstractcore/server/app.py:1948:    Useful when you want explicit
     provider routing or when the model name doesn't include the provider
     prefix.
     abstractcore/utils/version.py:8:This approach ensures reliable version
      access in all deployment scenarios,
     abstractcore/utils/vlm_token_calculator.py:276:                scale =
      max_res / max(width, height)
     abstractcore/utils/vlm_token_calculator.py:277:                width =
      int(width * scale)
     abstractcore/utils/vlm_token_calculator.py:278:                height
     = int(height * scale)
     abstractcore/utils/vlm_token_calculator.py:280:                scale =
      min_res / min(width, height)
     abstractcore/utils/vlm_token_calculator.py:281:                width =
      int(width * scale)
     abstractcore/utils/vlm_token_calculator.py:282:                height
     = int(height * scale)
     abstractcore/utils/vlm_token_calculator.py:313:
     min_scale_factor = float('inf')
     abstractcore/utils/vlm_token_calculator.py:316:            scale_w =
     res_width / width
     abstractcore/utils/vlm_token_calculator.py:317:            scale_h =
     res_height / height
     abstractcore/utils/vlm_token_calculator.py:318:
     scale_factor = max(scale_w, scale_h)  # Scale to fit
     abstractcore/utils/vlm_token_calculator.py:320:            if
     scale_factor < min_scale_factor:
     abstractcore/utils/vlm_token_calculator.py:321:
     min_scale_factor = scale_factor
     abstractcore/utils/vlm_token_calculator.py:337:
     'scale_factor': min_scale_factor,
     abstractcore/utils/vlm_token_calculator.py:357:            scale =
     min(max_dim / width, max_dim / height)
     abstractcore/utils/vlm_token_calculator.py:358:            width =
     int(width * scale)
     abstractcore/utils/vlm_token_calculator.py:359:            height =
     int(height * scale)
     abstractcore/utils/vlm_token_calculator.py:364:            scale =
     short_side_target / min(width, height)
     abstractcore/utils/vlm_token_calculator.py:365:            width =
     int(width * scale)
     abstractcore/utils/vlm_token_calculator.py:366:            height =
     int(height * scale)
     abstractcore/utils/vlm_token_calculator.py:400:            scale =
     min(max_dim / width, max_dim / height)
     abstractcore/utils/vlm_token_calculator.py:401:            width =
     int(width * scale)
     abstractcore/utils/vlm_token_calculator.py:402:            height =
     int(height * scale)
     abstractcore/utils/vlm_token_calculator.py:476:            # Scale by
     efficiency factor
     abstractcore/utils/knowledge-graph-explorer.html:516:
     .scaleExtent([0.1, 3])
     abstractcore/utils/knowledge-graph-explorer.html:1170:
     svg.transition().call(zoom.scaleBy, 1.3);
     abstractcore/utils/knowledge-graph-explorer.html:1176:
     svg.transition().call(zoom.scaleBy, 0.7);
     abstractcore/structured/README.md:43:| `handler.py` | Main structured
     output orchestration | `StructuredOutputHandler` |
     abstractcore/structured/README.md:116:The core orchestrator that
     manages structured output generation using native or prompted
     strategies.
     abstractcore/structured/README.md:451:    3. Deploy to staging (todo,
     priority 3)
     abstractcore/compression/__init__.py:12:from .orchestrator import
     CompressionOrchestrator
     abstractcore/compression/__init__.py:20:    'CompressionOrchestrator',

     abstractcore/compression/README.md:154:│   ├── orchestrator.py     #
     Decision-making API
     abstractcore/compression/README.md:168:- abstractcore.orchestrator:
     Automatic compression decisions
     abstractcore/compression/README.md:188:└── orchestrator.py          #
     High-level decision API
     abstractcore/compression/README.md:211:**Layer 6: Analytics &
     Orchestration**
     abstractcore/compression/README.md:214:- `orchestrator.py`:
     Intelligent compression decision-making
     abstractcore/compression/README.md:484:### 10. Orchestrator
     (`orchestrator.py`)
     abstractcore/compression/README.md:488:**Key Class**:
     `CompressionOrchestrator`
     abstractcore/compression/README.md:503:orchestrator =
     CompressionOrchestrator(config)
     abstractcore/compression/README.md:506:should_compress =
     orchestrator.should_compress(
     abstractcore/compression/README.md:514:recommendation =
     orchestrator.get_compression_recommendation(
     abstractcore/compression/README.md:522:media_contents =
     orchestrator.compress_content(
     abstractcore/compression/README.md:548:│ 1. DECISION PHASE
     (Orchestrator)                             │
     abstractcore/compression/README.md:626:# Step 1: Orchestrator decides
     to compress (> 10k threshold)
     abstractcore/compression/README.md:627:orchestrator =
     CompressionOrchestrator()
     abstractcore/compression/README.md:783:### Orchestrator (Recommended)
     abstractcore/compression/README.md:786:from abstractcore.compression
     import CompressionOrchestrator
     abstractcore/compression/README.md:789:orchestrator =
     CompressionOrchestrator()
     abstractcore/compression/README.md:792:recommendation =
     orchestrator.get_compression_recommendation(
     abstractcore/compression/README.md:804:    media_contents =
     orchestrator.compress_content(
     abstractcore/compression/README.md:1100:**5. Use Orchestrator for
     Automatic Decisions**:
     abstractcore/compression/README.md:1102:orchestrator =
     CompressionOrchestrator()
     abstractcore/compression/README.md:1103:media =
     orchestrator.compress_content(text, provider, model)
     abstractcore/compression/README.md:1255:def
     test_orchestrator_decision():
     abstractcore/compression/README.md:1256:    orchestrator =
     CompressionOrchestrator()
     abstractcore/compression/README.md:1258:    assert not
     orchestrator.should_compress("Short text", "openai", "gpt-4o")
     abstractcore/compression/README.md:1260:    assert
     orchestrator.should_compress(large_doc, "openai", "gpt-4o")
     abstractcore/compression/README.md:1331:from abstractcore.compression
     import CompressionOrchestrator
     abstractcore/compression/README.md:1361:# Orchestrator: Intelligent
     decision-making
     abstractcore/compression/README.md:1362:CompressionOrchestrator()
     abstractcore/compression/README.md:1405:- **Intelligent
     decision-making** via Orchestrator
     abstractcore/compression/README.md:1448:- [`core/`](../../../abstractcore/core/README.md)
      - Orchestrator in factory pattern
     abstractcore/compression/orchestrator.py:2:Compression orchestrator
     for intelligent decision-making.
     abstractcore/compression/orchestrator.py:15:class
     CompressionOrchestrator:
     abstractcore/compression/orchestrator.py:20:        Initialize
     compression orchestrator.
     abstractcore/compression/orchestrator.py:29:
     self.logger.debug("CompressionOrchestrator initialized")
     abstractcore/compression/pil_text_renderer.py:725:            return
     (config.page_width, self._scale_dimension(100, config))  # Minimum
     size
     abstractcore/compression/pil_text_renderer.py:731:
     scaled_line_height = self._scale_dimension(config.line_height, config)
     abstractcore/compression/pil_text_renderer.py:732:
     scaled_line_spacing = int(scaled_line_height * 1.3)  # 30% more space
     between lines
     abstractcore/compression/pil_text_renderer.py:733:
     scaled_margin_y = self._scale_dimension(config.margin_y, config)
     abstractcore/compression/pil_text_renderer.py:734:
     scaled_padding = self._scale_dimension(20, config)
     abstractcore/compression/pil_text_renderer.py:737:        total_height
      = max_lines_in_column * scaled_line_spacing + 2 * scaled_margin_y +
     scaled_padding
     abstractcore/compression/pil_text_renderer.py:740:        min_height =
      self._scale_dimension(100, config)
     abstractcore/compression/pil_text_renderer.py:745:
          f"scaled_line_height={scaled_line_height},
     scaled_line_spacing={scaled_line_spacing}, height={height}")
     abstractcore/assets/model_capabilities.json:389:      "notes": "Vision
      model, may be limited by deployment platform",
     abstractcore/assets/semantic_models.md:642:   - Consider hash-based
     identifiers for large-scale systems
     abstractcore/apps/deepsearch.py:182:    <meta name="viewport"
     content="width=device-width, initial-scale=1.0">
     abstractcore/media/auto_handler.py:20:    from
     ..compression.orchestrator import CompressionOrchestrator
     abstractcore/media/auto_handler.py:24:    CompressionOrchestrator =
     None
     abstractcore/media/auto_handler.py:65:
     self._compression_orchestrator = None
     abstractcore/media/auto_handler.py:142:    def
     _get_compression_orchestrator(self) -> 'CompressionOrchestrator':
     abstractcore/media/auto_handler.py:143:        """Get or create
     CompressionOrchestrator instance."""
     abstractcore/media/auto_handler.py:144:        if
     self._compression_orchestrator is None and GLYPH_AVAILABLE:
     abstractcore/media/auto_handler.py:146:
     self._compression_orchestrator = CompressionOrchestrator(config)
     abstractcore/media/auto_handler.py:147:        return
     self._compression_orchestrator
     abstractcore/media/auto_handler.py:332:            orchestrator =
     self._get_compression_orchestrator()
     abstractcore/media/auto_handler.py:333:            if orchestrator:
     abstractcore/media/auto_handler.py:334:                return
     orchestrator.should_compress(file_path, provider, model,
     glyph_compression)
     abstractcore/media/auto_handler.py:459:        orchestrator =
     self._get_compression_orchestrator()
     abstractcore/media/auto_handler.py:460:        if not orchestrator:
     abstractcore/media/auto_handler.py:461:            raise
     Exception("Compression orchestrator not available")
     abstractcore/media/auto_handler.py:476:        compressed_content =
     orchestrator.compress_content(text_content, provider, model,
     glyph_compression)
     abstractcore/media/utils/image_scaler.py:24:    FIT = "fit"          #
      Scale to fit within target size, maintaining aspect ratio
     abstractcore/media/utils/image_scaler.py:25:    FILL = "fill"        #
      Scale to fill target size, may crop, maintaining aspect ratio
     abstractcore/media/utils/image_scaler.py:27:    PAD = "pad"          #
      Scale to fit and pad with background to exact target size
     abstractcore/media/utils/image_scaler.py:28:    CROP_CENTER =
     "crop_center"  # Scale to fill and crop from center
     abstractcore/media/utils/image_scaler.py:31:class
     ModelOptimizedScaler:
     abstractcore/media/utils/image_scaler.py:33:    Intelligent image
     scaler that optimizes images for specific vision models.
     abstractcore/media/utils/image_scaler.py:88:        # Scale down if
     too large
     abstractcore/media/utils/image_scaler.py:112:        # Scale to fit
     within max size while maintaining aspect ratio
     abstractcore/media/utils/image_scaler.py:113:        scale =
     min(max_width / width, max_height / height)
     abstractcore/media/utils/image_scaler.py:115:        if scale < 1:  #
     Only scale down, never up
     abstractcore/media/utils/image_scaler.py:116:            new_width =
     int(width * scale)
     abstractcore/media/utils/image_scaler.py:117:            new_height =
     int(height * scale)
     abstractcore/media/utils/image_scaler.py:127:    def scale_image(self,
      image: Image.Image, target_size: Tuple[int, int],
     abstractcore/media/utils/image_scaler.py:131:        Scale image to
     target size using specified mode.
     abstractcore/media/utils/image_scaler.py:134:            image: PIL
     Image to scale
     abstractcore/media/utils/image_scaler.py:140:            Scaled PIL
     Image
     abstractcore/media/utils/image_scaler.py:145:            # Scale to
     fit within target size, maintaining aspect ratio
     abstractcore/media/utils/image_scaler.py:150:            # Scale to
     fill target size, may crop
     abstractcore/media/utils/image_scaler.py:158:            # Scale to
     fit and pad to exact size
     abstractcore/media/utils/image_scaler.py:164:            # Paste
     scaled image centered
     abstractcore/media/utils/image_scaler.py:172:            # Scale to
     fill and crop from center
     abstractcore/media/utils/image_scaler.py:178:    def
     scale_for_model(self, image: Image.Image, model_name: str,
     abstractcore/media/utils/image_scaler.py:182:        Scale image
     optimally for a specific model.
     abstractcore/media/utils/image_scaler.py:185:            image: PIL
     Image to scale
     abstractcore/media/utils/image_scaler.py:187:            scaling_mode:
      How to scale the image
     abstractcore/media/utils/image_scaler.py:191:            Optimally
     scaled PIL Image for the model
     abstractcore/media/utils/image_scaler.py:202:        return
     self.scale_image(image, target_size, scaling_mode)
     abstractcore/media/utils/image_scaler.py:265:_scaler_instance = None
     abstractcore/media/utils/image_scaler.py:267:def get_scaler() ->
     ModelOptimizedScaler:
     abstractcore/media/utils/image_scaler.py:268:    """Get shared scaler
     instance."""
     abstractcore/media/utils/image_scaler.py:269:    global
     _scaler_instance
     abstractcore/media/utils/image_scaler.py:270:    if _scaler_instance
     is None:
     abstractcore/media/utils/image_scaler.py:271:        _scaler_instance
     = ModelOptimizedScaler()
     abstractcore/media/utils/image_scaler.py:272:    return
     _scaler_instance
     abstractcore/media/utils/image_scaler.py:274:def
     scale_image_for_model(image: Union[Image.Image, str, Path],
     abstractcore/media/utils/image_scaler.py:278:    Convenience function
     to scale an image for a specific model.
     abstractcore/media/utils/image_scaler.py:283:        scaling_mode: How
      to scale the image
     abstractcore/media/utils/image_scaler.py:286:        Optimally scaled
     PIL Image
     abstractcore/media/utils/image_scaler.py:291:    scaler = get_scaler()
     abstractcore/media/utils/image_scaler.py:292:    return
     scaler.scale_for_model(image, model_name, scaling_mode)
     abstractcore/media/utils/image_scaler.py:305:    scaler = get_scaler()
     abstractcore/media/utils/image_scaler.py:306:    return
     scaler.get_optimal_resolution(model_name, original_size)
     abstractcore/media/utils/__init__.py:8:from .image_scaler import (
     abstractcore/media/utils/__init__.py:9:    ModelOptimizedScaler,
     abstractcore/media/utils/__init__.py:11:    get_scaler,
     abstractcore/media/utils/__init__.py:12:    scale_image_for_model,
     abstractcore/media/utils/__init__.py:17:    'ModelOptimizedScaler',
     abstractcore/media/utils/__init__.py:19:    'get_scaler',
     abstractcore/media/utils/__init__.py:20:    'scale_image_for_model',
     abstractcore/media/types.py:135:    This is used for intelligent
     routing and validation of media content
     abstractcore/media/types.py:249:    'dockerfile': MediaType.TEXT,
     'containerfile': MediaType.TEXT,
     abstractcore/media/README.md:20:| Any file | AutoMediaHandler |
     Automatic routing | All optional |
     abstractcore/media/README.md:38:- **Build/Scripts**: sh, bash, zsh,
     dockerfile, cmake, gradle, makefile
     abstractcore/media/README.md:137:    └── image_scaler.py          #
     Model-optimized image scaling
     abstractcore/media/README.md:593:- Intelligent routing (structured vs
     text-embedded)
     abstractcore/media/README.md:628:#### 15. `image_scaler.py` -
     Model-Optimized Scaling
     abstractcore/media/README.md:633:- **FIT**: Scale to fit within
     bounds, maintain aspect ratio
     abstractcore/media/README.md:634:- **FILL**: Scale to fill bounds, may
      crop
     abstractcore/media/README.md:636:- **PAD**: Scale to fit, pad with
     background
     abstractcore/media/README.md:637:- **CROP_CENTER**: Scale to fill,
     crop from center
     abstractcore/media/README.md:641:from
     abstractcore.media.utils.image_scaler import scale_image_for_model,
     ScalingMode
     abstractcore/media/README.md:644:scaled = scale_image_for_model(
     abstractcore/media/README.md:651:scaled = scale_image_for_model(
     abstractcore/media/README.md:973:        orchestrator =
     self._get_compression_orchestrator()
     abstractcore/media/README.md:974:        compressed =
     orchestrator.compress_content(text, provider, model)
     abstractcore/media/README.md:1351:from
     abstractcore.media.utils.image_scaler import (
     abstractcore/media/README.md:1352:    scale_image_for_model,
     abstractcore/media/processors/image_processor.py:27:from
     ..utils.image_scaler import get_scaler, ScalingMode
     abstractcore/media/processors/image_processor.py:525:                #
      Get model-optimized scaler
     abstractcore/media/processors/image_processor.py:526:
     scaler = get_scaler()
     abstractcore/media/processors/image_processor.py:528:                #
      Scale image for the specific model
     abstractcore/media/processors/image_processor.py:529:
     img = scaler.scale_for_model(img, model_name, scaling_mode)
     abstractcore/media/processors/image_processor.py:550:
     optimal_size = scaler.get_optimal_resolution(model_name, img.size)
     abstractcore/media/processors/image_processor.py:597:        scaler =
     get_scaler()
     abstractcore/media/processors/image_processor.py:598:        return
     scaler.get_optimal_resolution(model_name, original_size)
     abstractcore/media/processors/image_processor.py:612:
     scaler = get_scaler()
     abstractcore/media/processors/image_processor.py:613:
     scaler._get_model_capabilities(model_name)
     abstractcore/media/handlers/local_handler.py:226:        Create a
     multimodal message for local provider with intelligent vision routing.
     tests/embeddings/test_embeddings_no_mock.py:149:
     "Docker is a containerization platform that packages applications and
     their dependencies.",
     tests/embeddings/test_embeddings_no_mock.py:150:
     "Kubernetes is an orchestration system for automating deployment and
     management of containerized applications.",
     tests/embeddings/test_embeddings_no_mock.py:159:
     ("containerization", ["Docker", "Kubernetes"]),
     tests/embeddings/test_embeddings_no_mock.py:215:
     "Docker was first released in 2013 and revolutionized application
     deployment through containerization.",
     tests/embeddings/test_embeddings_no_mock.py:224:                ("What
      is Docker used for?", "containerization", 0.3),
     tests/embeddings/test_real_models_comprehensive.py:417:    def
     test_performance_at_scale(self):
     tests/embeddings/test_real_models_comprehensive.py:420:
     print("\n⚡ Performance at Scale Test")
     tests/embeddings/test_real_models_comprehensive.py:431:
      f"software development, and data science for testing performance at
     scale."
     tests/embeddings/test_embeddings_llm_integration.py:88:
      "Docker enables developers to package applications and dependencies
     into lightweight, portable containers.",
     tests/embeddings/test_embeddings_llm_integration.py:90:
      "Kubernetes automates deployment, scaling, and management of
     containerized applications across clusters."
     tests/embeddings/test_embeddings_llm_integration.py:138:
       "What does Docker enable?",
     tests/providers/test_openai_compatible_provider.py:308:        print("
       LocalAI: docker run -p 8080:8080 localai/localai")
     tests/server/media-data.py:252:1. Deploy to production environment
     tests/server/run_media_tests.py:339:            report.append("   1.
     Deploy to production environment")
     tests/media_examples/data.csv:3:High-Performance Computing
     (HPC),Large-Scale Data Analytics & Processing,Building & Leading
     Technical Teams
     tests/media_examples/data.csv:4:"DevOps & CI/CD, Docker, Kubernetes
     ",AI/ML Workflows & In-Silico Modeling,Stakeholder & Cross-Functional
     Partnership
     tests/media_examples/data.tsv:3:High-Performance Computing
     (HPC)	Large-Scale Data Analytics & Processing	Building & Leading
     Technical Teams
     tests/media_examples/data.tsv:4:DevOps & CI/CD, Docker, Kubernetes
     	AI/ML Workflows & In-Silico Modeling	Stakeholder & Cross-Functional
     Partnership
     tests/media_examples/christmas_carol_ebook_chapter_1.txt:171:lived
     hard by, and was brewing on a large scale.
     tests/media_examples/christmas_carol_ebook.txt:171:lived hard by, and
     was brewing on a large scale.
     tests/media_examples/christmas_carol_ebook.txt:1998:glimpses! It was
     not alone that the scales descending on the
     tests/media_examples/christmas_carol_ebook.txt:3004:files, scales,
     weights, and refuse iron of all kinds. Secrets
     tests/token_terminology/SUMMARY.md:123:**Action Required**: None -
     ready to deploy
     tests/token_terminology/README.md:211:**Recommendation**: Ready for
     immediate deployment
     tests/token_terminology/README.md:255:### For Deployment
     tests/token_terminology/README.md:258:3. Deploy with confidence - zero
      breaking changes
     tests/token_terminology/TEST_REPORT.md:319:**Confidence Level**:
     **100%** - Ready for immediate production deployment
     tests/glyph/20251031_170442-analysis.md:121:1. **Deploy Glyph
     compression** for production use with LMStudio and similar local
     models
     tests/glyph/20251031_170442-no-glyph.md:177:  - Train ML models before
      clinical deployment.
     tests/glyph/20251031_170442-no-glyph.md:183:- Roche and Institut Curie
      (authors’ institutions) can deploy MIIC-SDG internally to share
     real-world data with external partners or regulators.
     tests/glyph/20251031_170442-glyph.md:76:- **Impact**: QPS enables
     direct ranking of SDG methods on a unified scale—previously impossible
      in the literature.
     tests/glyph/20251031_170442-glyph.md:191:### (d) **Implementation &
     Deployment**
     tests/glyph/20251031_170442-glyph.md:289:- **Proves**: MIIC-SDG is the
      most robust method across sample sizes, especially small datasets
     (<1000). Deep learning methods (CTGAN, MedWGAN) excel at large scales
     but fail on small ones. PrivBayes performs worst overall.
     tests/glyph/20251031_170442-glyph.md:315:| **Sample size trend** |
     MIIC-SDG excels at small n (<500); Deep learning methods (CTGAN,
     MedWGAN) improve with scale (>1000). |
     tests/glyph/glyph_comparison_test.py:23:from abstractcore.compression
     import GlyphConfig, CompressionOrchestrator
     tests/glyph/benchmark_summary.md:96:1. **Deploy Glyph compression**
     for production document processing
     tests/glyph/benchmark_summary.md:99:4. **Scale testing** to larger
     document sets
     tests/vision_compression/christmas_carol/report.md:55:        1.
     **The "Postage Stamp Problem":** Sending a 4K image causes it to be
     downscaled, blurring all text into unreadability.
     tests/vision_compression/christmas_carol/report.md:70:2.  **The
     "Postage Stamp Problem" is Your Biggest Risk:** Sending images with
     resolutions much higher than the model's native processing size (e.g.,
      `1024x1024` for me, or Qwen3-VL) is the worst-case scenario. The
     image will be downscaled, and all text clarity will be permanently
     lost.
     tests/vision_compression/christmas_carol/report.md:86:* **DO NOT**
     send 4K or 8K images and "hope for the best." They will be downscaled
     and become unreadable.
     tests/vision_compression/christmas_carol/discussion.md:136:    "To see
      the dingy cloud come drooping down, obscuring everything, one might
     have thought that Nature lived hard by, and was brewing on a large
     scale.",
     tests/vision_compression/christmas_carol/christmas_carol_ebook_chapter
     _1.txt:171:lived hard by, and was brewing on a large scale.
     tests/reports/2025-10-11_streaming_tag_rewriting_fix_report.md:295:**R
     EADY FOR DEPLOYMENT** with recommendation to test with real LLM before
      full production rollout.
     tests/reports/2025-10-11_06-54-13_tool_execution_separation_validation
     .md:412:### Production Status: READY FOR DEPLOYMENT ✅
     tests/reports/2025-10-11_06-54-13_tool_execution_separation_validation
     .md:414:This architectural fix can be deployed to production with
     confidence. The separation of tool tag rewriting from tool execution
     is working correctly across all scenarios, and the user's exact use
     case has been validated.
     tests/reports/2025-10-11_streaming_enhancements_verification.md:357:1.
      **Deploy to Production**: All validation complete ✅
     tests/reports/2025-10-11_streaming_enhancements_verification.md:408:**
     Status**: READY FOR IMMEDIATE DEPLOYMENT ✅
     tests/reports/2025-10-11_streaming_tag_rewriting_comprehensive_fix.md:
     471:- ✅ **Production ready** for immediate deployment
     tests/reports/2025-10-11_streaming_tag_rewriting_comprehensive_fix.md:
     490:**Production Status**: ✅ Ready for Deployment
     tests/reports/2025-10-11_streaming_tool_execution_fix_FINAL.md:30:**VE
     RDICT**: ✅ **READY FOR PRODUCTION DEPLOYMENT**
     tests/reports/2025-10-11_streaming_tool_execution_fix_FINAL.md:403:1.
     ✅ **Deploy to production** - All validations passing
     tests/reports/2025-10-11_streaming_tool_execution_fix_FINAL.md:447:**R
     ecommendation**: **APPROVE FOR PRODUCTION DEPLOYMENT**
     tests/reports/2025-10-11_04-34-03_unified_streaming_tests.md:277:The
     unified streaming solution is **ready for production deployment** and
     provides a significant improvement over the previous dual-mode
     approach.
     tests/vision_examples/mystery4_wh.json:28:    "visual_elements":
     ["motion blur", "water spray", "dynamic action", "scale"],
     docs/research/vision-first/Image Tokenization for Visual
     Models.md:11:[Omitted long matching line]
     docs/research/vision-first/Image Tokenization for Visual
     Models.md:13:[Omitted long matching line]
     docs/research/vision-first/Image Tokenization for Visual
     Models.md:55:1. **Initial Resizing:** The image is first scaled down
     to fit within a 2048x2048 pixel square, preserving its original aspect
      ratio. Following this, it undergoes a second resizing step where its
     shortest side is set to 768 pixels.
     docs/research/vision-first/Image Tokenization for Visual
     Models.md:72:2. **Large Images:** If **either dimension of an image
     exceeds 384 pixels**, a different logic is triggered. The image is
     automatically cropped and scaled as needed into a series of 768x768
     pixel tiles. Each of these tiles then costs **258 tokens**. For
     example, an image that requires four tiles to be fully represented
     would cost $4 \\times 258 \= 1032$ tokens.
     docs/research/vision-first/Image Tokenization for Visual
     Models.md:84:This formula is applicable as long as the image remains
     within the model's optimal performance envelope. An image will be
     automatically scaled down, preserving its aspect ratio, if it meets
     either of the following conditions:
     docs/research/vision-first/Image Tokenization for Visual
     Models.md:89:Anthropic's documentation provides explicit guidance that
      submitting oversized images increases the latency
     (time-to-first-token) without providing any additional performance
     benefit, as the model will simply work with the downscaled version.13
     It also cautions that very small images (under 200 pixels on any side)
      may lead to degraded performance. This transparency allows developers
      to pre-process images to an optimal size, balancing detail with cost
     and latency.
     docs/research/vision-first/Image Tokenization for Visual
     Models.md:182:* **Google's** hybrid model provides a predictable,
     two-tiered cost structure that efficiently handles images of vastly
     different scales.
     docs/research/vision-first/Image Tokenization for Visual
     Models.md:191:2. An Image is Worth 16x16 Words: Transformers for Image
      ..., accessed October 31, 2025,
     [https://www.researchgate.net/publication/344828174_An_Image_is_Worth_16x16_Words_Transformers_for_Image_Recognition_at_Scale](https://www.researchgate.net/publication/344828174_An_Image_is_Worth_16x16_Words_Transformers_for_Image_Recognition_at_Scale)
     docs/research/vision-first/Image Tokenization for Visual
     Models.md:192:3. An Image is Worth 16x16 Words: Transformers for Image
      Recognition at Scale \- SciSpace, accessed October 31, 2025,
     [https://scispace.com/papers/an-image-is-worth-16x16-words-transformer
     s-for-image-v85s5ahlww](https://scispace.com/papers/an-image-is-worth-
     16x16-words-transformers-for-image-v85s5ahlww)
     docs/research/vision-first/glyph-implementation-summary.md:17:- ✅
     **CompressionOrchestrator**: Intelligent compression decision-making
     engine
     docs/research/vision-first/glyph-implementation-summary.md:150:- ✅
     **Production Patterns**: Enterprise deployment and monitoring
     docs/research/vision-first/glyph-implementation-summary.md:188:- **GPU
      Acceleration**: Hardware-accelerated rendering for large-scale
     operations
     docs/research/vision-first/glyph-implementation-summary.md:189:-
     **Distributed Processing**: Multi-node compression for enterprise
     deployments
     docs/research/vision-first/evaluation-methodology.md:469:
     """Calculate economics at scale."""
     docs/research/vision-first/evaluation-methodology.md:1100:Once
     deployed, continuously monitor:
     docs/research/vision-first/glyph-package-analysis.md:144:        if
     horizontal_scale != 1.0:
     docs/research/vision-first/glyph-package-analysis.md:145:
     img = img.resize((int(w * horizontal_scale), h))
     docs/research/vision-first/glyph-package-analysis.md:189:
        scale = math.sqrt(max_pixels / (w * h))
     docs/research/vision-first/glyph-package-analysis.md:191:
            max(1, int(w * scale)),
     docs/research/vision-first/glyph-package-analysis.md:192:
            max(1, int(h * scale))
     docs/research/vision-first/glyph-package-analysis.md:303:
     grayscale=False,
     docs/research/vision-first/glyph-package-analysis.md:883:
     "horizontal-scale": 1.0,         // Image width scaling
     docs/research/vision-first/glyph-package-analysis.md:1223:
             ├─► Horizontal scale: resize(width * scale, height)
     docs/research/vision-first/glyph-package-analysis.md:1226:
             │   • Convert to grayscale
     docs/research/vision-first/glyph-package-analysis.md:1247:    │
     scale = sqrt(max_pixels / (width * height))
     docs/research/vision-first/glyph-package-analysis.md:1287:- Processing
      time scales linearly with text length
     docs/research/vision-first/glyph-package-analysis.md:1425:    img =
     horizontal_scale(img, config['horizontal-scale'])
     docs/research/vision-first/glyph-package-analysis.md:1466:|
     `horizontal-scale` | number | 1.0 | 0.5-2.0 | Image width scaling
     factor |
     docs/research/vision-first/deepseek-ocr-report.md:616:    normalized
     to 0-999 scale
     docs/research/vision-first/deepseek-ocr-report.md:640:    2. Scale to
     actual image dimensions
     docs/research/vision-first/proposal-implementation.md:37:│   └──
     adaptive_router.py      # NEW: Intelligent routing
     docs/research/vision-first/proposal-implementation.md:519:Create
     intelligent routing based on content and requirements:
     docs/research/vision-first/proposal-implementation.md:1154:## 7.
     Deployment Considerations
     docs/research/vision-first/proposal-implementation.md:1159:#
     docker-compose.yml for DeepSeek-OCR deployment
     docs/research/vision-first/proposal-implementation.md:1172:    deploy:
     docs/research/vision-first/proposal-implementation.md:1211:Before
     deploying to production:
     docs/research/vision-first/proposal-implementation.md:1213:- [ ]
     DeepSeek-OCR model deployed and accessible
     docs/research/vision-first/Glyph_ Visual Text Compression
     Guide.md:7:[Omitted long matching line]
     docs/research/vision-first/Glyph_ Visual Text Compression
     Guide.md:9:[Omitted long matching line]
     docs/research/vision-first/Glyph_ Visual Text Compression
     Guide.md:69:| h\_scale | Horizontal scaling factor for glyphs. Values
     less than 1 condense the text horizontally. | Sampled from 0.75 to 1.0
      with decaying probability for smaller values. 1 | 1.0 | **Moderate
     Impact.** The optimal value of 1.0 indicates that horizontally
     distorting the characters provided no net benefit; the potential
     compression gain was likely offset by a decrease in OCR accuracy. |
     docs/research/vision-first/Glyph_ Visual Text Compression
     Guide.md:156:## **Practical Implementation and Deployment Guide**
     docs/research/vision-first/Glyph_ Visual Text Compression
     Guide.md:158:Despite the intricate, multi-stage research pipeline
     behind its creation, the Glyph framework has been engineered for
     accessible use and deployment. The complexity of the training and
     optimization processes is encapsulated, presenting the end-user with a
      straightforward, two-step workflow: first, render the long text into
     a sequence of images, and second, query the VLM with these images.4
     docs/research/vision-first/Glyph_ Visual Text Compression
     Guide.md:206:### **Model Inference and Deployment**
     docs/research/vision-first/Glyph_ Visual Text Compression
     Guide.md:233:2. **vLLM Accelerated Deployment:** For production
     environments requiring high throughput and low latency, deploying the
     model with vLLM is recommended. This involves starting a dedicated
     inference server and then sending requests to it.4
     docs/research/vision-first/glyph-complete-analysis.md:47:- **Batch
     Processing**: Multiprocessing support for large-scale operations
     docs/research/vision-first/glyph-complete-analysis.md:132:   -
     Academic cost optimization for large-scale analysis
     docs/research/vision-first/glyph-complete-analysis.md:137:   -
     Cost-effective large-scale document AI deployment
     docs/research/vision-first/glyph-complete-analysis.md:293:**2.
     CompressionOrchestrator (`abstractcore/compression/orchestrator.py`)**
     docs/research/vision-first/glyph-complete-analysis.md:295:class
     CompressionOrchestrator:
     docs/research/vision-first/glyph-complete-analysis.md:375:- Batch
     processing with multiprocessing support for large-scale operations
     docs/research/vision-first/glyph-complete-analysis.md:402:-
     **Typography**: `alignment`, `first-line-indent`, `horizontal-scale`
     docs/research/vision-first/glyph-complete-analysis.md:804:4.
     **Production Deployment**: Best practices and monitoring
     docs/research/vision-first/glyph-complete-analysis.md:1673:### 8.3
     Enterprise Production Deployment
     docs/research/vision-first/glyph-complete-analysis.md:1675:**Scenario*
     *: Large-scale document processing service
     docs/research/vision-first/glyph-complete-analysis.md:1697:
     """Setup comprehensive monitoring for production deployment."""
     docs/research/vision-first/glyph-complete-analysis.md:1834:# 5.
     Kubernetes deployment configuration
     docs/research/vision-first/glyph-complete-analysis.md:1836:#
     k8s-deployment.yaml
     docs/research/vision-first/glyph-complete-analysis.md:1838:kind:
     Deployment
     docs/research/vision-first/proposal.md:96:- Requires specific model
     deployment (380M encoder + 3B decoder)
     docs/research/vision-first/proposal.md:110:| **Infrastructure** |
     Lightweight | Model-dependent | Flexible deployment |
     docs/research/vision-first/proposal.md:146:For real-world deployment,
     intelligent routing ensures optimal quality/compression tradeoffs:
     docs/research/vision-first/proposal.md:167:- Infrastructure
     availability (DeepSeek deployment)
     docs/research/vision-first/proposal.md:211:- **Issue**: Requires
     DeepSeek model deployment (380M + 3B params)
     docs/research/vision-first/proposal.md:216:  - Progressive deployment
     strategy
     docs/research/vision-first/proposal.md:228:- Solution: Content-aware
     routing
     docs/research/vision-first/proposal.md:304:- [ ] Deploy DeepSeek-OCR
     locally
     docs/research/vision-first/proposal.md:312:- [ ] Implement adaptive
     routing logic
     docs/research/vision-first/proposal.md:318:- [ ] Deploy cloud
     compression service
     docs/research/vision-first/proposal.md:356:3. **Progressive
     deployment** - Start simple, evolve based on results
     docs/research/native-structured-output-transformers-mlx.md:524:###
     Deployment Risks
     docs/research/Agentic Deep Research Best Practices Guide.md:7:[Omitted
      long matching line]
     docs/research/Agentic Deep Research Best Practices Guide.md:47:###
     **Planning and Task Decomposition: From Simple Chains to Hierarchical
     Orchestration**
     docs/research/Agentic Deep Research Best Practices
     Guide.md:90:Anthropic's research system, which powers the Research
     feature in its Claude models, is built on a sophisticated
     **orchestrator-worker** multi-agent architecture.32 A "lead agent"
     acts as the orchestrator, analyzing the user's query, decomposing it
     into sub-tasks, and delegating them to multiple specialized
     "subagents" that operate in parallel. This design offers two
     significant advantages 32:
     docs/research/Agentic Deep Research Best Practices
     Guide.md:95:Anthropic places a strong emphasis on prompt engineering
     as a core design layer. They have developed detailed principles for
     teaching the lead agent how to delegate tasks effectively, how to
     scale the allocated effort based on query complexity, and how to guide
      subagents with proven search strategies, such as starting with broad
     queries before narrowing the focus.33
     docs/research/Agentic Deep Research Best Practices Guide.md:100:|
     **Core Architecture** | Single agent with a four-stage pipeline (Plan,
      Search, Reason, Report). | Single agent with a three-phase process
     (Plan, Iterate, Synthesize). | Hierarchical multi-agent
     (Orchestrator-Worker) with parallel subagents. |
     docs/research/Agentic Deep Research Best Practices Guide.md:105:|
     **Limitations** | Less emphasis on private/enterprise data. | Value is
      highly dependent on the quality and organization of a company's M365
     data. | Higher orchestration complexity, potential for increased token
      costs due to multiple agents. |
     docs/research/Agentic Deep Research Best Practices Guide.md:113:Three
     multi-agent frameworks have become dominant in the open-source
     community, each offering a different abstraction for orchestrating
     agent behavior. The choice between them often reflects a fundamental
     preference for how to achieve reliable and complex agentic workflows.
     docs/research/Agentic Deep Research Best Practices
     Guide.md:125:[Omitted long matching line]
     docs/research/Agentic Deep Research Best Practices
     Guide.md:151:Synthesizing the findings from both commercial and
     open-source ecosystems reveals a set of core trade-offs and emerging
     best practices that are essential for anyone looking to build, deploy,
      or utilize agentic deep research systems.
     docs/research/Agentic Deep Research Best Practices
     Guide.md:159:[Omitted long matching line]
     docs/research/Agentic Deep Research Best Practices Guide.md:163:When
     deploying deep research agents in domains like scientific research,
     legal analysis, or enterprise decision-making, raw performance is
     insufficient. Reliability, trustworthiness, and auditability become
     paramount.7 Three requirements are non-negotiable:
     docs/research/Agentic Deep Research Best Practices Guide.md:166:*
     **Human Oversight:** No autonomous system is infallible. Robust
     architectures must include built-in checkpoints and adjudication
     queues where the agent pauses and requests human input for critical or
      ambiguous decisions. This "human-in-the-loop" capability is crucial
     for mitigating risks and ensuring alignment with user intent.7
     docs/research/Agentic Deep Research Best Practices Guide.md:177:The
     optimal system is a **hierarchical multi-agent system** built upon a
     flexible, graph-based control framework like **LangGraph**. This
     choice provides the explicit state management, persistence, and
     human-in-the-loop capabilities necessary for robust, auditable
     workflows. The architecture adopts the proven **orchestrator-worker
     pattern** seen in Anthropic's system and numerous open-source
     projects.
     docs/research/Agentic Deep Research Best Practices Guide.md:181:*
     **Orchestration Layer (Supervisor Agent):**
     docs/research/Agentic Deep Research Best Practices Guide.md:202:*
     **Scalability & Resilience:** Addressed by the parallel, asynchronous
     worker agents and the robust, stateful orchestration layer that can
     recover from failures.
     docs/research/Agentic Deep Research Best Practices
     Guide.md:209:[Omitted long matching line]
     docs/research/Agentic Deep Research Best Practices Guide.md:218:*
     **Economic and Social Implications:** The increasing capability of
     these autonomous systems will inevitably reshape knowledge work.
     Further research is needed to understand these impacts and to address
     the profound challenges of ensuring the responsible, ethical, and safe
      deployment of agents that can independently generate and synthesize
     information at scale.
     docs/research/structured/lmstudio-http-structured-response.md:318:-
     **Scalability**: HTTP API is stateless and easier to load balance
     docs/research/CODEX_GUIDE.md:213:### Tool Routing
     docs/research/Unified LLM Tool Call Handling.md:265:## **6.0 Advanced
     Orchestration and Agentic Design**
     docs/research/Unified LLM Tool Call Handling.md:267:A unified
     tool-calling layer is not an end in itself; it is the foundational
     infrastructure upon which sophisticated, multi-step agentic systems
     are built. Handling a single tool call is a building block. The true
     power emerges when an application can orchestrate sequences of these
     calls to solve complex problems that require planning, reasoning, and
     state management.
     docs/research/Unified LLM Tool Call Handling.md:272:However, simple
     while loops can be brittle. They can get stuck in repetitive cycles,
     lose track of the overall goal, or fail to handle unexpected tool
     outputs gracefully.13 This motivates the need for more structured and
     controllable orchestration frameworks.
     docs/research/Unified LLM Tool Call Handling.md:299:* **Sub-Agents:**
     Complex tasks can be broken down and delegated. A main "orchestrator"
     agent can use tool calls to invoke specialized sub-agents. For
     example, a research agent might call a "data-scraping" sub-agent and a
      "data-analysis" sub-agent. Each sub-agent is, itself, a tool that the
      orchestrator can call.
     docs/research/Unified LLM Tool Call Handling.md:302:The unified
     tool-calling layer is the essential fabric that enables all of these
     advanced patterns. It provides the standardized API that planners,
     orchestrators, and memory systems use to interact with the LLM's core
     reasoning engine.
     docs/research/Unified LLM Tool Call Handling.md:326:[Omitted long
     matching line]
     docs/research/Inferring Intent in Psychological Text
     Analysis.md:151:The proliferation of digital text data has spurred the
      development of computational methods that can analyze language at an
     unprecedented scale. These quantitative approaches leverage natural
     language processing (NLP) and machine learning to systematically
     identify and measure psychological states, emotions, and intentions
     from vast textual corpora.
     docs/research/Inferring Intent in Psychological Text
     Analysis.md:189:[Omitted long matching line]
     docs/research/Inferring Intent in Psychological Text
     Analysis.md:249:[Omitted long matching line]
     docs/research/abstract-series.md:33:    - load balancing to distribute
      requests across multiple instances
     docs/research/abstract-series.md:75:- Queue logic to execute several
     tasks
     docs/examples.md:1409:### Load Balancing
     docs/examples.md:1417:class LoadBalancer:
     docs/examples.md:1462:balancer = LoadBalancer([
     docs/architecture.md:60:**Goal**: Flexible deployment as library or
     server
     docs/architecture.md:695:- **Multi-Worker Support**: Production
     deployment with multiple workers
     docs/architecture.md:841:- **AbstractSwarm**: Multi-agent
     orchestration
     docs/architecture.md:842:- **AbstractPipeline**: Complex workflow
     orchestration
     docs/architecture.md:853:6. **Flexibility** - Deploy as Python library
      or OpenAI-compatible HTTP server
     docs/comparison.md:138:- **LangGraph** = Complex workflow
     orchestration
     docs/comparison.md:149:- **Query Engines**: Sophisticated query
     processing and routing
     docs/comparison.md:313:**Why**: State management and workflow
     orchestration
     docs/comparison.md:342:- ✅ Complex chain orchestration
     docs/capabilities.md:101:**Why this helps**: Allows flexible tool
     execution control for different deployment scenarios.
     docs/capabilities.md:121:> **See**: [Structured Output
     Guide](../../structured-output.md) for native vs prompted strategies, schema
      design, and production deployment
     docs/capabilities.md:228:-
     **[LangGraph](https://github.com/langchain-ai/langgraph)** - Agent
     orchestration
     docs/capabilities.md:280:### 6. Multi-Agent Orchestration (Use
     Orchestration Frameworks)
     docs/archive/server-configuration.md:56:|
     `/{provider}/v1/chat/completions` | POST | Provider-specific routing |
      Same as above, provider in path |
     docs/archive/server-configuration.md:210:## Deployment Configurations
     docs/archive/server-configuration.md:212:### Production Docker Setup
     docs/archive/server-configuration.md:214:```dockerfile
     docs/archive/server-configuration.md:236:### Docker Compose Example
     docs/archive/server-configuration.md:257:### Kubernetes Deployment
     docs/archive/server-configuration.md:261:kind: Deployment
     docs/archive/server-configuration.md:357:# In your deployment, use a
     reverse proxy like nginx:
     docs/archive/openai-format-conversion-fix.md:328:### Deployment
     Checklist
     docs/archive/openai-format-conversion-fix.md:353:### For Server
     Deployments
     docs/archive/openai-format-conversion-fix.md:386:- ✅ Ready for
     immediate production deployment
     docs/archive/openai-format-conversion-fix.md:394:**Production
     status**: **READY FOR DEPLOYMENT** ✅
     docs/archive/proposals.md:12:- **AbstractAgent**: ReAct reasoning and
     task queues
     docs/archive/proposals.md:13:- **AbstractSwarm**: Multi-agent
     orchestration
     docs/archive/proposals.md:87:- **No RAG Orchestration**: Correctly
     deferred to RAGnarok
     docs/archive/structured-output-verification-report.md:94:-
     **Routing**: Correctly routes to `_generate_native()` for native
     providers
     docs/archive/chat-completions-endpoint.md:52:ID of the model to use.
     Use `provider/model` format for automatic routing.
     docs/archive/chat-completions-endpoint.md:778:- Provider-specific
     routing
     docs/archive/improved-structured-response.md:353:**Finding**: Native
     structured outputs validate complex schemas successfully across all
     tested providers. Response time scales with schema complexity.
     docs/archive/improved-structured-response.md:488:### 6. Production
     Deployment Checklist
     docs/archive/improved-structured-response.md:490:Before deploying
     structured outputs to production:
     docs/archive/media/report_final_status_20251019_050000.md:222:-
     Intelligent auto-routing
     docs/archive/media/phase_3_system_enhancement.md:5:**Goal**: Add
     advanced features for production-scale usage
     docs/archive/media/phase_3_system_enhancement.md:9:After Phase 1 (core
      integration) and Phase 2 (documentation accuracy), the media system
     will be **functionally complete** for core use cases. Phase 3 focuses
     on **production-scale enhancements** and **advanced capabilities**.
     docs/archive/media/implementation_justification.md:7:The AbstractCore
     media system follows a **3-phase development approach** designed to
     deliver immediate value while building toward enterprise-scale
     capabilities. This document justifies the key architectural decisions
     and explains why specific implementation choices were made.
     docs/archive/media/implementation_justification.md:205:**Problem**:
     Core system works but lacks enterprise-scale capabilities.
     docs/archive/media/implementation_justification.md:210:- **Production
     scaling**: Core system scales to enterprise needs
     docs/archive/media/implementation_justification.md:281:- **Production
     debugging**: Essential for diagnosing issues at scale
     docs/archive/media/implementation_justification.md:303:-
     **Container-friendly**: Works well with Docker, Kubernetes
     docs/archive/media/implementation_justification.md:328:├──
     performance/   # Performance and scale tests
     docs/archive/media/implementation_justification.md:393:- **Deployment
     simplicity**: Can deploy without all dependencies
     docs/embeddings.md:596:- **Use REST API**: Deploy embeddings as HTTP
     service with the server
     docs/embeddings.md:606:- **[Server Guide](../../server.md)** - Server setup
     and deployment
     docs/backlog/completed/structured_logging_audit_report.md:34:11. ✅
     abstractcore/media/utils/image_scaler.py - **Instance logger in
     __init__**
     docs/backlog/completed/structured_logging_audit_report.md:100:-
     image_scaler.py: ✅ Correct (class needs instance logger)
     docs/backlog/completed/structured_logging_audit_report.md:214:The
     structured logging migration Phase 1 is **complete, verified, and
     production-ready**. The implementation follows SOTA best practices
     from Python, Django, FastAPI, and cloud-native patterns. Zero
     over-engineering detected. All edge cases handled correctly. Ready for
      deployment.
     docs/backlog/completed/v2.6.5-dynamic-base-url.md:9:Successfully
     implemented server-side dynamic `base_url` parameter support for
     `/v1/chat/completions` endpoint. This enables per-request routing to
     custom OpenAI-compatible endpoints without requiring environment
     variable configuration.
     docs/backlog/completed/v2.6.5-dynamic-base-url.md:11:**Key
     Achievement**: Users can now route individual chat completion requests
      to different endpoints dynamically, perfect for multi-tenant
     applications, testing scenarios, and flexible deployment
     architectures.
     docs/backlog/completed/v2.6.5-dynamic-base-url.md:51:| **Multiple
     Requests** | Multiple POST | ✅ PASSED | Consistent routing |
     docs/backlog/completed/v2.6.5-dynamic-base-url.md:65:- ✅ Correct
     routing to localhost:1234 (not default :8080)
     docs/backlog/completed/v2.6.5-dynamic-base-url.md:160:### 3. Dynamic
     Load Balancing
     docs/backlog/completed/v2.6.5-dynamic-base-url.md:182:- ✅ **No
     environment variables needed** for dynamic routing
     docs/backlog/completed/v2.6.5-dynamic-base-url.md:191:- ✅ **Load
     balancing** - distribute requests dynamically
     docs/backlog/completed/v2.6.5-dynamic-base-url.md:310:- ✅ Dynamic
     routing to custom endpoints working
     docs/backlog/completed/v2.6.5-dynamic-base-url.md:320:v2.6.5
     successfully implements dynamic `base_url` parameter support for
     server-side chat completions. The feature enables flexible,
     per-request endpoint routing without environment variables, perfect
     for multi-tenant applications, testing scenarios, and dynamic load
     balancing.
     docs/backlog/completed/011-vllm-provider-implementation.md:45:##
     Real-World Deployment Experience
     docs/backlog/completed/011-vllm-provider-implementation.md:50:- Cloud
     provider: Scaleway (Paris)
     docs/backlog/completed/011-vllm-provider-implementation.md:113:Based
     on deployment experience with 4x NVIDIA L4 GPUs (23GB each):
     docs/backlog/completed/011-vllm-provider-implementation.md:263:###
     Deployment Best Practices
     docs/backlog/completed/011-vllm-provider-implementation.md:276:4.
     **Server mode, not library mode**: Production deployments run `vllm
     serve` as separate process
     docs/backlog/completed/011-vllm-provider-implementation.md:284:✅
     **Production-ready documentation** with real deployment experience
     docs/backlog/completed/009-base-url-openai-anthropic.md:121:### 3.
     Digital Article Docker Deployment
     docs/backlog/completed/009-base-url-openai-anthropic.md:152:**Medium-H
     igh** - This is a common requirement for enterprise deployments and
     proxy integrations.
     docs/backlog/completed/media-roadmap.md:38:- **✅ Intelligent
     routing**: Media content automatically routed based on model
     capabilities
     docs/backlog/completed/media-roadmap.md:75:- **Capability awareness**:
      Uses model_capabilities.json for routing
     docs/backlog/completed/media-roadmap.md:182:- ✅ OpenAI: Multi-image
     messages, vision model routing
     docs/backlog/completed/011-provider-request-vllm.md:11:**Objective:**
     Implement a dedicated `VLLMProvider` in AbstractCore to unlock
     high-performance inference capabilities specifically for self-hosted
     GPU clusters (e.g., AWS EC2, Kubernetes).
     docs/backlog/completed/011-provider-request-vllm.md:46:        #
     Default to localhost if no URL provided, supporting the Docker setup
     docs/backlog/completed/011-provider-request-vllm.md:143:This is where
     the new provider shines. You can build a routing agent in
     AbstractCore:
     docs/backlog/completed/011-provider-request-vllm.md:166:### Step 1:
     Update Docker
     docs/backlog/completed/011-provider-request-vllm.md:168:Switch your
     `docker-compose.yml` to vLLM (as discussed previously).
     docs/backlog/completed/openai-compatible-server-integration-v2.6.5.md:
     90:5. **Routing**: Server properly routes to custom endpoints
     docs/backlog/completed/openai-compatible-server-integration-v2.6.5.md:
     170:2. **Docker Deployments**: Configure endpoints at runtime via POST
      parameters
     docs/backlog/completed/openai-compatible-server-integration-v2.6.5.md:
     173:5. **Proxy Routing**: Route to different OpenAI-compatible servers
      dynamically
     docs/backlog/completed/004-structured-logging.md:37:11.
     abstractcore/media/utils/image_scaler.py
     docs/backlog/completed/server.md:5:A production-grade server that
     unlocks the full power of AbstractCore, going beyond simple API
     compatibility to provide intelligent routing, cost optimization, and
     enterprise features.
     docs/backlog/completed/server.md:12:- router.py - Intelligent request
     routing system
     docs/backlog/completed/server.md:46:- Semantic Routing: Use embeddings
      to route requests to best model
     docs/backlog/completed/server.md:48:- Load Balancing: Distribute
     across multiple providers
     docs/backlog/completed/server.md:71:3. abstractcore/server/router.py
     (300 lines) - Intelligent routing
     docs/backlog/completed/010-model-download-api.md:5:Digital Article is
     a computational notebook application that uses AbstractCore for LLM
     interactions. When deployed via Docker, users need to download models
     (Ollama, HuggingFace) through the web UI without leaving the
     application.
     docs/backlog/completed/abstractcore-custom-base-url-feature-request.md
     :17:4. **Dockerized deployments** with custom networking
     docs/backlog/completed/abstractcore-custom-base-url-feature-request.md
     :189:### 1. **Remote Deployments**
     docs/backlog/completed/abstractcore-custom-base-url-feature-request.md
     :194:### 2. **Docker/Kubernetes**
     docs/backlog/completed/abstractcore-custom-base-url-feature-request.md
     :368:**Why**: Applications need accurate provider availability
     detection for remote/custom deployments
     docs/backlog/completed/abstractcore-custom-base-url-feature-request.md
     :372:- Runs in Docker/Kubernetes
     docs/backlog/completed/abstractcore-custom-base-url-feature-request.md
     :384:Thank you for considering this feature request! AbstractCore is
     an excellent library, and this enhancement would make it even more
     robust for production deployments.
     docs/backlog/completed/abstractcore-custom-base-url-feature-request.md
     :515:**New Feature**: Runtime programmatic configuration API that
     extends v2.6.1 by enabling provider configuration without environment
     variables. Perfect for web UIs, Docker startup scripts, testing, and
     multi-tenant deployments.
     docs/backlog/completed/abstractcore-custom-base-url-feature-request.md
     :633:**2. Docker Startup Scripts** - Clean programmatic configuration:
     docs/backlog/completed/abstractcore-custom-base-url-feature-request.md
     :639:    """Configure providers from Docker environment on startup."""
     docs/backlog/completed/abstractcore-custom-base-url-feature-request.md
     :658:**4. Multi-tenant Deployments** - Configure per tenant:
     docs/backlog/completed/abstractcore-custom-base-url-feature-request.md
     :709:3. ✅ **llms-full.txt** - Comprehensive section with Web UI,
     Docker, testing, multi-tenant examples
     docs/backlog/completed/abstractcore-custom-base-url-feature-request.md
     :783:✅ **Docker-Friendly**: Configure via env vars or
     programmatically on startup
     docs/backlog/completed/abstractcore-custom-base-url-feature-request.md
     :809:- **Use Case**: Docker/Kubernetes deployments where env vars are
     standard
     docs/backlog/completed/abstractcore-custom-base-url-feature-request.md
     :858:✅ Docker/Kubernetes friendly
     docs/backlog/async-mlx-hf.md:262:                    └─> Queue request
     docs/backlog/007-secrets-management.md:11:Add support for external
     secrets management systems (environment variables, HashiCorp Vault,
     AWS Secrets Manager, Azure Key Vault) to enable secure API key
     management in production environments. Currently, AbstractCore stores
     API keys in `~/.abstractcore/config/abstractcore.json`, which is
     suitable for development but not enterprise deployments.
     docs/backlog/007-secrets-management.md:462:For enterprise deployments:
     docs/backlog/abstract-processing.md:3:**Goal**: Create a lightweight
     package that orchestrates AbstractCore with advanced prompt
     engineering techniques for summarization and semantic fact extraction.
     docs/backlog/abstract-processing.md:7:AbstractProcessing leverages
     **pure LLM capabilities** through sophisticated prompting patterns. No
      external ML models, no heavy dependencies - just intelligent
     orchestration of AbstractCore with SOTA prompt engineering techniques
     from 2025.
     docs/backlog/abstract-processing.md:541:AbstractProcessing provides
     SOTA LLM-based text processing through intelligent orchestration of
     AbstractCore. By focusing on prompt engineering excellence over
     complex pipelines, it delivers high-quality results with minimal
     dependencies while maintaining the lightweight, reliable philosophy of
      the Abstract* ecosystem.
     docs/backlog/multi-provider-requests.md:63:- ❌ Can't reuse across
     different deployment modes
     docs/backlog/multi-provider-requests.md:87:- ✅ Consistent across
     deployment modes
     docs/backlog/multi-provider-requests.md:236:- Cost-aware routing and
     fallbacks
     docs/backlog/multi-provider-requests.md:409:- Focus on routing and
     cost optimization
     docs/backlog/multi-provider-requests.md:422:- **Sophisticated
     Strategies**: Beyond simple routing
     docs/backlog/multi-provider-requests.md:473:This feature transforms
     AbstractCore from "one API for all providers" to "intelligent
     orchestration across all providers" - a compelling evolution of the
     value proposition.
     docs/backlog/006-architecture-decision-records.md:149:LLM applications
      face two deployment scenarios:
     docs/backlog/006-architecture-decision-records.md:355:4. Queue:
     Streaming, scalable
     docs/glyphs.md:410:- **Ollama qwen2.5vl**: Balanced performance, good
     for local deployment
     docs/glyphs.md:570:*Glyph compression represents a paradigm shift in
     document processing, making large-scale text analysis more efficient
     while maintaining the quality and accuracy you expect from
     AbstractCore.*
     docs/prerequisites.md:327:**Best for**: Production GPU deployments,
     high-throughput inference, tensor parallelism
     docs/prerequisites.md:708:- Consider firewall rules for production
     deployments
     docs/README.md:57:  - Deployment (Docker, production, cloud)
     docs/README.md:177:- Deploy to production → [Server
     Documentation](../../server.md#deployment)
     docs/vision-capabilities.md:9:- **15+ vision models** supported across
      cloud and local deployments
     docs/vision-capabilities.md:122:- Local deployment with REST API
     docs/vision-capabilities.md:148:   - **Best for**: General vision,
     local deployment, cost efficiency
     docs/vision-capabilities.md:168:   - **Best for**: Lightweight local
     deployment
     docs/vision-capabilities.md:181:| Qwen2.5-VL 7B | Ollama/LMStudio | 7B
      | Medium | Very Good | 3584px | Free | Local deployment |
     docs/vision-capabilities.md:1111:   - Edge deployment options
     docs/structured-output.md:16:8. [Production
     Deployment](../../structured-output.md#production-deployment)
     docs/structured-output.md:549:    proficiency: int  # 1-10 scale
     docs/structured-output.md:971:## Production Deployment
     docs/structured-output.md:973:### Pre-Deployment Checklist
     docs/structured-output.md:975:Before deploying structured outputs to
     production:
     docs/server.md:700:## Deployment
     docs/server.md:702:### Docker
     docs/server.md:704:```dockerfile
     docs/server.md:719:docker build -t abstractcore-server .
     docs/server.md:720:docker run -p 8000:8000 -e
     OPENAI_API_KEY=$OPENAI_API_KEY abstractcore-server
     docs/server.md:723:### Docker Compose
     docs/server.md:910:- **Production Ready**: Docker, multi-worker,
     health checks
     docs/apps/basic-intent.md:720:- **Scale with context**: Appropriate
     guidance for standalone vs conversational analysis
     docs/apps/basic-judge.md:81:Uses a **1-5 scale** with clear
     definitions:
     docs/apps/basic-judge.md:633:- "code review for production deployment"
     docs/apps/basic-summarizer.md:393:- **Scale with length**: Appropriate
      guidance for brief vs comprehensive
     docs/reports/INVESTIGATION_SUMMARY.md:22:### 1. Project Structure &
     Scale
     docs/reports/INVESTIGATION_SUMMARY.md:128:Media Orchestrator (entry
     point for all media handling)
     docs/reports/INVESTIGATION_SUMMARY.md:417:The codebase is
     well-positioned for production deployment and future enhancements.
     docs/reports/vision-compression-reality.md:176:- ❌ Needs model
     deployment infrastructure
     docs/reports/2025-10-11-sprint-documentation-improvements-report.md:37
     :- ✅ Production deployment configurations included
     docs/reports/2025-10-11-sprint-documentation-improvements-report.md:55
     :5. **Server & Agentic CLI** (540 lines) - Production deployment
     docs/reports/2025-10-11-sprint-documentation-improvements-report.md:20
     5:- **Learning Path**: Complete progression from beginner to
     production deployment
     docs/reports/2025-10-11-sprint-documentation-improvements-report.md:22
     7:- ✅ **Production Readiness**: Security and deployment
     considerations included
     docs/reports/2025-10-11-sprint-documentation-improvements-report.md:30
     5:### **Production Deployment Approval**
     docs/reports/2025-10-11-sprint-documentation-improvements-report.md:30
     6:All deliverables are **APPROVED FOR IMMEDIATE PRODUCTION
     DEPLOYMENT** with the following certifications:
     docs/reports/2025-10-11-sprint-documentation-improvements-report.md:32
     2:**Next Review**: Post-deployment feedback analysis
     docs/reports/2025-10-10-report.md:190:The library demonstrates good
     engineering practices overall, with comprehensive testing, proper
     abstractions, and production-ready features. However, the security
     issues must be addressed before deployment.
     docs/reports/glyph-compression-technical-report.md:143:### Compression
      Orchestrator
     docs/reports/glyph-compression-technical-report.md:146:from
     abstractcore.compression import CompressionOrchestrator
     docs/reports/glyph-compression-technical-report.md:148:orchestrator =
     CompressionOrchestrator()
     docs/reports/glyph-compression-technical-report.md:151:recommendation
     = orchestrator.get_compression_recommendation(
     docs/reports/glyph-compression-technical-report.md:237:- **Ollama
     qwen2.5vl**: Balanced performance, good for local deployment
     docs/reports/glyph-compression-technical-report.md:337:### Production
     Deployment
     docs/reports/accurate-vlm-token-calculation-report.md:427:**Next
     Steps**: Deploy enhanced calculator and monitor real-world accuracy
     improvements
     docs/reports/2025-10-18-report-config.md:403:4. **Docker
     Integration**: Environment-based configuration
     docs/reports/DOCUMENTATION_INDEX.md:101:- File type routing
     docs/reports/DOCUMENTATION_INDEX.md:130:  - Project structure & scale
     docs/reports/CODEBASE_OVERVIEW.md:287:**ImageScaler**
     (`image_scaler.py`):
     docs/reports/CODEBASE_OVERVIEW.md:636:The system is production-ready
     with 137+ models across 7 providers, comprehensive media support for 5
      media types and 50+ file formats, and a mature configuration system
     for flexible deployment.
     docs/reports/MEDIA_SYSTEM_ARCHITECTURE.md:43:│
     MEDIA SYSTEM ORCHESTRATOR                            │
     docs/reports/MEDIA_SYSTEM_ARCHITECTURE.md:536:         │   │ Detection
         │  │(Scaler) │
     docs/reports/v2.6.5-verification.md:91:| **Server Integration** | ✅
     VERIFIED | Dynamic routing to custom endpoints |
     docs/reports/v2.6.5-release-summary.md:12:Implemented dynamic
     `base_url` parameter support for `/v1/chat/completions` endpoint,
     enabling per-request routing to custom OpenAI-compatible endpoints
     **without environment variable configuration**.
     docs/reports/v2.6.5-release-summary.md:18:1. **Dynamic Routing**: POST
      parameter enables per-request endpoint selection
     docs/reports/v2.6.5-release-summary.md:42:✅ Routing confirmed:
     localhost:1234 (LMStudio), not :8080 (default)
     docs/reports/v2.6.5-release-summary.md:109:### 2. Dynamic Load
     Balancing
     docs/reports/v2.6.5-release-summary.md:175:### Test Dynamic Routing
     docs/reports/v2.6.5-release-summary.md:203:| **Overhead** | 25ms |
     Parameter extraction + routing |
     docs/reports/v2.6.5-release-summary.md:210:- ✅ Dynamic routing to
     custom endpoints working
     docs/reports/v2.6.5-release-summary.md:232:   # Test dynamic routing
     docs/reports/v2.6.5-release-summary.md:259:**Feature enables**:
     Multi-tenant applications, dynamic load balancing, A/B testing,
     flexible deployments - all without environment variable configuration.
     README.md:691:AbstractCore supports environment variables for provider
      base URLs, enabling remote servers, Docker deployments, and
     non-standard ports:
     README.md:739:- **Docker Startup**: Read from custom env vars and
     configure programmatically
     README.md:767:- **[Server Documentation](../../server.md)** - Complete
     server setup, API reference, and deployment
     llms-full.txt:34:- **Dynamic Base URL** (v2.6.5): POST `base_url`
     parameter for runtime endpoint routing without environment variables
     llms-full.txt:43:- **Structured Output**: Native vs prompted
     strategies, schema validation, production deployment
     ([docs/structured-output.md](../../structured-output.md))
     llms-full.txt:330:AbstractCore supports environment variables for
     configuring provider base URLs, enabling remote servers, Docker
     deployments, and non-standard ports. Provider discovery
     (`get_all_providers_with_models()`) automatically respects these
     environment variables.
     llms-full.txt:375:# Docker deployment with LMStudio
     llms-full.txt:393:- Docker-friendly networking configuration
     llms-full.txt:394:- Non-standard port support for multi-instance
     deployments
     llms-full.txt:402:Configure provider settings at runtime without
     relying on environment variables. Perfect for web UIs, Docker startup
     scripts, testing, and multi-tenant deployments.
     llms-full.txt:458:**Docker Startup Script:**
     llms-full.txt:465:    """Configure providers from Docker environment
     on startup."""
     llms-full.txt:488:**Multi-tenant Deployment:**
     llms-full.txt:611:- **Build & Scripts**: Shell (.sh, .bash, .zsh),
     Dockerfile, CMake, Gradle, Makefile
     llms-full.txt:942:# - Schema design patterns and production deployment
     llms-full.txt:1968:- Docker deployments: Download models through web
     UI
     llms-full.txt:2122:**Via AbstractCore Server (Dynamic Routing):**
     llms-full.txt:2135:1. POST `base_url` parameter (highest) - for
     dynamic routing
     llms-full.txt:2554:Comprehensive OpenAI-compatible REST API with
     advanced routing and agent CLI integration:
     llms-full.txt:2565:# Production deployment
     llms-full.txt:3162:### Production Deployment
     llms-full.txt:3164:#### Docker Deployment
     llms-full.txt:3166:```dockerfile
     llms-full.txt:3167:# Dockerfile
     llms-full.txt:3180:#### Kubernetes Deployment
     llms-full.txt:3183:# k8s-deployment.yaml
     llms-full.txt:3185:kind: Deployment
     llms-full.txt:3219:  type: LoadBalancer
     llms-full.txt:3240:- **Multi-Provider Routing**: Route to any provider
      using model prefix
     llms-full.txt:3242:- **Scalable**: Supports horizontal scaling with
     load balancers
     ACKNOWLEDGEMENTS.md:93:### Cloud and Deployment
     ACKNOWLEDGEMENTS.md:94:- **[Docker](https://github.com/docker)** -
     Containerization platform
     CONTRIBUTING.md:91:- **Reliability**: Static version works in all
     deployment scenarios (development, installed packages, Docker, etc.)
     docs/server.md:1:# Server and API Integration
     docs/architecture.md:1:# Architecture
     docs/endpoint.md:1:# Endpoint
     strategies.
     examples/rag/complete_rag_example.py:137:        "Docker is a
     containerization platform that packages applications and their
     dependencies into lightweight, portable containers. It was first
     released in 2013 and revolutionized application deployment.",
     examples/rag/complete_rag_example.py:141:        "Kubernetes is an
     open-source container orchestration platform originally developed by
     Google. It automates deployment, scaling, and management of
     containerized applications across clusters.",
     examples/rag/complete_rag_example.py:147:        "TensorFlow is an
     open-source machine learning framework developed by Google. It
     provides tools for building and deploying machine learning models at
     scale."
     examples/rag/complete_rag_example.py:156:        "What are the main
     benefits of Docker?",
     examples/rag/complete_rag_example.py:222:        print("• Scale up
     knowledge base for production use")
     examples/media/glyph_compression_demo.py:20:from abstractcore.compression
     import GlyphConfig, CompressionOrchestrator
     examples/media/glyph_compression_demo.py:33:including improvements in model
     efficiency, training methodologies, and deployment strategies.
     examples/media/glyph_compression_demo.py:84:### Deployment and Optimization
     examples/media/glyph_compression_demo.py:86:Practical deployment
     considerations have become increasingly important:
     examples/media/glyph_compression_demo.py:89:  distillation have enabled
     deployment of large models on resource-constrained devices.
     examples/media/glyph_compression_demo.py:95:  have enabled serving of
     extremely large models at scale.
     examples/media/glyph_compression_demo.py:104:The scaled dot-product
     attention mechanism computes attention weights as:
     examples/media/glyph_compression_demo.py:146:- **Edge Computing**:
     Deployment on mobile and IoT devices
     examples/media/glyph_compression_demo.py:211:        # Initialize
     compression orchestrator
     examples/media/glyph_compression_demo.py:212:        orchestrator =
     CompressionOrchestrator()
     examples/media/glyph_compression_demo.py:215:        recommendation =
     orchestrator.get_compression_recommendation(
     examples/README.md:49:6. **Production Deployment**
     (`examples/learning_path/06_production_patterns.py`)
     examples/README.md:62:  - Docker
     examples/embeddings/embeddings_demo.py:76:            "Docker containers provide
     lightweight, portable environments for application deployment.",
     examples/embeddings/embeddings_demo.py:77:            "Kubernetes orchestrates
     containerized applications across distributed computing clusters.",
     examples/embeddings/embeddings_demo.py:85:            "container deployment
     tools",
     examples/learning_path/06_production_patterns.py:460:    #
     Strategy 1: Model Routing
     examples/learning_path/06_production_patterns.py:461:
     print("\n1️⃣ Intelligent Model Routing:")
     examples/learning_path/06_production_patterns.py:776:
     "Queue depth",
     examples/learning_path/05_server_agentic_cli.py:10:-
     Multi-provider server deployment
     examples/learning_path/05_server_agentic_cli.py:125:    # Docker
     deployment
     examples/learning_path/05_server_agentic_cli.py:126:
     print("\n4️⃣ Docker Deployment:")
     examples/learning_path/05_server_agentic_cli.py:127:    print("
      ```dockerfile")
     examples/learning_path/05_server_agentic_cli.py:344:def
     multi_provider_deployment():
     examples/learning_path/05_server_agentic_cli.py:346:
     Demonstrates multi-provider deployment patterns.
     examples/learning_path/05_server_agentic_cli.py:349:    - Load
     balancing across providers
     examples/learning_path/05_server_agentic_cli.py:354:
     print("Multi-Provider Deployment Patterns")
     examples/learning_path/05_server_agentic_cli.py:357:
     print("\n🌐 Deployment Architecture:")
     examples/learning_path/05_server_agentic_cli.py:360:
            │   Load Balancer     │
     examples/learning_path/05_server_agentic_cli.py:398:    print("
      routing:")
     examples/learning_path/05_server_agentic_cli.py:403:    # Load
     balancing strategies
     examples/learning_path/05_server_agentic_cli.py:404:
     print("\n⚖️ Load Balancing Strategies:")
     examples/learning_path/05_server_agentic_cli.py:480:def
     production_deployment_checklist():
     examples/learning_path/05_server_agentic_cli.py:482:    Provides
     a production deployment checklist.
     examples/learning_path/05_server_agentic_cli.py:490:
     print("Production Deployment Checklist")
     examples/learning_path/05_server_agentic_cli.py:524:
     "Horizontal scaling with load balancer",
     examples/learning_path/05_server_agentic_cli.py:534:    #
     Deployment
     examples/learning_path/05_server_agentic_cli.py:535:
     print("\n🚀 Deployment:")
     examples/learning_path/05_server_agentic_cli.py:536:
     deployment = [
     examples/learning_path/05_server_agentic_cli.py:537:
     "Docker containerization",
     examples/learning_path/05_server_agentic_cli.py:538:
     "Kubernetes orchestration",
     examples/learning_path/05_server_agentic_cli.py:540:
     "Blue-green deployment",
     examples/learning_path/05_server_agentic_cli.py:544:    for item
     in deployment:
     examples/learning_path/05_server_agentic_cli.py:651:
     multi_provider_deployment()
     examples/learning_path/05_server_agentic_cli.py:653:
     production_deployment_checklist()
     examples/learning_path/05_server_agentic_cli.py:662:    print("•
     Production-ready deployment patterns")
     examples/embeddings/simple_embeddings_examples.py:85:        "Docker provides
     containerization technology for packaging applications and
     dependencies.",
     examples/embeddings/simple_embeddings_examples.py:87:        "Kubernetes
     orchestrates containerized applications across distributed computing
     clusters.",
     examples/embeddings/simple_embeddings_examples.py:176:        "Docker was first
     released in 2013 and revolutionized application deployment through
     containers.",
     examples/embeddings/simple_embeddings_examples.py:177:        "Kubernetes was
     originally developed by Google and released in 2014 for container
     orchestration."
     examples/learning_path/06_production_patterns.py:1:Example 6: Production Patterns

⏺ Read(docs/architecture.md)
  ⎿  Read 855 lines

⏺ Read(docs/server.md)
  ⎿  Read 925 lines

∴ Thinking…

  Now I have a comprehensive understanding of AbstractCore's architecture. Let 
  me also search for SOTA patterns and look at what exists in the backlog for 
  multi-provider requests and scaling. Let me also look at the examples 
  documentation for load balancing patterns.

⏺ Let me also examine the existing backlog and research for multi-provider
  and scaling patterns:

⏺ Read(docs/backlog/multi-provider-requests.md)
  ⎿  Read 473 lines

⏺ Read(docs/examples.md · lines 1380-1529)
  ⎿  Read 150 lines

∴ Thinking…

  Now I have a comprehensive understanding of AbstractCore's architecture, 
  current capabilities, and planned features. Let me now research SOTA patterns 
  for LLM infrastructure deployment to provide an informed opinion.

  The user's question is fundamentally about deployment topology and 
  scalability:

  1. Should AbstractCore live on the same server as certain providers (HF, MLX, 
  Ollama, LMStudio, vLLM)?
  2. Or should it be a passthrough to distant servers?
  3. Who handles scalability, routing, and query queues?
  4. Who orchestrates that?

  Let me now synthesize what I've learned and provide a comprehensive answer 
  based on:
  - The codebase exploration
  - SOTA patterns from LangChain, LiteLLM, OpenRouter, vLLM
  - Industry best practices for LLM infrastructure

  Key observations from the codebase:

  1. Current Architecture:
    - AbstractCore is a library-first design with optional HTTP server
    - It acts as a passthrough/router to provider APIs
    - Local providers (Ollama, LMStudio, vLLM) run as separate processes
    - No built-in load balancing, queuing, or advanced routing
    - Relies on individual provider retry logic and circuit breakers
  2. Provider Types:
    - API Providers (OpenAI, Anthropic): Always remote, just API calls
    - Local HTTP Providers (Ollama, LMStudio, vLLM): Separate processes, local 
  HTTP
    - In-Process Providers (MLX, HuggingFace): Actually load models in 
  AbstractCore's process
  3. Missing Capabilities (as identified in the explore):
    - No request queuing
    - No load balancing
    - No connection pooling across providers
    - No auto-scaling
    - No multi-provider orchestration
    - No batch processing API

  I see emerging infrastructure patterns that could address these architectural 
  limitations. The industry is converging on specialized architectural 
  approaches for LLM serving that emphasize modular, scalable design. Dedicated 
  model servers with lightweight routing layers seem to be the most promising 
  strategy, allowing precise GPU resource management while maintaining 
  flexibility in provider selection and request handling.

  The key will be implementing a clean separation between inference, routing, 
  and orchestration layers to create a robust, performant LLM infrastructure 
  that can dynamically adapt to different provider capabilities and workload 
  demands.

  Emerging best practices suggest creating stateless routing layers that can 
  efficiently distribute requests across multiple backend providers, with 
  intelligent mechanisms for rate limiting, fallback strategies, and cost 
  optimization. This approach enables more resilient and cost-effective model 
  serving architectures.

  The design philosophy emphasizes separating inference capabilities from 
  routing infrastructure, allowing horizontal scaling of routers while 
  maintaining vertical scaling for GPU-intensive inference servers. Implementing
   robust queuing mechanisms and health monitoring becomes critical for managing
   request bursts and ensuring system reliability.

  AbstractCore emerges as a flexible client library that can adapt to different 
  deployment scenarios. In development environments, it enables simple, 
  low-latency local deployments, while in production settings, it can function 
  as a lightweight stateless router coordinating requests across distributed GPU
   resources.

  The key architectural considerations revolve around creating a modular system 
  that can dynamically route requests, handle provider complexity, and maintain 
  high availability without becoming a monolithic inference engine itself.

⏺ Now I have a comprehensive understanding of AbstractCore's architecture.
  Let me provide you with a well-researched opinion on your architectural
  question.

⏺ Updated plan
  ⎿  Wrote 447 lines to /Users/albou/.claude/plans/breezy-singing-prism.md 
     # AbstractCore Deployment Architecture Analysis

     ## User's Question

     Should AbstractCore live on the same server as certain providers (HF,
     MLX, Ollama, LMStudio, vLLM) or should it live independently as a
     passthrough to distant servers? Who handles scalability, routing, and
     query queuing? Who orchestrates that?

     ---

     ## Executive Summary

     After thorough analysis of AbstractCore's codebase (~45,000 LOC),
     documentation, and SOTA LLM infrastructure patterns, my recommendation
     is:

     **AbstractCore should be a lightweight, stateless routing layer that 
     lives INDEPENDENTLY from inference servers in production deployments.**

     The key insight is **separation of concerns**: GPU-bound inference and
     request routing are fundamentally different workloads with different
     scaling characteristics.

     ---

     ## Current AbstractCore Architecture Analysis

     ### What AbstractCore IS Today

     ```
     ┌─────────────────────────────────────────────────────────────┐
     │                    AbstractCore                              │
     │  ┌─────────────────────────────────────────────────────┐    │
     │  │  Library Mode (create_llm)                           │    │
     │  │    ├── Provider Abstraction                          │    │
     │  │    ├── Universal Tools (@tool)                       │    │
     │  │    ├── Session Management                            │    │
     │  │    ├── Retry/Circuit Breaker                         │    │
     │  │    └── Event System                                  │    │
     │  └─────────────────────────────────────────────────────┘    │
     │  ┌─────────────────────────────────────────────────────┐    │
     │  │  Server Mode (FastAPI)                               │    │
     │  │    ├── OpenAI-compatible API                         │    │
     │  │    ├── Provider routing                              │    │
     │  │    └── Streaming support                             │    │
     │  └─────────────────────────────────────────────────────┘    │
     └─────────────────────────────────────────────────────────────┘
                                │
                                ▼
     ┌──────────────────────────────────────────────────────────────┐
     │                   Provider Backends                           │
     ├────────────┬────────────┬────────────┬────────────┬─────────┤
     │  OpenAI   │ Anthropic  │  Ollama    │  vLLM      │  MLX    │
     │  (API)    │  (API)     │  (HTTP)    │  (HTTP)    │(in-proc)│
     └────────────┴────────────┴────────────┴────────────┴─────────┘
     ```

     ### Provider Classification by Execution Model

     | Provider | Execution Model | Memory Model | Scaling Pattern |
     |----------|----------------|--------------|-----------------|
     | **OpenAI** | Remote API | None | Cloud-managed |
     | **Anthropic** | Remote API | None | Cloud-managed |
     | **Ollama** | Local HTTP service | Separate process | Single-GPU |
     | **LMStudio** | Local HTTP service | Separate process | Single-GPU |
     | **vLLM** | Remote/Local HTTP | Separate process | Multi-GPU, tensor
     parallel |
     | **HuggingFace** | **In-process** | Loads into AbstractCore |
     Single-GPU |
     | **MLX** | **In-process** | Loads into AbstractCore | Apple Silicon |

     ### Current Gaps (Identified in Codebase)

     1. **No request queuing** - Direct passthrough to providers
     2. **No load balancing** - Single provider per request
     3. **No connection pooling** - Each provider instance creates own HTTP
     client
     4. **No auto-scaling** - Fixed server capacity
     5. **No multi-provider orchestration** - Only backlog proposal exists

     ---

     ## SOTA LLM Infrastructure Patterns

     ### Pattern 1: Inference/Routing Separation (RECOMMENDED)

     ```
     ┌─────────────────────────────────────────────────────────────────┐
     │                      ROUTING LAYER                               │
     │  ┌──────────────────────────────────────────────────────────┐   │
     │  │  AbstractCore Server (Stateless, Horizontally Scalable)   │   │
     │  │    • Request validation & routing                         │   │
     │  │    • Provider abstraction                                 │   │
     │  │    • Tool call handling                                   │   │
     │  │    • Response normalization                               │   │
     │  │    • Event emission & observability                       │   │
     │  └──────────────────────────────────────────────────────────┘   │
     └─────────────────────────────────────────────────────────────────┘
                                    │
                                    ▼
     ┌─────────────────────────────────────────────────────────────────┐
     │                    INFERENCE LAYER                               │
     │  ┌──────────┐  ┌──────────┐  ┌──────────┐  ┌──────────────────┐ │
     │  │ Ollama   │  │ vLLM     │  │ TGI      │  │ Cloud APIs       │ │
     │  │ Server   │  │ Server   │  │ Server   │  │ (OpenAI/Anthro)  │ │
     │  │ (GPU 1)  │  │ (GPU 2-4)│  │ (GPU 5)  │  │                  │ │
     │  └──────────┘  └──────────┘  └──────────┘  └──────────────────┘ │
     └─────────────────────────────────────────────────────────────────┘
     ```

     **Why This Pattern?**

     1. **GPU Memory Isolation**: Inference servers manage GPU memory,
     continuous batching, tensor parallelism
     2. **Independent Scaling**: Route layer scales horizontally (cheap),
     inference scales vertically (expensive)
     3. **Fault Isolation**: Provider failure doesn't crash the router
     4. **Simpler Deployment**: Each component has single responsibility

     **Who Uses This Pattern?**
     - vLLM (server mode)
     - Text Generation Inference (TGI)
     - OpenRouter
     - LiteLLM Proxy
     - Anyscale/Ray Serve

     ### Pattern 2: Monolithic (Dev/Single-User Only)

     ```
     ┌─────────────────────────────────────────────────────┐
     │              Single Server/Process                   │
     │  ┌──────────────────────────────────────────────┐   │
     │  │  AbstractCore + HuggingFace/MLX (in-process)  │   │
     │  │  • Model loaded in same process               │   │
     │  │  • Low latency (no HTTP overhead)             │   │
     │  │  • Simple deployment                          │   │
     │  │  • Cannot scale                               │   │
     │  └──────────────────────────────────────────────┘   │
     └─────────────────────────────────────────────────────┘
     ```

     **When Acceptable:**
     - Development/testing
     - Single-user applications
     - Apple Silicon personal use (MLX)
     - Demo/prototype deployments

     ### Pattern 3: Queue-Based (High Throughput)

     ```
     ┌───────────────┐     ┌───────────────┐     ┌───────────────┐
     │   Clients     │────▶│  Load Balancer│────▶│ AbstractCore  │
     │               │     │  (nginx/HAP)  │     │ (Stateless)   │
     └───────────────┘     └───────────────┘     └───────┬───────┘
                                                          │
                                                          ▼
                                                 ┌───────────────┐
                                                 │ Message Queue │
                                                 │ (Redis/RabbitMQ)│
                                                 └───────┬───────┘
                                                          │

     ┌──────────────────────────────┼──────────────────────────────┐
                           ▼                              ▼
                  ▼
                   ┌───────────────┐            ┌───────────────┐
      ┌───────────────┐
                   │ Worker 1      │            │ Worker 2      │
      │ Worker 3      │
                   │ (vLLM GPU)    │            │ (Ollama GPU)  │
      │ (Cloud API)   │
                   └───────────────┘            └───────────────┘
      └───────────────┘
     ```

     **Benefits:**
     - Decouples request acceptance from processing
     - Handles burst traffic gracefully
     - Enables priority queuing
     - Supports retry without client waiting

     ---

     ## Recommended Architecture for AbstractCore

     ### Tier 1: Development/Personal (Current)

     ```
     Same machine, co-located:
     ┌─────────────────────────────────────────┐
     │  Developer Machine                       │
     │  ├── AbstractCore (library or server)   │
     │  ├── Ollama (local)                      │
     │  ├── LMStudio (local)                    │
     │  └── MLX (in-process)                    │
     └─────────────────────────────────────────┘
     ```

     - **AbstractCore Role**: Library + optional local server
     - **Scaling**: None needed
     - **Queue**: Not needed
     - **Orchestration**: Manual/none

     ### Tier 2: Team/Production (Recommended Enhancement)

     ```
     ┌─────────────────────────────────────────────────────────────────┐
     │                    ROUTING LAYER (Stateless)                     │
     │  ┌────────────────────────────────────────────────────────────┐ │
     │  │  Load Balancer (nginx/HAProxy/cloud LB)                     │ │
     │  └────────────────────────────────────────────────────────────┘ │
     │                           │                                      │
     │  ┌────────────┐  ┌────────────┐  ┌────────────┐                 │
     │  │AbstractCore│  │AbstractCore│  │AbstractCore│  (N replicas)   │
     │  │  Server 1  │  │  Server 2  │  │  Server 3  │                 │
     │  └────────────┘  └────────────┘  └────────────┘                 │
     └─────────────────────────────────────────────────────────────────┘
                                │
                                ▼
     ┌─────────────────────────────────────────────────────────────────┐
     │                   INFERENCE LAYER (Stateful)                     │
     │  ┌──────────────────────────────────────────────────────────┐   │
     │  │  GPU Server 1                                             │   │
     │  │  └── vLLM (4x L4, tensor-parallel-size=4)                │   │
     │  ├──────────────────────────────────────────────────────────┤   │
     │  │  GPU Server 2                                             │   │
     │  │  └── Ollama (various models)                              │   │
     │  ├──────────────────────────────────────────────────────────┤   │
     │  │  Cloud APIs                                               │   │
     │  │  └── OpenAI, Anthropic (remote)                          │   │
     │  └──────────────────────────────────────────────────────────┘   │
     └─────────────────────────────────────────────────────────────────┘
     ```

     - **AbstractCore Role**: Stateless routing/abstraction
     - **Scaling**: Horizontal (add more AbstractCore replicas)
     - **Queue**: Optional (handled by inference servers)
     - **Orchestration**: Load balancer + health checks

     ### Tier 3: Enterprise Scale (Future Enhancement)

     ```
     ┌─────────────────────────────────────────────────────────────────┐
     │  API Gateway (Kong/Ambassador)                                   │
     │  ├── Rate limiting                                               │
     │  ├── Authentication                                              │
     │  └── Tenant isolation                                            │
     └─────────────────────────────────────────────────────────────────┘
                                │
                                ▼
     ┌─────────────────────────────────────────────────────────────────┐
     │  AbstractCore Fleet (Kubernetes Deployment)                      │
     │  ├── Auto-scaling based on request rate                         │
     │  ├── Health checks + circuit breakers                            │
     │  └── Centralized configuration                                   │
     └─────────────────────────────────────────────────────────────────┘
                                │
               ┌────────────────┼────────────────┐
               ▼                ▼                ▼
     ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
     │ Request Queue   │ │ Priority Queue  │ │ Batch Queue     │
     │ (Fast requests) │ │ (VIP customers) │ │ (Bulk jobs)     │
     └─────────────────┘ └─────────────────┘ └─────────────────┘
               │                │                │
               └────────────────┼────────────────┘
                                ▼
     ┌─────────────────────────────────────────────────────────────────┐
     │  GPU Workers (Kubernetes StatefulSets)                           │
     │  ├── vLLM pods (auto-scaled based on queue depth)               │
     │  ├── Ollama pods                                                 │
     │  └── Specialized model pods                                      │
     └─────────────────────────────────────────────────────────────────┘
     ```

     ---

     ## Who Handles What?

     ### Current State

     | Component | Who Handles | Notes |
     |-----------|-------------|-------|
     | **Request routing** | AbstractCore | Model string parsing → provider
     |
     | **Retry/fallback** | AbstractCore | Per-provider circuit breaker |
     | **Queue** | Nobody | Direct passthrough |
     | **Load balancing** | Nobody | Single-provider requests |
     | **Inference** | Providers | Ollama, vLLM, cloud APIs |
     | **GPU memory** | Providers | Managed by inference servers |
     | **Scaling** | Manual | No auto-scaling |

     ### Recommended State

     | Component | Who Should Handle | Why |
     |-----------|-------------------|-----|
     | **Request routing** | AbstractCore | Core competency, provider
     abstraction |
     | **Retry/fallback** | AbstractCore | Already implemented, enhance with
      multi-provider |
     | **Queue** | External (Redis) + AbstractCore | AbstractCore submits to
      queue, workers consume |
     | **Load balancing** | External (nginx/k8s) + AbstractCore | LB for
     AbstractCore replicas, AC for provider selection |
     | **Inference** | Providers | Keep separate - different scaling
     characteristics |
     | **GPU memory** | Providers | Never mix with routing logic |
     | **Scaling** | Orchestrator (k8s) | Horizontal for AC, vertical for
     GPU |

     ---

     ## Specific Recommendations

     ### 1. Keep HuggingFace/MLX In-Process for Dev Only

     **Current**: HuggingFace and MLX load models into AbstractCore's
     process.

     **Recommendation**:
     - Keep for development/personal use
     - For production, move to HTTP server mode (vLLM or similar)
     - Document clearly that in-process mode doesn't scale

     ### 2. Add Optional Queue Integration (Future Feature)

     ```python
     # Proposed API for queue-based requests
     from abstractcore import create_llm, QueueConfig

     # Configure queue
     queue_config = QueueConfig(
         backend="redis",
         url="redis://localhost:6379",
         max_queue_size=1000,
         priority_levels=3
     )

     llm = create_llm("ollama", model="llama3:8b",
     queue_config=queue_config)

     # Async submit to queue
     job_id = await llm.submit("Long-running task")  # Returns immediately
     result = await llm.get_result(job_id)  # Poll or wait
     ```

     ### 3. Enhance Multi-Provider Routing (Use Existing Backlog)

     The `docs/backlog/multi-provider-requests.md` is excellent. Implement
     the hybrid approach:
     - Phase 1: Server API for parallel/cascade
     - Phase 2: Core library `MultiLLM` class

     ### 4. Add Connection Pooling (Quick Win)

     ```python
     # Proposed enhancement to base provider
     class BaseProvider:
         _connection_pool = None  # Shared across instances

         @classmethod
         def get_http_client(cls):
             if cls._connection_pool is None:
                 cls._connection_pool = httpx.AsyncClient(
                     limits=httpx.Limits(max_connections=100),
                     timeout=30.0
                 )
             return cls._connection_pool
     ```

     ### 5. Document Deployment Patterns

     Create `docs/deployment-patterns.md`:
     - Tier 1: Development (co-located)
     - Tier 2: Production (separated routing/inference)
     - Tier 3: Enterprise (k8s, queues, auto-scaling)

     ---

     ## Answering Your Questions Directly

     ### Q1: Should AbstractCore live on the same server as providers?

     **Short Answer**: **Depends on the tier.**

     | Tier | Same Server? | Rationale |
     |------|--------------|-----------|
     | Dev/Personal | Yes | Simplicity, low latency |
     | Team/Production | No | Separate concerns, fault isolation,
     independent scaling |
     | Enterprise | No | Must scale independently |

     ### Q2: Should it be a passthrough to distant servers?

     **Short Answer**: **Yes, for production.**

     AbstractCore's core value is **abstraction and unification**, not
     inference. Keep it as a lightweight routing layer that:
     - Validates and routes requests
     - Handles retries and fallbacks
     - Normalizes responses
     - Emits events for observability

     Let specialized inference servers (vLLM, Ollama, TGI) handle:
     - GPU memory management
     - Continuous batching
     - Tensor parallelism
     - Model quantization

     ### Q3: Who handles scalability?

     **Short Answer**: **Split responsibility.**

     | Layer | Scaling Approach | Who Manages |
     |-------|------------------|-------------|
     | AbstractCore | Horizontal (add replicas) | Kubernetes/Docker Swarm |
     | Inference | Vertical (bigger GPU) + Horizontal (more GPU servers) |
     vLLM/Ollama |
     | Queue | Horizontal (Redis cluster) | Redis/RabbitMQ |

     ### Q4: Who handles routing of multiple queries?

     **Short Answer**: **AbstractCore + External LB.**

     1. **External Load Balancer**: Distributes across AbstractCore replicas
     2. **AbstractCore**: Routes to correct provider based on model string
     3. **Future Enhancement**: Multi-provider strategies (parallel,
     cascade, race)

     ### Q5: Who orchestrates query queues?

     **Short Answer**: **External queue + AbstractCore workers.**

     For high-throughput scenarios:
     1. **AbstractCore Server**: Accepts requests, pushes to queue
     2. **Queue (Redis/RabbitMQ)**: Buffers requests, handles backpressure
     3. **AbstractCore Workers**: Consume from queue, call inference servers
     4. **Inference Servers**: Process requests, return results

     ---

     ## Implementation Roadmap

     ### Phase 1: Documentation (1 day)
     - Create `docs/deployment-patterns.md`
     - Document current architecture limitations
     - Recommend SOTA patterns for different scales

     ### Phase 2: Connection Pooling (1 week)
     - Implement shared connection pools per provider
     - 15-30% latency improvement, 20-40% memory savings
     - See backlog `003-connection-pool-optimization.md`

     ### Phase 3: Multi-Provider Routing (2-4 weeks)
     - Implement server API first (Phase 1 of backlog)
     - Add parallel, cascade, race strategies
     - See backlog `multi-provider-requests.md`

     ### Phase 4: Queue Integration (Optional, 2-3 weeks)
     - Design queue abstraction
     - Implement Redis backend
     - Add async submit/poll API

     ---

     ## Conclusion

     **AbstractCore is correctly designed as a routing/abstraction layer**,
     not an inference engine. The key architectural principle is:

     > **"AbstractCore should be thin, stateless, and horizontally scalable.
      Inference should be delegated to specialized servers that manage GPU 
     resources independently."**

     For production deployments:
     1. ✅ Keep AbstractCore as stateless router
     2. ✅ Deploy inference servers separately (vLLM, Ollama)
     3. ✅ Use external load balancer for AbstractCore replicas
     4. ✅ Consider queue for high-throughput scenarios
     5. ❌ Don't mix inference with routing (except dev mode)

     This architecture aligns with SOTA patterns from vLLM, TGI, OpenRouter,
      and LiteLLM, while preserving AbstractCore's core value proposition:
     **"Write once, run everywhere"** with provider abstraction and
     universal tools.















∴ Thinking…

  I've written a comprehensive analysis to the plan file. Now I need to present 
  this to the user in a clear, conversational way and ask if they have any 
  follow-up questions or want me to elaborate on specific aspects.

  This is not an implementation task - it's a strategic architecture question. 
  The user wants my opinion, not code changes. I should summarize my findings 
  and recommendations clearly, then check if they want more detail on any 
  aspect.

⏺ I've completed a thorough analysis of your architectural question. Here's
  my informed opinion:

  ---
  My Recommendation: Separation of Concerns

  AbstractCore should live INDEPENDENTLY from inference servers in 
  production deployments.

  The fundamental insight is that GPU-bound inference and request routing 
  are completely different workloads:

  | Aspect       | Routing (AbstractCore)      | Inference (vLLM, Ollama)
  |
  |--------------|-----------------------------|----------------------------
  |
  | CPU/Memory   | Low                         | High (GPU)
  |
  | Statefulness | Stateless                   | Stateful (model in memory)
  |
  | Scaling      | Horizontal (cheap replicas) | Vertical (bigger GPU)
  |
  | Failure mode | Restart instantly           | Model reload takes minutes
  |

  ---
  The SOTA Pattern (Used by vLLM, TGI, OpenRouter, LiteLLM)

  ┌─────────────────────────────────────────────────────┐
  │  ROUTING LAYER (Stateless, Horizontally Scalable)   │
  │  • AbstractCore instances behind load balancer      │
  │  • Request validation, provider abstraction         │
  │  • Retry logic, circuit breakers                    │
  │  • Event emission, observability                    │
  └────────────────────────┬────────────────────────────┘
                           │
  ┌────────────────────────▼────────────────────────────┐
  │  INFERENCE LAYER (Stateful, GPU-Bound)              │
  │  • vLLM (multi-GPU tensor parallelism)              │
  │  • Ollama (single GPU, easy model switching)        │
  │  • Cloud APIs (OpenAI, Anthropic)                   │
  └─────────────────────────────────────────────────────┘

  ---
  Who Handles What?

  | Component       | Best Handler
                      | Rationale                                        |
  |-----------------|-------------------------------------------------------
  --------------------|--------------------------------------------------|
  | Request routing | AbstractCore
                      | Core competency, already excellent               |
  | Retry/fallback  | AbstractCore
                      | Already implemented, enhance with multi-provider |
  | Queue           | External (Redis)
                      | AbstractCore submits, workers consume            |
  | Load balancing  | External (nginx/k8s) for AC replicas; AbstractCore for
   provider selection |                                                  |
  | Inference       | Providers (vLLM, Ollama)
                      | Keep separate - different scaling                |
  | GPU memory      | Providers ONLY
                      | Never mix with routing                           |
  | Orchestration   | Kubernetes/Docker Swarm
                      | Auto-scaling, health checks                      |

  ---
  Exception: Development/Personal Use

  Co-location is fine when:
  - Single user
  - Development/testing
  - Apple Silicon (MLX in-process is natural)
  - No scaling needed

  ---
  What AbstractCore Currently Lacks (For Production Scale)

  Based on my codebase analysis:

  1. No queue integration - Direct passthrough (fine for dev, not for burst
  traffic)
  2. No multi-provider routing - Backlog exists but not implemented
  3. No connection pooling - Each provider creates own HTTP client
  4. No load balancer awareness - Stateless design is good, but no health
  endpoints for k8s

  These are documented in your backlog (multi-provider-requests.md,
  003-connection-pool-optimization.md) - the architecture is sound, just
  needs enhancement for scale.

  ---
  Bottom Line

  AbstractCore is correctly designed as a routing/abstraction layer, not an
  inference engine. Your instinct to question this is good architectural
  thinking - the answer is:

  "Thin, stateless, horizontally scalable routing (AbstractCore) + Thick, 
  stateful, vertically scalable inference (vLLM/Ollama) = SOTA pattern"

  I've written a detailed analysis to
  /Users/albou/.claude/plans/breezy-singing-prism.md with diagrams,
  tier-based recommendations, and a roadmap for enhancements.
                                                                         
⏺ User answered Claude's questions:
  ⎿  · Would you like me to dive deeper into any specific aspect of this 
     architecture analysis? → Queue implementation design

∴ Thinking…

  The user wants me to dive deeper into queue implementation design. Let me 
  research SOTA patterns for queue integration in LLM systems and design a 
  clean, simple implementation for AbstractCore.

  I should look at:
  1. How existing LLM systems handle queuing (vLLM, TGI, Ray Serve)
  2. Common queue backends (Redis, RabbitMQ, Celery)
  3. What patterns make sense for AbstractCore's architecture
  4. A clean API design that maintains AbstractCore's simplicity

  Let me research and then update the plan file with a detailed queue 
  implementation design.

⏺ Let me research SOTA queue patterns for LLM systems and design a clean
  implementation for AbstractCore:

⏺ Web Search("vLLM request queue batching architecture 2025")
  ⎿  Did 1 search in 19s                                                   

⏺ Web Search("LLM inference queue Redis Celery architecture pattern 2025")
  ⎿  Did 1 search in 26s                                                   

∴ Thinking…

  I now have excellent information about SOTA queue patterns for LLM systems. 
  Let me update the plan file with a detailed queue implementation design for 
  AbstractCore.

  Key insights:
  1. vLLM uses internal continuous batching with a scheduler that maintains 
  waiting deque and running list
  2. For external queue (what AbstractCore would need), the Redis + Celery 
  pattern is standard
  3. Key components: FastAPI → Redis broker → Celery workers → Result backend 
  (Redis)
  4. SSE streaming pattern: Workers update Redis, FastAPI pulls updates and 
  streams to clients

  Let me design a clean queue implementation for AbstractCore that:
  1. Maintains the library-first philosophy
  2. Is optional (doesn't affect current users)
  3. Supports multiple backends (Redis, in-memory for dev)
  4. Integrates with existing event system
  5. Supports priority queuing
  6. Enables streaming via SSE

⏺ Now let me update the plan with a detailed queue implementation design:

⏺ Updated plan
  ⎿  Updated /Users/albou/.claude/plans/breezy-singing-prism.md with 574 
     additions
        430    
        431    ---
        432    
        433 +  # Appendix: Queue Implementation Design
        434 +  
        435 +  ## SOTA Research Summary
        436 +  
        437 +  Based on research from [vLLM's continuous batching 
            + architecture](https://blog.vllm.ai/2025/09/05/anatomy-of-vl
            + lm.html), [LLM + Celery + Redis 
            + patterns](https://www.jellyfishtechnologies.com/llm-driven-
            + api-orchestration-using-langchain-celery-redis-queue/), and
            +  [FastAPI + Celery deployment 
            + guides](https://towardsdatascience.com/deploying-ml-models-
            + in-production-with-fastapi-and-celery-7063e539a5db/), 
            + here's the SOTA pattern:
        438 +  
        439 +  ```
        440 +  ┌─────────────────┐     ┌─────────────────┐     
            + ┌─────────────────┐
        441 +  │   Client        │────▶│  AbstractCore   │────▶│  Redis
            +  Broker   │
        442 +  │   (submit job)  │     │  (FastAPI)      │     │  (queue)
            +         │
        443 +  └─────────────────┘     └────────┬────────┘     
            + └────────┬────────┘
        444 +                                   │                        
            + │
        445 +                                   │ SSE Stream             
            + │ Task Dispatch
        446 +                                   ▼                        
            + ▼
        447 +                          ┌─────────────────┐     
            + ┌─────────────────┐
        448 +                          │  Redis Results  │◀────│  Celery
            +  Workers │
        449 +                          │  (progress/done)│     │  
            + (inference)    │
        450 +                          └─────────────────┘     
            + └─────────────────┘
        451 +  ```
        452 +  
        453 +  **Key Insights:**
        454 +  1. **vLLM**: Internal scheduler with waiting deque + 
            + running list; continuous batching at GPU level
        455 +  2. **External Queue**: Redis as broker + result backend; 
            + Celery workers consume and process
        456 +  3. **Streaming**: Workers update Redis, FastAPI polls and 
            + streams via SSE
        457 +  4. **Timeouts**: Soft limit ~9 min, hard limit ~10 min for
            +  LLM operations
        458 +  
        459 +  ---
        460 +  
        461 +  ## AbstractCore Queue Design
        462 +  
        463 +  ### Design Principles
        464 +  
        465 +  1. **Optional** - Default behavior unchanged; queue is 
            + opt-in
        466 +  2. **Backend Agnostic** - Abstract interface with Redis, 
            + In-Memory implementations
        467 +  3. **Library-First** - Works without server; server just 
            + adds HTTP access
        468 +  4. **Event Integration** - Queue events flow through 
            + existing EventBus
        469 +  5. **Priority Support** - Multiple priority levels for 
            + different use cases
        470 +  6. **Streaming Compatible** - SSE streaming for real-time 
            + progress
        471 +  
        472 +  ### Architecture
        473 +  
        474 +  ```
        475 +  ┌─────────────────────────────────────────────────────────
            + ────────┐
        476 +  │                    AbstractCore Queue Layer             
            +          │
        477 +  │  ┌──────────────────────────────────────────────────────
            + ─────┐  │
        478 +  │  │  QueueManager (Singleton)                            
            +       │  │
        479 +  │  │    ├── submit(request) → job_id                      
            +      │  │
        480 +  │  │    ├── get_status(job_id) → JobStatus                
            +      │  │
        481 +  │  │    ├── get_result(job_id) → GenerateResponse         
            +      │  │
        482 +  │  │    ├── cancel(job_id) → bool                         
            +      │  │
        483 +  │  │    └── stream(job_id) → AsyncIterator[ProgressEvent] 
            +      │  │
        484 +  │  └──────────────────────────────────────────────────────
            + ─────┘  │
        485 +  │                              │                          
            +          │
        486 +  │  ┌───────────────────────────▼──────────────────────────
            + ─────┐  │
        487 +  │  │  QueueBackend (Abstract Interface)                   
            +       │  │
        488 +  │  │    ├── enqueue(job: Job) → None                      
            +      │  │
        489 +  │  │    ├── dequeue() → Optional[Job]                     
            +      │  │
        490 +  │  │    ├── get_job(job_id) → Optional[Job]               
            +      │  │
        491 +  │  │    ├── update_job(job_id, status, result) → None     
            +      │  │
        492 +  │  │    └── subscribe(job_id) → AsyncIterator[Event]      
            +      │  │
        493 +  │  └──────────────────────────────────────────────────────
            + ─────┘  │
        494 +  │                              │                          
            +          │
        495 +  │         ┌────────────────────┼────────────────────┐     
            +         │
        496 +  │         ▼                    ▼                    ▼     
            +         │
        497 +  │  ┌─────────────┐     ┌─────────────┐     ┌─────────────┐
            +        │
        498 +  │  │ InMemory    │     │   Redis     │     │  Future:    │
            +        │
        499 +  │  │ Backend     │     │   Backend   │     │  RabbitMQ   │
            +        │
        500 +  │  │ (dev/test)  │     │ (production)│     │  Kafka      │
            +        │
        501 +  │  └─────────────┘     └─────────────┘     └─────────────┘
            +        │
        502 +  └─────────────────────────────────────────────────────────
            + ────────┘
        503 +  ```
        504 +  
        505 +  ### Data Models
        506 +  
        507 +  ```python
        508 +  # abstractcore/queue/models.py
        509 +  
        510 +  from enum import Enum
        511 +  from dataclasses import dataclass
        512 +  from datetime import datetime
        513 +  from typing import Optional, Dict, Any, List
        514 +  
        515 +  class JobStatus(Enum):
        516 +      PENDING = "pending"       # In queue, not started
        517 +      RUNNING = "running"       # Being processed by worker
        518 +      COMPLETED = "completed"   # Successfully finished
        519 +      FAILED = "failed"         # Failed with error
        520 +      CANCELLED = "cancelled"   # Cancelled by user
        521 +      TIMEOUT = "timeout"       # Exceeded time limit
        522 +  
        523 +  class Priority(Enum):
        524 +      LOW = 0
        525 +      NORMAL = 1
        526 +      HIGH = 2
        527 +      URGENT = 3
        528 +  
        529 +  @dataclass
        530 +  class Job:
        531 +      job_id: str
        532 +      provider: str
        533 +      model: str
        534 +      prompt: str
        535 +      messages: Optional[List[Dict]] = None
        536 +      kwargs: Optional[Dict[str, Any]] = None
        537 +      priority: Priority = Priority.NORMAL
        538 +      status: JobStatus = JobStatus.PENDING
        539 +      created_at: datetime = None
        540 +      started_at: Optional[datetime] = None
        541 +      completed_at: Optional[datetime] = None
        542 +      result: Optional[Dict] = None  # GenerateResponse as 
            + dict
        543 +      error: Optional[str] = None
        544 +      progress: float = 0.0  # 0.0 to 1.0
        545 +      metadata: Optional[Dict] = None  # User-defined 
            + metadata
        546 +  
        547 +  @dataclass
        548 +  class ProgressEvent:
        549 +      job_id: str
        550 +      status: JobStatus
        551 +      progress: float
        552 +      message: Optional[str] = None
        553 +      partial_content: Optional[str] = None  # For streaming
        554 +      tokens_generated: int = 0
        555 +  ```
        556 +  
        557 +  ### API Design
        558 +  
        559 +  ```python
        560 +  # abstractcore/queue/__init__.py
        561 +  
        562 +  from abstractcore.queue.manager import QueueManager
        563 +  from abstractcore.queue.models import Job, JobStatus, 
            + Priority, ProgressEvent
        564 +  from abstractcore.queue.backends import InMemoryBackend, 
            + RedisBackend
        565 +  
        566 +  __all__ = [
        567 +      'QueueManager',
        568 +      'Job', 'JobStatus', 'Priority', 'ProgressEvent',
        569 +      'InMemoryBackend', 'RedisBackend'
        570 +  ]
        571 +  
        572 +  # Convenience functions at package level
        573 +  _default_manager: Optional[QueueManager] = None
        574 +  
        575 +  def configure_queue(backend: str = "memory", **kwargs):
        576 +      """Configure the default queue manager."""
        577 +      global _default_manager
        578 +      if backend == "memory":
        579 +          _default_manager = QueueManager(InMemoryBackend())
        580 +      elif backend == "redis":
        581 +          _default_manager = 
            + QueueManager(RedisBackend(**kwargs))
        582 +      else:
        583 +          raise ValueError(f"Unknown backend: {backend}")
        584 +  
        585 +  def get_queue() -> QueueManager:
        586 +      """Get the configured queue manager."""
        587 +      if _default_manager is None:
        588 +          raise RuntimeError("Queue not configured. Call 
            + configure_queue() first.")
        589 +      return _default_manager
        590 +  ```
        591 +  
        592 +  ### Usage Examples
        593 +  
        594 +  #### Basic Queue Usage (Library Mode)
        595 +  
        596 +  ```python
        597 +  from abstractcore import create_llm
        598 +  from abstractcore.queue import configure_queue, get_queue,
            +  Priority
        599 +  
        600 +  # Configure queue (development - in-memory)
        601 +  configure_queue(backend="memory")
        602 +  
        603 +  # Or production - Redis
        604 +  configure_queue(
        605 +      backend="redis",
        606 +      url="redis://localhost:6379",
        607 +      db=0,
        608 +      prefix="abstractcore:"
        609 +  )
        610 +  
        611 +  queue = get_queue()
        612 +  
        613 +  # Submit job (returns immediately)
        614 +  job_id = await queue.submit(
        615 +      provider="ollama",
        616 +      model="llama3:8b",
        617 +      prompt="Write a long essay about quantum computing",
        618 +      priority=Priority.NORMAL,
        619 +      metadata={"user_id": "user123", "request_type": 
            + "essay"}
        620 +  )
        621 +  
        622 +  print(f"Job submitted: {job_id}")
        623 +  
        624 +  # Check status
        625 +  status = await queue.get_status(job_id)
        626 +  print(f"Status: {status.status}, Progress: 
            + {status.progress:.0%}")
        627 +  
        628 +  # Wait for result (blocking)
        629 +  result = await queue.get_result(job_id, timeout=300)  # 5 
            + min timeout
        630 +  print(f"Result: {result.content[:100]}...")
        631 +  
        632 +  # Or poll with async for
        633 +  async for event in queue.stream(job_id):
        634 +      print(f"Progress: {event.progress:.0%} - 
            + {event.message}")
        635 +      if event.partial_content:
        636 +          print(f"Partial: {event.partial_content}", end="")
        637 +      if event.status in (JobStatus.COMPLETED, 
            + JobStatus.FAILED):
        638 +          break
        639 +  ```
        640 +  
        641 +  #### With Priority Queuing
        642 +  
        643 +  ```python
        644 +  # High priority request (VIP customer)
        645 +  high_priority_job = await queue.submit(
        646 +      provider="openai",
        647 +      model="gpt-4",
        648 +      prompt="Urgent: summarize this contract",
        649 +      priority=Priority.URGENT
        650 +  )
        651 +  
        652 +  # Low priority request (batch job)
        653 +  low_priority_job = await queue.submit(
        654 +      provider="ollama",
        655 +      model="llama3:8b",
        656 +      prompt="Generate 100 product descriptions",
        657 +      priority=Priority.LOW
        658 +  )
        659 +  
        660 +  # Urgent jobs processed before normal, normal before low
        661 +  ```
        662 +  
        663 +  #### Worker Process (Separate from API)
        664 +  
        665 +  ```python
        666 +  # worker.py - Run this in separate processes
        667 +  
        668 +  from abstractcore import create_llm
        669 +  from abstractcore.queue import configure_queue, get_queue,
            +  JobStatus
        670 +  
        671 +  configure_queue(backend="redis", 
            + url="redis://localhost:6379")
        672 +  queue = get_queue()
        673 +  
        674 +  async def worker_loop():
        675 +      """Continuously process jobs from queue."""
        676 +      while True:
        677 +          job = await queue.dequeue()  # Blocking wait for 
            + next job
        678 +          if job is None:
        679 +              continue
        680 +  
        681 +          try:
        682 +              # Update status to running
        683 +              await queue.update_status(job.job_id, 
            + JobStatus.RUNNING)
        684 +  
        685 +              # Create LLM and generate
        686 +              llm = create_llm(job.provider, 
            + model=job.model)
        687 +  
        688 +              if job.kwargs.get("stream"):
        689 +                  # Stream with progress updates
        690 +                  content = ""
        691 +                  async for chunk in 
            + llm.agenerate(job.prompt, stream=True):
        692 +                      content += chunk.content
        693 +                      await queue.update_progress(
        694 +                          job.job_id,
        695 +                          progress=len(content) / 1000,  # 
            + Estimate
        696 +                          partial_content=chunk.content
        697 +                      )
        698 +                  result = GenerateResponse(content=content,
            +  ...)
        699 +              else:
        700 +                  result = await llm.agenerate(job.prompt, 
            + **job.kwargs)
        701 +  
        702 +              # Mark completed
        703 +              await queue.update_status(
        704 +                  job.job_id,
        705 +                  JobStatus.COMPLETED,
        706 +                  result=result.to_dict()
        707 +              )
        708 +  
        709 +          except Exception as e:
        710 +              await queue.update_status(
        711 +                  job.job_id,
        712 +                  JobStatus.FAILED,
        713 +                  error=str(e)
        714 +              )
        715 +  
        716 +  # Run with: python worker.py
        717 +  asyncio.run(worker_loop())
        718 +  ```
        719 +  
        720 +  #### Server API Integration
        721 +  
        722 +  ```python
        723 +  # abstractcore/server/app.py additions
        724 +  
        725 +  from abstractcore.queue import get_queue, JobStatus
        726 +  
        727 +  @app.post("/v1/queue/submit")
        728 +  async def submit_to_queue(request: ChatCompletionRequest):
        729 +      """Submit request to queue, return job ID 
            + immediately."""
        730 +      queue = get_queue()
        731 +  
        732 +      job_id = await queue.submit(
        733 +          provider=request.provider,
        734 +          model=request.model,
        735 +          messages=request.messages,
        736 +          kwargs=request.dict(exclude={"messages", "model"})
        737 +      )
        738 +  
        739 +      return {"job_id": job_id, "status": "pending"}
        740 +  
        741 +  @app.get("/v1/queue/status/{job_id}")
        742 +  async def get_job_status(job_id: str):
        743 +      """Get current status of a queued job."""
        744 +      queue = get_queue()
        745 +      status = await queue.get_status(job_id)
        746 +  
        747 +      return {
        748 +          "job_id": job_id,
        749 +          "status": status.status.value,
        750 +          "progress": status.progress,
        751 +          "created_at": status.created_at.isoformat(),
        752 +          "started_at": status.started_at.isoformat() if 
            + status.started_at else None
        753 +      }
        754 +  
        755 +  @app.get("/v1/queue/result/{job_id}")
        756 +  async def get_job_result(job_id: str, wait: bool = False, 
            + timeout: int = 60):
        757 +      """Get result of completed job."""
        758 +      queue = get_queue()
        759 +  
        760 +      if wait:
        761 +          result = await queue.get_result(job_id, 
            + timeout=timeout)
        762 +      else:
        763 +          result = await queue.get_result_nowait(job_id)
        764 +          if result is None:
        765 +              raise HTTPException(404, "Job not completed 
            + yet")
        766 +  
        767 +      return result
        768 +  
        769 +  @app.get("/v1/queue/stream/{job_id}")
        770 +  async def stream_job_progress(job_id: str):
        771 +      """Stream job progress via SSE."""
        772 +      queue = get_queue()
        773 +  
        774 +      async def event_generator():
        775 +          async for event in queue.stream(job_id):
        776 +              yield {
        777 +                  "event": event.status.value,
        778 +                  "data": {
        779 +                      "progress": event.progress,
        780 +                      "message": event.message,
        781 +                      "partial_content": 
            + event.partial_content
        782 +                  }
        783 +              }
        784 +              if event.status in (JobStatus.COMPLETED, 
            + JobStatus.FAILED):
        785 +                  break
        786 +  
        787 +      return EventSourceResponse(event_generator())
        788 +  
        789 +  @app.delete("/v1/queue/cancel/{job_id}")
        790 +  async def cancel_job(job_id: str):
        791 +      """Cancel a pending or running job."""
        792 +      queue = get_queue()
        793 +      cancelled = await queue.cancel(job_id)
        794 +  
        795 +      return {"job_id": job_id, "cancelled": cancelled}
        796 +  ```
        797 +  
        798 +  ### Redis Backend Implementation
        799 +  
        800 +  ```python
        801 +  # abstractcore/queue/backends/redis_backend.py
        802 +  
        803 +  import redis.asyncio as redis
        804 +  import json
        805 +  from typing import Optional, AsyncIterator
        806 +  from datetime import datetime
        807 +  
        808 +  class RedisBackend(QueueBackend):
        809 +      """Redis-based queue backend for production use."""
        810 +  
        811 +      def __init__(
        812 +          self,
        813 +          url: str = "redis://localhost:6379",
        814 +          db: int = 0,
        815 +          prefix: str = "abstractcore:",
        816 +          max_connections: int = 10
        817 +      ):
        818 +          self.url = url
        819 +          self.prefix = prefix
        820 +          self._pool = redis.ConnectionPool.from_url(
        821 +              url, db=db, max_connections=max_connections
        822 +          )
        823 +          self._client = 
            + redis.Redis(connection_pool=self._pool)
        824 +  
        825 +      def _key(self, key_type: str, key_id: str) -> str:
        826 +          """Generate Redis key with prefix."""
        827 +          return f"{self.prefix}{key_type}:{key_id}"
        828 +  
        829 +      async def enqueue(self, job: Job) -> None:
        830 +          """Add job to priority queue."""
        831 +          job_data = job.to_dict()
        832 +          job_data["created_at"] = 
            + datetime.utcnow().isoformat()
        833 +  
        834 +          async with self._client.pipeline() as pipe:
        835 +              # Store job data
        836 +              await pipe.hset(
        837 +                  self._key("job", job.job_id),
        838 +                  mapping={"data": json.dumps(job_data)}
        839 +              )
        840 +              # Add to priority queue (sorted set, score = 
            + priority * 1e12 + timestamp)
        841 +              score = job.priority.value * 1e12 + 
            + datetime.utcnow().timestamp()
        842 +              await pipe.zadd(
        843 +                  self._key("queue", "pending"),
        844 +                  {job.job_id: score}
        845 +              )
        846 +              await pipe.execute()
        847 +  
        848 +      async def dequeue(self) -> Optional[Job]:
        849 +          """Get highest priority job from queue 
            + (blocking)."""
        850 +          # BZPOPMAX: blocking pop of highest score
        851 +          result = await self._client.bzpopmax(
        852 +              self._key("queue", "pending"),
        853 +              timeout=10  # Check every 10s for shutdown 
            + signals
        854 +          )
        855 +  
        856 +          if result is None:
        857 +              return None
        858 +  
        859 +          _, job_id, _ = result
        860 +          job_id = job_id.decode() if isinstance(job_id, 
            + bytes) else job_id
        861 +  
        862 +          # Get full job data
        863 +          job_data = await self._client.hget(
        864 +              self._key("job", job_id), "data"
        865 +          )
        866 +  
        867 +          if job_data is None:
        868 +              return None
        869 +  
        870 +          return Job.from_dict(json.loads(job_data))
        871 +  
        872 +      async def update_job(
        873 +          self,
        874 +          job_id: str,
        875 +          status: JobStatus,
        876 +          result: Optional[dict] = None,
        877 +          error: Optional[str] = None,
        878 +          progress: float = 0.0
        879 +      ) -> None:
        880 +          """Update job status and publish event."""
        881 +          updates = {
        882 +              "status": status.value,
        883 +              "progress": progress,
        884 +              "updated_at": datetime.utcnow().isoformat()
        885 +          }
        886 +  
        887 +          if status == JobStatus.RUNNING:
        888 +              updates["started_at"] = 
            + datetime.utcnow().isoformat()
        889 +          elif status in (JobStatus.COMPLETED, 
            + JobStatus.FAILED):
        890 +              updates["completed_at"] = 
            + datetime.utcnow().isoformat()
        891 +              if result:
        892 +                  updates["result"] = json.dumps(result)
        893 +              if error:
        894 +                  updates["error"] = error
        895 +  
        896 +          # Update job hash
        897 +          await self._client.hset(
        898 +              self._key("job", job_id),
        899 +              mapping=updates
        900 +          )
        901 +  
        902 +          # Publish event for SSE subscribers
        903 +          await self._client.publish(
        904 +              self._key("events", job_id),
        905 +              json.dumps({
        906 +                  "status": status.value,
        907 +                  "progress": progress,
        908 +                  "message": f"Job {status.value}"
        909 +              })
        910 +          )
        911 +  
        912 +      async def subscribe(self, job_id: str) -> 
            + AsyncIterator[ProgressEvent]:
        913 +          """Subscribe to job progress events."""
        914 +          pubsub = self._client.pubsub()
        915 +          await pubsub.subscribe(self._key("events", 
            + job_id))
        916 +  
        917 +          async for message in pubsub.listen():
        918 +              if message["type"] == "message":
        919 +                  data = json.loads(message["data"])
        920 +                  yield ProgressEvent(
        921 +                      job_id=job_id,
        922 +                      status=JobStatus(data["status"]),
        923 +                      progress=data["progress"],
        924 +                      message=data.get("message"),
        925 +                      
            + partial_content=data.get("partial_content")
        926 +                  )
        927 +  
        928 +                  if data["status"] in ("completed", 
            + "failed", "cancelled"):
        929 +                      break
        930 +  
        931 +          await pubsub.unsubscribe()
        932 +  ```
        933 +  
        934 +  ### Event System Integration
        935 +  
        936 +  ```python
        937 +  # New event types for queue operations
        938 +  class EventType(Enum):
        939 +      # Existing...
        940 +      GENERATION_STARTED = "generation_started"
        941 +      GENERATION_COMPLETED = "generation_completed"
        942 +  
        943 +      # New queue events
        944 +      QUEUE_JOB_SUBMITTED = "queue_job_submitted"
        945 +      QUEUE_JOB_STARTED = "queue_job_started"
        946 +      QUEUE_JOB_PROGRESS = "queue_job_progress"
        947 +      QUEUE_JOB_COMPLETED = "queue_job_completed"
        948 +      QUEUE_JOB_FAILED = "queue_job_failed"
        949 +      QUEUE_JOB_CANCELLED = "queue_job_cancelled"
        950 +  
        951 +  # Queue manager emits events
        952 +  class QueueManager:
        953 +      async def submit(self, ...) -> str:
        954 +          job_id = str(uuid.uuid4())
        955 +          job = Job(job_id=job_id, ...)
        956 +  
        957 +          await self.backend.enqueue(job)
        958 +  
        959 +          emit_global(EventType.QUEUE_JOB_SUBMITTED, {
        960 +              "job_id": job_id,
        961 +              "provider": job.provider,
        962 +              "model": job.model,
        963 +              "priority": job.priority.value
        964 +          })
        965 +  
        966 +          return job_id
        967 +  ```
        968 +  
        969 +  ---
        970 +  
        971 +  ## Implementation Phases
        972 +  
        973 +  ### Phase 1: Core Queue Infrastructure (1 week)
        974 +  - `abstractcore/queue/models.py` - Data models
        975 +  - `abstractcore/queue/backends/base.py` - Abstract backend
            +  interface
        976 +  - `abstractcore/queue/backends/memory.py` - In-memory 
            + backend (dev/test)
        977 +  - `abstractcore/queue/manager.py` - QueueManager class
        978 +  - `abstractcore/queue/__init__.py` - Public API
        979 +  
        980 +  ### Phase 2: Redis Backend (3-4 days)
        981 +  - `abstractcore/queue/backends/redis.py` - Redis 
            + implementation
        982 +  - Add `redis` to optional dependencies (`pip install 
            + abstractcore[queue]`)
        983 +  - Connection pooling and error handling
        984 +  - Pub/sub for progress streaming
        985 +  
        986 +  ### Phase 3: Server API (2-3 days)
        987 +  - Add queue endpoints to `abstractcore/server/app.py`
        988 +  - SSE streaming for progress
        989 +  - OpenAPI documentation
        990 +  
        991 +  ### Phase 4: Worker Framework (3-4 days)
        992 +  - `abstractcore/queue/worker.py` - Worker process 
            + implementation
        993 +  - Graceful shutdown handling
        994 +  - Concurrent worker support
        995 +  - Retry logic for failed jobs
        996 +  
        997 +  ### Phase 5: Documentation & Testing (2-3 days)
        998 +  - Comprehensive test suite
        999 +  - `docs/queue-integration.md` - User guide
       1000 +  - Docker Compose example with Redis
       1001 +  - Benchmark results
       1002 +  
       1003 +  **Total Effort: ~3 weeks**
       1004 +  
       1005 +  ---
       1006 +  
       1007    ## Conclusion
       1008    
       1009    **AbstractCore is correctly designed as a 
              routing/abstraction layer**, not an inference engine. The 
              key architectural principle is:

∴ Thinking…

  I've added a comprehensive queue implementation design to the plan file. Now I
   should summarize this for the user and check if they have any more questions 
  or are satisfied with the analysis.

⏺ I've added a comprehensive queue implementation design to the plan. Here's
   a summary:

  ---
  Queue Architecture Design for AbstractCore

  SOTA Pattern (from https://blog.vllm.ai/2025/09/05/anatomy-of-vllm.html, 
  https://www.jellyfishtechnologies.com/llm-driven-api-orchestration-using-l
  angchain-celery-redis-queue/)

  Client → AbstractCore (FastAPI) → Redis Broker → Workers → Inference
  Servers
                  ↓
          SSE Stream (progress updates from Redis Pub/Sub)

  Key Design Decisions

  | Decision | Choice                               | Rationale
                               |
  |----------|--------------------------------------|-----------------------
  -----------------------------|
  | Backend  | Abstract + Redis/InMemory            | Redis for prod,
  InMemory for dev/test              |
  | Priority | 4 levels (LOW, NORMAL, HIGH, URGENT) | Redis sorted sets with
   priority * 1e12 + timestamp |
  | Progress | Redis Pub/Sub → SSE                  | Real-time updates
  without polling                  |
  | Workers  | Separate processes                   | Scale independently
  from API                       |
  | Optional | pip install abstractcore[queue]      | Don't bloat core
  library                           |

  API Design (Library Mode)

  from abstractcore.queue import configure_queue, get_queue, Priority

  # Configure (dev)
  configure_queue(backend="memory")

  # Configure (prod)
  configure_queue(backend="redis", url="redis://localhost:6379")

  # Submit (returns immediately)
  job_id = await queue.submit(provider="ollama", model="llama3:8b",
  prompt="...")

  # Stream progress
  async for event in queue.stream(job_id):
      print(f"Progress: {event.progress:.0%}")

  Server API

  | Endpoint              | Method | Purpose                |
  |-----------------------|--------|------------------------|
  | /v1/queue/submit      | POST   | Submit job, get job_id |
  | /v1/queue/status/{id} | GET    | Check status           |
  | /v1/queue/result/{id} | GET    | Get completed result   |
  | /v1/queue/stream/{id} | GET    | SSE progress stream    |
  | /v1/queue/cancel/{id} | DELETE | Cancel job             |

  Implementation Phases (~3 weeks total)

  1. Core Infrastructure (1 week) - Models, abstract backend, QueueManager
  2. Redis Backend (3-4 days) - Production implementation with Pub/Sub
  3. Server API (2-3 days) - HTTP endpoints with SSE
  4. Worker Framework (3-4 days) - Separate worker processes
  5. Docs & Testing (2-3 days) - Comprehensive tests + Docker Compose
  example

  ---
  The complete design with code examples is in the plan file at
  /Users/albou/.claude/plans/breezy-singing-prism.md.
                                                                          
⏺ User answered Claude's questions:
  ⎿  · Is there anything else you'd like me to elaborate on regarding the 
     deployment architecture or queue design? → Kubernetes manifests

∴ Thinking…

  The user wants Kubernetes manifests for auto-scaling. Let me add a 
  comprehensive Kubernetes deployment section to the plan, including:

  1. Deployment manifests for AbstractCore (stateless, horizontally scalable)2. 
  Service definitions
  2. HPA (Horizontal Pod Autoscaler) for auto-scaling
  3. Redis deployment (or recommend external Redis)
  4. Worker deployment (separate from API)6. ConfigMaps and Secrets7. Ingress 
  configuration

  I'll add this to the plan file with production-ready k8s manifests.

⏺ Let me add comprehensive Kubernetes manifests for production deployment:

⏺ Updated plan
  ⎿  Updated /Users/albou/.claude/plans/breezy-singing-prism.md with 743 
     additions
       1004    
       1005    ---
       1006    
       1007 +  # Appendix: Kubernetes Deployment
       1008 +  
       1009 +  ## Overview
       1010 +  
       1011 +  Production-grade Kubernetes deployment for AbstractCore 
            + with:
       1012 +  - **AbstractCore API** - Stateless, horizontally scalable 
            + routing layer
       1013 +  - **Workers** - Separate pods for queue processing
       1014 +  - **Redis** - Queue broker and result backend
       1015 +  - **Inference Servers** - vLLM/Ollama as StatefulSets
       1016 +  
       1017 +  ## Architecture
       1018 +  
       1019 +  ```
       1020 +  ┌─────────────────────────────────────────────────────────
            + ───────────┐
       1021 +  │  Kubernetes Cluster                                     
            +             │
       1022 +  │  ┌──────────────────────────────────────────────────────
            + ────────┐  │
       1023 +  │  │  Ingress (nginx/traefik)                             
            +          │  │
       1024 +  │  │    └── abstractcore.yourdomain.com                   
            +          │  │
       1025 +  │  └──────────────────────────────────────────────────────
            + ────────┘  │
       1026 +  │                               │                         
            +             │
       1027 +  │  ┌────────────────────────────▼─────────────────────────
            + ────────┐  │
       1028 +  │  │  Service: abstractcore-api (ClusterIP)               
            +          │  │
       1029 +  │  │    └── Deployment: abstractcore-api (HPA: 2-10 
            + replicas)     │  │
       1030 +  │  │         └── Pod: abstractcore-api                    
            +          │  │
       1031 +  │  │              ├── Container: abstractcore             
            +          │  │
       1032 +  │  │              └── Resources: 256Mi-512Mi, 0.25-0.5 CPU
            +         │  │
       1033 +  │  └──────────────────────────────────────────────────────
            + ────────┘  │
       1034 +  │                               │                         
            +             │
       1035 +  │  ┌────────────────────────────▼─────────────────────────
            + ────────┐  │
       1036 +  │  │  Service: redis (ClusterIP)                          
            +          │  │
       1037 +  │  │    └── StatefulSet: redis (1 replica)                
            +         │  │
       1038 +  │  │         └── Pod: redis                               
            +          │  │
       1039 +  │  │              └── Volume: redis-data (PVC)            
            +          │  │
       1040 +  │  └──────────────────────────────────────────────────────
            + ────────┘  │
       1041 +  │                               │                         
            +             │
       1042 +  │  ┌────────────────────────────▼─────────────────────────
            + ────────┐  │
       1043 +  │  │  Deployment: abstractcore-worker (HPA: 1-5 replicas) 
            +         │  │
       1044 +  │  │    └── Pod: abstractcore-worker                      
            +          │  │
       1045 +  │  │         └── Container: abstractcore-worker           
            +          │  │
       1046 +  │  │              └── Resources: 512Mi-1Gi, 0.5-1 CPU     
            +         │  │
       1047 +  │  └──────────────────────────────────────────────────────
            + ────────┘  │
       1048 +  │                               │                         
            +             │
       1049 +  │  ┌────────────────────────────▼─────────────────────────
            + ────────┐  │
       1050 +  │  │  StatefulSet: vllm (GPU nodes)                       
            +          │  │
       1051 +  │  │    └── Pod: vllm-0                                   
            +          │  │
       1052 +  │  │         ├── Container: vllm                          
            +          │  │
       1053 +  │  │         ├── Resources: nvidia.com/gpu: 4             
            +         │  │
       1054 +  │  │         └── Volume: model-cache (PVC)                
            +          │  │
       1055 +  │  └──────────────────────────────────────────────────────
            + ────────┘  │
       1056 +  └─────────────────────────────────────────────────────────
            + ───────────┘
       1057 +  ```
       1058 +  
       1059 +  ## Manifests
       1060 +  
       1061 +  ### 1. Namespace and ConfigMap
       1062 +  
       1063 +  ```yaml
       1064 +  # k8s/namespace.yaml
       1065 +  apiVersion: v1
       1066 +  kind: Namespace
       1067 +  metadata:
       1068 +    name: abstractcore
       1069 +    labels:
       1070 +      app.kubernetes.io/name: abstractcore
       1071 +  ---
       1072 +  # k8s/configmap.yaml
       1073 +  apiVersion: v1
       1074 +  kind: ConfigMap
       1075 +  metadata:
       1076 +    name: abstractcore-config
       1077 +    namespace: abstractcore
       1078 +  data:
       1079 +    # Provider configuration
       1080 +    OLLAMA_BASE_URL: "http://ollama:11434"
       1081 +    LMSTUDIO_BASE_URL: "http://lmstudio:1234"
       1082 +    VLLM_BASE_URL: "http://vllm:8000/v1"
       1083 +  
       1084 +    # Queue configuration
       1085 +    QUEUE_BACKEND: "redis"
       1086 +    REDIS_URL: "redis://redis:6379"
       1087 +    QUEUE_PREFIX: "abstractcore:"
       1088 +  
       1089 +    # Server configuration
       1090 +    ABSTRACTCORE_LOG_LEVEL: "INFO"
       1091 +    ABSTRACTCORE_DEBUG: "false"
       1092 +  
       1093 +    # Worker configuration
       1094 +    WORKER_CONCURRENCY: "4"
       1095 +    WORKER_MAX_RETRIES: "3"
       1096 +    WORKER_TIMEOUT: "600"  # 10 minutes
       1097 +  ```
       1098 +  
       1099 +  ### 2. Secrets
       1100 +  
       1101 +  ```yaml
       1102 +  # k8s/secrets.yaml
       1103 +  apiVersion: v1
       1104 +  kind: Secret
       1105 +  metadata:
       1106 +    name: abstractcore-secrets
       1107 +    namespace: abstractcore
       1108 +  type: Opaque
       1109 +  stringData:
       1110 +    # API keys (replace with actual values or use external 
            + secrets manager)
       1111 +    OPENAI_API_KEY: "sk-..."
       1112 +    ANTHROPIC_API_KEY: "sk-ant-..."
       1113 +    VLLM_API_KEY: ""  # Optional
       1114 +  
       1115 +    # Redis password (if using password-protected Redis)
       1116 +    REDIS_PASSWORD: ""
       1117 +  ```
       1118 +  
       1119 +  ### 3. AbstractCore API Deployment
       1120 +  
       1121 +  ```yaml
       1122 +  # k8s/api-deployment.yaml
       1123 +  apiVersion: apps/v1
       1124 +  kind: Deployment
       1125 +  metadata:
       1126 +    name: abstractcore-api
       1127 +    namespace: abstractcore
       1128 +    labels:
       1129 +      app: abstractcore-api
       1130 +      component: api
       1131 +  spec:
       1132 +    replicas: 2  # Minimum replicas (HPA will scale)
       1133 +    selector:
       1134 +      matchLabels:
       1135 +        app: abstractcore-api
       1136 +    template:
       1137 +      metadata:
       1138 +        labels:
       1139 +          app: abstractcore-api
       1140 +          component: api
       1141 +      spec:
       1142 +        containers:
       1143 +        - name: abstractcore
       1144 +          image: abstractcore/server:latest  # Or your 
            + registry
       1145 +          ports:
       1146 +          - containerPort: 8000
       1147 +            name: http
       1148 +          envFrom:
       1149 +          - configMapRef:
       1150 +              name: abstractcore-config
       1151 +          - secretRef:
       1152 +              name: abstractcore-secrets
       1153 +          resources:
       1154 +            requests:
       1155 +              memory: "256Mi"
       1156 +              cpu: "250m"
       1157 +            limits:
       1158 +              memory: "512Mi"
       1159 +              cpu: "500m"
       1160 +          livenessProbe:
       1161 +            httpGet:
       1162 +              path: /health
       1163 +              port: 8000
       1164 +            initialDelaySeconds: 10
       1165 +            periodSeconds: 10
       1166 +            failureThreshold: 3
       1167 +          readinessProbe:
       1168 +            httpGet:
       1169 +              path: /health
       1170 +              port: 8000
       1171 +            initialDelaySeconds: 5
       1172 +            periodSeconds: 5
       1173 +            successThreshold: 1
       1174 +          securityContext:
       1175 +            runAsNonRoot: true
       1176 +            runAsUser: 1000
       1177 +            readOnlyRootFilesystem: true
       1178 +            allowPrivilegeEscalation: false
       1179 +  ---
       1180 +  # k8s/api-service.yaml
       1181 +  apiVersion: v1
       1182 +  kind: Service
       1183 +  metadata:
       1184 +    name: abstractcore-api
       1185 +    namespace: abstractcore
       1186 +    labels:
       1187 +      app: abstractcore-api
       1188 +  spec:
       1189 +    type: ClusterIP
       1190 +    ports:
       1191 +    - port: 8000
       1192 +      targetPort: 8000
       1193 +      protocol: TCP
       1194 +      name: http
       1195 +    selector:
       1196 +      app: abstractcore-api
       1197 +  ```
       1198 +  
       1199 +  ### 4. Horizontal Pod Autoscaler (HPA)
       1200 +  
       1201 +  ```yaml
       1202 +  # k8s/api-hpa.yaml
       1203 +  apiVersion: autoscaling/v2
       1204 +  kind: HorizontalPodAutoscaler
       1205 +  metadata:
       1206 +    name: abstractcore-api-hpa
       1207 +    namespace: abstractcore
       1208 +  spec:
       1209 +    scaleTargetRef:
       1210 +      apiVersion: apps/v1
       1211 +      kind: Deployment
       1212 +      name: abstractcore-api
       1213 +    minReplicas: 2
       1214 +    maxReplicas: 10
       1215 +    metrics:
       1216 +    # Scale based on CPU usage
       1217 +    - type: Resource
       1218 +      resource:
       1219 +        name: cpu
       1220 +        target:
       1221 +          type: Utilization
       1222 +          averageUtilization: 70
       1223 +    # Scale based on memory usage
       1224 +    - type: Resource
       1225 +      resource:
       1226 +        name: memory
       1227 +        target:
       1228 +          type: Utilization
       1229 +          averageUtilization: 80
       1230 +    # Scale based on request rate (requires metrics-server +
            +  custom metrics)
       1231 +    # - type: Pods
       1232 +    #   pods:
       1233 +    #     metric:
       1234 +    #       name: http_requests_per_second
       1235 +    #     target:
       1236 +    #       type: AverageValue
       1237 +    #       averageValue: "100"
       1238 +    behavior:
       1239 +      scaleDown:
       1240 +        stabilizationWindowSeconds: 300  # Wait 5 min before
            +  scaling down
       1241 +        policies:
       1242 +        - type: Percent
       1243 +          value: 50
       1244 +          periodSeconds: 60
       1245 +      scaleUp:
       1246 +        stabilizationWindowSeconds: 0  # Scale up 
            + immediately
       1247 +        policies:
       1248 +        - type: Percent
       1249 +          value: 100
       1250 +          periodSeconds: 15
       1251 +        - type: Pods
       1252 +          value: 4
       1253 +          periodSeconds: 15
       1254 +        selectPolicy: Max
       1255 +  ```
       1256 +  
       1257 +  ### 5. Worker Deployment
       1258 +  
       1259 +  ```yaml
       1260 +  # k8s/worker-deployment.yaml
       1261 +  apiVersion: apps/v1
       1262 +  kind: Deployment
       1263 +  metadata:
       1264 +    name: abstractcore-worker
       1265 +    namespace: abstractcore
       1266 +    labels:
       1267 +      app: abstractcore-worker
       1268 +      component: worker
       1269 +  spec:
       1270 +    replicas: 1  # Start with 1, HPA will scale
       1271 +    selector:
       1272 +      matchLabels:
       1273 +        app: abstractcore-worker
       1274 +    template:
       1275 +      metadata:
       1276 +        labels:
       1277 +          app: abstractcore-worker
       1278 +          component: worker
       1279 +      spec:
       1280 +        containers:
       1281 +        - name: worker
       1282 +          image: abstractcore/worker:latest  # Or your 
            + registry
       1283 +          command: ["python", "-m", 
            + "abstractcore.queue.worker"]
       1284 +          envFrom:
       1285 +          - configMapRef:
       1286 +              name: abstractcore-config
       1287 +          - secretRef:
       1288 +              name: abstractcore-secrets
       1289 +          env:
       1290 +          - name: WORKER_MODE
       1291 +            value: "true"
       1292 +          resources:
       1293 +            requests:
       1294 +              memory: "512Mi"
       1295 +              cpu: "500m"
       1296 +            limits:
       1297 +              memory: "1Gi"
       1298 +              cpu: "1000m"
       1299 +          livenessProbe:
       1300 +            exec:
       1301 +              command:
       1302 +              - python
       1303 +              - -c
       1304 +              - "import redis; r = 
            + redis.from_url('redis://redis:6379'); r.ping()"
       1305 +            initialDelaySeconds: 30
       1306 +            periodSeconds: 30
       1307 +            failureThreshold: 3
       1308 +          securityContext:
       1309 +            runAsNonRoot: true
       1310 +            runAsUser: 1000
       1311 +  ---
       1312 +  # k8s/worker-hpa.yaml
       1313 +  apiVersion: autoscaling/v2
       1314 +  kind: HorizontalPodAutoscaler
       1315 +  metadata:
       1316 +    name: abstractcore-worker-hpa
       1317 +    namespace: abstractcore
       1318 +  spec:
       1319 +    scaleTargetRef:
       1320 +      apiVersion: apps/v1
       1321 +      kind: Deployment
       1322 +      name: abstractcore-worker
       1323 +    minReplicas: 1
       1324 +    maxReplicas: 5
       1325 +    metrics:
       1326 +    - type: Resource
       1327 +      resource:
       1328 +        name: cpu
       1329 +        target:
       1330 +          type: Utilization
       1331 +          averageUtilization: 60  # Workers should be kept 
            + busy
       1332 +    # Custom metric: Scale based on queue depth (requires 
            + KEDA or custom metrics adapter)
       1333 +    # - type: External
       1334 +    #   external:
       1335 +    #     metric:
       1336 +    #       name: redis_queue_length
       1337 +    #       selector:
       1338 +    #         matchLabels:
       1339 +    #           queue: abstractcore:queue:pending
       1340 +    #     target:
       1341 +    #       type: AverageValue
       1342 +    #       averageValue: "10"  # 10 jobs per worker
       1343 +  ```
       1344 +  
       1345 +  ### 6. Redis StatefulSet
       1346 +  
       1347 +  ```yaml
       1348 +  # k8s/redis-statefulset.yaml
       1349 +  apiVersion: apps/v1
       1350 +  kind: StatefulSet
       1351 +  metadata:
       1352 +    name: redis
       1353 +    namespace: abstractcore
       1354 +  spec:
       1355 +    serviceName: redis
       1356 +    replicas: 1
       1357 +    selector:
       1358 +      matchLabels:
       1359 +        app: redis
       1360 +    template:
       1361 +      metadata:
       1362 +        labels:
       1363 +          app: redis
       1364 +      spec:
       1365 +        containers:
       1366 +        - name: redis
       1367 +          image: redis:7-alpine
       1368 +          ports:
       1369 +          - containerPort: 6379
       1370 +            name: redis
       1371 +          command:
       1372 +          - redis-server
       1373 +          - "--appendonly"
       1374 +          - "yes"
       1375 +          - "--maxmemory"
       1376 +          - "256mb"
       1377 +          - "--maxmemory-policy"
       1378 +          - "allkeys-lru"
       1379 +          resources:
       1380 +            requests:
       1381 +              memory: "128Mi"
       1382 +              cpu: "100m"
       1383 +            limits:
       1384 +              memory: "256Mi"
       1385 +              cpu: "200m"
       1386 +          volumeMounts:
       1387 +          - name: redis-data
       1388 +            mountPath: /data
       1389 +          livenessProbe:
       1390 +            exec:
       1391 +              command:
       1392 +              - redis-cli
       1393 +              - ping
       1394 +            initialDelaySeconds: 5
       1395 +            periodSeconds: 10
       1396 +          readinessProbe:
       1397 +            exec:
       1398 +              command:
       1399 +              - redis-cli
       1400 +              - ping
       1401 +            initialDelaySeconds: 5
       1402 +            periodSeconds: 5
       1403 +    volumeClaimTemplates:
       1404 +    - metadata:
       1405 +        name: redis-data
       1406 +      spec:
       1407 +        accessModes: ["ReadWriteOnce"]
       1408 +        storageClassName: standard  # Adjust for your 
            + cluster
       1409 +        resources:
       1410 +          requests:
       1411 +            storage: 1Gi
       1412 +  ---
       1413 +  # k8s/redis-service.yaml
       1414 +  apiVersion: v1
       1415 +  kind: Service
       1416 +  metadata:
       1417 +    name: redis
       1418 +    namespace: abstractcore
       1419 +  spec:
       1420 +    type: ClusterIP
       1421 +    ports:
       1422 +    - port: 6379
       1423 +      targetPort: 6379
       1424 +    selector:
       1425 +      app: redis
       1426 +  ```
       1427 +  
       1428 +  ### 7. vLLM StatefulSet (GPU Nodes)
       1429 +  
       1430 +  ```yaml
       1431 +  # k8s/vllm-statefulset.yaml
       1432 +  apiVersion: apps/v1
       1433 +  kind: StatefulSet
       1434 +  metadata:
       1435 +    name: vllm
       1436 +    namespace: abstractcore
       1437 +  spec:
       1438 +    serviceName: vllm
       1439 +    replicas: 1  # Scale manually based on GPU availability
       1440 +    selector:
       1441 +      matchLabels:
       1442 +        app: vllm
       1443 +    template:
       1444 +      metadata:
       1445 +        labels:
       1446 +          app: vllm
       1447 +      spec:
       1448 +        nodeSelector:
       1449 +          # Ensure scheduling on GPU nodes
       1450 +          nvidia.com/gpu.present: "true"
       1451 +        tolerations:
       1452 +        - key: nvidia.com/gpu
       1453 +          operator: Exists
       1454 +          effect: NoSchedule
       1455 +        containers:
       1456 +        - name: vllm
       1457 +          image: vllm/vllm-openai:latest
       1458 +          args:
       1459 +          - "--model"
       1460 +          - "Qwen/Qwen2.5-Coder-7B-Instruct"
       1461 +          - "--host"
       1462 +          - "0.0.0.0"
       1463 +          - "--port"
       1464 +          - "8000"
       1465 +          - "--tensor-parallel-size"
       1466 +          - "4"  # For 4 GPUs
       1467 +          - "--gpu-memory-utilization"
       1468 +          - "0.9"
       1469 +          - "--max-model-len"
       1470 +          - "8192"
       1471 +          - "--max-num-seqs"
       1472 +          - "128"
       1473 +          ports:
       1474 +          - containerPort: 8000
       1475 +            name: http
       1476 +          resources:
       1477 +            requests:
       1478 +              nvidia.com/gpu: 4
       1479 +              memory: "32Gi"
       1480 +              cpu: "8"
       1481 +            limits:
       1482 +              nvidia.com/gpu: 4
       1483 +              memory: "64Gi"
       1484 +              cpu: "16"
       1485 +          volumeMounts:
       1486 +          - name: model-cache
       1487 +            mountPath: /root/.cache/huggingface
       1488 +          - name: shm
       1489 +            mountPath: /dev/shm  # Required for tensor 
            + parallelism
       1490 +          env:
       1491 +          - name: HF_HOME
       1492 +            value: "/root/.cache/huggingface"
       1493 +          livenessProbe:
       1494 +            httpGet:
       1495 +              path: /health
       1496 +              port: 8000
       1497 +            initialDelaySeconds: 300  # Model loading takes 
            + time
       1498 +            periodSeconds: 30
       1499 +            failureThreshold: 3
       1500 +          readinessProbe:
       1501 +            httpGet:
       1502 +              path: /health
       1503 +              port: 8000
       1504 +            initialDelaySeconds: 300
       1505 +            periodSeconds: 10
       1506 +        volumes:
       1507 +        - name: shm
       1508 +          emptyDir:
       1509 +            medium: Memory
       1510 +            sizeLimit: "16Gi"  # For tensor parallelism IPC
       1511 +    volumeClaimTemplates:
       1512 +    - metadata:
       1513 +        name: model-cache
       1514 +      spec:
       1515 +        accessModes: ["ReadWriteOnce"]
       1516 +        storageClassName: standard
       1517 +        resources:
       1518 +          requests:
       1519 +            storage: 100Gi  # For model weights
       1520 +  ---
       1521 +  # k8s/vllm-service.yaml
       1522 +  apiVersion: v1
       1523 +  kind: Service
       1524 +  metadata:
       1525 +    name: vllm
       1526 +    namespace: abstractcore
       1527 +  spec:
       1528 +    type: ClusterIP
       1529 +    ports:
       1530 +    - port: 8000
       1531 +      targetPort: 8000
       1532 +    selector:
       1533 +      app: vllm
       1534 +  ```
       1535 +  
       1536 +  ### 8. Ingress
       1537 +  
       1538 +  ```yaml
       1539 +  # k8s/ingress.yaml
       1540 +  apiVersion: networking.k8s.io/v1
       1541 +  kind: Ingress
       1542 +  metadata:
       1543 +    name: abstractcore-ingress
       1544 +    namespace: abstractcore
       1545 +    annotations:
       1546 +      nginx.ingress.kubernetes.io/proxy-body-size: "50m"
       1547 +      nginx.ingress.kubernetes.io/proxy-read-timeout: "600" 
            +  # 10 min for LLM requests
       1548 +      nginx.ingress.kubernetes.io/proxy-send-timeout: "600"
       1549 +      nginx.ingress.kubernetes.io/proxy-connect-timeout: 
            + "60"
       1550 +      # Enable SSE support
       1551 +      nginx.ingress.kubernetes.io/proxy-buffering: "off"
       1552 +      nginx.ingress.kubernetes.io/proxy-http-version: "1.1"
       1553 +      # Rate limiting (optional)
       1554 +      nginx.ingress.kubernetes.io/limit-rps: "100"
       1555 +      nginx.ingress.kubernetes.io/limit-connections: "50"
       1556 +      # TLS (use cert-manager)
       1557 +      cert-manager.io/cluster-issuer: "letsencrypt-prod"
       1558 +  spec:
       1559 +    ingressClassName: nginx
       1560 +    tls:
       1561 +    - hosts:
       1562 +      - abstractcore.yourdomain.com
       1563 +      secretName: abstractcore-tls
       1564 +    rules:
       1565 +    - host: abstractcore.yourdomain.com
       1566 +      http:
       1567 +        paths:
       1568 +        - path: /
       1569 +          pathType: Prefix
       1570 +          backend:
       1571 +            service:
       1572 +              name: abstractcore-api
       1573 +              port:
       1574 +                number: 8000
       1575 +  ```
       1576 +  
       1577 +  ### 9. Network Policy (Security)
       1578 +  
       1579 +  ```yaml
       1580 +  # k8s/network-policy.yaml
       1581 +  apiVersion: networking.k8s.io/v1
       1582 +  kind: NetworkPolicy
       1583 +  metadata:
       1584 +    name: abstractcore-network-policy
       1585 +    namespace: abstractcore
       1586 +  spec:
       1587 +    podSelector: {}  # Apply to all pods in namespace
       1588 +    policyTypes:
       1589 +    - Ingress
       1590 +    - Egress
       1591 +    ingress:
       1592 +    # Allow ingress controller to reach API
       1593 +    - from:
       1594 +      - namespaceSelector:
       1595 +          matchLabels:
       1596 +            kubernetes.io/metadata.name: ingress-nginx
       1597 +      ports:
       1598 +      - protocol: TCP
       1599 +        port: 8000
       1600 +    # Allow internal communication
       1601 +    - from:
       1602 +      - podSelector: {}
       1603 +    egress:
       1604 +    # Allow DNS
       1605 +    - to:
       1606 +      - namespaceSelector: {}
       1607 +      ports:
       1608 +      - protocol: UDP
       1609 +        port: 53
       1610 +    # Allow Redis
       1611 +    - to:
       1612 +      - podSelector:
       1613 +          matchLabels:
       1614 +            app: redis
       1615 +      ports:
       1616 +      - protocol: TCP
       1617 +        port: 6379
       1618 +    # Allow vLLM
       1619 +    - to:
       1620 +      - podSelector:
       1621 +          matchLabels:
       1622 +            app: vllm
       1623 +      ports:
       1624 +      - protocol: TCP
       1625 +        port: 8000
       1626 +    # Allow external APIs (OpenAI, Anthropic)
       1627 +    - to:
       1628 +      - ipBlock:
       1629 +          cidr: 0.0.0.0/0
       1630 +      ports:
       1631 +      - protocol: TCP
       1632 +        port: 443
       1633 +  ```
       1634 +  
       1635 +  ### 10. Pod Disruption Budget (High Availability)
       1636 +  
       1637 +  ```yaml
       1638 +  # k8s/pdb.yaml
       1639 +  apiVersion: policy/v1
       1640 +  kind: PodDisruptionBudget
       1641 +  metadata:
       1642 +    name: abstractcore-api-pdb
       1643 +    namespace: abstractcore
       1644 +  spec:
       1645 +    minAvailable: 1  # Always keep at least 1 pod running
       1646 +    selector:
       1647 +      matchLabels:
       1648 +        app: abstractcore-api
       1649 +  ---
       1650 +  apiVersion: policy/v1
       1651 +  kind: PodDisruptionBudget
       1652 +  metadata:
       1653 +    name: abstractcore-worker-pdb
       1654 +    namespace: abstractcore
       1655 +  spec:
       1656 +    minAvailable: 1
       1657 +    selector:
       1658 +      matchLabels:
       1659 +        app: abstractcore-worker
       1660 +  ```
       1661 +  
       1662 +  ## Deployment Commands
       1663 +  
       1664 +  ```bash
       1665 +  # Create namespace and apply all manifests
       1666 +  kubectl apply -f k8s/namespace.yaml
       1667 +  kubectl apply -f k8s/configmap.yaml
       1668 +  kubectl apply -f k8s/secrets.yaml  # Edit with real values
            +  first!
       1669 +  kubectl apply -f k8s/redis-statefulset.yaml
       1670 +  kubectl apply -f k8s/redis-service.yaml
       1671 +  kubectl apply -f k8s/api-deployment.yaml
       1672 +  kubectl apply -f k8s/api-service.yaml
       1673 +  kubectl apply -f k8s/api-hpa.yaml
       1674 +  kubectl apply -f k8s/worker-deployment.yaml
       1675 +  kubectl apply -f k8s/worker-hpa.yaml
       1676 +  kubectl apply -f k8s/vllm-statefulset.yaml  # If using 
            + vLLM
       1677 +  kubectl apply -f k8s/vllm-service.yaml
       1678 +  kubectl apply -f k8s/ingress.yaml
       1679 +  kubectl apply -f k8s/network-policy.yaml
       1680 +  kubectl apply -f k8s/pdb.yaml
       1681 +  
       1682 +  # Or apply all at once
       1683 +  kubectl apply -f k8s/
       1684 +  
       1685 +  # Check status
       1686 +  kubectl -n abstractcore get pods
       1687 +  kubectl -n abstractcore get hpa
       1688 +  kubectl -n abstractcore get svc
       1689 +  kubectl -n abstractcore get ingress
       1690 +  
       1691 +  # View logs
       1692 +  kubectl -n abstractcore logs -f 
            + deployment/abstractcore-api
       1693 +  kubectl -n abstractcore logs -f 
            + deployment/abstractcore-worker
       1694 +  
       1695 +  # Scale manually
       1696 +  kubectl -n abstractcore scale deployment/abstractcore-api 
            + --replicas=5
       1697 +  kubectl -n abstractcore scale 
            + deployment/abstractcore-worker --replicas=3
       1698 +  ```
       1699 +  
       1700 +  ## Monitoring with Prometheus/Grafana
       1701 +  
       1702 +  ```yaml
       1703 +  # k8s/servicemonitor.yaml
       1704 +  apiVersion: monitoring.coreos.com/v1
       1705 +  kind: ServiceMonitor
       1706 +  metadata:
       1707 +    name: abstractcore-api
       1708 +    namespace: abstractcore
       1709 +    labels:
       1710 +      app: abstractcore-api
       1711 +  spec:
       1712 +    selector:
       1713 +      matchLabels:
       1714 +        app: abstractcore-api
       1715 +    endpoints:
       1716 +    - port: http
       1717 +      path: /metrics
       1718 +      interval: 15s
       1719 +  ```
       1720 +  
       1721 +  ## KEDA for Queue-Based Scaling (Advanced)
       1722 +  
       1723 +  ```yaml
       1724 +  # k8s/keda-scaledobject.yaml
       1725 +  # Requires KEDA installed: https://keda.sh/docs/deploy/
       1726 +  apiVersion: keda.sh/v1alpha1
       1727 +  kind: ScaledObject
       1728 +  metadata:
       1729 +    name: abstractcore-worker-scaler
       1730 +    namespace: abstractcore
       1731 +  spec:
       1732 +    scaleTargetRef:
       1733 +      name: abstractcore-worker
       1734 +    minReplicaCount: 1
       1735 +    maxReplicaCount: 10
       1736 +    triggers:
       1737 +    - type: redis
       1738 +      metadata:
       1739 +        address: redis:6379
       1740 +        listName: abstractcore:queue:pending
       1741 +        listLength: "10"  # 10 jobs per worker
       1742 +  ```
       1743 +  
       1744 +  ---
       1745 +  
       1746 +  **Total Effort: ~3 weeks**
       1747 +  
       1748 +  ---
       1749 +  
       1750    ## Conclusion
       1751    
       1752    **AbstractCore is correctly designed as a 
              routing/abstraction layer**, not an inference engine. The 
              key architectural principle is:

∴ Thinking…

  I've added comprehensive Kubernetes manifests to the plan file. Now let me 
  summarize this for the user and see if they're satisfied with the complete 
  analysis.

  The plan file now includes:
  1. Architecture analysis answering their original question2. Queue 
  implementation design
  2. Kubernetes deployment manifests with:
    - Namespace and ConfigMap
    - Secrets management
    - API Deployment with HPA
    - Worker Deployment with HPA   - Redis StatefulSet
    - vLLM StatefulSet for GPU inference   - Ingress with SSE support   - 
  Network Policy for security
    - Pod Disruption Budget for HA
    - ServiceMonitor for Prometheus
    - KEDA ScaledObject for queue-based scaling

  Let me provide a summary and check if they're satisfied.

⏺ I've added comprehensive Kubernetes manifests to the plan. Here's a
  summary of what's included:

  ---
  Kubernetes Deployment Summary

  Components

  | Component           | Kind        | Scaling       | Purpose
      |
  |---------------------|-------------|---------------|---------------------
  ----|
  | abstractcore-api    | Deployment  | HPA 2-10 pods | Stateless routing
  layer |
  | abstractcore-worker | Deployment  | HPA 1-5 pods  | Queue processing
      |
  | redis               | StatefulSet | 1 replica     | Queue broker +
  results  |
  | vllm                | StatefulSet | Manual (GPU)  | Inference server
      |

  Key Features

  1. Auto-scaling
    - API: CPU/memory-based HPA (70%/80% thresholds)
    - Workers: CPU-based HPA (60% threshold)
    - KEDA example for queue-depth scaling
  2. High Availability
    - PodDisruptionBudget (minAvailable: 1)
    - Liveness/readiness probes
    - Scale-up: immediate | Scale-down: 5-min stabilization
  3. Security
    - NetworkPolicy (ingress/egress rules)
    - Secrets for API keys
    - Non-root containers, read-only filesystem
  4. Ingress
    - SSE support (proxy-buffering: off)
    - 10-min timeout for LLM requests
    - TLS with cert-manager
  5. GPU Support
    - vLLM with tensor parallelism
    - Node selector + tolerations
    - Shared memory volume for IPC

  Quick Deploy

  kubectl apply -f k8s/  # Apply all manifests
  kubectl -n abstractcore get pods  # Check status
  kubectl -n abstractcore get hpa   # Check auto-scaling

  ---
  The complete plan is now at
  /Users/albou/.claude/plans/breezy-singing-prism.md (~1750 lines) covering:

  1. Architecture Analysis - Should AbstractCore live with providers or
  separately?
  2. Queue Implementation Design - Redis-based with priority queuing
  3. Kubernetes Deployment - Production-ready manifests with auto-scaling
                                                                                  
⏺ User answered Claude's questions:
  ⎿  · Is this analysis complete for your needs tonight? → Yes, this is 
     comprehensive

∴ Thinking…

  The user is satisfied with the comprehensive analysis. Since this was an 
  architectural research question (not an implementation task), I should exit plan 
  mode. However, the plan file I created is really a research document/analysis rather 
  than an implementation plan - which is appropriate given the nature of the question.

  I should exit plan mode since the user is satisfied with the analysis.

⏺ Excellent! The comprehensive analysis document is ready at
  /Users/albou/.claude/plans/breezy-singing-prism.md.

  Summary

  Your question was strategic and shows good architectural thinking. Here are the
  key takeaways:

  Core Answer

  AbstractCore should be thin, stateless, and horizontally scalable. Inference 
  should be delegated to specialized servers (vLLM, Ollama) that manage GPU 
  resources independently.

  SOTA Pattern

  Routing Layer (AbstractCore)  →  Queue (Redis)  →  Workers  →  Inference Servers
       ↓ SSE                                                         ↑
    Progress                                                      GPU-bound

  Who Handles What

  | Concern       | Handler         | Why                                         |
  |---------------|-----------------|---------------------------------------------|
  | Routing       | AbstractCore    | Core competency                             |
  | Scaling (API) | Kubernetes HPA  | Cheap horizontal scaling                    |
  | Scaling (GPU) | Manual/KEDA     | Expensive, vertical                         |
  | Queue         | Redis + Workers | Decouple request acceptance from processing |
  | Inference     | vLLM/Ollama     | GPU memory management                       |

  The document includes:
  - ~400 lines of architecture analysis
  - ~550 lines of queue implementation design with code
  - ~750 lines of production Kubernetes manifests

  This should serve as a reference for your deployment evolution.
                                                                                  
⏺ User approved Claude's plan
  ⎿  Plan saved to: ~/.claude/plans/breezy-singing-prism.md · /plan to edit
     AbstractCore Deployment Architecture Analysis

     User's Question

     Should AbstractCore live on the same server as certain providers (HF, MLX, 
     Ollama, LMStudio, vLLM) or should it live independently as a passthrough to
     distant servers? Who handles scalability, routing, and query queuing? Who
     orchestrates that?

     ---
     Executive Summary

     After thorough analysis of AbstractCore's codebase (~45,000 LOC), 
     documentation, and SOTA LLM infrastructure patterns, my recommendation is:

     AbstractCore should be a lightweight, stateless routing layer that lives 
     INDEPENDENTLY from inference servers in production deployments.

     The key insight is separation of concerns: GPU-bound inference and request 
     routing are fundamentally different workloads with different scaling
     characteristics.

     ---
     Current AbstractCore Architecture Analysis

     What AbstractCore IS Today

     ┌─────────────────────────────────────────────────────────────┐
     │                    AbstractCore                              │
     │  ┌─────────────────────────────────────────────────────┐    │
     │  │  Library Mode (create_llm)                           │    │
     │  │    ├── Provider Abstraction                          │    │
     │  │    ├── Universal Tools (@tool)                       │    │
     │  │    ├── Session Management                            │    │
     │  │    ├── Retry/Circuit Breaker                         │    │
     │  │    └── Event System                                  │    │
     │  └─────────────────────────────────────────────────────┘    │
     │  ┌─────────────────────────────────────────────────────┐    │
     │  │  Server Mode (FastAPI)                               │    │
     │  │    ├── OpenAI-compatible API                         │    │
     │  │    ├── Provider routing                              │    │
     │  │    └── Streaming support                             │    │
     │  └─────────────────────────────────────────────────────┘    │
     └─────────────────────────────────────────────────────────────┘
                                │
                                ▼
     ┌──────────────────────────────────────────────────────────────┐
     │                   Provider Backends                           │
     ├────────────┬────────────┬────────────┬────────────┬─────────┤
     │  OpenAI   │ Anthropic  │  Ollama    │  vLLM      │  MLX    │
     │  (API)    │  (API)     │  (HTTP)    │  (HTTP)    │(in-proc)│
     └────────────┴────────────┴────────────┴────────────┴─────────┘

     Provider Classification by Execution Model

     | Provider    | Execution Model    | Memory Model            | Scaling Pattern
                 |
     |-------------|--------------------|-------------------------|----------------
     ------------|
     | OpenAI      | Remote API         | None                    | Cloud-managed  
                 |
     | Anthropic   | Remote API         | None                    | Cloud-managed  
                 |
     | Ollama      | Local HTTP service | Separate process        | Single-GPU     
                 |
     | LMStudio    | Local HTTP service | Separate process        | Single-GPU     
                 |
     | vLLM        | Remote/Local HTTP  | Separate process        | Multi-GPU, 
     tensor parallel |
     | HuggingFace | In-process         | Loads into AbstractCore | Single-GPU     
                 |
     | MLX         | In-process         | Loads into AbstractCore | Apple Silicon  
                 |

     Current Gaps (Identified in Codebase)

     1. No request queuing - Direct passthrough to providers
     2. No load balancing - Single provider per request
     3. No connection pooling - Each provider instance creates own HTTP client
     4. No auto-scaling - Fixed server capacity
     5. No multi-provider orchestration - Only backlog proposal exists

     ---
     SOTA LLM Infrastructure Patterns

     Pattern 1: Inference/Routing Separation (RECOMMENDED)

     ┌─────────────────────────────────────────────────────────────────┐
     │                      ROUTING LAYER                               │
     │  ┌──────────────────────────────────────────────────────────┐   │
     │  │  AbstractCore Server (Stateless, Horizontally Scalable)   │   │
     │  │    • Request validation & routing                         │   │
     │  │    • Provider abstraction                                 │   │
     │  │    • Tool call handling                                   │   │
     │  │    • Response normalization                               │   │
     │  │    • Event emission & observability                       │   │
     │  └──────────────────────────────────────────────────────────┘   │
     └─────────────────────────────────────────────────────────────────┘
                                    │
                                    ▼
     ┌─────────────────────────────────────────────────────────────────┐
     │                    INFERENCE LAYER                               │
     │  ┌──────────┐  ┌──────────┐  ┌──────────┐  ┌──────────────────┐ │
     │  │ Ollama   │  │ vLLM     │  │ TGI      │  │ Cloud APIs       │ │
     │  │ Server   │  │ Server   │  │ Server   │  │ (OpenAI/Anthro)  │ │
     │  │ (GPU 1)  │  │ (GPU 2-4)│  │ (GPU 5)  │  │                  │ │
     │  └──────────┘  └──────────┘  └──────────┘  └──────────────────┘ │
     └─────────────────────────────────────────────────────────────────┘

     Why This Pattern?

     1. GPU Memory Isolation: Inference servers manage GPU memory, continuous 
     batching, tensor parallelism
     2. Independent Scaling: Route layer scales horizontally (cheap), inference 
     scales vertically (expensive)
     3. Fault Isolation: Provider failure doesn't crash the router
     4. Simpler Deployment: Each component has single responsibility

     Who Uses This Pattern?
     - vLLM (server mode)
     - Text Generation Inference (TGI)
     - OpenRouter
     - LiteLLM Proxy
     - Anyscale/Ray Serve

     Pattern 2: Monolithic (Dev/Single-User Only)

     ┌─────────────────────────────────────────────────────┐
     │              Single Server/Process                   │
     │  ┌──────────────────────────────────────────────┐   │
     │  │  AbstractCore + HuggingFace/MLX (in-process)  │   │
     │  │  • Model loaded in same process               │   │
     │  │  • Low latency (no HTTP overhead)             │   │
     │  │  • Simple deployment                          │   │
     │  │  • Cannot scale                               │   │
     │  └──────────────────────────────────────────────┘   │
     └─────────────────────────────────────────────────────┘

     When Acceptable:
     - Development/testing
     - Single-user applications
     - Apple Silicon personal use (MLX)
     - Demo/prototype deployments

     Pattern 3: Queue-Based (High Throughput)

     ┌───────────────┐     ┌───────────────┐     ┌───────────────┐
     │   Clients     │────▶│  Load Balancer│────▶│ AbstractCore  │
     │               │     │  (nginx/HAP)  │     │ (Stateless)   │
     └───────────────┘     └───────────────┘     └───────┬───────┘
                                                          │
                                                          ▼
                                                 ┌───────────────┐
                                                 │ Message Queue │
                                                 │ (Redis/RabbitMQ)│
                                                 └───────┬───────┘
                                                          │
                           
     ┌──────────────────────────────┼──────────────────────────────┐
                           ▼                              ▼                        
           ▼
                   ┌───────────────┐            ┌───────────────┐            
     ┌───────────────┐
                   │ Worker 1      │            │ Worker 2      │            │ 
     Worker 3      │
                   │ (vLLM GPU)    │            │ (Ollama GPU)  │            │ 
     (Cloud API)   │
                   └───────────────┘            └───────────────┘            
     └───────────────┘

     Benefits:
     - Decouples request acceptance from processing
     - Handles burst traffic gracefully
     - Enables priority queuing
     - Supports retry without client waiting

     ---
     Recommended Architecture for AbstractCore

     Tier 1: Development/Personal (Current)

     Same machine, co-located:
     ┌─────────────────────────────────────────┐
     │  Developer Machine                       │
     │  ├── AbstractCore (library or server)   │
     │  ├── Ollama (local)                      │
     │  ├── LMStudio (local)                    │
     │  └── MLX (in-process)                    │
     └─────────────────────────────────────────┘

     - AbstractCore Role: Library + optional local server
     - Scaling: None needed
     - Queue: Not needed
     - Orchestration: Manual/none

     Tier 2: Team/Production (Recommended Enhancement)

     ┌─────────────────────────────────────────────────────────────────┐
     │                    ROUTING LAYER (Stateless)                     │
     │  ┌────────────────────────────────────────────────────────────┐ │
     │  │  Load Balancer (nginx/HAProxy/cloud LB)                     │ │
     │  └────────────────────────────────────────────────────────────┘ │
     │                           │                                      │
     │  ┌────────────┐  ┌────────────┐  ┌────────────┐                 │
     │  │AbstractCore│  │AbstractCore│  │AbstractCore│  (N replicas)   │
     │  │  Server 1  │  │  Server 2  │  │  Server 3  │                 │
     │  └────────────┘  └────────────┘  └────────────┘                 │
     └─────────────────────────────────────────────────────────────────┘
                                │
                                ▼
     ┌─────────────────────────────────────────────────────────────────┐
     │                   INFERENCE LAYER (Stateful)                     │
     │  ┌──────────────────────────────────────────────────────────┐   │
     │  │  GPU Server 1                                             │   │
     │  │  └── vLLM (4x L4, tensor-parallel-size=4)                │   │
     │  ├──────────────────────────────────────────────────────────┤   │
     │  │  GPU Server 2                                             │   │
     │  │  └── Ollama (various models)                              │   │
     │  ├──────────────────────────────────────────────────────────┤   │
     │  │  Cloud APIs                                               │   │
     │  │  └── OpenAI, Anthropic (remote)                          │   │
     │  └──────────────────────────────────────────────────────────┘   │
     └─────────────────────────────────────────────────────────────────┘

     - AbstractCore Role: Stateless routing/abstraction
     - Scaling: Horizontal (add more AbstractCore replicas)
     - Queue: Optional (handled by inference servers)
     - Orchestration: Load balancer + health checks

     Tier 3: Enterprise Scale (Future Enhancement)

     ┌─────────────────────────────────────────────────────────────────┐
     │  API Gateway (Kong/Ambassador)                                   │
     │  ├── Rate limiting                                               │
     │  ├── Authentication                                              │
     │  └── Tenant isolation                                            │
     └─────────────────────────────────────────────────────────────────┘
                                │
                                ▼
     ┌─────────────────────────────────────────────────────────────────┐
     │  AbstractCore Fleet (Kubernetes Deployment)                      │
     │  ├── Auto-scaling based on request rate                         │
     │  ├── Health checks + circuit breakers                            │
     │  └── Centralized configuration                                   │
     └─────────────────────────────────────────────────────────────────┘
                                │
               ┌────────────────┼────────────────┐
               ▼                ▼                ▼
     ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
     │ Request Queue   │ │ Priority Queue  │ │ Batch Queue     │
     │ (Fast requests) │ │ (VIP customers) │ │ (Bulk jobs)     │
     └─────────────────┘ └─────────────────┘ └─────────────────┘
               │                │                │
               └────────────────┼────────────────┘
                                ▼
     ┌─────────────────────────────────────────────────────────────────┐
     │  GPU Workers (Kubernetes StatefulSets)                           │
     │  ├── vLLM pods (auto-scaled based on queue depth)               │
     │  ├── Ollama pods                                                 │
     │  └── Specialized model pods                                      │
     └─────────────────────────────────────────────────────────────────┘

     ---
     Who Handles What?

     Current State

     | Component       | Who Handles  | Notes                           |
     |-----------------|--------------|---------------------------------|
     | Request routing | AbstractCore | Model string parsing → provider |
     | Retry/fallback  | AbstractCore | Per-provider circuit breaker    |
     | Queue           | Nobody       | Direct passthrough              |
     | Load balancing  | Nobody       | Single-provider requests        |
     | Inference       | Providers    | Ollama, vLLM, cloud APIs        |
     | GPU memory      | Providers    | Managed by inference servers    |
     | Scaling         | Manual       | No auto-scaling                 |

     Recommended State

     | Component       | Who Should Handle                   | Why                 
                                         |
     |-----------------|-------------------------------------|---------------------
     ------------------------------------|
     | Request routing | AbstractCore                        | Core competency, 
     provider abstraction                   |
     | Retry/fallback  | AbstractCore                        | Already implemented,
      enhance with multi-provider        |
     | Queue           | External (Redis) + AbstractCore     | AbstractCore submits
      to queue, workers consume          |
     | Load balancing  | External (nginx/k8s) + AbstractCore | LB for AbstractCore 
     replicas, AC for provider selection |
     | Inference       | Providers                           | Keep separate - 
     different scaling characteristics       |
     | GPU memory      | Providers                           | Never mix with 
     routing logic                            |
     | Scaling         | Orchestrator (k8s)                  | Horizontal for AC, 
     vertical for GPU                     |

     ---
     Specific Recommendations

     1. Keep HuggingFace/MLX In-Process for Dev Only

     Current: HuggingFace and MLX load models into AbstractCore's process.

     Recommendation:
     - Keep for development/personal use
     - For production, move to HTTP server mode (vLLM or similar)
     - Document clearly that in-process mode doesn't scale

     2. Add Optional Queue Integration (Future Feature)

     # Proposed API for queue-based requests
     from abstractcore import create_llm, QueueConfig

     # Configure queue
     queue_config = QueueConfig(
         backend="redis",
         url="redis://localhost:6379",
         max_queue_size=1000,
         priority_levels=3
     )

     llm = create_llm("ollama", model="llama3:8b", queue_config=queue_config)

     # Async submit to queue
     job_id = await llm.submit("Long-running task")  # Returns immediately
     result = await llm.get_result(job_id)  # Poll or wait

     3. Enhance Multi-Provider Routing (Use Existing Backlog)

     The docs/backlog/multi-provider-requests.md is excellent. Implement the hybrid
      approach:
     - Phase 1: Server API for parallel/cascade
     - Phase 2: Core library MultiLLM class

     4. Add Connection Pooling (Quick Win)

     # Proposed enhancement to base provider
     class BaseProvider:
         _connection_pool = None  # Shared across instances

         @classmethod
         def get_http_client(cls):
             if cls._connection_pool is None:
                 cls._connection_pool = httpx.AsyncClient(
                     limits=httpx.Limits(max_connections=100),
                     timeout=30.0
                 )
             return cls._connection_pool

     5. Document Deployment Patterns

     Create docs/deployment-patterns.md:
     - Tier 1: Development (co-located)
     - Tier 2: Production (separated routing/inference)
     - Tier 3: Enterprise (k8s, queues, auto-scaling)

     ---
     Answering Your Questions Directly

     Q1: Should AbstractCore live on the same server as providers?

     Short Answer: Depends on the tier.

     | Tier            | Same Server? | Rationale                                  
                  |
     |-----------------|--------------|--------------------------------------------
     -------------|
     | Dev/Personal    | Yes          | Simplicity, low latency                    
                  |
     | Team/Production | No           | Separate concerns, fault isolation, 
     independent scaling |
     | Enterprise      | No           | Must scale independently                   
                  |

     Q2: Should it be a passthrough to distant servers?

     Short Answer: Yes, for production.

     AbstractCore's core value is abstraction and unification, not inference. Keep 
     it as a lightweight routing layer that:
     - Validates and routes requests
     - Handles retries and fallbacks
     - Normalizes responses
     - Emits events for observability

     Let specialized inference servers (vLLM, Ollama, TGI) handle:
     - GPU memory management
     - Continuous batching
     - Tensor parallelism
     - Model quantization

     Q3: Who handles scalability?

     Short Answer: Split responsibility.

     | Layer        | Scaling Approach                                      | Who 
     Manages             |
     |--------------|-------------------------------------------------------|------
     -------------------|
     | AbstractCore | Horizontal (add replicas)                             | 
     Kubernetes/Docker Swarm |
     | Inference    | Vertical (bigger GPU) + Horizontal (more GPU servers) | 
     vLLM/Ollama             |
     | Queue        | Horizontal (Redis cluster)                            | 
     Redis/RabbitMQ          |

     Q4: Who handles routing of multiple queries?

     Short Answer: AbstractCore + External LB.

     1. External Load Balancer: Distributes across AbstractCore replicas
     2. AbstractCore: Routes to correct provider based on model string
     3. Future Enhancement: Multi-provider strategies (parallel, cascade, race)

     Q5: Who orchestrates query queues?

     Short Answer: External queue + AbstractCore workers.

     For high-throughput scenarios:
     1. AbstractCore Server: Accepts requests, pushes to queue
     2. Queue (Redis/RabbitMQ): Buffers requests, handles backpressure
     3. AbstractCore Workers: Consume from queue, call inference servers
     4. Inference Servers: Process requests, return results

     ---
     Implementation Roadmap

     Phase 1: Documentation (1 day)

     - Create docs/deployment-patterns.md
     - Document current architecture limitations
     - Recommend SOTA patterns for different scales

     Phase 2: Connection Pooling (1 week)

     - Implement shared connection pools per provider
     - 15-30% latency improvement, 20-40% memory savings
     - See backlog 003-connection-pool-optimization.md

     Phase 3: Multi-Provider Routing (2-4 weeks)

     - Implement server API first (Phase 1 of backlog)
     - Add parallel, cascade, race strategies
     - See backlog multi-provider-requests.md

     Phase 4: Queue Integration (Optional, 2-3 weeks)

     - Design queue abstraction
     - Implement Redis backend
     - Add async submit/poll API

     ---
     Appendix: Queue Implementation Design

     SOTA Research Summary

     Based on research from https://blog.vllm.ai/2025/09/05/anatomy-of-vllm.html, 
     https://www.jellyfishtechnologies.com/llm-driven-api-orchestration-using-langc
     hain-celery-redis-queue/, and https://towardsdatascience.com/deploying-ml-mode
     ls-in-production-with-fastapi-and-celery-7063e539a5db/, here's the SOTA
     pattern:

     ┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
     │   Client        │────▶│  AbstractCore   │────▶│  Redis Broker   │
     │   (submit job)  │     │  (FastAPI)      │     │  (queue)        │
     └─────────────────┘     └────────┬────────┘     └────────┬────────┘
                                      │                        │
                                      │ SSE Stream             │ Task Dispatch
                                      ▼                        ▼
                             ┌─────────────────┐     ┌─────────────────┐
                             │  Redis Results  │◀────│  Celery Workers │
                             │  (progress/done)│     │  (inference)    │
                             └─────────────────┘     └─────────────────┘

     Key Insights:
     1. vLLM: Internal scheduler with waiting deque + running list; continuous 
     batching at GPU level
     2. External Queue: Redis as broker + result backend; Celery workers consume 
     and process
     3. Streaming: Workers update Redis, FastAPI polls and streams via SSE
     4. Timeouts: Soft limit ~9 min, hard limit ~10 min for LLM operations

     ---
     AbstractCore Queue Design

     Design Principles

     1. Optional - Default behavior unchanged; queue is opt-in
     2. Backend Agnostic - Abstract interface with Redis, In-Memory implementations
     3. Library-First - Works without server; server just adds HTTP access
     4. Event Integration - Queue events flow through existing EventBus
     5. Priority Support - Multiple priority levels for different use cases
     6. Streaming Compatible - SSE streaming for real-time progress

     Architecture

     ┌─────────────────────────────────────────────────────────────────┐
     │                    AbstractCore Queue Layer                      │
     │  ┌───────────────────────────────────────────────────────────┐  │
     │  │  QueueManager (Singleton)                                  │  │
     │  │    ├── submit(request) → job_id                           │  │
     │  │    ├── get_status(job_id) → JobStatus                     │  │
     │  │    ├── get_result(job_id) → GenerateResponse              │  │
     │  │    ├── cancel(job_id) → bool                              │  │
     │  │    └── stream(job_id) → AsyncIterator[ProgressEvent]      │  │
     │  └───────────────────────────────────────────────────────────┘  │
     │                              │                                   │
     │  ┌───────────────────────────▼───────────────────────────────┐  │
     │  │  QueueBackend (Abstract Interface)                         │  │
     │  │    ├── enqueue(job: Job) → None                           │  │
     │  │    ├── dequeue() → Optional[Job]                          │  │
     │  │    ├── get_job(job_id) → Optional[Job]                    │  │
     │  │    ├── update_job(job_id, status, result) → None          │  │
     │  │    └── subscribe(job_id) → AsyncIterator[Event]           │  │
     │  └───────────────────────────────────────────────────────────┘  │
     │                              │                                   │
     │         ┌────────────────────┼────────────────────┐             │
     │         ▼                    ▼                    ▼             │
     │  ┌─────────────┐     ┌─────────────┐     ┌─────────────┐       │
     │  │ InMemory    │     │   Redis     │     │  Future:    │       │
     │  │ Backend     │     │   Backend   │     │  RabbitMQ   │       │
     │  │ (dev/test)  │     │ (production)│     │  Kafka      │       │
     │  └─────────────┘     └─────────────┘     └─────────────┘       │
     └─────────────────────────────────────────────────────────────────┘

     Data Models

     # abstractcore/queue/models.py

     from enum import Enum
     from dataclasses import dataclass
     from datetime import datetime
     from typing import Optional, Dict, Any, List

     class JobStatus(Enum):
         PENDING = "pending"       # In queue, not started
         RUNNING = "running"       # Being processed by worker
         COMPLETED = "completed"   # Successfully finished
         FAILED = "failed"         # Failed with error
         CANCELLED = "cancelled"   # Cancelled by user
         TIMEOUT = "timeout"       # Exceeded time limit

     class Priority(Enum):
         LOW = 0
         NORMAL = 1
         HIGH = 2
         URGENT = 3

     @dataclass
     class Job:
         job_id: str
         provider: str
         model: str
         prompt: str
         messages: Optional[List[Dict]] = None
         kwargs: Optional[Dict[str, Any]] = None
         priority: Priority = Priority.NORMAL
         status: JobStatus = JobStatus.PENDING
         created_at: datetime = None
         started_at: Optional[datetime] = None
         completed_at: Optional[datetime] = None
         result: Optional[Dict] = None  # GenerateResponse as dict
         error: Optional[str] = None
         progress: float = 0.0  # 0.0 to 1.0
         metadata: Optional[Dict] = None  # User-defined metadata

     @dataclass
     class ProgressEvent:
         job_id: str
         status: JobStatus
         progress: float
         message: Optional[str] = None
         partial_content: Optional[str] = None  # For streaming
         tokens_generated: int = 0

     API Design

     # abstractcore/queue/__init__.py

     from abstractcore.queue.manager import QueueManager
     from abstractcore.queue.models import Job, JobStatus, Priority, ProgressEvent
     from abstractcore.queue.backends import InMemoryBackend, RedisBackend

     __all__ = [
         'QueueManager',
         'Job', 'JobStatus', 'Priority', 'ProgressEvent',
         'InMemoryBackend', 'RedisBackend'
     ]

     # Convenience functions at package level
     _default_manager: Optional[QueueManager] = None

     def configure_queue(backend: str = "memory", **kwargs):
         """Configure the default queue manager."""
         global _default_manager
         if backend == "memory":
             _default_manager = QueueManager(InMemoryBackend())
         elif backend == "redis":
             _default_manager = QueueManager(RedisBackend(**kwargs))
         else:
             raise ValueError(f"Unknown backend: {backend}")

     def get_queue() -> QueueManager:
         """Get the configured queue manager."""
         if _default_manager is None:
             raise RuntimeError("Queue not configured. Call configure_queue() 
     first.")
         return _default_manager

     Usage Examples

     Basic Queue Usage (Library Mode)

     from abstractcore import create_llm
     from abstractcore.queue import configure_queue, get_queue, Priority

     # Configure queue (development - in-memory)
     configure_queue(backend="memory")

     # Or production - Redis
     configure_queue(
         backend="redis",
         url="redis://localhost:6379",
         db=0,
         prefix="abstractcore:"
     )

     queue = get_queue()

     # Submit job (returns immediately)
     job_id = await queue.submit(
         provider="ollama",
         model="llama3:8b",
         prompt="Write a long essay about quantum computing",
         priority=Priority.NORMAL,
         metadata={"user_id": "user123", "request_type": "essay"}
     )

     print(f"Job submitted: {job_id}")

     # Check status
     status = await queue.get_status(job_id)
     print(f"Status: {status.status}, Progress: {status.progress:.0%}")

     # Wait for result (blocking)
     result = await queue.get_result(job_id, timeout=300)  # 5 min timeout
     print(f"Result: {result.content[:100]}...")

     # Or poll with async for
     async for event in queue.stream(job_id):
         print(f"Progress: {event.progress:.0%} - {event.message}")
         if event.partial_content:
             print(f"Partial: {event.partial_content}", end="")
         if event.status in (JobStatus.COMPLETED, JobStatus.FAILED):
             break

     With Priority Queuing

     # High priority request (VIP customer)
     high_priority_job = await queue.submit(
         provider="openai",
         model="gpt-4",
         prompt="Urgent: summarize this contract",
         priority=Priority.URGENT
     )

     # Low priority request (batch job)
     low_priority_job = await queue.submit(
         provider="ollama",
         model="llama3:8b",
         prompt="Generate 100 product descriptions",
         priority=Priority.LOW
     )

     # Urgent jobs processed before normal, normal before low

     Worker Process (Separate from API)

     # worker.py - Run this in separate processes

     from abstractcore import create_llm
     from abstractcore.queue import configure_queue, get_queue, JobStatus

     configure_queue(backend="redis", url="redis://localhost:6379")
     queue = get_queue()

     async def worker_loop():
         """Continuously process jobs from queue."""
         while True:
             job = await queue.dequeue()  # Blocking wait for next job
             if job is None:
                 continue

             try:
                 # Update status to running
                 await queue.update_status(job.job_id, JobStatus.RUNNING)

                 # Create LLM and generate
                 llm = create_llm(job.provider, model=job.model)

                 if job.kwargs.get("stream"):
                     # Stream with progress updates
                     content = ""
                     async for chunk in llm.agenerate(job.prompt, stream=True):
                         content += chunk.content
                         await queue.update_progress(
                             job.job_id,
                             progress=len(content) / 1000,  # Estimate
                             partial_content=chunk.content
                         )
                     result = GenerateResponse(content=content, ...)
                 else:
                     result = await llm.agenerate(job.prompt, **job.kwargs)

                 # Mark completed
                 await queue.update_status(
                     job.job_id,
                     JobStatus.COMPLETED,
                     result=result.to_dict()
                 )

             except Exception as e:
                 await queue.update_status(
                     job.job_id,
                     JobStatus.FAILED,
                     error=str(e)
                 )

     # Run with: python worker.py
     asyncio.run(worker_loop())

     Server API Integration

     # abstractcore/server/app.py additions

     from abstractcore.queue import get_queue, JobStatus

     @app.post("/v1/queue/submit")
     async def submit_to_queue(request: ChatCompletionRequest):
         """Submit request to queue, return job ID immediately."""
         queue = get_queue()

         job_id = await queue.submit(
             provider=request.provider,
             model=request.model,
             messages=request.messages,
             kwargs=request.dict(exclude={"messages", "model"})
         )

         return {"job_id": job_id, "status": "pending"}

     @app.get("/v1/queue/status/{job_id}")
     async def get_job_status(job_id: str):
         """Get current status of a queued job."""
         queue = get_queue()
         status = await queue.get_status(job_id)

         return {
             "job_id": job_id,
             "status": status.status.value,
             "progress": status.progress,
             "created_at": status.created_at.isoformat(),
             "started_at": status.started_at.isoformat() if status.started_at else 
     None
         }

     @app.get("/v1/queue/result/{job_id}")
     async def get_job_result(job_id: str, wait: bool = False, timeout: int = 60):
         """Get result of completed job."""
         queue = get_queue()

         if wait:
             result = await queue.get_result(job_id, timeout=timeout)
         else:
             result = await queue.get_result_nowait(job_id)
             if result is None:
                 raise HTTPException(404, "Job not completed yet")

         return result

     @app.get("/v1/queue/stream/{job_id}")
     async def stream_job_progress(job_id: str):
         """Stream job progress via SSE."""
         queue = get_queue()

         async def event_generator():
             async for event in queue.stream(job_id):
                 yield {
                     "event": event.status.value,
                     "data": {
                         "progress": event.progress,
                         "message": event.message,
                         "partial_content": event.partial_content
                     }
                 }
                 if event.status in (JobStatus.COMPLETED, JobStatus.FAILED):
                     break

         return EventSourceResponse(event_generator())

     @app.delete("/v1/queue/cancel/{job_id}")
     async def cancel_job(job_id: str):
         """Cancel a pending or running job."""
         queue = get_queue()
         cancelled = await queue.cancel(job_id)

         return {"job_id": job_id, "cancelled": cancelled}

     Redis Backend Implementation

     # abstractcore/queue/backends/redis_backend.py

     import redis.asyncio as redis
     import json
     from typing import Optional, AsyncIterator
     from datetime import datetime

     class RedisBackend(QueueBackend):
         """Redis-based queue backend for production use."""

         def __init__(
             self,
             url: str = "redis://localhost:6379",
             db: int = 0,
             prefix: str = "abstractcore:",
             max_connections: int = 10
         ):
             self.url = url
             self.prefix = prefix
             self._pool = redis.ConnectionPool.from_url(
                 url, db=db, max_connections=max_connections
             )
             self._client = redis.Redis(connection_pool=self._pool)

         def _key(self, key_type: str, key_id: str) -> str:
             """Generate Redis key with prefix."""
             return f"{self.prefix}{key_type}:{key_id}"

         async def enqueue(self, job: Job) -> None:
             """Add job to priority queue."""
             job_data = job.to_dict()
             job_data["created_at"] = datetime.utcnow().isoformat()

             async with self._client.pipeline() as pipe:
                 # Store job data
                 await pipe.hset(
                     self._key("job", job.job_id),
                     mapping={"data": json.dumps(job_data)}
                 )
                 # Add to priority queue (sorted set, score = priority * 1e12 + 
     timestamp)
                 score = job.priority.value * 1e12 + datetime.utcnow().timestamp()
                 await pipe.zadd(
                     self._key("queue", "pending"),
                     {job.job_id: score}
                 )
                 await pipe.execute()

         async def dequeue(self) -> Optional[Job]:
             """Get highest priority job from queue (blocking)."""
             # BZPOPMAX: blocking pop of highest score
             result = await self._client.bzpopmax(
                 self._key("queue", "pending"),
                 timeout=10  # Check every 10s for shutdown signals
             )

             if result is None:
                 return None

             _, job_id, _ = result
             job_id = job_id.decode() if isinstance(job_id, bytes) else job_id

             # Get full job data
             job_data = await self._client.hget(
                 self._key("job", job_id), "data"
             )

             if job_data is None:
                 return None

             return Job.from_dict(json.loads(job_data))

         async def update_job(
             self,
             job_id: str,
             status: JobStatus,
             result: Optional[dict] = None,
             error: Optional[str] = None,
             progress: float = 0.0
         ) -> None:
             """Update job status and publish event."""
             updates = {
                 "status": status.value,
                 "progress": progress,
                 "updated_at": datetime.utcnow().isoformat()
             }

             if status == JobStatus.RUNNING:
                 updates["started_at"] = datetime.utcnow().isoformat()
             elif status in (JobStatus.COMPLETED, JobStatus.FAILED):
                 updates["completed_at"] = datetime.utcnow().isoformat()
                 if result:
                     updates["result"] = json.dumps(result)
                 if error:
                     updates["error"] = error

             # Update job hash
             await self._client.hset(
                 self._key("job", job_id),
                 mapping=updates
             )

             # Publish event for SSE subscribers
             await self._client.publish(
                 self._key("events", job_id),
                 json.dumps({
                     "status": status.value,
                     "progress": progress,
                     "message": f"Job {status.value}"
                 })
             )

         async def subscribe(self, job_id: str) -> AsyncIterator[ProgressEvent]:
             """Subscribe to job progress events."""
             pubsub = self._client.pubsub()
             await pubsub.subscribe(self._key("events", job_id))

             async for message in pubsub.listen():
                 if message["type"] == "message":
                     data = json.loads(message["data"])
                     yield ProgressEvent(
                         job_id=job_id,
                         status=JobStatus(data["status"]),
                         progress=data["progress"],
                         message=data.get("message"),
                         partial_content=data.get("partial_content")
                     )

                     if data["status"] in ("completed", "failed", "cancelled"):
                         break

             await pubsub.unsubscribe()

     Event System Integration

     # New event types for queue operations
     class EventType(Enum):
         # Existing...
         GENERATION_STARTED = "generation_started"
         GENERATION_COMPLETED = "generation_completed"

         # New queue events
         QUEUE_JOB_SUBMITTED = "queue_job_submitted"
         QUEUE_JOB_STARTED = "queue_job_started"
         QUEUE_JOB_PROGRESS = "queue_job_progress"
         QUEUE_JOB_COMPLETED = "queue_job_completed"
         QUEUE_JOB_FAILED = "queue_job_failed"
         QUEUE_JOB_CANCELLED = "queue_job_cancelled"

     # Queue manager emits events
     class QueueManager:
         async def submit(self, ...) -> str:
             job_id = str(uuid.uuid4())
             job = Job(job_id=job_id, ...)

             await self.backend.enqueue(job)

             emit_global(EventType.QUEUE_JOB_SUBMITTED, {
                 "job_id": job_id,
                 "provider": job.provider,
                 "model": job.model,
                 "priority": job.priority.value
             })

             return job_id

     ---
     Implementation Phases

     Phase 1: Core Queue Infrastructure (1 week)

     - abstractcore/queue/models.py - Data models
     - abstractcore/queue/backends/base.py - Abstract backend interface
     - abstractcore/queue/backends/memory.py - In-memory backend (dev/test)
     - abstractcore/queue/manager.py - QueueManager class
     - abstractcore/queue/__init__.py - Public API

     Phase 2: Redis Backend (3-4 days)

     - abstractcore/queue/backends/redis.py - Redis implementation
     - Add redis to optional dependencies (pip install abstractcore[queue])
     - Connection pooling and error handling
     - Pub/sub for progress streaming

     Phase 3: Server API (2-3 days)

     - Add queue endpoints to abstractcore/server/app.py
     - SSE streaming for progress
     - OpenAPI documentation

     Phase 4: Worker Framework (3-4 days)

     - abstractcore/queue/worker.py - Worker process implementation
     - Graceful shutdown handling
     - Concurrent worker support
     - Retry logic for failed jobs

     Phase 5: Documentation & Testing (2-3 days)

     - Comprehensive test suite
     - docs/queue-integration.md - User guide
     - Docker Compose example with Redis
     - Benchmark results

     Total Effort: ~3 weeks

     ---
     Appendix: Kubernetes Deployment

     Overview

     Production-grade Kubernetes deployment for AbstractCore with:
     - AbstractCore API - Stateless, horizontally scalable routing layer
     - Workers - Separate pods for queue processing
     - Redis - Queue broker and result backend
     - Inference Servers - vLLM/Ollama as StatefulSets

     Architecture

     ┌────────────────────────────────────────────────────────────────────┐
     │  Kubernetes Cluster                                                 │
     │  ┌──────────────────────────────────────────────────────────────┐  │
     │  │  Ingress (nginx/traefik)                                      │  │
     │  │    └── abstractcore.yourdomain.com                            │  │
     │  └──────────────────────────────────────────────────────────────┘  │
     │                               │                                     │
     │  ┌────────────────────────────▼─────────────────────────────────┐  │
     │  │  Service: abstractcore-api (ClusterIP)                        │  │
     │  │    └── Deployment: abstractcore-api (HPA: 2-10 replicas)     │  │
     │  │         └── Pod: abstractcore-api                             │  │
     │  │              ├── Container: abstractcore                      │  │
     │  │              └── Resources: 256Mi-512Mi, 0.25-0.5 CPU        │  │
     │  └──────────────────────────────────────────────────────────────┘  │
     │                               │                                     │
     │  ┌────────────────────────────▼─────────────────────────────────┐  │
     │  │  Service: redis (ClusterIP)                                   │  │
     │  │    └── StatefulSet: redis (1 replica)                        │  │
     │  │         └── Pod: redis                                        │  │
     │  │              └── Volume: redis-data (PVC)                     │  │
     │  └──────────────────────────────────────────────────────────────┘  │
     │                               │                                     │
     │  ┌────────────────────────────▼─────────────────────────────────┐  │
     │  │  Deployment: abstractcore-worker (HPA: 1-5 replicas)         │  │
     │  │    └── Pod: abstractcore-worker                               │  │
     │  │         └── Container: abstractcore-worker                    │  │
     │  │              └── Resources: 512Mi-1Gi, 0.5-1 CPU             │  │
     │  └──────────────────────────────────────────────────────────────┘  │
     │                               │                                     │
     │  ┌────────────────────────────▼─────────────────────────────────┐  │
     │  │  StatefulSet: vllm (GPU nodes)                                │  │
     │  │    └── Pod: vllm-0                                            │  │
     │  │         ├── Container: vllm                                   │  │
     │  │         ├── Resources: nvidia.com/gpu: 4                     │  │
     │  │         └── Volume: model-cache (PVC)                         │  │
     │  └──────────────────────────────────────────────────────────────┘  │
     └────────────────────────────────────────────────────────────────────┘

     Manifests

     1. Namespace and ConfigMap

     # k8s/namespace.yaml
     apiVersion: v1
     kind: Namespace
     metadata:
       name: abstractcore
       labels:
         app.kubernetes.io/name: abstractcore
     ---
     # k8s/configmap.yaml
     apiVersion: v1
     kind: ConfigMap
     metadata:
       name: abstractcore-config
       namespace: abstractcore
     data:
       # Provider configuration
       OLLAMA_BASE_URL: "http://ollama:11434"
       LMSTUDIO_BASE_URL: "http://lmstudio:1234"
       VLLM_BASE_URL: "http://vllm:8000/v1"

       # Queue configuration
       QUEUE_BACKEND: "redis"
       REDIS_URL: "redis://redis:6379"
       QUEUE_PREFIX: "abstractcore:"

       # Server configuration
       ABSTRACTCORE_LOG_LEVEL: "INFO"
       ABSTRACTCORE_DEBUG: "false"

       # Worker configuration
       WORKER_CONCURRENCY: "4"
       WORKER_MAX_RETRIES: "3"
       WORKER_TIMEOUT: "600"  # 10 minutes

     2. Secrets

     # k8s/secrets.yaml
     apiVersion: v1
     kind: Secret
     metadata:
       name: abstractcore-secrets
       namespace: abstractcore
     type: Opaque
     stringData:
       # API keys (replace with actual values or use external secrets manager)
       OPENAI_API_KEY: "sk-..."
       ANTHROPIC_API_KEY: "sk-ant-..."
       VLLM_API_KEY: ""  # Optional

       # Redis password (if using password-protected Redis)
       REDIS_PASSWORD: ""

     3. AbstractCore API Deployment

     # k8s/api-deployment.yaml
     apiVersion: apps/v1
     kind: Deployment
     metadata:
       name: abstractcore-api
       namespace: abstractcore
       labels:
         app: abstractcore-api
         component: api
     spec:
       replicas: 2  # Minimum replicas (HPA will scale)
       selector:
         matchLabels:
           app: abstractcore-api
       template:
         metadata:
           labels:
             app: abstractcore-api
             component: api
         spec:
           containers:
           - name: abstractcore
             image: abstractcore/server:latest  # Or your registry
             ports:
             - containerPort: 8000
               name: http
             envFrom:
             - configMapRef:
                 name: abstractcore-config
             - secretRef:
                 name: abstractcore-secrets
             resources:
               requests:
                 memory: "256Mi"
                 cpu: "250m"
               limits:
                 memory: "512Mi"
                 cpu: "500m"
             livenessProbe:
               httpGet:
                 path: /health
                 port: 8000
               initialDelaySeconds: 10
               periodSeconds: 10
               failureThreshold: 3
             readinessProbe:
               httpGet:
                 path: /health
                 port: 8000
               initialDelaySeconds: 5
               periodSeconds: 5
               successThreshold: 1
             securityContext:
               runAsNonRoot: true
               runAsUser: 1000
               readOnlyRootFilesystem: true
               allowPrivilegeEscalation: false
     ---
     # k8s/api-service.yaml
     apiVersion: v1
     kind: Service
     metadata:
       name: abstractcore-api
       namespace: abstractcore
       labels:
         app: abstractcore-api
     spec:
       type: ClusterIP
       ports:
       - port: 8000
         targetPort: 8000
         protocol: TCP
         name: http
       selector:
         app: abstractcore-api

     4. Horizontal Pod Autoscaler (HPA)

     # k8s/api-hpa.yaml
     apiVersion: autoscaling/v2
     kind: HorizontalPodAutoscaler
     metadata:
       name: abstractcore-api-hpa
       namespace: abstractcore
     spec:
       scaleTargetRef:
         apiVersion: apps/v1
         kind: Deployment
         name: abstractcore-api
       minReplicas: 2
       maxReplicas: 10
       metrics:
       # Scale based on CPU usage
       - type: Resource
         resource:
           name: cpu
           target:
             type: Utilization
             averageUtilization: 70
       # Scale based on memory usage
       - type: Resource
         resource:
           name: memory
           target:
             type: Utilization
             averageUtilization: 80
       # Scale based on request rate (requires metrics-server + custom metrics)
       # - type: Pods
       #   pods:
       #     metric:
       #       name: http_requests_per_second
       #     target:
       #       type: AverageValue
       #       averageValue: "100"
       behavior:
         scaleDown:
           stabilizationWindowSeconds: 300  # Wait 5 min before scaling down
           policies:
           - type: Percent
             value: 50
             periodSeconds: 60
         scaleUp:
           stabilizationWindowSeconds: 0  # Scale up immediately
           policies:
           - type: Percent
             value: 100
             periodSeconds: 15
           - type: Pods
             value: 4
             periodSeconds: 15
           selectPolicy: Max

     5. Worker Deployment

     # k8s/worker-deployment.yaml
     apiVersion: apps/v1
     kind: Deployment
     metadata:
       name: abstractcore-worker
       namespace: abstractcore
       labels:
         app: abstractcore-worker
         component: worker
     spec:
       replicas: 1  # Start with 1, HPA will scale
       selector:
         matchLabels:
           app: abstractcore-worker
       template:
         metadata:
           labels:
             app: abstractcore-worker
             component: worker
         spec:
           containers:
           - name: worker
             image: abstractcore/worker:latest  # Or your registry
             command: ["python", "-m", "abstractcore.queue.worker"]
             envFrom:
             - configMapRef:
                 name: abstractcore-config
             - secretRef:
                 name: abstractcore-secrets
             env:
             - name: WORKER_MODE
               value: "true"
             resources:
               requests:
                 memory: "512Mi"
                 cpu: "500m"
               limits:
                 memory: "1Gi"
                 cpu: "1000m"
             livenessProbe:
               exec:
                 command:
                 - python
                 - -c
                 - "import redis; r = redis.from_url('redis://redis:6379'); 
     r.ping()"
               initialDelaySeconds: 30
               periodSeconds: 30
               failureThreshold: 3
             securityContext:
               runAsNonRoot: true
               runAsUser: 1000
     ---
     # k8s/worker-hpa.yaml
     apiVersion: autoscaling/v2
     kind: HorizontalPodAutoscaler
     metadata:
       name: abstractcore-worker-hpa
       namespace: abstractcore
     spec:
       scaleTargetRef:
         apiVersion: apps/v1
         kind: Deployment
         name: abstractcore-worker
       minReplicas: 1
       maxReplicas: 5
       metrics:
       - type: Resource
         resource:
           name: cpu
           target:
             type: Utilization
             averageUtilization: 60  # Workers should be kept busy
       # Custom metric: Scale based on queue depth (requires KEDA or custom metrics
      adapter)
       # - type: External
       #   external:
       #     metric:
       #       name: redis_queue_length
       #       selector:
       #         matchLabels:
       #           queue: abstractcore:queue:pending
       #     target:
       #       type: AverageValue
       #       averageValue: "10"  # 10 jobs per worker

     6. Redis StatefulSet

     # k8s/redis-statefulset.yaml
     apiVersion: apps/v1
     kind: StatefulSet
     metadata:
       name: redis
       namespace: abstractcore
     spec:
       serviceName: redis
       replicas: 1
       selector:
         matchLabels:
           app: redis
       template:
         metadata:
           labels:
             app: redis
         spec:
           containers:
           - name: redis
             image: redis:7-alpine
             ports:
             - containerPort: 6379
               name: redis
             command:
             - redis-server
             - "--appendonly"
             - "yes"
             - "--maxmemory"
             - "256mb"
             - "--maxmemory-policy"
             - "allkeys-lru"
             resources:
               requests:
                 memory: "128Mi"
                 cpu: "100m"
               limits:
                 memory: "256Mi"
                 cpu: "200m"
             volumeMounts:
             - name: redis-data
               mountPath: /data
             livenessProbe:
               exec:
                 command:
                 - redis-cli
                 - ping
               initialDelaySeconds: 5
               periodSeconds: 10
             readinessProbe:
               exec:
                 command:
                 - redis-cli
                 - ping
               initialDelaySeconds: 5
               periodSeconds: 5
       volumeClaimTemplates:
       - metadata:
           name: redis-data
         spec:
           accessModes: ["ReadWriteOnce"]
           storageClassName: standard  # Adjust for your cluster
           resources:
             requests:
               storage: 1Gi
     ---
     # k8s/redis-service.yaml
     apiVersion: v1
     kind: Service
     metadata:
       name: redis
       namespace: abstractcore
     spec:
       type: ClusterIP
       ports:
       - port: 6379
         targetPort: 6379
       selector:
         app: redis

     7. vLLM StatefulSet (GPU Nodes)

     # k8s/vllm-statefulset.yaml
     apiVersion: apps/v1
     kind: StatefulSet
     metadata:
       name: vllm
       namespace: abstractcore
     spec:
       serviceName: vllm
       replicas: 1  # Scale manually based on GPU availability
       selector:
         matchLabels:
           app: vllm
       template:
         metadata:
           labels:
             app: vllm
         spec:
           nodeSelector:
             # Ensure scheduling on GPU nodes
             nvidia.com/gpu.present: "true"
           tolerations:
           - key: nvidia.com/gpu
             operator: Exists
             effect: NoSchedule
           containers:
           - name: vllm
             image: vllm/vllm-openai:latest
             args:
             - "--model"
             - "Qwen/Qwen2.5-Coder-7B-Instruct"
             - "--host"
             - "0.0.0.0"
             - "--port"
             - "8000"
             - "--tensor-parallel-size"
             - "4"  # For 4 GPUs
             - "--gpu-memory-utilization"
             - "0.9"
             - "--max-model-len"
             - "8192"
             - "--max-num-seqs"
             - "128"
             ports:
             - containerPort: 8000
               name: http
             resources:
               requests:
                 nvidia.com/gpu: 4
                 memory: "32Gi"
                 cpu: "8"
               limits:
                 nvidia.com/gpu: 4
                 memory: "64Gi"
                 cpu: "16"
             volumeMounts:
             - name: model-cache
               mountPath: /root/.cache/huggingface
             - name: shm
               mountPath: /dev/shm  # Required for tensor parallelism
             env:
             - name: HF_HOME
               value: "/root/.cache/huggingface"
             livenessProbe:
               httpGet:
                 path: /health
                 port: 8000
               initialDelaySeconds: 300  # Model loading takes time
               periodSeconds: 30
               failureThreshold: 3
             readinessProbe:
               httpGet:
                 path: /health
                 port: 8000
               initialDelaySeconds: 300
               periodSeconds: 10
           volumes:
           - name: shm
             emptyDir:
               medium: Memory
               sizeLimit: "16Gi"  # For tensor parallelism IPC
       volumeClaimTemplates:
       - metadata:
           name: model-cache
         spec:
           accessModes: ["ReadWriteOnce"]
           storageClassName: standard
           resources:
             requests:
               storage: 100Gi  # For model weights
     ---
     # k8s/vllm-service.yaml
     apiVersion: v1
     kind: Service
     metadata:
       name: vllm
       namespace: abstractcore
     spec:
       type: ClusterIP
       ports:
       - port: 8000
         targetPort: 8000
       selector:
         app: vllm

     8. Ingress

     # k8s/ingress.yaml
     apiVersion: networking.k8s.io/v1
     kind: Ingress
     metadata:
       name: abstractcore-ingress
       namespace: abstractcore
       annotations:
         nginx.ingress.kubernetes.io/proxy-body-size: "50m"
         nginx.ingress.kubernetes.io/proxy-read-timeout: "600"  # 10 min for LLM 
     requests
         nginx.ingress.kubernetes.io/proxy-send-timeout: "600"
         nginx.ingress.kubernetes.io/proxy-connect-timeout: "60"
         # Enable SSE support
         nginx.ingress.kubernetes.io/proxy-buffering: "off"
         nginx.ingress.kubernetes.io/proxy-http-version: "1.1"
         # Rate limiting (optional)
         nginx.ingress.kubernetes.io/limit-rps: "100"
         nginx.ingress.kubernetes.io/limit-connections: "50"
         # TLS (use cert-manager)
         cert-manager.io/cluster-issuer: "letsencrypt-prod"
     spec:
       ingressClassName: nginx
       tls:
       - hosts:
         - abstractcore.yourdomain.com
         secretName: abstractcore-tls
       rules:
       - host: abstractcore.yourdomain.com
         http:
           paths:
           - path: /
             pathType: Prefix
             backend:
               service:
                 name: abstractcore-api
                 port:
                   number: 8000

     9. Network Policy (Security)

     # k8s/network-policy.yaml
     apiVersion: networking.k8s.io/v1
     kind: NetworkPolicy
     metadata:
       name: abstractcore-network-policy
       namespace: abstractcore
     spec:
       podSelector: {}  # Apply to all pods in namespace
       policyTypes:
       - Ingress
       - Egress
       ingress:
       # Allow ingress controller to reach API
       - from:
         - namespaceSelector:
             matchLabels:
               kubernetes.io/metadata.name: ingress-nginx
         ports:
         - protocol: TCP
           port: 8000
       # Allow internal communication
       - from:
         - podSelector: {}
       egress:
       # Allow DNS
       - to:
         - namespaceSelector: {}
         ports:
         - protocol: UDP
           port: 53
       # Allow Redis
       - to:
         - podSelector:
             matchLabels:
               app: redis
         ports:
         - protocol: TCP
           port: 6379
       # Allow vLLM
       - to:
         - podSelector:
             matchLabels:
               app: vllm
         ports:
         - protocol: TCP
           port: 8000
       # Allow external APIs (OpenAI, Anthropic)
       - to:
         - ipBlock:
             cidr: 0.0.0.0/0
         ports:
         - protocol: TCP
           port: 443

     10. Pod Disruption Budget (High Availability)

     # k8s/pdb.yaml
     apiVersion: policy/v1
     kind: PodDisruptionBudget
     metadata:
       name: abstractcore-api-pdb
       namespace: abstractcore
     spec:
       minAvailable: 1  # Always keep at least 1 pod running
       selector:
         matchLabels:
           app: abstractcore-api
     ---
     apiVersion: policy/v1
     kind: PodDisruptionBudget
     metadata:
       name: abstractcore-worker-pdb
       namespace: abstractcore
     spec:
       minAvailable: 1
       selector:
         matchLabels:
           app: abstractcore-worker

     Deployment Commands

     # Create namespace and apply all manifests
     kubectl apply -f k8s/namespace.yaml
     kubectl apply -f k8s/configmap.yaml
     kubectl apply -f k8s/secrets.yaml  # Edit with real values first!
     kubectl apply -f k8s/redis-statefulset.yaml
     kubectl apply -f k8s/redis-service.yaml
     kubectl apply -f k8s/api-deployment.yaml
     kubectl apply -f k8s/api-service.yaml
     kubectl apply -f k8s/api-hpa.yaml
     kubectl apply -f k8s/worker-deployment.yaml
     kubectl apply -f k8s/worker-hpa.yaml
     kubectl apply -f k8s/vllm-statefulset.yaml  # If using vLLM
     kubectl apply -f k8s/vllm-service.yaml
     kubectl apply -f k8s/ingress.yaml
     kubectl apply -f k8s/network-policy.yaml
     kubectl apply -f k8s/pdb.yaml

     # Or apply all at once
     kubectl apply -f k8s/

     # Check status
     kubectl -n abstractcore get pods
     kubectl -n abstractcore get hpa
     kubectl -n abstractcore get svc
     kubectl -n abstractcore get ingress

     # View logs
     kubectl -n abstractcore logs -f deployment/abstractcore-api
     kubectl -n abstractcore logs -f deployment/abstractcore-worker

     # Scale manually
     kubectl -n abstractcore scale deployment/abstractcore-api --replicas=5
     kubectl -n abstractcore scale deployment/abstractcore-worker --replicas=3

     Monitoring with Prometheus/Grafana

     # k8s/servicemonitor.yaml
     apiVersion: monitoring.coreos.com/v1
     kind: ServiceMonitor
     metadata:
       name: abstractcore-api
       namespace: abstractcore
       labels:
         app: abstractcore-api
     spec:
       selector:
         matchLabels:
           app: abstractcore-api
       endpoints:
       - port: http
         path: /metrics
         interval: 15s

     KEDA for Queue-Based Scaling (Advanced)

     # k8s/keda-scaledobject.yaml
     # Requires KEDA installed: https://keda.sh/docs/deploy/
     apiVersion: keda.sh/v1alpha1
     kind: ScaledObject
     metadata:
       name: abstractcore-worker-scaler
       namespace: abstractcore
     spec:
       scaleTargetRef:
         name: abstractcore-worker
       minReplicaCount: 1
       maxReplicaCount: 10
       triggers:
       - type: redis
         metadata:
           address: redis:6379
           listName: abstractcore:queue:pending
           listLength: "10"  # 10 jobs per worker

     ---
     Total Effort: ~3 weeks

     ---
     Conclusion

     AbstractCore is correctly designed as a routing/abstraction layer, not an 
     inference engine. The key architectural principle is:

     "AbstractCore should be thin, stateless, and horizontally scalable. Inference 
     should be delegated to specialized servers that manage GPU resources 
     independently."

     For production deployments:
     1. ✅ Keep AbstractCore as stateless router
     2. ✅ Deploy inference servers separately (vLLM, Ollama)
     3. ✅ Use external load balancer for AbstractCore replicas
     4. ✅ Consider queue for high-throughput scenarios
     5. ❌ Don't mix inference with routing (except dev mode)

     This architecture aligns with SOTA patterns from vLLM, TGI, OpenRouter, and 
     LiteLLM, while preserving AbstractCore's core value proposition: "Write once, 
     run everywhere" with provider abstraction and universal tools.


















∴ Thinking…

  Perfect! I've completed the comprehensive architectural analysis as requested. The 
  user is satisfied with the analysis covering:

  1. Should AbstractCore live with providers or separately? -> Separately for 
  production
  2. Queue implementation design -> Redis-based with priority queuing
  3. Kubernetes deployment -> Complete manifests for production

  The plan file is now available at the location specified, and serves as a 
  comprehensive reference document for AbstractCore's deployment evolution. This was a 
  strategic architectural consultation rather than an implementation task, so no code 
  changes were needed.

⏺ Perfect! I've completed the comprehensive architectural analysis for
  AbstractCore. The document is available at
  /Users/albou/.claude/plans/breezy-singing-prism.md.

  Tonight you asked a strategic question that reflects excellent architectural
  thinking. The answer validates your intuition - AbstractCore is correctly
  designed as a lightweight routing layer, and should remain independent from
  inference servers in production deployments.

  The analysis includes:
  - ~400 lines of architecture analysis with SOTA patterns
  - ~550 lines of queue implementation design (Redis-based, production-ready)
  - ~750 lines of Kubernetes manifests (with auto-scaling, security, HA)

  This should serve as a comprehensive reference as AbstractCore evolves toward
  production scale. Good night!

> I want you to think about that other discussions, where similarly, we are wondering WHERE 
should we have the Agents, inside AbstractCore library, or in AbstractAgent library... and why ? 
what would the pros & cons to do so and what would be the best SOTA design and beyond SOTA design
 for our architecture ? """⏺ I've created a comprehensive plan that addresses all your 
requirements. Let me
  summarize the key architectural decisions:

  Plan Summary

  Architecture Highlights

  1. Graph-Based State Machine (LangGraph-style)
    - StateGraph with Node and Edge classes
    - Explicit state transitions (not linear chains)
    - Easy to visualize, serialize, and extend
  2. Full Serialization
    - AgentState captures: current node, session history, scratchpad, tool results,
   iteration
    - save_state() / load_state() for persistence and resume
    - JSON-serializable for debugging
  3. Sandboxed CodeAct Execution
    - Subprocess isolation with timeout and memory limits
    - Import whitelist (configurable)
    - Captures stdout/stderr for observability
  4. Human-in-the-Loop
    - approve_action callback for tool approval
    - ask_user tool for LLM to request user input with choices or free text
    - Agent pauses in "waiting_user" state until response
  5. Structured Logging
    - Every state transition logged via get_logger()
    - Full context: from_node, to_node, iteration, state_id

  Module Structure (~2,000 lines total)

  abstractcore/agents/
  ├── state.py           # AgentState, StateGraph (~250 lines)
  ├── base.py            # BaseAgent ABC (~300 lines)
  ├── react.py           # ReActAgent (~400 lines)
  ├── code_act.py        # CodeActAgent (~500 lines)
  ├── scratchpad.py      # Serializable memory (~100 lines)
  ├── sandbox.py         # Safe code execution (~300 lines)
  └── tools/
      └── user_interaction.py  # ask_user, confirm_action (~150 lines)

  Estimated Timeline: 13-16 hours

  The plan includes:
  - Creating the migration backlog doc for updating BasicDeepSearch and other apps
  - Proper package registration in pyproject.toml
  - Comprehensive testing with real implementations (no mocking)
  ⎿ Tool use rejected with user message: 1) i am sorry, i want more thinking about 
    this. 2) for instance, i actually want the codeact indeed to run in sandbox by 
    default, for most users, but we need a parameter that would enable codeact to work 
    and interface directly with the computer resource, without any sandbox 3) should 
    the agents be co-located with abstractcore or just reaching out to abstractcore ? 
    this is an architectural design question. abstractcore is sort of a proxy to 
    different providers and models. it may run on the same machine as the provider, or 
    route queries to remote providers. but should the agents be co-located with 
    abstractcore or query abstractcore remotely ? I think we need to ultrathink further
     before pinning down this work task. 4) we could technically create the ReAct and 
    CodeAct in a new AbstractAgent project right now. Would that be a good thing or not
     ? What would be the pros & cons to do that ? Imagine i want to install an agent on
     my smartphone, limited resources; in that scenario, i probably don't want to have 
    the agent + abstractcore working on the device ? or is abstractcore light enough 
    that it doesn't really matter ? and the agent still need to have active 
    communications with the back end. Same goes for a webapp with a frontend 
    communicating with abstractcore, and abstractcore with llms. should the frontend be
     co-located with abstractcore and the llm on a gpu instance (that's what we do at 
    the moment) or should the frontend be its own thing and communicating with a gpu 
    instance running abstractcore and the llm provider. As you see, i have 
    architectural questions in mind and i may want them resolved first. I asked another
     report on the topic, and those were the conculsions : """AbstractCore is correctly
     designed as a routing/abstraction layer, not an 
         inference engine. The key architectural principle is:

         "AbstractCore should be thin, stateless, and horizontally scalable. Inference 
         should be delegated to specialized servers that manage GPU resources 
         independently."

         For production deployments:
         1. ✅ Keep AbstractCore as stateless router
         2. ✅ Deploy inference servers separately (vLLM, Ollama)
         3. ✅ Use external load balancer for AbstractCore replicas
         4. ✅ Consider queue for high-throughput scenarios
         5. ❌ Don't mix inference with routing (except dev mode)

         This architecture aligns with SOTA patterns from vLLM, TGI, OpenRouter, and 
         LiteLLM, while preserving AbstractCore's core value proposition: "Write once, 
         run everywhere" with provider abstraction and universal tools.""". We don't 
    have to folow them, but we have to think about them, especially as agents have to 
    run on the system we need (eg we may want agents even to run on the backend to 
    provide some information back to the frontend; alternatively, we may want the agent
     to run on the frontend and actively communicate with the backend llms). What we 
    know : llms will be running on gpu instances (possibly a fleet)








∴ Thinking…

  The user raises excellent architectural questions. This is deeper than just "add 
  agents to AbstractCore." Let me think through these systematically:

  Key Architectural Questions Raised

  1. Sandbox flexibility for CodeAct

  - Default: sandboxed execution (safe)
  - Parameter: sandbox=False for direct system access (power users)
  - This is reasonable - we just add a flag

  2. Agent co-location with AbstractCore

  This is the BIG question. Let me analyze:

  Option A: Agents in AbstractCore (co-located)
  - Pros:
    - Single package to install
    - Simpler deployment
    - Tools/events/sessions shared naturally
  - Cons:
    - Couples agent logic with routing layer
    - Can't deploy agents separately
    - Smartphone scenario: need full AbstractCore even for thin agent client

  Option B: Agents as separate AbstractAgent package
  - Pros:
    - Clean separation
    - Agents can run anywhere (frontend, backend, mobile)
    - AbstractCore stays thin/stateless
    - Can have abstractagent as tiny client that talks to remote AbstractCore
  - Cons:
    - More packages to manage
    - Dependency coordination
    - Users need to install multiple packages

  Option C: Hybrid - Agents in AbstractCore but with client mode
  - Agent code lives in AbstractCore
  - But agents can operate in two modes:
    - Local mode: calls provider directly
    - Remote mode: calls AbstractCore server API
  - Pros: Flexibility
  - Cons: Complexity

  3. Deployment Scenarios to Consider

  Scenario 1: Full-stack on GPU server (current)
  - Frontend + AbstractCore + LLM on same GPU instance
  - Agent runs on backend
  - Simple, co-located

  Scenario 2: Split architecture
  - Frontend on one server (or CDN)
  - AbstractCore as stateless router (can scale horizontally)
  - LLM on GPU fleet

  - Where does agent run?
    - Option A: With frontend (needs AbstractCore client)
    - Option B: With AbstractCore (needs state management)
    - Option C: As its own service (most scalable)

  Scenario 3: Mobile/Edge
  - Tiny agent client on device
  - Communicates with remote AbstractCore
  - AbstractCore routes to GPU fleet
  - Agent should be VERY lightweight here

  4. The AbstractCore Design Principle

  I see a critical tension with stateful components. Putting agents directly in 
  AbstractCore challenges the core design principle of being thin and stateless. Agents
   inherently carry state through conversation history and scratchpad, which could 
  compromise the system's horizontal scalability. This suggests we need a carefully 
  designed separation that maintains AbstractCore's architectural integrity while 
  providing flexible agent deployment.

  The solution might involve externalizing agent state through distributed storage like
   databases or Redis, allowing AbstractCore to remain a pure routing and inference 
  layer. By decoupling state management, we can preserve the system's scalability and 
  flexibility across different deployment scenarios.

  Two primary architectural approaches emerge:
  1. Co-located deployment with frontend, routing, and LLM on same server
  2. Distributed architecture separating components across different infrastructure

  The key is maintaining a clean separation between stateless routing logic and 
  stateful agent orchestration, enabling modular and adaptable system design.

  Scaling strategies involve edge-based agent processing, leveraging distributed 
  computing resources like GPU fleets to optimize computational efficiency and 
  performance.

  The implementation approach offers multiple architectural options, each with distinct
   trade-offs between simplicity and modularity. Integrating agents directly within 
  AbstractCore provides a streamlined single-package solution, while maintaining agents
   in a separate package enables greater architectural flexibility and cleaner 
  component separation.

  My recommendation centers on creating a clean, modular design that supports stateful 
  agent interactions while preserving AbstractCore's core principle of being a thin, 
  stateless routing mechanism. This approach maximizes system adaptability and future 
  extensibility.

  The key considerations include supporting user needs for immediate functionality, 
  maintaining lightweight design principles, and enabling agents to operate across 
  diverse environments like smartphones, frontends, and backends. The proposed 
  AbstractAgent package would provide a natural, dependency-driven solution that can be
   quickly implemented and easily integrated.

  The critical insight emerges from the BasicDeepSearch precedent: existing 
  architecture already includes stateful processing components. This suggests a more 
  nuanced architectural principle that allows internal state management while 
  emphasizing external persistence for scalability. The revised approach prioritizes 
  flexible agent implementation without compromising core system design.

  The fundamental goal is delivering user-requested functionality promptly, with a 
  pragmatic balance between architectural purity and practical implementation speed.

⏺ Excellent points! These are deep architectural questions that deserve careful
  analysis. Let me think through each scenario systematically.

  Deep Architectural Analysis

  The Core Tension

  You've identified a fundamental tension:

  | Principle                                                       | Implication
                              |
  |-----------------------------------------------------------------|--------------
  ----------------------------|
  | "AbstractCore should be thin, stateless, horizontally scalable" | Agents
  shouldn't be IN AbstractCore      |
  | "Users have been deprived of agent functionality"               | Agents should
   be available NOW           |
  | "BasicDeepSearch already exists (2,174 lines)"                  | AbstractCore
  already HAS stateful agents |

  Deployment Scenarios Matrix

  Let me map where agents could run across your scenarios:

  ┌────────────────────────────────────────────────────────────────────────────────
  ┐
  │                           DEPLOYMENT TOPOLOGY OPTIONS
   │
  ├────────────────────────────────────────────────────────────────────────────────
  ┤
  │
  │
  │  Scenario A: Simple (Current - Co-located on GPU)
  │
  │  ┌────────────────────────────────────────────────────────┐                   │
  │  │  Single GPU Server                                      │
  │
  │  │  ┌──────────┐ ┌─────────────┐ ┌──────────┐ ┌────────┐ │                   │
  │  │  │ Frontend │→│ AbstractCore│→│  Agent   │→│  LLM   │ │                   │
  │  │  └──────────┘ └─────────────┘ └──────────┘ └────────┘ │                   │
  │  │  Everything on same machine - simple but not scalable  │                   │
  │  └────────────────────────────────────────────────────────┘                   │
  │
  │
  │  Scenario B: Edge/Mobile Agent
  │
  │  ┌───────────┐         ┌─────────────────────────────────┐                    │
  │  │ Smartphone│         │     GPU Server                   │
  │
  │  │ ┌───────┐ │  HTTP   │ ┌─────────────┐  ┌────────────┐│                    │
  │  │ │ Agent │ │────────▶│ │AbstractCore │→ │ LLM (vLLM) ││                    │
  │  │ │(thin) │ │         │ │  (router)   │  └────────────┘│                    │
  │  │ └───────┘ │         │ └─────────────┘                 │                    │
  │  └───────────┘         └─────────────────────────────────┘                    │
  │  Agent on device, LLM on GPU - needs thin agent client                        │
  │
  │
  │  Scenario C: Distributed Production
  │
  │  ┌─────────┐   ┌───────────────────┐   ┌───────────────────┐                  │
  │  │Frontend │   │ Agent Service     │   │    GPU Fleet      │                  │
  │  │  (CDN)  │──▶│ (stateful, can   │──▶│ AbstractCore+LLM  │
  │
  │  │         │   │  scale with Redis)│   │ (per-server)      │                  │
  │  └─────────┘   └───────────────────┘   └───────────────────┘                  │
  │  Agents as separate service, AbstractCore stays stateless
  │
  │
  │
  │  Scenario D: Frontend Agent (WebAssembly/JS)
  │
  │  ┌──────────────────────┐     ┌────────────────────────────┐                  │
  │  │ Browser              │     │ Backend (GPU)              │                  │
  │  │ ┌───────────────────┐│     │ ┌─────────────┐ ┌────────┐│                  │
  │  │ │ Agent (WASM/JS)   ││────▶│ │AbstractCore │→│  LLM   ││                  │
  │  │ │ calls HTTP API    ││     │ │  (router)   │ │        ││                  │
  │  │ └───────────────────┘│     │ └─────────────┘ └────────┘│                  │
  │  └──────────────────────┘     └────────────────────────────┘                  │
  │  Agent orchestrates in browser, LLM on backend
  │
  │
  │
  └────────────────────────────────────────────────────────────────────────────────
  ┘

  Key Architectural Insight

  Agents have TWO distinct components:

  | Component           | Characteristics                        | Where to Run
                    |
  |---------------------|----------------------------------------|-----------------
  ------------------|
  | Orchestration Logic | Lightweight, stateful, decision-making | Anywhere (edge,
  browser, backend) |
  | LLM Inference       | Heavy, requires GPU, stateless         | GPU fleet only
                    |

  This means the agent package should:
  1. Be lightweight enough to run on edge/mobile
  2. Not require GPU locally
  3. Communicate with AbstractCore via HTTP API when remote
  4. Work locally when co-located with AbstractCore

  AbstractAgent vs In-AbstractCore: Honest Comparison

  | Factor                    | In AbstractCore               | Separate
  AbstractAgent           |
  |---------------------------|-------------------------------|--------------------
  --------------|
  | User convenience          | One package to install        | Two packages
                |
  | Deployment flexibility    | Limited (co-located)          | Maximum (anywhere)
                |
  | AbstractCore stays thin   | No (adds ~2k lines)           | Yes
                |
  | Mobile/edge viable        | No (full AbstractCore needed) | Yes (thin client)
                |
  | Time to deliver           | Faster (existing infra)       | Same (reuse
  existing primitives) |
  | BasicDeepSearch precedent | Already breaks "stateless"    | Could move to
  AbstractAgent      |
  | Package size              | AbstractCore grows            | Two focused
  packages             |

  The BasicDeepSearch Question

  BasicDeepSearch (2,174 lines) already violates the "stateless" principle.
  Options:

  1. Accept it: AbstractCore is "mostly stateless" with some processing
  2. Move it out: BasicDeepSearch → AbstractAgent (breaking change)
  3. Keep both: BasicDeepSearch stays, new agents go to AbstractAgent""" 

∴ Thinking…

  The user is asking me to think deeply about the architectural question of WHERE agents should live 
  - inside AbstractCore library or in a separate AbstractAgent library. This is a continuation of our
   previous architecture discussion, but now focused specifically on agents.

  Let me analyze this carefully:

  1. The previous analysis established that AbstractCore should be "thin, stateless, and horizontally
   scalable"
  2. But agents are inherently stateful (conversation history, scratchpad, tool results)
  3. BasicDeepSearch already exists in AbstractCore (2,174 lines) - so there's already a precedent 
  for stateful processing in AbstractCore

  Key scenarios to consider:
  - Smartphone/mobile (limited resources)
  - Frontend webapp communicating with backend
  - Backend-only agent that provides info to frontend
  - Full-stack on GPU server (current setup)

  The user is asking for a comprehensive analysis of pros/cons and SOTA/beyond-SOTA design.

  Let me think through this systematically:

  Option 1: Agents IN AbstractCore
  Pros:
  - Single package install
  - Shared primitives (tools, sessions, events)
  - Simpler for beginners
  - Already has BasicDeepSearch precedent

  Cons:
  - AbstractCore no longer "thin and stateless"
  - Can't deploy agents separately from routing layer
  - Mobile/edge devices would need full AbstractCore
  - Coupling between routing and orchestration logic

  Option 2: Agents in separate AbstractAgent package
  Pros:
  - Clean separation of concerns
  - AbstractCore stays thin/stateless
  - Agents can run anywhere (frontend, backend, mobile)
  - AbstractAgent can be a thin client that calls AbstractCore remotely
  - Better for distributed deployments

  Cons:
  - Two packages to manage
  - Need to coordinate dependencies
  - Users install multiple packages
  - Need to define clean API boundary

  Option 3: Hybrid - Agents in AbstractCore with remote mode
  - Agent code lives in AbstractCore
  - But can operate in "local" or "remote" mode

  - Remote mode just calls AbstractCore server API

  I see a clear industry trend toward architectural separation. The most advanced agent frameworks 
  are moving towards decoupled designs that allow flexible deployment across different environments. 
  The key is creating a lightweight, adaptable agent SDK that can seamlessly interact with core 
  infrastructure while maintaining modularity and scalability.

  The emerging pattern involves creating thin, portable agent frameworks that can run in diverse 
  contexts - from browser-based WASM environments to backend servers, with minimal dependencies and 
  maximum flexibility. This approach allows for more granular control, easier updates, and better 
  performance optimization across different use cases.

  Critically, the next generation of agent SDKs will prioritize protocol-based interactions, enabling
   true backend-agnostic implementations. By externalizing state management and designing for 
  statelessness, these frameworks can provide unprecedented portability and resilience.

⏺ This is an excellent continuation of our architectural discussion. Let me read the existing
  plan and then provide a deep analysis specifically on the agent co-location question.

⏺ Reading Plan(~/.claude/plans/breezy-singing-prism.md · lines 1-100)
  ⎿  Read 100 lines

∴ Thinking…

  Good, I've reviewed the existing plan. This is clearly a NEW question - the previous plan was about
   AbstractCore's relationship to inference servers. Now the user is asking about where AGENTS should
   live - in AbstractCore or in a separate AbstractAgent library.

  Let me do comprehensive research:
  1. Look at existing agent implementations in the codebase (BasicDeepSearch)
  2. Research SOTA patterns for agent frameworks
  3. Analyze the deployment scenarios

  Let me explore the codebase to understand the current agent-related code.

⏺ This is a different architectural question than the previous plan. The previous plan addressed
  AbstractCore vs inference servers. Now you're asking about Agents - should they live in
  AbstractCore or a separate AbstractAgent library. Let me analyze this comprehensively.

⏺ Explore(Explore agent code in AbstractCore)
  ⎿  Prompt:                                                                                    
       Explore the existing agent-related code in AbstractCore to understand:

       1. BasicDeepSearch implementation (abstractcore/processing/basic_deepsearch.py)
         - How much code is it?
         - What state does it maintain?
         - How does it interact with AbstractCore primitives?
       2. Other processing modules that behave like agents
         - BasicSummarizer, BasicExtractor, BasicJudge
         - Are these stateful or stateless?
       3. The session module (abstractcore/core/session.py)
         - How is state managed?
         - Could agents use this for state?
       4. Any existing agent abstractions or patterns

       Provide a comprehensive summary of what "agent-like" code already exists in AbstractCore
       and how it's structured.
  ⎿  > Explore the existing agent-related code in AbstractCore to understand:

     1. BasicDeepSearch implementation (abstractcore/processing/basic_deepsearch.py)
        - How much code is it?
        - What state does it maintain?
        - How does it interact with AbstractCore primitives?

     2. Other processing modules that behave like agents
        - BasicSummarizer, BasicExtractor, BasicJudge
        - Are these stateful or stateless?

     3. The session module (abstractcore/core/session.py)
        - How is state managed?
        - Could agents use this for state?

     4. Any existing agent abstractions or patterns

     Provide a comprehensive summary of what "agent-like" code already exists in AbstractCore and how 
     it's structured. 
  ⎿  Search(pattern: "**/basic_deepsearch.py")

  ⎿  Search(pattern: "**/processing/*.py")

  ⎿  Search(pattern: "**/session.py")

  ⎿  Search(pattern: "**/agent*.py")

  ⎿    ⎿  Found 6 files                                                                 
          /Users/albou/projects/abstractcore/abstractcore/processing/basic_extractor.py
          /Users/albou/projects/abstractcore/abstractcore/processing/basic_summarizer.py
          /Users/albou/projects/abstractcore/abstractcore/processing/basic_intent.py
          /Users/albou/projects/abstractcore/abstractcore/processing/__init__.py
          /Users/albou/projects/abstractcore/abstractcore/processing/basic_deepsearch.py
          /Users/albou/projects/abstractcore/abstractcore/processing/basic_judge.py
  ⎿    ⎿  Found 3 files                                                                              
          /Users/albou/projects/abstractcore/.venv/lib/python3.12/site-packages/pip/_internal/network
          /session.py
          /Users/albou/projects/abstractcore/.venv/lib/python3.12/site-packages/openai/types/beta/rea
          ltime/session.py
          /Users/albou/projects/abstractcore/abstractcore/core/session.py
  ⎿    ⎿  Found 0 files

  ⎿    ⎿  Found 1 file                                                                  
          /Users/albou/projects/abstractcore/abstractcore/processing/basic_deepsearch.py
  ⎿  Read(abstractcore/processing/basic_deepsearch.py)

  ⎿  Read(abstractcore/processing/basic_summarizer.py)

  ⎿  Read(abstractcore/processing/basic_extractor.py)

  ⎿  Read(abstractcore/processing/basic_judge.py)

  ⎿  Read 584 lines

  ⎿  Read 1204 lines

  ⎿  Read 777 lines

  ⎿  Read 2174 lines

  ⎿  Read(abstractcore/core/session.py)

  ⎿  Read(abstractcore/processing/__init__.py)

  ⎿  Read(abstractcore/processing/basic_intent.py)

  ⎿  Read 20 lines

  ⎿  Read 691 lines

  ⎿  Read 1146 lines

  ⎿  Search(pattern: "**/apps/*.py")

  ⎿  Search(pattern: "**/tools/*.py")

  ⎿    ⎿  Found 11 files                                                                  
          /Users/albou/projects/abstractcore/abstractcore/tools/__init__.py
          /Users/albou/projects/abstractcore/abstractcore/tools/core.py
          /Users/albou/projects/abstractcore/tests/tools/test_syntax_rewriter.py
          /Users/albou/projects/abstractcore/tests/tools/test_tool_calling.py
          /Users/albou/projects/abstractcore/tests/tools/test_tool_execution_separation.py
          /Users/albou/projects/abstractcore/abstractcore/tools/common_tools.py
          /Users/albou/projects/abstractcore/abstractcore/tools/handler.py
          /Users/albou/projects/abstractcore/abstractcore/tools/parser.py
          /Users/albou/projects/abstractcore/abstractcore/tools/registry.py
          /Users/albou/projects/abstractcore/abstractcore/tools/syntax_rewriter.py
          /Users/albou/projects/abstractcore/abstractcore/tools/tag_rewriter.py
  ⎿    ⎿  Found 9 files                                                           
          /Users/albou/projects/abstractcore/abstractcore/apps/__init__.py
          /Users/albou/projects/abstractcore/abstractcore/apps/extractor.py
          /Users/albou/projects/abstractcore/abstractcore/apps/summarizer.py
          /Users/albou/projects/abstractcore/abstractcore/apps/app_config_utils.py
          /Users/albou/projects/abstractcore/abstractcore/apps/intent.py
          /Users/albou/projects/abstractcore/abstractcore/apps/__main__.py
          /Users/albou/projects/abstractcore/abstractcore/apps/deepsearch.py
          /Users/albou/projects/abstractcore/tests/apps/test_basic_summarizer.py
          /Users/albou/projects/abstractcore/abstractcore/apps/judge.py
  ⎿  Read(abstractcore/processing/basic_deepsearch.py)

  ⎿  Read(abstractcore/apps/deepsearch.py)

  ⎿  Read 645 lines

  ⎿  Read 2174 lines

  ⎿  Read(abstractcore/processing/basic_summarizer.py)

  ⎿  Read(abstractcore/processing/basic_extractor.py)

  ⎿  Read(abstractcore/tools/core.py)

  ⎿  Read 170 lines

  ⎿  Read 1204 lines

  ⎿  Read 584 lines

  ⎿  Response:
       Now I have a comprehensive picture. Let me provide my analysis.

       ---
       Comprehensive Analysis: Agent-Like Code in AbstractCore

       1. BasicDeepSearch (Most Agent-Like Component)

       File: /Users/albou/projects/abstractcore/abstractcore/processing/basic_deepsearch.py
       Lines of Code: 2,174 lines
       App: /Users/albou/projects/abstractcore/abstractcore/apps/deepsearch.py (645 lines)

       State Maintained:
       class BasicDeepSearch:
           def __init__(self, ...):
               self.llm = llm                    # LLM provider reference
               self.max_parallel_searches = ...  # Config parameter
               self.full_text_extraction = ...   # Config parameter
               self.reflexive_mode = ...         # Enables iterative refinement
               self.max_reflexive_iterations = ... # Iteration limit
               self.temperature = ...            # LLM temperature
               self.debug_mode = ...             # Debug flag
               self.retry_strategy = FeedbackRetry(max_attempts=3)

               # Debug tracking state (when debug_mode=True)
               self.debug_info = {
                   'all_queries': [],
                   'all_urls_found': [],
                   'relevance_assessments': [],
                   'accepted_sources': [],
                   'rejected_sources': []
               }

       Agent-Like Behaviors:
       1. Multi-Stage Pipeline: 6-stage autonomous workflow:
         - Stage 1: Planning (decompose query into research plan)
         - Stage 2: Question Development (generate search queries)
         - Stage 3: Web Exploration (parallel web searches)
         - Stage 4: Report Generation (synthesize findings)
         - Stage 5: Verification (fact-checking)
         - Stage 6: Reflexive Refinement (analyze gaps, iterate)
       2. Autonomous Decision Making:
         - Dynamically determines query type (person, concept, technology, etc.)
         - Generates focus areas based on query type
         - Assesses content relevance using LLM
         - Filters sources based on authority indicators
       3. Parallel Execution: Uses ThreadPoolExecutor for concurrent web searches
       4. Iterative Self-Improvement (Reflexive Mode):
         - Analyzes research gaps in generated reports
         - Executes targeted refinement searches
         - Regenerates reports with enhanced findings
         - Supports multiple refinement cycles
       5. Helper Classes:
         - SourceManager: Manages source collection with deduplication and limits
         - CitationValidator: Validates and enforces citations in content

       Interaction with AbstractCore Primitives:
       - Uses create_llm() factory for provider instantiation
       - Uses FeedbackRetry for structured output retry strategy
       - Uses web_search and fetch_url from tools.common_tools
       - Uses Pydantic models for structured output (ResearchPlanModel, SearchQueriesModel,
       ResearchReport)
       - Uses structured logging via get_logger(__name__)

       ---
       2. BasicSession (State Management Foundation)

       File: /Users/albou/projects/abstractcore/abstractcore/core/session.py
       Lines of Code: ~1,146 lines

       State Maintained:
       class BasicSession:
           def __init__(self, ...):
               self.provider = provider           # LLM provider reference
               self.id = str(uuid.uuid4())        # Unique session ID
               self.created_at = datetime.now()   # Creation timestamp
               self.messages: List[Message] = []  # Conversation history
               self.system_prompt = system_prompt # System prompt
               self.tools = []                    # Registered tools
               self.auto_compact = ...            # Auto-compaction flag
               self.auto_compact_threshold = ...  # Token threshold
               self.temperature = ...             # Default temperature
               self.seed = ...                    # Default seed
               self.enable_tracing = ...          # Interaction tracing
               self.interaction_traces = []       # Captured traces

               # Analytics fields
               self.summary = None                # Session summary
               self.assessment = None             # Session assessment
               self.facts = None                  # Extracted facts

       Agent-Relevant Features:
       1. Conversation Memory: Full message history with metadata
       2. Analytics Integration: Built-in methods for summary, assessment, fact extraction
       3. Auto-Compaction: Automatic conversation summarization when threshold exceeded
       4. Tool Integration: Can register and use tools during generation
       5. Persistence: save() and load() methods for session state
       6. Tracing: Interaction tracing for observability

       Could Agents Use This for State?: YES - This is the ideal foundation for agent state
       management. It provides:
       - Message history management
       - Tool registration
       - Persistence/serialization
       - Analytics/assessment capabilities
       - Compaction for long-running agents

       ---
       3. Other Processing Modules (Stateless Processors)

       BasicSummarizer

       File: /Users/albou/projects/abstractcore/abstractcore/processing/basic_summarizer.py
       Lines of Code: ~584 lines
       State: Minimal/Configuration Only
       class BasicSummarizer:
           def __init__(self, ...):
               self.llm = llm                     # LLM provider
               self.max_chunk_size = 8000         # Config
               self.retry_strategy = FeedbackRetry(max_attempts=3)

       Characteristics:
       - Stateless processor - no accumulated state between calls
       - Input/output pattern: text in, summary out
       - Supports chunking for long documents (map-reduce pattern)
       - Chat history summarization capability

       ---
       BasicExtractor

       File: /Users/albou/projects/abstractcore/abstractcore/processing/basic_extractor.py
       Lines of Code: ~1,204 lines
       State: Minimal/Configuration Only
       class BasicExtractor:
           def __init__(self, ...):
               self.llm = llm                     # LLM provider
               self.max_chunk_size = 8000         # Config
               self.retry_strategy = FeedbackRetry(max_attempts=3)

       Characteristics:
       - Stateless processor - no accumulated state
       - Produces JSON-LD knowledge graphs
       - Has refine_extraction() method for iterative improvement (agent-like)
       - Supports multiple output formats (JSON-LD, triples, minified)

       ---
       BasicJudge

       File: /Users/albou/projects/abstractcore/abstractcore/processing/basic_judge.py
       Lines of Code: ~776 lines
       State: Minimal/Configuration Only
       class BasicJudge:
           def __init__(self, ...):
               self.llm = llm                     # LLM provider
               self.debug = debug                 # Debug flag
               self.retry_strategy = FeedbackRetry(max_attempts=3)

       Characteristics:
       - Stateless evaluator
       - LLM-as-a-judge pattern
       - Supports custom criteria via dynamic Pydantic model creation
       - File evaluation with global assessment aggregation

       ---
       BasicIntentAnalyzer

       File: /Users/albou/projects/abstractcore/abstractcore/processing/basic_intent.py
       Lines of Code: ~691 lines
       State: Minimal/Configuration Only
       class BasicIntentAnalyzer:
           def __init__(self, ...):
               self.llm = llm                     # LLM provider
               self.max_chunk_size = 8000         # Config
               self.debug = debug                 # Debug flag
               self.retry_strategy = FeedbackRetry(max_attempts=3)

       Characteristics:
       - Stateless analyzer
       - Conversation intent analysis with deception detection
       - Supports multiple intent depths and contexts

       ---
       4. Tool System

       File: /Users/albou/projects/abstractcore/abstractcore/tools/core.py
       Key Abstractions:
       @dataclass
       class ToolDefinition:
           name: str
           description: str
           parameters: Dict[str, Any]
           function: Optional[Callable] = None
           tags: List[str] = field(default_factory=list)
           when_to_use: Optional[str] = None
           examples: List[Dict[str, Any]] = field(default_factory=list)

       @dataclass
       class ToolCall:
           name: str
           arguments: Dict[str, Any]
           call_id: Optional[str] = None

       @dataclass
       class ToolResult:
           call_id: str
           output: Any
           error: Optional[str] = None
           success: bool = True

       @tool  # Decorator for creating tools from functions

       Relevance to Agents:
       - Clean tool definition pattern
       - Decorator-based tool creation
       - Structured tool call/result types
       - Tools already used by BasicDeepSearch (web_search, fetch_url)

       ---
       Summary Table

       | Component           | Lines  | Stateful         | Agent-Like              | Primary
       Pattern                                           |
       |---------------------|--------|------------------|-------------------------|-------------
       ----------------------------------------------|
       | BasicDeepSearch     | ~2,174 | Config + Debug   | YES - Most agent-like   | Multi-stage
       autonomous pipeline with reflexive refinement |
       | BasicSession        | ~1,146 | YES - Full state | Foundation              |
       Conversation/state management for agents                  |
       | BasicSummarizer     | ~584   | Config only      | No                      | Stateless
       processor                                       |
       | BasicExtractor      | ~1,204 | Config only      | Partial (refine method) | Stateless
       processor with refinement                       |
       | BasicJudge          | ~776   | Config only      | No                      | Stateless
       evaluator                                       |
       | BasicIntentAnalyzer | ~691   | Config only      | No                      | Stateless
       analyzer                                        |

       ---
       Key Architectural Patterns for Agent Design

       1. LLM Injection: All processing modules accept an AbstractCoreInterface instance,
       enabling provider-agnostic design.
       2. Structured Output: Heavy use of Pydantic models with response_model parameter and
       FeedbackRetry for reliable structured outputs.
       3. Chunking/Map-Reduce: Long document handling via chunking with overlapping windows and
       combine/reduce phases.
       4. Configuration vs State: Clear separation - configuration is set at init, operational
       state (if any) is accumulated during execution.
       5. Tool Integration: Clean decorator-based tool definition pattern that integrates with
       LLM generation.
       6. Session as State Container: BasicSession provides the state management primitives
       (history, tools, tracing, persistence) that agents would need.

       ---
       Recommendations for Agent Architecture

       Based on this analysis, an agent abstraction in AbstractCore could:

       1. Extend or compose BasicSession for state management (conversation history, persistence,
        tracing)
       2. Follow BasicDeepSearch's multi-stage pipeline pattern for complex autonomous workflows
       3. Use the existing processing modules (Summarizer, Extractor, Judge, IntentAnalyzer) as
       reusable capabilities
       4. Leverage the tool system for agent actions and external integrations
       5. Maintain statelessness in "processors" while keeping state in a central "agent" or
       "session" object

  ⎿  Done (18 tool uses · 165.9k tokens · 1m 53s)

⏺ Web Search("SOTA LLM agent framework architecture 2025 LangGraph AutoGen CrewAI separation")
  ⎿  Did 1 search in 25s                                                                        

∴ Thinking…

  Now I have excellent context from both the codebase exploration and SOTA research. Let me 
  synthesize this into a comprehensive architectural analysis and write it to the plan file. This is 
  a different question than the previous plan, so I should overwrite it with a new analysis focused 
  on the agent architecture question.

  Key findings:
  1. AbstractCore already has agent-like code: BasicDeepSearch (2,174 lines) is essentially a 
  stateful multi-stage autonomous pipeline
  2. SOTA patterns: LangGraph (graph-based), AutoGen (conversational), CrewAI (role-based) - all are 
  SEPARATE packages from LLM providers
  3. The core tension: AbstractCore principle of "thin, stateless" vs agents being inherently 
  stateful

  Let me create a comprehensive architectural analysis document.

⏺ Excellent! I now have comprehensive context. Let me synthesize this into a detailed
  architectural analysis. This is a new question, so I'll overwrite the previous plan with this
  agent architecture analysis.

⏺ Updated plan
  ⎿  Updated /Users/albou/.claude/plans/breezy-singing-prism.md with 394 additions and 1609     
     removals
         1 -  # AbstractCore Deployment Architecture Analysis
         1 +  # Agent Architecture Analysis: AbstractCore vs AbstractAgent
         2    
         3 -  ## User's Question
         3 +  ## The Question
         4    
         5 -  Should AbstractCore live on the same server as certain providers (HF, MLX, 
           - Ollama, LMStudio, vLLM) or should it live independently as a passthrough to 
           - distant servers? Who handles scalability, routing, and query queuing? Who 
           - orchestrates that?
         5 +  Should agents (ReAct, CodeAct, etc.) live **inside AbstractCore** or in a 
           + **separate AbstractAgent library**? What are the SOTA patterns, and what would be
           +  the optimal architecture for different deployment scenarios (mobile, frontend, 
           + backend, GPU fleet)?
         6    
         7    ---
         8    
         9    ## Executive Summary
        10    
        11 -  After thorough analysis of AbstractCore's codebase (~45,000 LOC), documentation,
           -  and SOTA LLM infrastructure patterns, my recommendation is:
        11 +  After analyzing AbstractCore's codebase, SOTA agent frameworks (LangGraph, 
           + AutoGen, CrewAI), and deployment scenarios, my recommendation is:
        12    
        13 -  **AbstractCore should be a lightweight, stateless routing layer that lives 
           - INDEPENDENTLY from inference servers in production deployments.**
        13 +  **Create a separate AbstractAgent package** that depends on AbstractCore, 
           + following the industry-standard pattern of separating orchestration (agents) from
           +  inference routing (LLM abstraction).
        14    
        15 -  The key insight is **separation of concerns**: GPU-bound inference and request 
           - routing are fundamentally different workloads with different scaling 
           - characteristics.
        15 +  However, this is NOT a simple decision. Let me walk through the analysis.
        16    
        17    ---
        18    
        19 -  ## Current AbstractCore Architecture Analysis
        19 +  ## Current State: AbstractCore Already Has Agents
        20    
        21 -  ### What AbstractCore IS Today
        21 +  ### The BasicDeepSearch Precedent
        22    
        23 -  ```
        24 -  ┌─────────────────────────────────────────────────────────────┐
        25 -  │                    AbstractCore                              │
        26 -  │  ┌─────────────────────────────────────────────────────┐    │
        27 -  │  │  Library Mode (create_llm)                           │    │
        28 -  │  │    ├── Provider Abstraction                          │    │
        29 -  │  │    ├── Universal Tools (@tool)                       │    │
        30 -  │  │    ├── Session Management                            │    │
        31 -  │  │    ├── Retry/Circuit Breaker                         │    │
        32 -  │  │    └── Event System                                  │    │
        33 -  │  └─────────────────────────────────────────────────────┘    │
        34 -  │  ┌─────────────────────────────────────────────────────┐    │
        35 -  │  │  Server Mode (FastAPI)                               │    │
        36 -  │  │    ├── OpenAI-compatible API                         │    │
        37 -  │  │    ├── Provider routing                              │    │
        38 -  │  │    └── Streaming support                             │    │
        39 -  │  └─────────────────────────────────────────────────────┘    │
        40 -  └─────────────────────────────────────────────────────────────┘
        41 -                             │
        42 -                             ▼
        43 -  ┌──────────────────────────────────────────────────────────────┐
        44 -  │                   Provider Backends                           │
        45 -  ├────────────┬────────────┬────────────┬────────────┬─────────┤
        46 -  │  OpenAI   │ Anthropic  │  Ollama    │  vLLM      │  MLX    │
        47 -  │  (API)    │  (API)     │  (HTTP)    │  (HTTP)    │(in-proc)│
        48 -  └────────────┴────────────┴────────────┴────────────┴─────────┘
        49 -  ```
        23 +  AbstractCore already contains significant agent-like code:
        24    
        25 -  ### Provider Classification by Execution Model
        25 +  | Component | Lines | Stateful | Agent-Like |
        26 +  |-----------|-------|----------|------------|
        27 +  | **BasicDeepSearch** | ~2,174 | Yes (debug tracking) | **YES** - 6-stage 
           + autonomous pipeline |
        28 +  | **BasicSession** | ~1,146 | Yes (full state) | Foundation for agent state |
        29 +  | **BasicSummarizer** | ~584 | Config only | No |
        30 +  | **BasicExtractor** | ~1,204 | Config only | Partial (refine method) |
        31 +  | **BasicJudge** | ~776 | Config only | No |
        32    
        33 -  | Provider | Execution Model | Memory Model | Scaling Pattern |
        34 -  |----------|----------------|--------------|-----------------|
        35 -  | **OpenAI** | Remote API | None | Cloud-managed |
        36 -  | **Anthropic** | Remote API | None | Cloud-managed |
        37 -  | **Ollama** | Local HTTP service | Separate process | Single-GPU |
        38 -  | **LMStudio** | Local HTTP service | Separate process | Single-GPU |
        39 -  | **vLLM** | Remote/Local HTTP | Separate process | Multi-GPU, tensor parallel |
        40 -  | **HuggingFace** | **In-process** | Loads into AbstractCore | Single-GPU |
        41 -  | **MLX** | **In-process** | Loads into AbstractCore | Apple Silicon |
        33 +  **BasicDeepSearch is essentially a ReAct-style agent**:
        34 +  - Multi-stage autonomous pipeline (6 stages)
        35 +  - Reflexive refinement (self-improvement loops)
        36 +  - Parallel tool execution (ThreadPoolExecutor)
        37 +  - Dynamic decision making
        38 +  - State tracking (debug_info dict)
        39    
        40 -  ### Current Gaps (Identified in Codebase)
        40 +  **This creates a tension**: AbstractCore was designed to be "thin, stateless, 
           + horizontally scalable" but already contains a ~2,200 line stateful agent.
        41    
        42 -  1. **No request queuing** - Direct passthrough to providers
        43 -  2. **No load balancing** - Single provider per request
        44 -  3. **No connection pooling** - Each provider instance creates own HTTP client
        45 -  4. **No auto-scaling** - Fixed server capacity
        46 -  5. **No multi-provider orchestration** - Only backlog proposal exists
        47 -  
        42    ---
        43    
        44 -  ## SOTA LLM Infrastructure Patterns
        44 +  ## SOTA Agent Framework Patterns (2025)
        45    
        46 -  ### Pattern 1: Inference/Routing Separation (RECOMMENDED)
        46 +  Based on research from 
           + [Turing](https://www.turing.com/resources/ai-agent-frameworks), 
           + [Softcery](https://softcery.com/lab/top-14-ai-agent-frameworks-of-2025-a-founders
           + -guide-to-building-smarter-systems), and [technical 
           + comparisons](https://ai.plainenglish.io/technical-comparison-of-autogen-crewai-la
           + nggraph-and-openai-swarm-1e4e9571d725):
        47    
        48 -  ```
        49 -  ┌─────────────────────────────────────────────────────────────────┐
        50 -  │                      ROUTING LAYER                               │
        51 -  │  ┌──────────────────────────────────────────────────────────┐   │
        52 -  │  │  AbstractCore Server (Stateless, Horizontally Scalable)   │   │
        53 -  │  │    • Request validation & routing                         │   │
        54 -  │  │    • Provider abstraction                                 │   │
        55 -  │  │    • Tool call handling                                   │   │
        56 -  │  │    • Response normalization                               │   │
        57 -  │  │    • Event emission & observability                       │   │
        58 -  │  └──────────────────────────────────────────────────────────┘   │
        59 -  └─────────────────────────────────────────────────────────────────┘
        60 -                                 │
        61 -                                 ▼
        62 -  ┌─────────────────────────────────────────────────────────────────┐
        63 -  │                    INFERENCE LAYER                               │
        64 -  │  ┌──────────┐  ┌──────────┐  ┌──────────┐  ┌──────────────────┐ │
        65 -  │  │ Ollama   │  │ vLLM     │  │ TGI      │  │ Cloud APIs       │ │
        66 -  │  │ Server   │  │ Server   │  │ Server   │  │ (OpenAI/Anthro)  │ │
        67 -  │  │ (GPU 1)  │  │ (GPU 2-4)│  │ (GPU 5)  │  │                  │ │
        68 -  │  └──────────┘  └──────────┘  └──────────┘  └──────────────────┘ │
        69 -  └─────────────────────────────────────────────────────────────────┘
        70 -  ```
        48 +  ### Framework Architecture Comparison
        49    
        50 -  **Why This Pattern?**
        50 +  | Framework | Architecture | LLM Coupling | Package Separation |
        51 +  |-----------|-------------|--------------|-------------------|
        52 +  | **LangGraph** | Graph-based state machines | Part of LangChain ecosystem | 
           + `langgraph` separate from `langchain-core` |
        53 +  | **AutoGen** | Multi-agent conversations | LLM-agnostic (uses config) | 
           + Standalone package |
        54 +  | **CrewAI** | Role-based teams | Uses LiteLLM (separate pkg) | Standalone 
           + package |
        55 +  | **OpenAI Swarm** | Handoff-based orchestration | OpenAI SDK only | Standalone 
           + package |
        56 +  | **Claude Agent SDK** | Tool-use orchestration | Anthropic SDK | Standalone 
           + package |
        57    
        58 -  1. **GPU Memory Isolation**: Inference servers manage GPU memory, continuous 
           - batching, tensor parallelism
        59 -  2. **Independent Scaling**: Route layer scales horizontally (cheap), inference 
           - scales vertically (expensive)
        60 -  3. **Fault Isolation**: Provider failure doesn't crash the router
        61 -  4. **Simpler Deployment**: Each component has single responsibility
        58 +  ### Key SOTA Pattern: **Separation**
        59    
        60 -  **Who Uses This Pattern?**
        61 -  - vLLM (server mode)
        62 -  - Text Generation Inference (TGI)
        63 -  - OpenRouter
        64 -  - LiteLLM Proxy
        65 -  - Anyscale/Ray Serve
        60 +  **Every major agent framework is SEPARATE from LLM provider libraries.**
        61    
        62 -  ### Pattern 2: Monolithic (Dev/Single-User Only)
        62 +  - LangGraph ≠ LangChain (separate packages)
        63 +  - CrewAI uses LiteLLM (separate dependency)
        64 +  - AutoGen is provider-agnostic (config-based)
        65 +  - OpenAI Swarm is separate from `openai` SDK
        66    
        67 -  ```
        68 -  ┌─────────────────────────────────────────────────────┐
        69 -  │              Single Server/Process                   │
        70 -  │  ┌──────────────────────────────────────────────┐   │
        71 -  │  │  AbstractCore + HuggingFace/MLX (in-process)  │   │
        72 -  │  │  • Model loaded in same process               │   │
        73 -  │  │  • Low latency (no HTTP overhead)             │   │
        74 -  │  │  • Simple deployment                          │   │
        75 -  │  │  • Cannot scale                               │   │
        76 -  │  └──────────────────────────────────────────────┘   │
        77 -  └─────────────────────────────────────────────────────┘
        78 -  ```
        67 +  This separation exists because:
        68 +  1. **Different scaling characteristics**: Agents are stateful, LLM routing is 
           + stateless
        69 +  2. **Different deployment targets**: Agents can run on edge, LLM inference on 
           + GPU
        70 +  3. **Clean dependencies**: Don't want full LLM SDK just for agent orchestration
        71 +  4. **Modularity**: Mix and match agent frameworks with LLM providers
        72    
        73 -  **When Acceptable:**
        74 -  - Development/testing
        75 -  - Single-user applications
        76 -  - Apple Silicon personal use (MLX)
        77 -  - Demo/prototype deployments
        73 +  ---
        74    
        75 -  ### Pattern 3: Queue-Based (High Throughput)
        75 +  ## The Two Components of an Agent
        76    
        77 +  Agents have fundamentally different components with different characteristics:
        78 +  
        79    ```
        80 -  ┌───────────────┐     ┌───────────────┐     ┌───────────────┐
        81 -  │   Clients     │────▶│  Load Balancer│────▶│ AbstractCore  │
        82 -  │               │     │  (nginx/HAP)  │     │ (Stateless)   │
        83 -  └───────────────┘     └───────────────┘     └───────┬───────┘
        84 -                                                       │
        85 -                                                       ▼
        86 -                                              ┌───────────────┐
        87 -                                              │ Message Queue │
        88 -                                              │ (Redis/RabbitMQ)│
        89 -                                              └───────┬───────┘
        90 -                                                       │
        91 -                        
           - ┌──────────────────────────────┼──────────────────────────────┐
        92 -                        ▼                              ▼                          
           -     ▼
        93 -                ┌───────────────┐            ┌───────────────┐            
           - ┌───────────────┐
        94 -                │ Worker 1      │            │ Worker 2      │            │ Worker
           -  3      │
        95 -                │ (vLLM GPU)    │            │ (Ollama GPU)  │            │ (Cloud
           -  API)   │
        96 -                └───────────────┘            └───────────────┘            
           - └───────────────┘
        80 +  ┌─────────────────────────────────────────────────────────────────────────┐
        81 +  │                         AGENT ANATOMY                                    │
        82 +  ├─────────────────────────────────────────────────────────────────────────┤
        83 +  │                                                                          │
        84 +  │  ┌────────────────────────────────────────────────────────────────────┐ │
        85 +  │  │  ORCHESTRATION LAYER (Stateful, Lightweight)                        │ │
        86 +  │  │  ────────────────────────────────────────────────────────────────── │ │
        87 +  │  │  • Decision making (what tool to call next)                        │ │
        88 +  │  │  • State management (scratchpad, history, iteration)               │ │
        89 +  │  │  • Tool coordination (execute, parse results)                       │ │
        90 +  │  │  • Human-in-the-loop (approvals, questions)                         │ │
        91 +  │  │  • Control flow (loops, conditionals, stopping)                     │ │
        92 +  │  │                                                                      │ │
        93 +  │  │  Characteristics:                                                    │ │
        94 +  │  │  - CPU-bound (no GPU required)                                      │ │
        95 +  │  │  - Memory: ~50-200MB                                                 │ │
        96 +  │  │  - Can run ANYWHERE (mobile, browser, edge, backend)               │ │
        97 +  │  └────────────────────────────────────────────────────────────────────┘ │
        98 +  │                               │                                          │
        99 +  │                               │ HTTP/SDK calls                          │
       100 +  │                               ▼                                          │
       101 +  │  ┌────────────────────────────────────────────────────────────────────┐ │
       102 +  │  │  INFERENCE LAYER (Stateless, Heavy)                                 │ │
       103 +  │  │  ────────────────────────────────────────────────────────────────── │ │
       104 +  │  │  • LLM generation (text, structured output)                        │ │
       105 +  │  │  • Token processing                                                 │ │
       106 +  │  │  • GPU memory management                                            │ │
       107 +  │  │  • Continuous batching                                              │ │
       108 +  │  │                                                                      │ │
       109 +  │  │  Characteristics:                                                    │ │
       110 +  │  │  - GPU-bound (requires NVIDIA/Apple Silicon)                        │ │
       111 +  │  │  - Memory: 8-128GB VRAM                                             │ │
       112 +  │  │  - Must run on GPU servers                                          │ │
       113 +  │  └────────────────────────────────────────────────────────────────────┘ │
       114 +  │                                                                          │
       115 +  └─────────────────────────────────────────────────────────────────────────┘
       116    ```
       117    
       118 -  **Benefits:**
       119 -  - Decouples request acceptance from processing
       120 -  - Handles burst traffic gracefully
       121 -  - Enables priority queuing
       122 -  - Supports retry without client waiting
       118 +  **Key Insight**: The orchestration layer CAN and SHOULD be able to run 
           + separately from the inference layer.
       119    
       120    ---
       121    
       122 -  ## Recommended Architecture for AbstractCore
       122 +  ## Deployment Scenarios Analysis
       123    
       124 -  ### Tier 1: Development/Personal (Current)
       124 +  ### Scenario A: Simple (Co-located on GPU) - Current
       125    
       126    ```
       127 -  Same machine, co-located:
       128 -  ┌─────────────────────────────────────────┐
       129 -  │  Developer Machine                       │
       130 -  │  ├── AbstractCore (library or server)   │
       131 -  │  ├── Ollama (local)                      │
       132 -  │  ├── LMStudio (local)                    │
       133 -  │  └── MLX (in-process)                    │
       134 -  └─────────────────────────────────────────┘
       127 +  ┌────────────────────────────────────────────────────────┐
       128 +  │  Single GPU Server                                      │
       129 +  │  ┌──────────┐ ┌─────────────┐ ┌────────┐ ┌──────────┐ │
       130 +  │  │ Frontend │→│ Agent Logic │→│Abstract│→│ LLM      │ │
       131 +  │  │          │ │             │ │ Core   │ │(Ollama)  │ │
       132 +  │  └──────────┘ └─────────────┘ └────────┘ └──────────┘ │
       133 +  │  Everything co-located - works fine                     │
       134 +  └────────────────────────────────────────────────────────┘
       135 +  
       136 +  Works with agents IN AbstractCore: ✅
       137 +  Works with separate AbstractAgent: ✅
       138    ```
       139    
       140 -  - **AbstractCore Role**: Library + optional local server
       141 -  - **Scaling**: None needed
       142 -  - **Queue**: Not needed
       143 -  - **Orchestration**: Manual/none
       140 +  ### Scenario B: Mobile/Edge Agent
       141    
       142 -  ### Tier 2: Team/Production (Recommended Enhancement)
       142 +  ```
       143 +  ┌───────────────────┐         ┌─────────────────────────────┐
       144 +  │ Smartphone        │         │     GPU Server               │
       145 +  │ ┌───────────────┐ │  HTTP   │ ┌─────────────┐ ┌─────────┐ │
       146 +  │ │ Agent Logic   │ │────────▶│ │AbstractCore │→│ vLLM    │ │
       147 +  │ │ (thin client) │ │         │ │  (router)   │ │         │ │
       148 +  │ └───────────────┘ │         │ └─────────────┘ └─────────┘ │
       149 +  └───────────────────┘         └─────────────────────────────┘
       150 +  Agent on device, LLM on GPU
       151    
       152 +  Works with agents IN AbstractCore: ❌ (full AbstractCore on phone?)
       153 +  Works with separate AbstractAgent: ✅ (thin client)
       154    ```
       155 -  ┌─────────────────────────────────────────────────────────────────┐
       156 -  │                    ROUTING LAYER (Stateless)                     │
       157 -  │  ┌────────────────────────────────────────────────────────────┐ │
       158 -  │  │  Load Balancer (nginx/HAProxy/cloud LB)                     │ │
       159 -  │  └────────────────────────────────────────────────────────────┘ │
       160 -  │                           │                                      │
       161 -  │  ┌────────────┐  ┌────────────┐  ┌────────────┐                 │
       162 -  │  │AbstractCore│  │AbstractCore│  │AbstractCore│  (N replicas)   │
       163 -  │  │  Server 1  │  │  Server 2  │  │  Server 3  │                 │
       164 -  │  └────────────┘  └────────────┘  └────────────┘                 │
       165 -  └─────────────────────────────────────────────────────────────────┘
       166 -                             │
       167 -                             ▼
       168 -  ┌─────────────────────────────────────────────────────────────────┐
       169 -  │                   INFERENCE LAYER (Stateful)                     │
       170 -  │  ┌──────────────────────────────────────────────────────────┐   │
       171 -  │  │  GPU Server 1                                             │   │
       172 -  │  │  └── vLLM (4x L4, tensor-parallel-size=4)                │   │
       173 -  │  ├──────────────────────────────────────────────────────────┤   │
       174 -  │  │  GPU Server 2                                             │   │
       175 -  │  │  └── Ollama (various models)                              │   │
       176 -  │  ├──────────────────────────────────────────────────────────┤   │
       177 -  │  │  Cloud APIs                                               │   │
       178 -  │  │  └── OpenAI, Anthropic (remote)                          │   │
       179 -  │  └──────────────────────────────────────────────────────────┘   │
       180 -  └─────────────────────────────────────────────────────────────────┘
       181 -  ```
       155    
       156 -  - **AbstractCore Role**: Stateless routing/abstraction
       157 -  - **Scaling**: Horizontal (add more AbstractCore replicas)
       158 -  - **Queue**: Optional (handled by inference servers)
       159 -  - **Orchestration**: Load balancer + health checks
       156 +  ### Scenario C: Frontend Agent (Browser/WASM)
       157    
       158 -  ### Tier 3: Enterprise Scale (Future Enhancement)
       158 +  ```
       159 +  ┌──────────────────────────┐     ┌────────────────────────────┐
       160 +  │ Browser                  │     │ Backend (GPU)              │
       161 +  │ ┌──────────────────────┐ │     │ ┌─────────────┐ ┌────────┐ │
       162 +  │ │ Agent (JS/WASM)      │ │────▶│ │AbstractCore │→│  LLM   │ │
       163 +  │ │ orchestrates locally │ │HTTP │ │  (router)   │ │        │ │
       164 +  │ └──────────────────────┘ │     │ └─────────────┘ └────────┘ │
       165 +  └──────────────────────────┘     └────────────────────────────┘
       166 +  Agent in browser, LLM on backend
       167    
       168 +  Works with agents IN AbstractCore: ❌ (no Python in browser)
       169 +  Works with separate AbstractAgent: ✅ (could port to JS/WASM)
       170    ```
       171 -  ┌─────────────────────────────────────────────────────────────────┐
       172 -  │  API Gateway (Kong/Ambassador)                                   │
       173 -  │  ├── Rate limiting                                               │
       174 -  │  ├── Authentication                                              │
       175 -  │  └── Tenant isolation                                            │
       176 -  └─────────────────────────────────────────────────────────────────┘
       177 -                             │
       178 -                             ▼
       179 -  ┌─────────────────────────────────────────────────────────────────┐
       180 -  │  AbstractCore Fleet (Kubernetes Deployment)                      │
       181 -  │  ├── Auto-scaling based on request rate                         │
       182 -  │  ├── Health checks + circuit breakers                            │
       183 -  │  └── Centralized configuration                                   │
       184 -  └─────────────────────────────────────────────────────────────────┘
       185 -                             │
       186 -            ┌────────────────┼────────────────┐
       187 -            ▼                ▼                ▼
       188 -  ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
       189 -  │ Request Queue   │ │ Priority Queue  │ │ Batch Queue     │
       190 -  │ (Fast requests) │ │ (VIP customers) │ │ (Bulk jobs)     │
       191 -  └─────────────────┘ └─────────────────┘ └─────────────────┘
       192 -            │                │                │
       193 -            └────────────────┼────────────────┘
       194 -                             ▼
       195 -  ┌─────────────────────────────────────────────────────────────────┐
       196 -  │  GPU Workers (Kubernetes StatefulSets)                           │
       197 -  │  ├── vLLM pods (auto-scaled based on queue depth)               │
       198 -  │  ├── Ollama pods                                                 │
       199 -  │  └── Specialized model pods                                      │
       200 -  └─────────────────────────────────────────────────────────────────┘
       201 -  ```
       171    
       172 -  ---
       172 +  ### Scenario D: Backend Agent Service (Production Scale)
       173    
       174 -  ## Who Handles What?
       174 +  ```
       175 +  ┌─────────┐   ┌───────────────────┐   ┌───────────────────┐
       176 +  │Frontend │   │ Agent Service     │   │    GPU Fleet      │
       177 +  │  (CDN)  │──▶│ (stateful, scales │──▶│ AbstractCore+LLM  │
       178 +  │         │   │  with Redis/DB)   │   │ (per-server)      │
       179 +  └─────────┘   └───────────────────┘   └───────────────────┘
       180 +  Agents as separate microservice
       181    
       182 -  ### Current State
       182 +  Works with agents IN AbstractCore: ⚠️ (forces coupling)
       183 +  Works with separate AbstractAgent: ✅ (clean microservice)
       184 +  ```
       185    
       186 -  | Component | Who Handles | Notes |
       187 -  |-----------|-------------|-------|
       188 -  | **Request routing** | AbstractCore | Model string parsing → provider |
       189 -  | **Retry/fallback** | AbstractCore | Per-provider circuit breaker |
       190 -  | **Queue** | Nobody | Direct passthrough |
       191 -  | **Load balancing** | Nobody | Single-provider requests |
       192 -  | **Inference** | Providers | Ollama, vLLM, cloud APIs |
       193 -  | **GPU memory** | Providers | Managed by inference servers |
       194 -  | **Scaling** | Manual | No auto-scaling |
       186 +  ### Scenario E: Agent on Backend, LLM Fleet
       187    
       188 -  ### Recommended State
       188 +  ```
       189 +  ┌─────────┐   ┌───────────────────────────────────────────┐
       190 +  │Frontend │   │ Backend Server (no GPU)                   │
       191 +  │  (CDN)  │──▶│ ┌─────────────┐ ┌───────────────────────┐ │
       192 +  │         │   │ │   Agent     │→│ AbstractCore (client) │ │
       193 +  └─────────┘   │ │   Logic     │ │ calls remote LLMs     │ │
       194 +                │ └─────────────┘ └───────────┬───────────┘ │
       195 +                └─────────────────────────────┼─────────────┘
       196 +                                              │ HTTP
       197 +                ┌─────────────────────────────▼─────────────┐
       198 +                │ GPU Fleet (vLLM, Ollama, etc.)            │
       199 +                │ ┌─────────────┐ ┌─────────────┐          │
       200 +                │ │ GPU Server 1│ │ GPU Server 2│  ...     │
       201 +                │ └─────────────┘ └─────────────┘          │
       202 +                └───────────────────────────────────────────┘
       203 +  Agent on backend without GPU, LLM on fleet
       204    
       205 -  | Component | Who Should Handle | Why |
       206 -  |-----------|-------------------|-----|
       207 -  | **Request routing** | AbstractCore | Core competency, provider abstraction |
       208 -  | **Retry/fallback** | AbstractCore | Already implemented, enhance with 
           - multi-provider |
       209 -  | **Queue** | External (Redis) + AbstractCore | AbstractCore submits to queue, 
           - workers consume |
       210 -  | **Load balancing** | External (nginx/k8s) + AbstractCore | LB for AbstractCore
           -  replicas, AC for provider selection |
       211 -  | **Inference** | Providers | Keep separate - different scaling characteristics 
           - |
       212 -  | **GPU memory** | Providers | Never mix with routing logic |
       213 -  | **Scaling** | Orchestrator (k8s) | Horizontal for AC, vertical for GPU |
       205 +  Works with agents IN AbstractCore: ✅ (AbstractCore routes to fleet)
       206 +  Works with separate AbstractAgent: ✅ (depends on AbstractCore)
       207 +  ```
       208    
       209    ---
       210    
       211 -  ## Specific Recommendations
       211 +  ## Package Size & Dependency Analysis
       212    
       213 -  ### 1. Keep HuggingFace/MLX In-Process for Dev Only
       213 +  ### Current AbstractCore
       214    
       215 -  **Current**: HuggingFace and MLX load models into AbstractCore's process.
       216 -  
       217 -  **Recommendation**:
       218 -  - Keep for development/personal use
       219 -  - For production, move to HTTP server mode (vLLM or similar)
       220 -  - Document clearly that in-process mode doesn't scale
       221 -  
       222 -  ### 2. Add Optional Queue Integration (Future Feature)
       223 -  
       224 -  ```python
       225 -  # Proposed API for queue-based requests
       226 -  from abstractcore import create_llm, QueueConfig
       227 -  
       228 -  # Configure queue
       229 -  queue_config = QueueConfig(
       230 -      backend="redis",
       231 -      url="redis://localhost:6379",
       232 -      max_queue_size=1000,
       233 -      priority_levels=3
       234 -  )
       235 -  
       236 -  llm = create_llm("ollama", model="llama3:8b", queue_config=queue_config)
       237 -  
       238 -  # Async submit to queue
       239 -  job_id = await llm.submit("Long-running task")  # Returns immediately
       240 -  result = await llm.get_result(job_id)  # Poll or wait
       215    ```
       216 +  abstractcore/
       217 +  ├── core/          # ~3,500 lines (factory, session, retry)
       218 +  ├── providers/     # ~10,000 lines (8 providers)
       219 +  ├── processing/    # ~5,500 lines (summarizer, extractor, judge, deepsearch)
       220 +  ├── tools/         # ~1,500 lines (tool system)
       221 +  ├── media/         # ~3,000 lines (file handlers)
       222 +  ├── server/        # ~3,000 lines (FastAPI)
       223 +  ├── structured/    # ~1,500 lines (Pydantic integration)
       224 +  ├── events/        # ~500 lines
       225 +  ├── utils/         # ~2,000 lines (CLI, logging)
       226 +  └── apps/          # ~2,000 lines (CLI apps)
       227    
       228 -  ### 3. Enhance Multi-Provider Routing (Use Existing Backlog)
       229 -  
       230 -  The `docs/backlog/multi-provider-requests.md` is excellent. Implement the hybrid
           -  approach:
       231 -  - Phase 1: Server API for parallel/cascade
       232 -  - Phase 2: Core library `MultiLLM` class
       233 -  
       234 -  ### 4. Add Connection Pooling (Quick Win)
       235 -  
       236 -  ```python
       237 -  # Proposed enhancement to base provider
       238 -  class BaseProvider:
       239 -      _connection_pool = None  # Shared across instances
       240 -  
       241 -      @classmethod
       242 -      def get_http_client(cls):
       243 -          if cls._connection_pool is None:
       244 -              cls._connection_pool = httpx.AsyncClient(
       245 -                  limits=httpx.Limits(max_connections=100),
       246 -                  timeout=30.0
       247 -              )
       248 -          return cls._connection_pool
       228 +  Total: ~45,000 lines
       229 +  Dependencies: openai, anthropic, httpx, pydantic, rich, tenacity, ...
       230 +  Install size: ~50-100MB (with all providers)
       231    ```
       232    
       233 -  ### 5. Document Deployment Patterns
       233 +  ### Proposed AbstractAgent
       234    
       235 -  Create `docs/deployment-patterns.md`:
       236 -  - Tier 1: Development (co-located)
       237 -  - Tier 2: Production (separated routing/inference)
       238 -  - Tier 3: Enterprise (k8s, queues, auto-scaling)
       239 -  
       240 -  ---
       241 -  
       242 -  ## Answering Your Questions Directly
       243 -  
       244 -  ### Q1: Should AbstractCore live on the same server as providers?
       245 -  
       246 -  **Short Answer**: **Depends on the tier.**
       247 -  
       248 -  | Tier | Same Server? | Rationale |
       249 -  |------|--------------|-----------|
       250 -  | Dev/Personal | Yes | Simplicity, low latency |
       251 -  | Team/Production | No | Separate concerns, fault isolation, independent scaling
           -  |
       252 -  | Enterprise | No | Must scale independently |
       253 -  
       254 -  ### Q2: Should it be a passthrough to distant servers?
       255 -  
       256 -  **Short Answer**: **Yes, for production.**
       257 -  
       258 -  AbstractCore's core value is **abstraction and unification**, not inference. 
           - Keep it as a lightweight routing layer that:
       259 -  - Validates and routes requests
       260 -  - Handles retries and fallbacks
       261 -  - Normalizes responses
       262 -  - Emits events for observability
       263 -  
       264 -  Let specialized inference servers (vLLM, Ollama, TGI) handle:
       265 -  - GPU memory management
       266 -  - Continuous batching
       267 -  - Tensor parallelism
       268 -  - Model quantization
       269 -  
       270 -  ### Q3: Who handles scalability?
       271 -  
       272 -  **Short Answer**: **Split responsibility.**
       273 -  
       274 -  | Layer | Scaling Approach | Who Manages |
       275 -  |-------|------------------|-------------|
       276 -  | AbstractCore | Horizontal (add replicas) | Kubernetes/Docker Swarm |
       277 -  | Inference | Vertical (bigger GPU) + Horizontal (more GPU servers) | 
           - vLLM/Ollama |
       278 -  | Queue | Horizontal (Redis cluster) | Redis/RabbitMQ |
       279 -  
       280 -  ### Q4: Who handles routing of multiple queries?
       281 -  
       282 -  **Short Answer**: **AbstractCore + External LB.**
       283 -  
       284 -  1. **External Load Balancer**: Distributes across AbstractCore replicas
       285 -  2. **AbstractCore**: Routes to correct provider based on model string
       286 -  3. **Future Enhancement**: Multi-provider strategies (parallel, cascade, race)
       287 -  
       288 -  ### Q5: Who orchestrates query queues?
       289 -  
       290 -  **Short Answer**: **External queue + AbstractCore workers.**
       291 -  
       292 -  For high-throughput scenarios:
       293 -  1. **AbstractCore Server**: Accepts requests, pushes to queue
       294 -  2. **Queue (Redis/RabbitMQ)**: Buffers requests, handles backpressure
       295 -  3. **AbstractCore Workers**: Consume from queue, call inference servers
       296 -  4. **Inference Servers**: Process requests, return results
       297 -  
       298 -  ---
       299 -  
       300 -  ## Implementation Roadmap
       301 -  
       302 -  ### Phase 1: Documentation (1 day)
       303 -  - Create `docs/deployment-patterns.md`
       304 -  - Document current architecture limitations
       305 -  - Recommend SOTA patterns for different scales
       306 -  
       307 -  ### Phase 2: Connection Pooling (1 week)
       308 -  - Implement shared connection pools per provider
       309 -  - 15-30% latency improvement, 20-40% memory savings
       310 -  - See backlog `003-connection-pool-optimization.md`
       311 -  
       312 -  ### Phase 3: Multi-Provider Routing (2-4 weeks)
       313 -  - Implement server API first (Phase 1 of backlog)
       314 -  - Add parallel, cascade, race strategies
       315 -  - See backlog `multi-provider-requests.md`
       316 -  
       317 -  ### Phase 4: Queue Integration (Optional, 2-3 weeks)
       318 -  - Design queue abstraction
       319 -  - Implement Redis backend
       320 -  - Add async submit/poll API
       321 -  
       322 -  ---
       323 -  
       324 -  # Appendix: Queue Implementation Design
       325 -  
       326 -  ## SOTA Research Summary
       327 -  
       328 -  Based on research from [vLLM's continuous batching 
           - architecture](https://blog.vllm.ai/2025/09/05/anatomy-of-vllm.html), [LLM + 
           - Celery + Redis patterns](https://www.jellyfishtechnologies.com/llm-driven-api-orc
           - hestration-using-langchain-celery-redis-queue/), and [FastAPI + Celery deployment
           -  guides](https://towardsdatascience.com/deploying-ml-models-in-production-with-fa
           - stapi-and-celery-7063e539a5db/), here's the SOTA pattern:
       329 -  
       235    ```
       236 -  ┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
       237 -  │   Client        │────▶│  AbstractCore   │────▶│  Redis Broker   │
       238 -  │   (submit job)  │     │  (FastAPI)      │     │  (queue)        │
       239 -  └─────────────────┘     └────────┬────────┘     └────────┬────────┘
       240 -                                   │                        │
       241 -                                   │ SSE Stream             │ Task Dispatch
       242 -                                   ▼                        ▼
       243 -                          ┌─────────────────┐     ┌─────────────────┐
       244 -                          │  Redis Results  │◀────│  Celery Workers │
       245 -                          │  (progress/done)│     │  (inference)    │
       246 -                          └─────────────────┘     └─────────────────┘
       247 -  ```
       236 +  abstractagent/
       237 +  ├── core/
       238 +  │   ├── state.py        # StateGraph, AgentState (~250 lines)
       239 +  │   ├── base.py         # BaseAgent ABC (~300 lines)
       240 +  │   └── scratchpad.py   # Memory management (~100 lines)
       241 +  ├── agents/
       242 +  │   ├── react.py        # ReActAgent (~400 lines)
       243 +  │   └── code_act.py     # CodeActAgent (~500 lines)
       244 +  ├── sandbox/
       245 +  │   └── executor.py     # Safe code execution (~300 lines)
       246 +  ├── tools/
       247 +  │   └── user.py         # Human-in-the-loop (~150 lines)
       248 +  └── client/
       249 +      └── remote.py       # Remote AbstractCore client (~200 lines)
       250    
       251 -  **Key Insights:**
       252 -  1. **vLLM**: Internal scheduler with waiting deque + running list; continuous 
           - batching at GPU level
       253 -  2. **External Queue**: Redis as broker + result backend; Celery workers consume 
           - and process
       254 -  3. **Streaming**: Workers update Redis, FastAPI polls and streams via SSE
       255 -  4. **Timeouts**: Soft limit ~9 min, hard limit ~10 min for LLM operations
       256 -  
       257 -  ---
       258 -  
       259 -  ## AbstractCore Queue Design
       260 -  
       261 -  ### Design Principles
       262 -  
       263 -  1. **Optional** - Default behavior unchanged; queue is opt-in
       264 -  2. **Backend Agnostic** - Abstract interface with Redis, In-Memory 
           - implementations
       265 -  3. **Library-First** - Works without server; server just adds HTTP access
       266 -  4. **Event Integration** - Queue events flow through existing EventBus
       267 -  5. **Priority Support** - Multiple priority levels for different use cases
       268 -  6. **Streaming Compatible** - SSE streaming for real-time progress
       269 -  
       270 -  ### Architecture
       271 -  
       251 +  Total: ~2,200 lines (MUCH smaller)
       252 +  Dependencies: abstractcore OR httpx (client mode)
       253 +  Install size: ~5-10MB
       254    ```
       255 -  ┌─────────────────────────────────────────────────────────────────┐
       256 -  │                    AbstractCore Queue Layer                      │
       257 -  │  ┌───────────────────────────────────────────────────────────┐  │
       258 -  │  │  QueueManager (Singleton)                                  │  │
       259 -  │  │    ├── submit(request) → job_id                           │  │
       260 -  │  │    ├── get_status(job_id) → JobStatus                     │  │
       261 -  │  │    ├── get_result(job_id) → GenerateResponse              │  │
       262 -  │  │    ├── cancel(job_id) → bool                              │  │
       263 -  │  │    └── stream(job_id) → AsyncIterator[ProgressEvent]      │  │
       264 -  │  └───────────────────────────────────────────────────────────┘  │
       265 -  │                              │                                   │
       266 -  │  ┌───────────────────────────▼───────────────────────────────┐  │
       267 -  │  │  QueueBackend (Abstract Interface)                         │  │
       268 -  │  │    ├── enqueue(job: Job) → None                           │  │
       269 -  │  │    ├── dequeue() → Optional[Job]                          │  │
       270 -  │  │    ├── get_job(job_id) → Optional[Job]                    │  │
       271 -  │  │    ├── update_job(job_id, status, result) → None          │  │
       272 -  │  │    └── subscribe(job_id) → AsyncIterator[Event]           │  │
       273 -  │  └───────────────────────────────────────────────────────────┘  │
       274 -  │                              │                                   │
       275 -  │         ┌────────────────────┼────────────────────┐             │
       276 -  │         ▼                    ▼                    ▼             │
       277 -  │  ┌─────────────┐     ┌─────────────┐     ┌─────────────┐       │
       278 -  │  │ InMemory    │     │   Redis     │     │  Future:    │       │
       279 -  │  │ Backend     │     │   Backend   │     │  RabbitMQ   │       │
       280 -  │  │ (dev/test)  │     │ (production)│     │  Kafka      │       │
       281 -  │  └─────────────┘     └─────────────┘     └─────────────┘       │
       282 -  └─────────────────────────────────────────────────────────────────┘
       283 -  ```
       255    
       256 -  ### Data Models
       256 +  ### The Mobile/Edge Consideration
       257    
       258 -  ```python
       259 -  # abstractcore/queue/models.py
       258 +  On a smartphone or resource-constrained device:
       259    
       260 -  from enum import Enum
       261 -  from dataclasses import dataclass
       262 -  from datetime import datetime
       263 -  from typing import Optional, Dict, Any, List
       260 +  | Package | Size | Dependencies | Viable? |
       261 +  |---------|------|--------------|---------|
       262 +  | Full AbstractCore | ~50-100MB | Heavy (providers, media handlers) | ❌ 
           + Overkill |
       263 +  | AbstractAgent (client mode) | ~5-10MB | Just httpx | ✅ Perfect |
       264 +  | AbstractAgent (full mode) | ~10-15MB | + abstractcore-core | ⚠️ Depends |
       265    
       266 -  class JobStatus(Enum):
       267 -      PENDING = "pending"       # In queue, not started
       268 -      RUNNING = "running"       # Being processed by worker
       269 -      COMPLETED = "completed"   # Successfully finished
       270 -      FAILED = "failed"         # Failed with error
       271 -      CANCELLED = "cancelled"   # Cancelled by user
       272 -      TIMEOUT = "timeout"       # Exceeded time limit
       266 +  ---
       267    
       268 -  class Priority(Enum):
       269 -      LOW = 0
       270 -      NORMAL = 1
       271 -      HIGH = 2
       272 -      URGENT = 3
       268 +  ## The Options
       269    
       270 -  @dataclass
       271 -  class Job:
       272 -      job_id: str
       273 -      provider: str
       274 -      model: str
       275 -      prompt: str
       276 -      messages: Optional[List[Dict]] = None
       277 -      kwargs: Optional[Dict[str, Any]] = None
       278 -      priority: Priority = Priority.NORMAL
       279 -      status: JobStatus = JobStatus.PENDING
       280 -      created_at: datetime = None
       281 -      started_at: Optional[datetime] = None
       282 -      completed_at: Optional[datetime] = None
       283 -      result: Optional[Dict] = None  # GenerateResponse as dict
       284 -      error: Optional[str] = None
       285 -      progress: float = 0.0  # 0.0 to 1.0
       286 -      metadata: Optional[Dict] = None  # User-defined metadata
       270 +  ### Option 1: Agents IN AbstractCore
       271    
       272 -  @dataclass
       273 -  class ProgressEvent:
       274 -      job_id: str
       275 -      status: JobStatus
       276 -      progress: float
       277 -      message: Optional[str] = None
       278 -      partial_content: Optional[str] = None  # For streaming
       279 -      tokens_generated: int = 0
       280 -  ```
       281 -  
       282 -  ### API Design
       283 -  
       272    ```python
       273 -  # abstractcore/queue/__init__.py
       273 +  from abstractcore.agents import ReActAgent, CodeActAgent
       274 +  from abstractcore import create_llm
       275    
       276 -  from abstractcore.queue.manager import QueueManager
       277 -  from abstractcore.queue.models import Job, JobStatus, Priority, ProgressEvent
       278 -  from abstractcore.queue.backends import InMemoryBackend, RedisBackend
       279 -  
       280 -  __all__ = [
       281 -      'QueueManager',
       282 -      'Job', 'JobStatus', 'Priority', 'ProgressEvent',
       283 -      'InMemoryBackend', 'RedisBackend'
       284 -  ]
       285 -  
       286 -  # Convenience functions at package level
       287 -  _default_manager: Optional[QueueManager] = None
       288 -  
       289 -  def configure_queue(backend: str = "memory", **kwargs):
       290 -      """Configure the default queue manager."""
       291 -      global _default_manager
       292 -      if backend == "memory":
       293 -          _default_manager = QueueManager(InMemoryBackend())
       294 -      elif backend == "redis":
       295 -          _default_manager = QueueManager(RedisBackend(**kwargs))
       296 -      else:
       297 -          raise ValueError(f"Unknown backend: {backend}")
       298 -  
       299 -  def get_queue() -> QueueManager:
       300 -      """Get the configured queue manager."""
       301 -      if _default_manager is None:
       302 -          raise RuntimeError("Queue not configured. Call configure_queue() 
           - first.")
       303 -      return _default_manager
       276 +  llm = create_llm("ollama", model="qwen3:4b")
       277 +  agent = ReActAgent(llm=llm, tools=[...])
       278 +  result = agent.run("Research quantum computing")
       279    ```
       280    
       281 -  ### Usage Examples
       281 +  **Pros:**
       282 +  - ✅ Single package install
       283 +  - ✅ Tighter integration with existing primitives
       284 +  - ✅ Easier for beginners
       285 +  - ✅ BasicDeepSearch precedent exists
       286 +  - ✅ Shared test infrastructure
       287    
       288 -  #### Basic Queue Usage (Library Mode)
       288 +  **Cons:**
       289 +  - ❌ Violates "thin, stateless" principle
       290 +  - ❌ Can't deploy agents separately
       291 +  - ❌ Full AbstractCore needed even for thin agent client
       292 +  - ❌ Not viable for mobile/browser scenarios
       293 +  - ❌ Package grows larger
       294    
       295 +  ### Option 2: Separate AbstractAgent Package
       296 +  
       297    ```python
       298 +  from abstractagent import ReActAgent
       299    from abstractcore import create_llm
       300 -  from abstractcore.queue import configure_queue, get_queue, Priority
       300    
       301 -  # Configure queue (development - in-memory)
       302 -  configure_queue(backend="memory")
       301 +  # Option A: With local AbstractCore
       302 +  llm = create_llm("ollama", model="qwen3:4b")
       303 +  agent = ReActAgent(llm=llm, tools=[...])
       304    
       305 -  # Or production - Redis
       306 -  configure_queue(
       307 -      backend="redis",
       308 -      url="redis://localhost:6379",
       309 -      db=0,
       310 -      prefix="abstractcore:"
       311 -  )
       305 +  # Option B: With remote AbstractCore (thin client)
       306 +  from abstractagent.client import RemoteClient
       307 +  client = RemoteClient("https://abstractcore.yourdomain.com")
       308 +  agent = ReActAgent(client=client, tools=[...])
       309 +  ```
       310    
       311 -  queue = get_queue()
       311 +  **Pros:**
       312 +  - ✅ Clean separation of concerns
       313 +  - ✅ AbstractCore stays thin/stateless
       314 +  - ✅ Agents can run anywhere (mobile, browser via port, backend)
       315 +  - ✅ Follows SOTA pattern (LangGraph, CrewAI, AutoGen)
       316 +  - ✅ Remote client mode for edge/mobile
       317 +  - ✅ Smaller footprint for orchestration-only installs
       318    
       319 -  # Submit job (returns immediately)
       320 -  job_id = await queue.submit(
       321 -      provider="ollama",
       322 -      model="llama3:8b",
       323 -      prompt="Write a long essay about quantum computing",
       324 -      priority=Priority.NORMAL,
       325 -      metadata={"user_id": "user123", "request_type": "essay"}
       326 -  )
       319 +  **Cons:**
       320 +  - ❌ Two packages to manage
       321 +  - ❌ Dependency coordination
       322 +  - ❌ More initial setup
       323 +  - ❌ Need to move BasicDeepSearch (breaking change)
       324    
       325 -  print(f"Job submitted: {job_id}")
       325 +  ### Option 3: Hybrid - Agents in AbstractCore with Remote Mode
       326    
       327 -  # Check status
       328 -  status = await queue.get_status(job_id)
       329 -  print(f"Status: {status.status}, Progress: {status.progress:.0%}")
       330 -  
       331 -  # Wait for result (blocking)
       332 -  result = await queue.get_result(job_id, timeout=300)  # 5 min timeout
       333 -  print(f"Result: {result.content[:100]}...")
       334 -  
       335 -  # Or poll with async for
       336 -  async for event in queue.stream(job_id):
       337 -      print(f"Progress: {event.progress:.0%} - {event.message}")
       338 -      if event.partial_content:
       339 -          print(f"Partial: {event.partial_content}", end="")
       340 -      if event.status in (JobStatus.COMPLETED, JobStatus.FAILED):
       341 -          break
       342 -  ```
       343 -  
       344 -  #### With Priority Queuing
       345 -  
       327    ```python
       328 -  # High priority request (VIP customer)
       329 -  high_priority_job = await queue.submit(
       330 -      provider="openai",
       331 -      model="gpt-4",
       332 -      prompt="Urgent: summarize this contract",
       333 -      priority=Priority.URGENT
       334 -  )
       328 +  from abstractcore.agents import ReActAgent
       329    
       330 -  # Low priority request (batch job)
       331 -  low_priority_job = await queue.submit(
       332 -      provider="ollama",
       333 -      model="llama3:8b",
       334 -      prompt="Generate 100 product descriptions",
       335 -      priority=Priority.LOW
       336 -  )
       330 +  # Local mode
       331 +  agent = ReActAgent(provider="ollama", model="qwen3:4b")
       332    
       333 -  # Urgent jobs processed before normal, normal before low
       333 +  # Remote mode (doesn't load full providers)
       334 +  agent = ReActAgent.remote("https://abstractcore.yourdomain.com")
       335    ```
       336    
       337 -  #### Worker Process (Separate from API)
       337 +  **Pros:**
       338 +  - ✅ Single package conceptually
       339 +  - ✅ Remote mode reduces footprint
       340 +  - ✅ Gradual migration path
       341    
       342 -  ```python
       343 -  # worker.py - Run this in separate processes
       342 +  **Cons:**
       343 +  - ❌ Complexity in one package
       344 +  - ❌ Still pulls AbstractCore deps even in remote mode
       345 +  - ❌ Confusing API
       346    
       347 -  from abstractcore import create_llm
       348 -  from abstractcore.queue import configure_queue, get_queue, JobStatus
       347 +  ---
       348    
       349 -  configure_queue(backend="redis", url="redis://localhost:6379")
       350 -  queue = get_queue()
       349 +  ## Beyond SOTA: Optimal Architecture
       350    
       351 -  async def worker_loop():
       352 -      """Continuously process jobs from queue."""
       353 -      while True:
       354 -          job = await queue.dequeue()  # Blocking wait for next job
       355 -          if job is None:
       356 -              continue
       351 +  If I were designing from scratch with no constraints:
       352    
       353 -          try:
       354 -              # Update status to running
       355 -              await queue.update_status(job.job_id, JobStatus.RUNNING)
       356 -  
       357 -              # Create LLM and generate
       358 -              llm = create_llm(job.provider, model=job.model)
       359 -  
       360 -              if job.kwargs.get("stream"):
       361 -                  # Stream with progress updates
       362 -                  content = ""
       363 -                  async for chunk in llm.agenerate(job.prompt, stream=True):
       364 -                      content += chunk.content
       365 -                      await queue.update_progress(
       366 -                          job.job_id,
       367 -                          progress=len(content) / 1000,  # Estimate
       368 -                          partial_content=chunk.content
       369 -                      )
       370 -                  result = GenerateResponse(content=content, ...)
       371 -              else:
       372 -                  result = await llm.agenerate(job.prompt, **job.kwargs)
       373 -  
       374 -              # Mark completed
       375 -              await queue.update_status(
       376 -                  job.job_id,
       377 -                  JobStatus.COMPLETED,
       378 -                  result=result.to_dict()
       379 -              )
       380 -  
       381 -          except Exception as e:
       382 -              await queue.update_status(
       383 -                  job.job_id,
       384 -                  JobStatus.FAILED,
       385 -                  error=str(e)
       386 -              )
       387 -  
       388 -  # Run with: python worker.py
       389 -  asyncio.run(worker_loop())
       353    ```
       354 +  ┌─────────────────────────────────────────────────────────────────────────┐
       355 +  │                    IDEAL LAYERED ARCHITECTURE                            │
       356 +  ├─────────────────────────────────────────────────────────────────────────┤
       357 +  │                                                                          │
       358 +  │  Layer 4: Applications                                                   │
       359 +  │  ┌──────────────────────────────────────────────────────────────────┐   │
       360 +  │  │  Digital Article, DeepSearch CLI, Custom Apps                     │   │
       361 +  │  │  (Use AbstractAgent + AbstractCore + domain-specific logic)       │   │
       362 +  │  └──────────────────────────────────────────────────────────────────┘   │
       363 +  │                               │                                          │
       364 +  │  Layer 3: Agent Orchestration (AbstractAgent)                           │
       365 +  │  ┌──────────────────────────────────────────────────────────────────┐   │
       366 +  │  │  ReActAgent, CodeActAgent, CustomAgent                            │   │
       367 +  │  │  • State machine / graph execution                                │   │
       368 +  │  │  • Tool coordination                                              │   │
       369 +  │  │  • Human-in-the-loop                                              │   │
       370 +  │  │  • Scratchpad / memory                                            │   │
       371 +  │  │  • Can run anywhere (edge, browser, backend)                      │   │
       372 +  │  │  Dependencies: abstractcore-client OR abstractcore (full)         │   │
       373 +  │  └──────────────────────────────────────────────────────────────────┘   │
       374 +  │                               │                                          │
       375 +  │  Layer 2: LLM Abstraction (AbstractCore)                                │
       376 +  │  ┌──────────────────────────────────────────────────────────────────┐   │
       377 +  │  │  Provider abstraction, Tool system, Sessions, Structured output   │   │
       378 +  │  │  • Stateless routing layer                                        │   │
       379 +  │  │  • OpenAI-compatible API                                          │   │
       380 +  │  │  • Horizontally scalable                                          │   │
       381 +  │  │  Dependencies: provider SDKs (openai, anthropic, httpx, etc.)     │   │
       382 +  │  └──────────────────────────────────────────────────────────────────┘   │
       383 +  │                               │                                          │
       384 +  │  Layer 1: Inference (vLLM, Ollama, Cloud APIs)                          │
       385 +  │  ┌──────────────────────────────────────────────────────────────────┐   │
       386 +  │  │  GPU servers, cloud providers                                     │   │
       387 +  │  │  • Actual LLM inference                                           │   │
       388 +  │  │  • Token generation                                               │   │
       389 +  │  │  • Vertically scaled (GPU memory)                                 │   │
       390 +  │  └──────────────────────────────────────────────────────────────────┘   │
       391 +  │                                                                          │
       392 +  └─────────────────────────────────────────────────────────────────────────┘
       393 +  ```
       394    
       395 -  #### Server API Integration
       395 +  ### Key Design Principles
       396    
       397 -  ```python
       398 -  # abstractcore/server/app.py additions
       397 +  1. **Separate orchestration from inference**: Agent logic doesn't need GPU
       398 +  2. **Protocol-based communication**: Agents use HTTP/SDK, not direct coupling
       399 +  3. **State externalization**: Agent state in Redis/DB for scalability
       400 +  4. **Client mode**: AbstractAgent can work with remote AbstractCore
       401 +  5. **Modular dependencies**: Don't force full stack for simple use cases
       402    
       403 -  from abstractcore.queue import get_queue, JobStatus
       403 +  ---
       404    
       405 -  @app.post("/v1/queue/submit")
       406 -  async def submit_to_queue(request: ChatCompletionRequest):
       407 -      """Submit request to queue, return job ID immediately."""
       408 -      queue = get_queue()
       405 +  ## Migration Path for BasicDeepSearch
       406    
       407 -      job_id = await queue.submit(
       408 -          provider=request.provider,
       409 -          model=request.model,
       410 -          messages=request.messages,
       411 -          kwargs=request.dict(exclude={"messages", "model"})
       412 -      )
       407 +  If we go with Option 2 (separate AbstractAgent), what happens to 
           + BasicDeepSearch?
       408    
       409 -      return {"job_id": job_id, "status": "pending"}
       409 +  ### Option A: Keep in AbstractCore (Pragmatic)
       410    
       411 -  @app.get("/v1/queue/status/{job_id}")
       412 -  async def get_job_status(job_id: str):
       413 -      """Get current status of a queued job."""
       414 -      queue = get_queue()
       415 -      status = await queue.get_status(job_id)
       411 +  - BasicDeepSearch stays where it is
       412 +  - New agents (ReAct, CodeAct) go to AbstractAgent
       413 +  - Document that BasicDeepSearch is a "processing pipeline" not an "agent"
       414 +  - Future: Deprecate and migrate in major version
       415    
       416 -      return {
       417 -          "job_id": job_id,
       418 -          "status": status.status.value,
       419 -          "progress": status.progress,
       420 -          "created_at": status.created_at.isoformat(),
       421 -          "started_at": status.started_at.isoformat() if status.started_at else 
           - None
       422 -      }
       416 +  ### Option B: Move to AbstractAgent (Clean)
       417    
       418 -  @app.get("/v1/queue/result/{job_id}")
       419 -  async def get_job_result(job_id: str, wait: bool = False, timeout: int = 60):
       420 -      """Get result of completed job."""
       421 -      queue = get_queue()
       418 +  - Create AbstractAgent package
       419 +  - Move BasicDeepSearch to `abstractagent.pipelines.deepsearch`
       420 +  - AbstractCore `processing/basic_deepsearch.py` becomes thin wrapper
       421 +  - Allows clean "agents are in AbstractAgent" story
       422    
       423 -      if wait:
       424 -          result = await queue.get_result(job_id, timeout=timeout)
       425 -      else:
       426 -          result = await queue.get_result_nowait(job_id)
       427 -          if result is None:
       428 -              raise HTTPException(404, "Job not completed yet")
       423 +  ### Option C: Dual-Home (Transitional)
       424    
       425 -      return result
       425 +  - Create AbstractAgent with new agents
       426 +  - BasicDeepSearch stays in AbstractCore for now
       427 +  - Gradually encourage users to use AbstractAgent for agent workloads
       428 +  - In v3.0, move BasicDeepSearch to AbstractAgent
       429    
       430 -  @app.get("/v1/queue/stream/{job_id}")
       431 -  async def stream_job_progress(job_id: str):
       432 -      """Stream job progress via SSE."""
       433 -      queue = get_queue()
       430 +  **Recommendation**: Option C - Pragmatic transition without breaking users
       431    
       432 -      async def event_generator():
       433 -          async for event in queue.stream(job_id):
       434 -              yield {
       435 -                  "event": event.status.value,
       436 -                  "data": {
       437 -                      "progress": event.progress,
       438 -                      "message": event.message,
       439 -                      "partial_content": event.partial_content
       440 -                  }
       441 -              }
       442 -              if event.status in (JobStatus.COMPLETED, JobStatus.FAILED):
       443 -                  break
       432 +  ---
       433    
       434 -      return EventSourceResponse(event_generator())
       434 +  ## CodeAct Sandbox Consideration
       435    
       436 -  @app.delete("/v1/queue/cancel/{job_id}")
       437 -  async def cancel_job(job_id: str):
       438 -      """Cancel a pending or running job."""
       439 -      queue = get_queue()
       440 -      cancelled = await queue.cancel(job_id)
       436 +  You mentioned wanting sandbox=True by default but with an option to disable:
       437    
       438 -      return {"job_id": job_id, "cancelled": cancelled}
       439 -  ```
       440 -  
       441 -  ### Redis Backend Implementation
       442 -  
       438    ```python
       439 -  # abstractcore/queue/backends/redis_backend.py
       440 -  
       441 -  import redis.asyncio as redis
       442 -  import json
       443 -  from typing import Optional, AsyncIterator
       444 -  from datetime import datetime
       445 -  
       446 -  class RedisBackend(QueueBackend):
       447 -      """Redis-based queue backend for production use."""
       448 -  
       439 +  class CodeActAgent:
       440        def __init__(
       441            self,
       442 -          url: str = "redis://localhost:6379",
       443 -          db: int = 0,
       444 -          prefix: str = "abstractcore:",
       445 -          max_connections: int = 10
       442 +          llm,
       443 +          sandbox: bool = True,  # Safe by default
       444 +          sandbox_config: Optional[SandboxConfig] = None,
       445 +          allow_system_access: bool = False,  # Explicit opt-in
       446 +          ...
       447        ):
       448 -          self.url = url
       449 -          self.prefix = prefix
       450 -          self._pool = redis.ConnectionPool.from_url(
       451 -              url, db=db, max_connections=max_connections
       452 -          )
       453 -          self._client = redis.Redis(connection_pool=self._pool)
       454 -  
       455 -      def _key(self, key_type: str, key_id: str) -> str:
       456 -          """Generate Redis key with prefix."""
       457 -          return f"{self.prefix}{key_type}:{key_id}"
       458 -  
       459 -      async def enqueue(self, job: Job) -> None:
       460 -          """Add job to priority queue."""
       461 -          job_data = job.to_dict()
       462 -          job_data["created_at"] = datetime.utcnow().isoformat()
       463 -  
       464 -          async with self._client.pipeline() as pipe:
       465 -              # Store job data
       466 -              await pipe.hset(
       467 -                  self._key("job", job.job_id),
       468 -                  mapping={"data": json.dumps(job_data)}
       448 +          if sandbox:
       449 +              self.executor = SandboxedExecutor(sandbox_config)
       450 +          elif allow_system_access:
       451 +              self.executor = DirectExecutor()  # Unrestricted
       452 +          else:
       453 +              raise ValueError(
       454 +                  "Must enable sandbox=True OR explicitly set 
           + allow_system_access=True"
       455                )
       456 -              # Add to priority queue (sorted set, score = priority * 1e12 + 
           - timestamp)
       457 -              score = job.priority.value * 1e12 + datetime.utcnow().timestamp()
       458 -              await pipe.zadd(
       459 -                  self._key("queue", "pending"),
       460 -                  {job.job_id: score}
       461 -              )
       462 -              await pipe.execute()
       463 -  
       464 -      async def dequeue(self) -> Optional[Job]:
       465 -          """Get highest priority job from queue (blocking)."""
       466 -          # BZPOPMAX: blocking pop of highest score
       467 -          result = await self._client.bzpopmax(
       468 -              self._key("queue", "pending"),
       469 -              timeout=10  # Check every 10s for shutdown signals
       470 -          )
       471 -  
       472 -          if result is None:
       473 -              return None
       474 -  
       475 -          _, job_id, _ = result
       476 -          job_id = job_id.decode() if isinstance(job_id, bytes) else job_id
       477 -  
       478 -          # Get full job data
       479 -          job_data = await self._client.hget(
       480 -              self._key("job", job_id), "data"
       481 -          )
       482 -  
       483 -          if job_data is None:
       484 -              return None
       485 -  
       486 -          return Job.from_dict(json.loads(job_data))
       487 -  
       488 -      async def update_job(
       489 -          self,
       490 -          job_id: str,
       491 -          status: JobStatus,
       492 -          result: Optional[dict] = None,
       493 -          error: Optional[str] = None,
       494 -          progress: float = 0.0
       495 -      ) -> None:
       496 -          """Update job status and publish event."""
       497 -          updates = {
       498 -              "status": status.value,
       499 -              "progress": progress,
       500 -              "updated_at": datetime.utcnow().isoformat()
       501 -          }
       502 -  
       503 -          if status == JobStatus.RUNNING:
       504 -              updates["started_at"] = datetime.utcnow().isoformat()
       505 -          elif status in (JobStatus.COMPLETED, JobStatus.FAILED):
       506 -              updates["completed_at"] = datetime.utcnow().isoformat()
       507 -              if result:
       508 -                  updates["result"] = json.dumps(result)
       509 -              if error:
       510 -                  updates["error"] = error
       511 -  
       512 -          # Update job hash
       513 -          await self._client.hset(
       514 -              self._key("job", job_id),
       515 -              mapping=updates
       516 -          )
       517 -  
       518 -          # Publish event for SSE subscribers
       519 -          await self._client.publish(
       520 -              self._key("events", job_id),
       521 -              json.dumps({
       522 -                  "status": status.value,
       523 -                  "progress": progress,
       524 -                  "message": f"Job {status.value}"
       525 -              })
       526 -          )
       527 -  
       528 -      async def subscribe(self, job_id: str) -> AsyncIterator[ProgressEvent]:
       529 -          """Subscribe to job progress events."""
       530 -          pubsub = self._client.pubsub()
       531 -          await pubsub.subscribe(self._key("events", job_id))
       532 -  
       533 -          async for message in pubsub.listen():
       534 -              if message["type"] == "message":
       535 -                  data = json.loads(message["data"])
       536 -                  yield ProgressEvent(
       537 -                      job_id=job_id,
       538 -                      status=JobStatus(data["status"]),
       539 -                      progress=data["progress"],
       540 -                      message=data.get("message"),
       541 -                      partial_content=data.get("partial_content")
       542 -                  )
       543 -  
       544 -                  if data["status"] in ("completed", "failed", "cancelled"):
       545 -                      break
       546 -  
       547 -          await pubsub.unsubscribe()
       456    ```
       457    
       458 -  ### Event System Integration
       458 +  This is the right pattern - safe defaults with explicit opt-in for power users.
       459    
       460 -  ```python
       461 -  # New event types for queue operations
       462 -  class EventType(Enum):
       463 -      # Existing...
       464 -      GENERATION_STARTED = "generation_started"
       465 -      GENERATION_COMPLETED = "generation_completed"
       466 -  
       467 -      # New queue events
       468 -      QUEUE_JOB_SUBMITTED = "queue_job_submitted"
       469 -      QUEUE_JOB_STARTED = "queue_job_started"
       470 -      QUEUE_JOB_PROGRESS = "queue_job_progress"
       471 -      QUEUE_JOB_COMPLETED = "queue_job_completed"
       472 -      QUEUE_JOB_FAILED = "queue_job_failed"
       473 -      QUEUE_JOB_CANCELLED = "queue_job_cancelled"
       474 -  
       475 -  # Queue manager emits events
       476 -  class QueueManager:
       477 -      async def submit(self, ...) -> str:
       478 -          job_id = str(uuid.uuid4())
       479 -          job = Job(job_id=job_id, ...)
       480 -  
       481 -          await self.backend.enqueue(job)
       482 -  
       483 -          emit_global(EventType.QUEUE_JOB_SUBMITTED, {
       484 -              "job_id": job_id,
       485 -              "provider": job.provider,
       486 -              "model": job.model,
       487 -              "priority": job.priority.value
       488 -          })
       489 -  
       490 -          return job_id
       491 -  ```
       492 -  
       460    ---
       461    
       462 -  ## Implementation Phases
       462 +  ## Recommendation
       463    
       464 -  ### Phase 1: Core Queue Infrastructure (1 week)
       465 -  - `abstractcore/queue/models.py` - Data models
       466 -  - `abstractcore/queue/backends/base.py` - Abstract backend interface
       467 -  - `abstractcore/queue/backends/memory.py` - In-memory backend (dev/test)
       468 -  - `abstractcore/queue/manager.py` - QueueManager class
       469 -  - `abstractcore/queue/__init__.py` - Public API
       464 +  ### My Recommendation: **Option 2 (Separate AbstractAgent) with Pragmatic 
           + Migration**
       465    
       466 -  ### Phase 2: Redis Backend (3-4 days)
       467 -  - `abstractcore/queue/backends/redis.py` - Redis implementation
       468 -  - Add `redis` to optional dependencies (`pip install abstractcore[queue]`)
       469 -  - Connection pooling and error handling
       470 -  - Pub/sub for progress streaming
       466 +  **Rationale:**
       467    
       468 -  ### Phase 3: Server API (2-3 days)
       469 -  - Add queue endpoints to `abstractcore/server/app.py`
       470 -  - SSE streaming for progress
       471 -  - OpenAPI documentation
       468 +  1. **Follows SOTA**: Every major agent framework (LangGraph, AutoGen, CrewAI) 
           + separates agent logic from LLM providers
       469    
       470 -  ### Phase 4: Worker Framework (3-4 days)
       471 -  - `abstractcore/queue/worker.py` - Worker process implementation
       472 -  - Graceful shutdown handling
       473 -  - Concurrent worker support
       474 -  - Retry logic for failed jobs
       470 +  2. **Enables ALL deployment scenarios**: Mobile, browser, backend, edge - all 
           + viable with thin client
       471    
       472 -  ### Phase 5: Documentation & Testing (2-3 days)
       473 -  - Comprehensive test suite
       474 -  - `docs/queue-integration.md` - User guide
       475 -  - Docker Compose example with Redis
       476 -  - Benchmark results
       472 +  3. **Preserves AbstractCore's identity**: "Thin, stateless, horizontally 
           + scalable" routing layer
       473    
       474 -  **Total Effort: ~3 weeks**
       474 +  4. **Future-proof**: As agents get more complex, they deserve their own package
       475    
       476 -  ---
       476 +  5. **Clean dependency story**:
       477 +     - Just want LLM abstraction? `pip install abstractcore`
       478 +     - Want agents? `pip install abstractagent` (pulls abstractcore)
       479 +     - Want thin agent client? `pip install abstractagent[client]` (just httpx)
       480    
       481 -  # Appendix: Kubernetes Deployment
       481 +  ### Immediate Actions
       482    
       483 -  ## Overview
       483 +  1. **Create AbstractAgent repository** (or `abstractcore-agent` if monorepo)
       484 +  2. **Implement ReActAgent and CodeActAgent** in new package
       485 +  3. **Keep BasicDeepSearch in AbstractCore** for now (pragmatic)
       486 +  4. **Add `abstractagent.client.RemoteClient`** for thin client mode
       487 +  5. **Document the architecture** clearly
       488    
       489 -  Production-grade Kubernetes deployment for AbstractCore with:
       490 -  - **AbstractCore API** - Stateless, horizontally scalable routing layer
       491 -  - **Workers** - Separate pods for queue processing
       492 -  - **Redis** - Queue broker and result backend
       493 -  - **Inference Servers** - vLLM/Ollama as StatefulSets
       489 +  ### Package Structure
       490    
       491 -  ## Architecture
       492 -  
       491    ```
       492 -  ┌────────────────────────────────────────────────────────────────────┐
       493 -  │  Kubernetes Cluster                                                 │
       494 -  │  ┌──────────────────────────────────────────────────────────────┐  │
       495 -  │  │  Ingress (nginx/traefik)                                      │  │
       496 -  │  │    └── abstractcore.yourdomain.com                            │  │
       497 -  │  └──────────────────────────────────────────────────────────────┘  │
       498 -  │                               │                                     │
       499 -  │  ┌────────────────────────────▼─────────────────────────────────┐  │
       500 -  │  │  Service: abstractcore-api (ClusterIP)                        │  │
       501 -  │  │    └── Deployment: abstractcore-api (HPA: 2-10 replicas)     │  │
       502 -  │  │         └── Pod: abstractcore-api                             │  │
       503 -  │  │              ├── Container: abstractcore                      │  │
       504 -  │  │              └── Resources: 256Mi-512Mi, 0.25-0.5 CPU        │  │
       505 -  │  └──────────────────────────────────────────────────────────────┘  │
       506 -  │                               │                                     │
       507 -  │  ┌────────────────────────────▼─────────────────────────────────┐  │
       508 -  │  │  Service: redis (ClusterIP)                                   │  │
       509 -  │  │    └── StatefulSet: redis (1 replica)                        │  │
       510 -  │  │         └── Pod: redis                                        │  │
       511 -  │  │              └── Volume: redis-data (PVC)                     │  │
       512 -  │  └──────────────────────────────────────────────────────────────┘  │
       513 -  │                               │                                     │
       514 -  │  ┌────────────────────────────▼─────────────────────────────────┐  │
       515 -  │  │  Deployment: abstractcore-worker (HPA: 1-5 replicas)         │  │
       516 -  │  │    └── Pod: abstractcore-worker                               │  │
       517 -  │  │         └── Container: abstractcore-worker                    │  │
       518 -  │  │              └── Resources: 512Mi-1Gi, 0.5-1 CPU             │  │
       519 -  │  └──────────────────────────────────────────────────────────────┘  │
       520 -  │                               │                                     │
       521 -  │  ┌────────────────────────────▼─────────────────────────────────┐  │
       522 -  │  │  StatefulSet: vllm (GPU nodes)                                │  │
       523 -  │  │    └── Pod: vllm-0                                            │  │
       524 -  │  │         ├── Container: vllm                                   │  │
       525 -  │  │         ├── Resources: nvidia.com/gpu: 4                     │  │
       526 -  │  │         └── Volume: model-cache (PVC)                         │  │
       527 -  │  └──────────────────────────────────────────────────────────────┘  │
       528 -  └────────────────────────────────────────────────────────────────────┘
       492 +  abstractagent/               # New repository
       493 +  ├── setup.py                 # Depends on abstractcore OR just httpx
       494 +  ├── abstractagent/
       495 +  │   ├── __init__.py
       496 +  │   ├── core/
       497 +  │   │   ├── state.py         # AgentState, StateGraph
       498 +  │   │   ├── base.py          # BaseAgent ABC
       499 +  │   │   └── scratchpad.py    # Memory management
       500 +  │   ├── agents/
       501 +  │   │   ├── react.py         # ReActAgent
       502 +  │   │   └── code_act.py      # CodeActAgent
       503 +  │   ├── sandbox/
       504 +  │   │   └── executor.py      # Sandboxed code execution
       505 +  │   ├── tools/
       506 +  │   │   └── user.py          # ask_user, approve_action
       507 +  │   └── client/
       508 +  │       └── remote.py        # RemoteClient for thin mode
       509 +  └── tests/
       510    ```
       511    
       512 -  ## Manifests
       513 -  
       514 -  ### 1. Namespace and ConfigMap
       515 -  
       516 -  ```yaml
       517 -  # k8s/namespace.yaml
       518 -  apiVersion: v1
       519 -  kind: Namespace
       520 -  metadata:
       521 -    name: abstractcore
       522 -    labels:
       523 -      app.kubernetes.io/name: abstractcore
       512    ---
       513 -  # k8s/configmap.yaml
       514 -  apiVersion: v1
       515 -  kind: ConfigMap
       516 -  metadata:
       517 -    name: abstractcore-config
       518 -    namespace: abstractcore
       519 -  data:
       520 -    # Provider configuration
       521 -    OLLAMA_BASE_URL: "http://ollama:11434"
       522 -    LMSTUDIO_BASE_URL: "http://lmstudio:1234"
       523 -    VLLM_BASE_URL: "http://vllm:8000/v1"
       513    
       514 -    # Queue configuration
       515 -    QUEUE_BACKEND: "redis"
       516 -    REDIS_URL: "redis://redis:6379"
       517 -    QUEUE_PREFIX: "abstractcore:"
       514 +  ## Summary Table
       515    
       516 -    # Server configuration
       517 -    ABSTRACTCORE_LOG_LEVEL: "INFO"
       518 -    ABSTRACTCORE_DEBUG: "false"
       516 +  | Factor | In AbstractCore | Separate AbstractAgent |
       517 +  |--------|-----------------|------------------------|
       518 +  | User convenience | ✅ Single install | ⚠️ Two packages |
       519 +  | Mobile/edge viable | ❌ Full pkg needed | ✅ Thin client |
       520 +  | Browser possible | ❌ Python only | ✅ Could port to JS |
       521 +  | AbstractCore stays thin | ❌ Grows larger | ✅ Yes |
       522 +  | Follows SOTA | ❌ Uncommon | ✅ Standard pattern |
       523 +  | Breaking changes | ✅ None | ⚠️ For BasicDeepSearch |
       524 +  | Future flexibility | ❌ Coupled | ✅ Independent evolution |
       525 +  | Time to deliver | ✅ Faster | ⚠️ Slightly longer |
       526    
       527 -    # Worker configuration
       528 -    WORKER_CONCURRENCY: "4"
       529 -    WORKER_MAX_RETRIES: "3"
       530 -    WORKER_TIMEOUT: "600"  # 10 minutes
       531 -  ```
       532 -  
       533 -  ### 2. Secrets
       534 -  
       535 -  ```yaml
       536 -  # k8s/secrets.yaml
       537 -  apiVersion: v1
       538 -  kind: Secret
       539 -  metadata:
       540 -    name: abstractcore-secrets
       541 -    namespace: abstractcore
       542 -  type: Opaque
       543 -  stringData:
       544 -    # API keys (replace with actual values or use external secrets manager)
       545 -    OPENAI_API_KEY: "sk-..."
       546 -    ANTHROPIC_API_KEY: "sk-ant-..."
       547 -    VLLM_API_KEY: ""  # Optional
       548 -  
       549 -    # Redis password (if using password-protected Redis)
       550 -    REDIS_PASSWORD: ""
       551 -  ```
       552 -  
       553 -  ### 3. AbstractCore API Deployment
       554 -  
       555 -  ```yaml
       556 -  # k8s/api-deployment.yaml
       557 -  apiVersion: apps/v1
       558 -  kind: Deployment
       559 -  metadata:
       560 -    name: abstractcore-api
       561 -    namespace: abstractcore
       562 -    labels:
       563 -      app: abstractcore-api
       564 -      component: api
       565 -  spec:
       566 -    replicas: 2  # Minimum replicas (HPA will scale)
       567 -    selector:
       568 -      matchLabels:
       569 -        app: abstractcore-api
       570 -    template:
       571 -      metadata:
       572 -        labels:
       573 -          app: abstractcore-api
       574 -          component: api
       575 -      spec:
       576 -        containers:
       577 -        - name: abstractcore
       578 -          image: abstractcore/server:latest  # Or your registry
       579 -          ports:
       580 -          - containerPort: 8000
       581 -            name: http
       582 -          envFrom:
       583 -          - configMapRef:
       584 -              name: abstractcore-config
       585 -          - secretRef:
       586 -              name: abstractcore-secrets
       587 -          resources:
       588 -            requests:
       589 -              memory: "256Mi"
       590 -              cpu: "250m"
       591 -            limits:
       592 -              memory: "512Mi"
       593 -              cpu: "500m"
       594 -          livenessProbe:
       595 -            httpGet:
       596 -              path: /health
       597 -              port: 8000
       598 -            initialDelaySeconds: 10
       599 -            periodSeconds: 10
       600 -            failureThreshold: 3
       601 -          readinessProbe:
       602 -            httpGet:
       603 -              path: /health
       604 -              port: 8000
       605 -            initialDelaySeconds: 5
       606 -            periodSeconds: 5
       607 -            successThreshold: 1
       608 -          securityContext:
       609 -            runAsNonRoot: true
       610 -            runAsUser: 1000
       611 -            readOnlyRootFilesystem: true
       612 -            allowPrivilegeEscalation: false
       527    ---
       528 -  # k8s/api-service.yaml
       529 -  apiVersion: v1
       530 -  kind: Service
       531 -  metadata:
       532 -    name: abstractcore-api
       533 -    namespace: abstractcore
       534 -    labels:
       535 -      app: abstractcore-api
       536 -  spec:
       537 -    type: ClusterIP
       538 -    ports:
       539 -    - port: 8000
       540 -      targetPort: 8000
       541 -      protocol: TCP
       542 -      name: http
       543 -    selector:
       544 -      app: abstractcore-api
       545 -  ```
       528    
       529 -  ### 4. Horizontal Pod Autoscaler (HPA)
       529 +  ## Questions for You
       530    
       531 -  ```yaml
       532 -  # k8s/api-hpa.yaml
       533 -  apiVersion: autoscaling/v2
       534 -  kind: HorizontalPodAutoscaler
       535 -  metadata:
       536 -    name: abstractcore-api-hpa
       537 -    namespace: abstractcore
       538 -  spec:
       539 -    scaleTargetRef:
       540 -      apiVersion: apps/v1
       541 -      kind: Deployment
       542 -      name: abstractcore-api
       543 -    minReplicas: 2
       544 -    maxReplicas: 10
       545 -    metrics:
       546 -    # Scale based on CPU usage
       547 -    - type: Resource
       548 -      resource:
       549 -        name: cpu
       550 -        target:
       551 -          type: Utilization
       552 -          averageUtilization: 70
       553 -    # Scale based on memory usage
       554 -    - type: Resource
       555 -      resource:
       556 -        name: memory
       557 -        target:
       558 -          type: Utilization
       559 -          averageUtilization: 80
       560 -    # Scale based on request rate (requires metrics-server + custom metrics)
       561 -    # - type: Pods
       562 -    #   pods:
       563 -    #     metric:
       564 -    #       name: http_requests_per_second
       565 -    #     target:
       566 -    #       type: AverageValue
       567 -    #       averageValue: "100"
       568 -    behavior:
       569 -      scaleDown:
       570 -        stabilizationWindowSeconds: 300  # Wait 5 min before scaling down
       571 -        policies:
       572 -        - type: Percent
       573 -          value: 50
       574 -          periodSeconds: 60
       575 -      scaleUp:
       576 -        stabilizationWindowSeconds: 0  # Scale up immediately
       577 -        policies:
       578 -        - type: Percent
       579 -          value: 100
       580 -          periodSeconds: 15
       581 -        - type: Pods
       582 -          value: 4
       583 -          periodSeconds: 15
       584 -        selectPolicy: Max
       585 -  ```
       531 +  Before finalizing this architecture, I'd like to understand:
       532    
       533 -  ### 5. Worker Deployment
       533 +  1. **Priority**: Is mobile/edge deployment important now, or is it a future 
           + consideration?
       534    
       535 -  ```yaml
       536 -  # k8s/worker-deployment.yaml
       537 -  apiVersion: apps/v1
       538 -  kind: Deployment
       539 -  metadata:
       540 -    name: abstractcore-worker
       541 -    namespace: abstractcore
       542 -    labels:
       543 -      app: abstractcore-worker
       544 -      component: worker
       545 -  spec:
       546 -    replicas: 1  # Start with 1, HPA will scale
       547 -    selector:
       548 -      matchLabels:
       549 -        app: abstractcore-worker
       550 -    template:
       551 -      metadata:
       552 -        labels:
       553 -          app: abstractcore-worker
       554 -          component: worker
       555 -      spec:
       556 -        containers:
       557 -        - name: worker
       558 -          image: abstractcore/worker:latest  # Or your registry
       559 -          command: ["python", "-m", "abstractcore.queue.worker"]
       560 -          envFrom:
       561 -          - configMapRef:
       562 -              name: abstractcore-config
       563 -          - secretRef:
       564 -              name: abstractcore-secrets
       565 -          env:
       566 -          - name: WORKER_MODE
       567 -            value: "true"
       568 -          resources:
       569 -            requests:
       570 -              memory: "512Mi"
       571 -              cpu: "500m"
       572 -            limits:
       573 -              memory: "1Gi"
       574 -              cpu: "1000m"
       575 -          livenessProbe:
       576 -            exec:
       577 -              command:
       578 -              - python
       579 -              - -c
       580 -              - "import redis; r = redis.from_url('redis://redis:6379'); r.ping()"
       581 -            initialDelaySeconds: 30
       582 -            periodSeconds: 30
       583 -            failureThreshold: 3
       584 -          securityContext:
       585 -            runAsNonRoot: true
       586 -            runAsUser: 1000
       587 -  ---
       588 -  # k8s/worker-hpa.yaml
       589 -  apiVersion: autoscaling/v2
       590 -  kind: HorizontalPodAutoscaler
       591 -  metadata:
       592 -    name: abstractcore-worker-hpa
       593 -    namespace: abstractcore
       594 -  spec:
       595 -    scaleTargetRef:
       596 -      apiVersion: apps/v1
       597 -      kind: Deployment
       598 -      name: abstractcore-worker
       599 -    minReplicas: 1
       600 -    maxReplicas: 5
       601 -    metrics:
       602 -    - type: Resource
       603 -      resource:
       604 -        name: cpu
       605 -        target:
       606 -          type: Utilization
       607 -          averageUtilization: 60  # Workers should be kept busy
       608 -    # Custom metric: Scale based on queue depth (requires KEDA or custom metrics 
           - adapter)
       609 -    # - type: External
       610 -    #   external:
       611 -    #     metric:
       612 -    #       name: redis_queue_length
       613 -    #       selector:
       614 -    #         matchLabels:
       615 -    #           queue: abstractcore:queue:pending
       616 -    #     target:
       617 -    #       type: AverageValue
       618 -    #       averageValue: "10"  # 10 jobs per worker
       619 -  ```
       535 +  2. **Breaking changes**: How important is it to NOT break BasicDeepSearch users?
       536    
       537 -  ### 6. Redis StatefulSet
       537 +  3. **Monorepo vs separate**: Would you prefer `abstractcore-agent` in same repo 
           + or separate `abstractagent` repo?
       538    
       539 -  ```yaml
       540 -  # k8s/redis-statefulset.yaml
       541 -  apiVersion: apps/v1
       542 -  kind: StatefulSet
       543 -  metadata:
       544 -    name: redis
       545 -    namespace: abstractcore
       546 -  spec:
       547 -    serviceName: redis
       548 -    replicas: 1
       549 -    selector:
       550 -      matchLabels:
       551 -        app: redis
       552 -    template:
       553 -      metadata:
       554 -        labels:
       555 -          app: redis
       556 -      spec:
       557 -        containers:
       558 -        - name: redis
       559 -          image: redis:7-alpine
       560 -          ports:
       561 -          - containerPort: 6379
       562 -            name: redis
       563 -          command:
       564 -          - redis-server
       565 -          - "--appendonly"
       566 -          - "yes"
       567 -          - "--maxmemory"
       568 -          - "256mb"
       569 -          - "--maxmemory-policy"
       570 -          - "allkeys-lru"
       571 -          resources:
       572 -            requests:
       573 -              memory: "128Mi"
       574 -              cpu: "100m"
       575 -            limits:
       576 -              memory: "256Mi"
       577 -              cpu: "200m"
       578 -          volumeMounts:
       579 -          - name: redis-data
       580 -            mountPath: /data
       581 -          livenessProbe:
       582 -            exec:
       583 -              command:
       584 -              - redis-cli
       585 -              - ping
       586 -            initialDelaySeconds: 5
       587 -            periodSeconds: 10
       588 -          readinessProbe:
       589 -            exec:
       590 -              command:
       591 -              - redis-cli
       592 -              - ping
       593 -            initialDelaySeconds: 5
       594 -            periodSeconds: 5
       595 -    volumeClaimTemplates:
       596 -    - metadata:
       597 -        name: redis-data
       598 -      spec:
       599 -        accessModes: ["ReadWriteOnce"]
       600 -        storageClassName: standard  # Adjust for your cluster
       601 -        resources:
       602 -          requests:
       603 -            storage: 1Gi
       604 -  ---
       605 -  # k8s/redis-service.yaml
       606 -  apiVersion: v1
       607 -  kind: Service
       608 -  metadata:
       609 -    name: redis
       610 -    namespace: abstractcore
       611 -  spec:
       612 -    type: ClusterIP
       613 -    ports:
       614 -    - port: 6379
       615 -      targetPort: 6379
       616 -    selector:
       617 -      app: redis
       618 -  ```
       539 +  4. **Timeline**: Do you need agents ASAP (favors in-AbstractCore), or is clean 
           + architecture more important?
       540    
       541 -  ### 7. vLLM StatefulSet (GPU Nodes)
       542 -  
       543 -  ```yaml
       544 -  # k8s/vllm-statefulset.yaml
       545 -  apiVersion: apps/v1
       546 -  kind: StatefulSet
       547 -  metadata:
       548 -    name: vllm
       549 -    namespace: abstractcore
       550 -  spec:
       551 -    serviceName: vllm
       552 -    replicas: 1  # Scale manually based on GPU availability
       553 -    selector:
       554 -      matchLabels:
       555 -        app: vllm
       556 -    template:
       557 -      metadata:
       558 -        labels:
       559 -          app: vllm
       560 -      spec:
       561 -        nodeSelector:
       562 -          # Ensure scheduling on GPU nodes
       563 -          nvidia.com/gpu.present: "true"
       564 -        tolerations:
       565 -        - key: nvidia.com/gpu
       566 -          operator: Exists
       567 -          effect: NoSchedule
       568 -        containers:
       569 -        - name: vllm
       570 -          image: vllm/vllm-openai:latest
       571 -          args:
       572 -          - "--model"
       573 -          - "Qwen/Qwen2.5-Coder-7B-Instruct"
       574 -          - "--host"
       575 -          - "0.0.0.0"
       576 -          - "--port"
       577 -          - "8000"
       578 -          - "--tensor-parallel-size"
       579 -          - "4"  # For 4 GPUs
       580 -          - "--gpu-memory-utilization"
       581 -          - "0.9"
       582 -          - "--max-model-len"
       583 -          - "8192"
       584 -          - "--max-num-seqs"
       585 -          - "128"
       586 -          ports:
       587 -          - containerPort: 8000
       588 -            name: http
       589 -          resources:
       590 -            requests:
       591 -              nvidia.com/gpu: 4
       592 -              memory: "32Gi"
       593 -              cpu: "8"
       594 -            limits:
       595 -              nvidia.com/gpu: 4
       596 -              memory: "64Gi"
       597 -              cpu: "16"
       598 -          volumeMounts:
       599 -          - name: model-cache
       600 -            mountPath: /root/.cache/huggingface
       601 -          - name: shm
       602 -            mountPath: /dev/shm  # Required for tensor parallelism
       603 -          env:
       604 -          - name: HF_HOME
       605 -            value: "/root/.cache/huggingface"
       606 -          livenessProbe:
       607 -            httpGet:
       608 -              path: /health
       609 -              port: 8000
       610 -            initialDelaySeconds: 300  # Model loading takes time
       611 -            periodSeconds: 30
       612 -            failureThreshold: 3
       613 -          readinessProbe:
       614 -            httpGet:
       615 -              path: /health
       616 -              port: 8000
       617 -            initialDelaySeconds: 300
       618 -            periodSeconds: 10
       619 -        volumes:
       620 -        - name: shm
       621 -          emptyDir:
       622 -            medium: Memory
       623 -            sizeLimit: "16Gi"  # For tensor parallelism IPC
       624 -    volumeClaimTemplates:
       625 -    - metadata:
       626 -        name: model-cache
       627 -      spec:
       628 -        accessModes: ["ReadWriteOnce"]
       629 -        storageClassName: standard
       630 -        resources:
       631 -          requests:
       632 -            storage: 100Gi  # For model weights
       541    ---
       542 -  # k8s/vllm-service.yaml
       543 -  apiVersion: v1
       544 -  kind: Service
       545 -  metadata:
       546 -    name: vllm
       547 -    namespace: abstractcore
       548 -  spec:
       549 -    type: ClusterIP
       550 -    ports:
       551 -    - port: 8000
       552 -      targetPort: 8000
       553 -    selector:
       554 -      app: vllm
       555 -  ```
       542    
       543 -  ### 8. Ingress
       543 +  ## Sources
       544    
       545 -  ```yaml
       546 -  # k8s/ingress.yaml
       547 -  apiVersion: networking.k8s.io/v1
       548 -  kind: Ingress
       549 -  metadata:
       550 -    name: abstractcore-ingress
       551 -    namespace: abstractcore
       552 -    annotations:
       553 -      nginx.ingress.kubernetes.io/proxy-body-size: "50m"
       554 -      nginx.ingress.kubernetes.io/proxy-read-timeout: "600"  # 10 min for LLM 
           - requests
       555 -      nginx.ingress.kubernetes.io/proxy-send-timeout: "600"
       556 -      nginx.ingress.kubernetes.io/proxy-connect-timeout: "60"
       557 -      # Enable SSE support
       558 -      nginx.ingress.kubernetes.io/proxy-buffering: "off"
       559 -      nginx.ingress.kubernetes.io/proxy-http-version: "1.1"
       560 -      # Rate limiting (optional)
       561 -      nginx.ingress.kubernetes.io/limit-rps: "100"
       562 -      nginx.ingress.kubernetes.io/limit-connections: "50"
       563 -      # TLS (use cert-manager)
       564 -      cert-manager.io/cluster-issuer: "letsencrypt-prod"
       565 -  spec:
       566 -    ingressClassName: nginx
       567 -    tls:
       568 -    - hosts:
       569 -      - abstractcore.yourdomain.com
       570 -      secretName: abstractcore-tls
       571 -    rules:
       572 -    - host: abstractcore.yourdomain.com
       573 -      http:
       574 -        paths:
       575 -        - path: /
       576 -          pathType: Prefix
       577 -          backend:
       578 -            service:
       579 -              name: abstractcore-api
       580 -              port:
       581 -                number: 8000
       582 -  ```
       583 -  
       584 -  ### 9. Network Policy (Security)
       585 -  
       586 -  ```yaml
       587 -  # k8s/network-policy.yaml
       588 -  apiVersion: networking.k8s.io/v1
       589 -  kind: NetworkPolicy
       590 -  metadata:
       591 -    name: abstractcore-network-policy
       592 -    namespace: abstractcore
       593 -  spec:
       594 -    podSelector: {}  # Apply to all pods in namespace
       595 -    policyTypes:
       596 -    - Ingress
       597 -    - Egress
       598 -    ingress:
       599 -    # Allow ingress controller to reach API
       600 -    - from:
       601 -      - namespaceSelector:
       602 -          matchLabels:
       603 -            kubernetes.io/metadata.name: ingress-nginx
       604 -      ports:
       605 -      - protocol: TCP
       606 -        port: 8000
       607 -    # Allow internal communication
       608 -    - from:
       609 -      - podSelector: {}
       610 -    egress:
       611 -    # Allow DNS
       612 -    - to:
       613 -      - namespaceSelector: {}
       614 -      ports:
       615 -      - protocol: UDP
       616 -        port: 53
       617 -    # Allow Redis
       618 -    - to:
       619 -      - podSelector:
       620 -          matchLabels:
       621 -            app: redis
       622 -      ports:
       623 -      - protocol: TCP
       624 -        port: 6379
       625 -    # Allow vLLM
       626 -    - to:
       627 -      - podSelector:
       628 -          matchLabels:
       629 -            app: vllm
       630 -      ports:
       631 -      - protocol: TCP
       632 -        port: 8000
       633 -    # Allow external APIs (OpenAI, Anthropic)
       634 -    - to:
       635 -      - ipBlock:
       636 -          cidr: 0.0.0.0/0
       637 -      ports:
       638 -      - protocol: TCP
       639 -        port: 443
       640 -  ```
       641 -  
       642 -  ### 10. Pod Disruption Budget (High Availability)
       643 -  
       644 -  ```yaml
       645 -  # k8s/pdb.yaml
       646 -  apiVersion: policy/v1
       647 -  kind: PodDisruptionBudget
       648 -  metadata:
       649 -    name: abstractcore-api-pdb
       650 -    namespace: abstractcore
       651 -  spec:
       652 -    minAvailable: 1  # Always keep at least 1 pod running
       653 -    selector:
       654 -      matchLabels:
       655 -        app: abstractcore-api
       656 -  ---
       657 -  apiVersion: policy/v1
       658 -  kind: PodDisruptionBudget
       659 -  metadata:
       660 -    name: abstractcore-worker-pdb
       661 -    namespace: abstractcore
       662 -  spec:
       663 -    minAvailable: 1
       664 -    selector:
       665 -      matchLabels:
       666 -        app: abstractcore-worker
       667 -  ```
       668 -  
       669 -  ## Deployment Commands
       670 -  
       671 -  ```bash
       672 -  # Create namespace and apply all manifests
       673 -  kubectl apply -f k8s/namespace.yaml
       674 -  kubectl apply -f k8s/configmap.yaml
       675 -  kubectl apply -f k8s/secrets.yaml  # Edit with real values first!
       676 -  kubectl apply -f k8s/redis-statefulset.yaml
       677 -  kubectl apply -f k8s/redis-service.yaml
       678 -  kubectl apply -f k8s/api-deployment.yaml
       679 -  kubectl apply -f k8s/api-service.yaml
       680 -  kubectl apply -f k8s/api-hpa.yaml
       681 -  kubectl apply -f k8s/worker-deployment.yaml
       682 -  kubectl apply -f k8s/worker-hpa.yaml
       683 -  kubectl apply -f k8s/vllm-statefulset.yaml  # If using vLLM
       684 -  kubectl apply -f k8s/vllm-service.yaml
       685 -  kubectl apply -f k8s/ingress.yaml
       686 -  kubectl apply -f k8s/network-policy.yaml
       687 -  kubectl apply -f k8s/pdb.yaml
       688 -  
       689 -  # Or apply all at once
       690 -  kubectl apply -f k8s/
       691 -  
       692 -  # Check status
       693 -  kubectl -n abstractcore get pods
       694 -  kubectl -n abstractcore get hpa
       695 -  kubectl -n abstractcore get svc
       696 -  kubectl -n abstractcore get ingress
       697 -  
       698 -  # View logs
       699 -  kubectl -n abstractcore logs -f deployment/abstractcore-api
       700 -  kubectl -n abstractcore logs -f deployment/abstractcore-worker
       701 -  
       702 -  # Scale manually
       703 -  kubectl -n abstractcore scale deployment/abstractcore-api --replicas=5
       704 -  kubectl -n abstractcore scale deployment/abstractcore-worker --replicas=3
       705 -  ```
       706 -  
       707 -  ## Monitoring with Prometheus/Grafana
       708 -  
       709 -  ```yaml
       710 -  # k8s/servicemonitor.yaml
       711 -  apiVersion: monitoring.coreos.com/v1
       712 -  kind: ServiceMonitor
       713 -  metadata:
       714 -    name: abstractcore-api
       715 -    namespace: abstractcore
       716 -    labels:
       717 -      app: abstractcore-api
       718 -  spec:
       719 -    selector:
       720 -      matchLabels:
       721 -        app: abstractcore-api
       722 -    endpoints:
       723 -    - port: http
       724 -      path: /metrics
       725 -      interval: 15s
       726 -  ```
       727 -  
       728 -  ## KEDA for Queue-Based Scaling (Advanced)
       729 -  
       730 -  ```yaml
       731 -  # k8s/keda-scaledobject.yaml
       732 -  # Requires KEDA installed: https://keda.sh/docs/deploy/
       733 -  apiVersion: keda.sh/v1alpha1
       734 -  kind: ScaledObject
       735 -  metadata:
       736 -    name: abstractcore-worker-scaler
       737 -    namespace: abstractcore
       738 -  spec:
       739 -    scaleTargetRef:
       740 -      name: abstractcore-worker
       741 -    minReplicaCount: 1
       742 -    maxReplicaCount: 10
       743 -    triggers:
       744 -    - type: redis
       745 -      metadata:
       746 -        address: redis:6379
       747 -        listName: abstractcore:queue:pending
       748 -        listLength: "10"  # 10 jobs per worker
       749 -  ```
       750 -  
       751 -  ---
       752 -  
       753 -  **Total Effort: ~3 weeks**
       754 -  
       755 -  ---
       756 -  
       757 -  ## Conclusion
       758 -  
       759 -  **AbstractCore is correctly designed as a routing/abstraction layer**, not an 
           - inference engine. The key architectural principle is:
       760 -  
       761 -  > **"AbstractCore should be thin, stateless, and horizontally scalable. 
           - Inference should be delegated to specialized servers that manage GPU resources 
           - independently."**
       762 -  
       763 -  For production deployments:
       764 -  1. ✅ Keep AbstractCore as stateless router
       765 -  2. ✅ Deploy inference servers separately (vLLM, Ollama)
       766 -  3. ✅ Use external load balancer for AbstractCore replicas
       767 -  4. ✅ Consider queue for high-throughput scenarios
       768 -  5. ❌ Don't mix inference with routing (except dev mode)
       769 -  
       770 -  This architecture aligns with SOTA patterns from vLLM, TGI, OpenRouter, and 
           - LiteLLM, while preserving AbstractCore's core value proposition: **"Write once, 
           - run everywhere"** with provider abstraction and universal tools.
       545 +  - [Turing - AI Agent Frameworks 
           + Comparison](https://www.turing.com/resources/ai-agent-frameworks)
       546 +  - [Softcery - 14 AI Agent Frameworks Compared](https://softcery.com/lab/top-14-a
           + i-agent-frameworks-of-2025-a-founders-guide-to-building-smarter-systems)
       547 +  - [Technical Comparison of AutoGen, CrewAI, LangGraph](https://ai.plainenglish.i
           + o/technical-comparison-of-autogen-crewai-langgraph-and-openai-swarm-1e4e9571d725)
       548 +  - [LangGraph vs AutoGen vs CrewAI Architecture 
           + Analysis](https://latenode.com/blog/langgraph-vs-autogen-vs-crewai-complete-ai-ag
           + ent-framework-comparison-architecture-analysis-2025)

∴ Thinking…

  I've written a comprehensive architectural analysis. Now I should present the key findings to the 
  user and ask clarifying questions to help finalize the architecture decision. Let me summarize and 
  ask the questions.

⏺ I've written a comprehensive architectural analysis to the plan file. Let me summarize the key
  findings and ask you some clarifying questions:

  ---
  Key Findings

  1. SOTA Pattern: Separation is Standard

  Every major agent framework is SEPARATE from LLM provider libraries:
  - LangGraph ≠ LangChain (separate packages)
  - CrewAI uses LiteLLM (separate dependency)
  - AutoGen is provider-agnostic
  - OpenAI Swarm is separate from openai SDK

  2. The Two Components Have Different Characteristics

  | Component              | Characteristics                    | Where It Can Run
             |
  |------------------------|------------------------------------|--------------------------------
  -----------|
  | Orchestration (agents) | Stateful, CPU-bound, ~50-200MB     | Anywhere (mobile, browser,
  edge, backend) |
  | Inference (LLMs)       | Stateless, GPU-bound, 8-128GB VRAM | GPU servers only
             |

  3. AbstractCore Already Has the Tension

  BasicDeepSearch (~2,174 lines) is essentially a stateful agent already in AbstractCore, which
  contradicts the "thin, stateless" design principle.

  4. Deployment Scenarios

  | Scenario       | Agents in AbstractCore | Separate AbstractAgent |
  |----------------|------------------------|------------------------|
  | Co-located GPU | ✅ Works               | ✅ Works               |
  | Mobile/Edge    | ❌ Full pkg needed     | ✅ Thin client         |
  | Browser/WASM   | ❌ Not possible        | ✅ Portable            |
  | Microservice   | ⚠️ Coupled             | ✅ Clean separation    |

  My Recommendation: Separate AbstractAgent Package

  With a pragmatic migration path:
  1. Create AbstractAgent for ReAct/CodeAct
  2. Keep BasicDeepSearch in AbstractCore for now (no breaking changes)
  3. Add thin client mode for mobile/edge deployment
  4. Migrate BasicDeepSearch in v3.0 if desired

  ---
                                                                                                
⏺ User answered Claude's questions:
  ⎿  · Is mobile/edge deployment important now, or is it a future consideration? → Agents should 
     be able to run both with the front end / client AND the backend

     · How important is it to NOT break BasicDeepSearch users? → We don't have much users yet, so
      we can completly break BasicDeepSearch after, as long as we make it at least as good
     · Should AbstractAgent be in the same monorepo or a separate repository? → use this folder 
     for AbstractAgent /Users/albou/projects/abstractagent/ (it will need its own git init)
     · What's more important: speed of delivery or architectural purity? → Architecture - get it 
     right first


∴ Thinking…

  The user has provided excellent clarity:

  1. Mobile/Edge: Agents should run on BOTH frontend/client AND backend - this confirms the need for 
  a thin client mode
  2. Breaking changes: No users yet, so we can break BasicDeepSearch if we improve it
  3. Repo structure: Separate repository at /Users/albou/projects/abstractagent/
  4. Timeline: Architecture - get it right first

  This strongly confirms Option 2 (Separate AbstractAgent package) with:
  - Thin client mode for frontend/edge deployment
  - Can move BasicDeepSearch to AbstractAgent
  - New repo at /Users/albou/projects/abstractagent/
  - Focus on clean architecture over speed

  Let me update the plan with the finalized architecture based on these answers.

⏺ Excellent clarity! Your answers strongly confirm Separate AbstractAgent with:
  - Dual deployment: Must run on both frontend AND backend
  - Clean slate: Can break BasicDeepSearch, just make it better
  - Separate repo: /Users/albou/projects/abstractagent/
  - Architecture first: Get it right

  Let me update the plan with the finalized architecture:

⏺ Updated plan
  ⎿  Updated /Users/albou/.claude/plans/breezy-singing-prism.md with 345 additions and 52       
     removals
       459    
       460    ---
       461    
       462 -  ## Recommendation
       462 +  ## Final Decision: Separate AbstractAgent Repository
       463    
       464 -  ### My Recommendation: **Option 2 (Separate AbstractAgent) with Pragmatic 
           - Migration**
       464 +  Based on user requirements:
       465 +  - ✅ **Dual deployment**: Agents must run on BOTH frontend/client AND backend
       466 +  - ✅ **Clean slate**: Can break BasicDeepSearch (few users), just improve it
       467 +  - ✅ **Separate repo**: `/Users/albou/projects/abstractagent/`
       468 +  - ✅ **Architecture first**: Get it right, don't rush
       469    
       470 -  **Rationale:**
       470 +  ---
       471    
       472 -  1. **Follows SOTA**: Every major agent framework (LangGraph, AutoGen, CrewAI) 
           - separates agent logic from LLM providers
       472 +  ## Finalized Architecture
       473    
       474 -  2. **Enables ALL deployment scenarios**: Mobile, browser, backend, edge - all 
           - viable with thin client
       474 +  ### Repository Structure
       475    
       476 -  3. **Preserves AbstractCore's identity**: "Thin, stateless, horizontally 
           - scalable" routing layer
       477 -  
       478 -  4. **Future-proof**: As agents get more complex, they deserve their own package
       479 -  
       480 -  5. **Clean dependency story**:
       481 -     - Just want LLM abstraction? `pip install abstractcore`
       482 -     - Want agents? `pip install abstractagent` (pulls abstractcore)
       483 -     - Want thin agent client? `pip install abstractagent[client]` (just httpx)
       484 -  
       485 -  ### Immediate Actions
       486 -  
       487 -  1. **Create AbstractAgent repository** (or `abstractcore-agent` if monorepo)
       488 -  2. **Implement ReActAgent and CodeActAgent** in new package
       489 -  3. **Keep BasicDeepSearch in AbstractCore** for now (pragmatic)
       490 -  4. **Add `abstractagent.client.RemoteClient`** for thin client mode
       491 -  5. **Document the architecture** clearly
       492 -  
       493 -  ### Package Structure
       494 -  
       476    ```
       477 -  abstractagent/               # New repository
       478 -  ├── setup.py                 # Depends on abstractcore OR just httpx
       477 +  /Users/albou/projects/abstractagent/    # NEW REPO
       478 +  ├── .git/                               # Own git history
       479 +  ├── pyproject.toml                      # Modern Python packaging
       480 +  ├── README.md
       481 +  ├── CLAUDE.md                           # Project instructions
       482    ├── abstractagent/
       483 -  │   ├── __init__.py
       483 +  │   ├── __init__.py                     # Package exports
       484    │   ├── core/
       485 -  │   │   ├── state.py         # AgentState, StateGraph
       486 -  │   │   ├── base.py          # BaseAgent ABC
       487 -  │   │   └── scratchpad.py    # Memory management
       485 +  │   │   ├── __init__.py
       486 +  │   │   ├── state.py                    # StateGraph, AgentState, Node, Edge
       487 +  │   │   ├── base.py                     # BaseAgent ABC
       488 +  │   │   └── scratchpad.py               # Serializable memory/working memory
       489    │   ├── agents/
       490 -  │   │   ├── react.py         # ReActAgent
       491 -  │   │   └── code_act.py      # CodeActAgent
       490 +  │   │   ├── __init__.py
       491 +  │   │   ├── react.py                    # ReActAgent (Think-Act-Observe)
       492 +  │   │   ├── code_act.py                 # CodeActAgent (Python code execution)
       493 +  │   │   └── deepsearch.py               # DeepSearchAgent (migrated from AC)
       494    │   ├── sandbox/
       495 -  │   │   └── executor.py      # Sandboxed code execution
       495 +  │   │   ├── __init__.py
       496 +  │   │   └── executor.py                 # Safe code execution (subprocess)
       497    │   ├── tools/
       498 -  │   │   └── user.py          # ask_user, approve_action
       498 +  │   │   ├── __init__.py
       499 +  │   │   └── user.py                     # ask_user, approve_action (HITL)
       500    │   └── client/
       501 -  │       └── remote.py        # RemoteClient for thin mode
       501 +  │       ├── __init__.py
       502 +  │       └── remote.py                   # RemoteClient for thin/edge deployment
       503    └── tests/
       504 +      ├── __init__.py
       505 +      ├── agents/
       506 +      │   ├── test_react.py
       507 +      │   ├── test_code_act.py
       508 +      │   └── test_deepsearch.py
       509 +      └── client/
       510 +          └── test_remote.py
       511    ```
       512    
       513 +  ### Dependency Strategy
       514 +  
       515 +  ```toml
       516 +  # pyproject.toml
       517 +  [project]
       518 +  name = "abstractagent"
       519 +  dependencies = [
       520 +      "pydantic>=2.0",
       521 +      "httpx>=0.25.0",         # Always needed for HTTP
       522 +  ]
       523 +  
       524 +  [project.optional-dependencies]
       525 +  # Full mode: local LLM execution via AbstractCore
       526 +  full = [
       527 +      "abstractcore>=2.6.0",   # Full AbstractCore
       528 +  ]
       529 +  
       530 +  # Client mode: thin client for edge/frontend
       531 +  client = [
       532 +      # No abstractcore - just httpx (already in base deps)
       533 +  ]
       534 +  
       535 +  # Development
       536 +  dev = [
       537 +      "pytest>=7.0",
       538 +      "abstractcore>=2.6.0",   # Needed for testing
       539 +  ]
       540 +  ```
       541 +  
       542 +  ### Usage Modes
       543 +  
       544 +  ```python
       545 +  # ═══════════════════════════════════════════════════════════════════════════
       546 +  # MODE 1: Full Mode (Backend with Local LLM)
       547 +  # ═══════════════════════════════════════════════════════════════════════════
       548 +  # pip install abstractagent[full]
       549 +  
       550 +  from abstractagent import ReActAgent, CodeActAgent
       551 +  from abstractcore import create_llm
       552 +  
       553 +  llm = create_llm("ollama", model="qwen3:4b")
       554 +  agent = ReActAgent(
       555 +      llm=llm,
       556 +      tools=[my_tool1, my_tool2],
       557 +      max_iterations=20
       558 +  )
       559 +  result = agent.run("Research quantum computing advances in 2025")
       560 +  
       561 +  # ═══════════════════════════════════════════════════════════════════════════
       562 +  # MODE 2: Client Mode (Frontend/Edge/Mobile)
       563 +  # ═══════════════════════════════════════════════════════════════════════════
       564 +  # pip install abstractagent  (base install, no abstractcore)
       565 +  
       566 +  from abstractagent import ReActAgent
       567 +  from abstractagent.client import RemoteClient
       568 +  
       569 +  # Connect to remote AbstractCore server (GPU fleet)
       570 +  client = RemoteClient(
       571 +      base_url="https://abstractcore.yourdomain.com",
       572 +      api_key="your-api-key"  # Optional auth
       573 +  )
       574 +  
       575 +  agent = ReActAgent(
       576 +      client=client,                    # Uses HTTP, not local LLM
       577 +      model="ollama/qwen3:4b",          # Model selection
       578 +      tools=[my_tool1, my_tool2],
       579 +      max_iterations=20
       580 +  )
       581 +  result = agent.run("Research quantum computing advances in 2025")
       582 +  
       583 +  # ═══════════════════════════════════════════════════════════════════════════
       584 +  # MODE 3: CodeAct with Sandbox Control
       585 +  # ═══════════════════════════════════════════════════════════════════════════
       586 +  
       587 +  from abstractagent import CodeActAgent
       588 +  from abstractagent.sandbox import SandboxConfig
       589 +  
       590 +  # Default: Sandboxed (safe)
       591 +  safe_agent = CodeActAgent(
       592 +      llm=llm,
       593 +      sandbox=True,  # DEFAULT
       594 +      sandbox_config=SandboxConfig(
       595 +          timeout=30,
       596 +          max_memory_mb=512,
       597 +          allowed_imports=["math", "json", "re", "datetime"]
       598 +      )
       599 +  )
       600 +  
       601 +  # Power user: Direct system access
       602 +  power_agent = CodeActAgent(
       603 +      llm=llm,
       604 +      sandbox=False,
       605 +      allow_system_access=True  # EXPLICIT opt-in required
       606 +  )
       607 +  ```
       608 +  
       609 +  ### Deployment Topologies Enabled
       610 +  
       611 +  ```
       612 +  ═══════════════════════════════════════════════════════════════════════════════
       613 +  TOPOLOGY 1: Simple Backend (Co-located)
       614 +  ═══════════════════════════════════════════════════════════════════════════════
       615 +  ┌─────────────────────────────────────────────────────────────────┐
       616 +  │  GPU Server                                                      │
       617 +  │  ┌──────────┐  ┌─────────────┐  ┌─────────────┐  ┌────────────┐ │
       618 +  │  │ Frontend │→ │ AbstractAgent│→ │ AbstractCore│→ │ Ollama/LLM │ │
       619 +  │  │  (React) │  │  (Full Mode) │  │  (router)   │  │            │ │
       620 +  │  └──────────┘  └─────────────┘  └─────────────┘  └────────────┘ │
       621 +  └─────────────────────────────────────────────────────────────────┘
       622 +  
       623 +  ═══════════════════════════════════════════════════════════════════════════════
       624 +  TOPOLOGY 2: Split Frontend/Backend
       625 +  ═══════════════════════════════════════════════════════════════════════════════
       626 +  ┌─────────────────────────────────┐      ┌───────────────────────────────────┐
       627 +  │  Frontend Server (no GPU)       │      │  GPU Fleet                         │
       628 +  │  ┌───────────────────────────┐  │      │  ┌─────────────┐  ┌─────────────┐ │
       629 +  │  │  AbstractAgent            │  │ HTTP │  │AbstractCore │→ │ vLLM        │ │
       630 +  │  │  (Client Mode)            │─────────│─▶│  (router)   │  │             │ │
       631 +  │  │  • Orchestrates locally   │  │      │  └─────────────┘  └─────────────┘ │
       632 +  │  │  • LLM calls via HTTP     │  │      │                                   │
       633 +  │  └───────────────────────────┘  │      │  ┌─────────────┐  ┌─────────────┐ │
       634 +  └─────────────────────────────────┘      │  │AbstractCore │→ │ Ollama      │ │
       635 +                                           │  │  (replica)  │  │             │ │
       636 +                                           │  └─────────────┘  └─────────────┘ │
       637 +                                           └───────────────────────────────────┘
       638 +  
       639 +  ═══════════════════════════════════════════════════════════════════════════════
       640 +  TOPOLOGY 3: Edge/Mobile Agent
       641 +  ═══════════════════════════════════════════════════════════════════════════════
       642 +  ┌───────────────────────┐         ┌─────────────────────────────────────────┐
       643 +  │  Mobile Device        │         │  Cloud GPU                               │
       644 +  │  ┌─────────────────┐  │  HTTPS  │  ┌─────────────┐  ┌───────────────────┐ │
       645 +  │  │ AbstractAgent   │  │─────────│─▶│AbstractCore │→ │ LLM Fleet         │ │
       646 +  │  │ (Client Mode)   │  │         │  │  (API)      │  │ (vLLM, Ollama)    │ │
       647 +  │  │ ~5MB footprint  │  │         │  └─────────────┘  └───────────────────┘ │
       648 +  │  └─────────────────┘  │         │                                         │
       649 +  └───────────────────────┘         └─────────────────────────────────────────┘
       650 +  
       651 +  ═══════════════════════════════════════════════════════════════════════════════
       652 +  TOPOLOGY 4: Browser Agent (Future - TypeScript port)
       653 +  ═══════════════════════════════════════════════════════════════════════════════
       654 +  ┌──────────────────────────────┐     ┌────────────────────────────────────────┐
       655 +  │  Browser                     │     │  Backend                                │
       656 +  │  ┌────────────────────────┐  │     │  ┌─────────────┐  ┌──────────────────┐ │
       657 +  │  │ AbstractAgent.js       │  │ HTTP│  │AbstractCore │→ │ LLM              │ │
       658 +  │  │ (TypeScript port)      │──│─────│─▶│  (API)      │  │                  │ │
       659 +  │  │ • Agent loop in browser│  │     │  └─────────────┘  └──────────────────┘ │
       660 +  │  │ • Tools execute locally│  │     │                                        │
       661 +  │  └────────────────────────┘  │     │                                        │
       662 +  └──────────────────────────────┘     └────────────────────────────────────────┘
       663 +  ```
       664 +  
       665    ---
       666    
       667 -  ## Summary Table
       667 +  ## Implementation Plan
       668    
       669 -  | Factor | In AbstractCore | Separate AbstractAgent |
       670 -  |--------|-----------------|------------------------|
       671 -  | User convenience | ✅ Single install | ⚠️ Two packages |
       672 -  | Mobile/edge viable | ❌ Full pkg needed | ✅ Thin client |
       673 -  | Browser possible | ❌ Python only | ✅ Could port to JS |
       674 -  | AbstractCore stays thin | ❌ Grows larger | ✅ Yes |
       675 -  | Follows SOTA | ❌ Uncommon | ✅ Standard pattern |
       676 -  | Breaking changes | ✅ None | ⚠️ For BasicDeepSearch |
       677 -  | Future flexibility | ❌ Coupled | ✅ Independent evolution |
       678 -  | Time to deliver | ✅ Faster | ⚠️ Slightly longer |
       669 +  ### Phase 1: Repository Setup (1 hour)
       670 +  1. Create `/Users/albou/projects/abstractagent/`
       671 +  2. `git init`
       672 +  3. Create `pyproject.toml` with dependency strategy
       673 +  4. Create `CLAUDE.md` with project instructions
       674 +  5. Setup basic package structure
       675    
       676 +  ### Phase 2: Core Infrastructure (4-6 hours)
       677 +  1. `abstractagent/core/state.py` - StateGraph, AgentState, Node, Edge
       678 +  2. `abstractagent/core/base.py` - BaseAgent ABC with common lifecycle
       679 +  3. `abstractagent/core/scratchpad.py` - Serializable working memory
       680 +  
       681 +  ### Phase 3: Remote Client (3-4 hours)
       682 +  1. `abstractagent/client/remote.py` - HTTP client for AbstractCore server
       683 +  2. OpenAI-compatible API calls
       684 +  3. Streaming support
       685 +  4. Authentication handling
       686 +  
       687 +  ### Phase 4: ReActAgent (4-5 hours)
       688 +  1. `abstractagent/agents/react.py`
       689 +  2. Think-Act-Observe loop
       690 +  3. Tool execution
       691 +  4. State serialization (save/load)
       692 +  5. Human-in-the-loop hooks
       693 +  
       694 +  ### Phase 5: CodeActAgent (5-6 hours)
       695 +  1. `abstractagent/agents/code_act.py`
       696 +  2. Python code generation
       697 +  3. `abstractagent/sandbox/executor.py` - Sandboxed execution
       698 +  4. `allow_system_access=True` option for power users
       699 +  5. Output capture and integration
       700 +  
       701 +  ### Phase 6: DeepSearchAgent Migration (4-5 hours)
       702 +  1. `abstractagent/agents/deepsearch.py` - Port from AbstractCore
       703 +  2. Improve/refactor during migration
       704 +  3. Update AbstractCore to deprecate original (or remove)
       705 +  
       706 +  ### Phase 7: Human-in-the-Loop (2-3 hours)
       707 +  1. `abstractagent/tools/user.py`
       708 +  2. `ask_user()` tool for agent questions
       709 +  3. `approve_action()` callback for sensitive operations
       710 +  4. State persistence during user wait
       711 +  
       712 +  ### Phase 8: Testing & Documentation (4-5 hours)
       713 +  1. Comprehensive tests (real implementations, no mocking)
       714 +  2. README with usage examples
       715 +  3. Architecture documentation
       716 +  
       717 +  **Total Estimate: 27-35 hours**
       718 +  
       719    ---
       720    
       721 -  ## Questions for You
       721 +  ## Key Design Decisions
       722    
       723 -  Before finalizing this architecture, I'd like to understand:
       723 +  ### 1. State Graph Architecture (LangGraph-inspired)
       724    
       725 -  1. **Priority**: Is mobile/edge deployment important now, or is it a future 
           - consideration?
       725 +  ```python
       726 +  # abstractagent/core/state.py
       727    
       728 -  2. **Breaking changes**: How important is it to NOT break BasicDeepSearch users?
       728 +  @dataclass
       729 +  class AgentState:
       730 +      """Fully serializable agent state for persistence and resume."""
       731 +      state_id: str
       732 +      current_node: str
       733 +      iteration: int
       734 +      messages: List[Dict]           # Conversation history
       735 +      scratchpad: Dict[str, Any]     # Working memory
       736 +      tool_results: List[Dict]       # Tool execution results
       737 +      status: str                    # "running", "waiting_user", "completed", 
           + "failed"
       738 +      created_at: datetime
       739 +      updated_at: datetime
       740    
       741 -  3. **Monorepo vs separate**: Would you prefer `abstractcore-agent` in same repo 
           - or separate `abstractagent` repo?
       741 +      def save(self, path: str) -> None: ...
       742 +      def load(cls, path: str) -> "AgentState": ...
       743 +      def to_dict(self) -> Dict: ...
       744    
       745 -  4. **Timeline**: Do you need agents ASAP (favors in-AbstractCore), or is clean 
           - architecture more important?
       745 +  class StateGraph:
       746 +      """Graph-based state machine for agent execution."""
       747 +      def __init__(self):
       748 +          self.nodes: Dict[str, Node] = {}
       749 +          self.edges: List[Edge] = []
       750 +          self.entry_point: str = None
       751    
       752 +      def add_node(self, name: str, action: Callable) -> None: ...
       753 +      def add_edge(self, from_node: str, to_node: str, condition: 
           + Optional[Callable] = None) -> None: ...
       754 +      def compile(self) -> "CompiledGraph": ...
       755 +  ```
       756 +  
       757 +  ### 2. Dual-Mode LLM Access
       758 +  
       759 +  ```python
       760 +  # abstractagent/core/base.py
       761 +  
       762 +  class BaseAgent(ABC):
       763 +      def __init__(
       764 +          self,
       765 +          llm: Optional["AbstractCoreInterface"] = None,  # Full mode
       766 +          client: Optional["RemoteClient"] = None,         # Client mode
       767 +          model: Optional[str] = None,                     # For client mode
       768 +          **kwargs
       769 +      ):
       770 +          if llm is not None:
       771 +              self._llm = llm
       772 +              self._mode = "full"
       773 +          elif client is not None:
       774 +              self._llm = ClientLLMWrapper(client, model)
       775 +              self._mode = "client"
       776 +          else:
       777 +              raise ValueError("Must provide either 'llm' or 'client'")
       778 +  ```
       779 +  
       780 +  ### 3. Sandbox Configuration
       781 +  
       782 +  ```python
       783 +  # abstractagent/sandbox/executor.py
       784 +  
       785 +  @dataclass
       786 +  class SandboxConfig:
       787 +      timeout: int = 30                           # Seconds
       788 +      max_memory_mb: int = 512                    # Memory limit
       789 +      allowed_imports: List[str] = field(default_factory=lambda: [
       790 +          "math", "json", "re", "datetime", "itertools", "collections"
       791 +      ])
       792 +      allow_network: bool = False                 # Block network by default
       793 +      allow_file_write: bool = False              # Block writes by default
       794 +  
       795 +  class SandboxedExecutor:
       796 +      """Execute Python code in isolated subprocess."""
       797 +  
       798 +  class DirectExecutor:
       799 +      """Execute Python code directly (power user mode)."""
       800 +  ```
       801 +  
       802 +  ### 4. Human-in-the-Loop Pattern
       803 +  
       804 +  ```python
       805 +  # abstractagent/tools/user.py
       806 +  
       807 +  async def ask_user(
       808 +      question: str,
       809 +      options: Optional[List[str]] = None,
       810 +      allow_free_text: bool = True
       811 +  ) -> str:
       812 +      """Pause agent and ask user for input."""
       813 +  
       814 +  def approve_action(
       815 +      action: str,
       816 +      details: Dict[str, Any],
       817 +      callback: Callable[[bool], None]
       818 +  ) -> None:
       819 +      """Request user approval for sensitive action."""
       820 +  ```
       821 +  
       822    ---
       823    
       824 +  ## Migration from AbstractCore BasicDeepSearch
       825 +  
       826 +  Since breaking changes are acceptable:
       827 +  
       828 +  1. **Create** `abstractagent/agents/deepsearch.py` with improved implementation
       829 +  2. **Deprecate** `abstractcore/processing/basic_deepsearch.py` with warning
       830 +  3. **Update** `abstractcore/apps/deepsearch.py` to use AbstractAgent
       831 +  4. **Document** migration path in CHANGELOG
       832 +  5. **Remove** original in AbstractCore v3.0 (or immediately if preferred)
       833 +  
       834 +  ---
       835 +  
       836    ## Sources
       837    
       838    - [Turing - AI Agent Frameworks 
             Comparison](https://www.turing.com/resources/ai-agent-frameworks)

∴ Thinking…

  I've updated the plan with the finalized architecture based on the user's answers. The plan now 
  includes:1. Separate AbstractAgent repository at /Users/albou/projects/abstractagent/2. Dual-mode 
  architecture (Full mode with local AbstractCore, Client mode for edge/frontend)3. Detailed 
  repository structure4. Dependency strategy with optional dependencies
  5. Usage examples for all modes6. Deployment topologies7. Implementation phases with time estimates
   (~27-35 hours)8. Key design decisions (StateGraph, Dual-mode LLM, Sandbox, HITL)
  9. Migration plan for BasicDeepSearchNow I should present this to the user and exit plan mode since
   this is an architectural analysis document, not an implementation task that requires immediate 
  execution. The user wanted to think through this carefully before implementing.

⏺ I've finalized the comprehensive architectural plan. Here's a summary:

  ---
  Final Architecture Decision

  Separate AbstractAgent Repository

  Location: /Users/albou/projects/abstractagent/

  Key Features:
  1. Dual-mode operation: Full mode (with AbstractCore) + Client mode (thin HTTP client)
  2. ~5MB footprint in client mode for mobile/edge
  3. Graph-based state machine (LangGraph-inspired)
  4. Sandboxed CodeAct by default with opt-out for power users
  5. Human-in-the-loop tools (ask_user, approve_action)

  Installation Options

  # Backend with local LLM
  pip install abstractagent[full]

  # Frontend/Edge/Mobile (thin client)
  pip install abstractagent

  Deployment Topologies Enabled

  | Topology         | Agent Location         | LLM Location |
  |------------------|------------------------|--------------|
  | Co-located       | Backend                | Same server  |
  | Split            | Frontend (client mode) | GPU Fleet    |
  | Mobile/Edge      | Device (~5MB)          | Cloud GPU    |
  | Browser (future) | JS port                | Cloud GPU    |

  Implementation Estimate: 27-35 hours

  | Phase                | Hours |
  |----------------------|-------|
  | Repository Setup     | 1     |
  | Core Infrastructure  | 4-6   |
  | Remote Client        | 3-4   |
  | ReActAgent           | 4-5   |
  | CodeActAgent         | 5-6   |
  | DeepSearch Migration | 4-5   |
  | Human-in-the-Loop    | 2-3   |
  | Testing & Docs       | 4-5   |

  ---
  The complete plan is at /Users/albou/.claude/plans/breezy-singing-prism.md (~840 lines).

  This architectural analysis is now complete. The plan provides a clear foundation for
  implementing AbstractAgent as a separate package that:
  - Follows SOTA patterns (separation like LangGraph, CrewAI, AutoGen)
  - Enables ALL deployment scenarios (backend, frontend, mobile, edge)
  - Preserves AbstractCore as a thin, stateless routing layer
  - Provides safe defaults (sandbox) with power user options

─────────────────────────────────────────────────────────────────────────────────────────────────────
 Ready to code?

 Here is Claude's plan:
╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌
 Agent Architecture Analysis: AbstractCore vs AbstractAgent

 The Question

 Should agents (ReAct, CodeAct, etc.) live inside AbstractCore or in a separate AbstractAgent 
 library? What are the SOTA patterns, and what would be the optimal architecture for different
 deployment scenarios (mobile, frontend, backend, GPU fleet)?

 ---
 Executive Summary

 After analyzing AbstractCore's codebase, SOTA agent frameworks (LangGraph, AutoGen, CrewAI), and
 deployment scenarios, my recommendation is:

 Create a separate AbstractAgent package that depends on AbstractCore, following the
 industry-standard pattern of separating orchestration (agents) from inference routing (LLM
 abstraction).

 However, this is NOT a simple decision. Let me walk through the analysis.

 ---
 Current State: AbstractCore Already Has Agents

 The BasicDeepSearch Precedent

 AbstractCore already contains significant agent-like code:

 | Component       | Lines  | Stateful             | Agent-Like                        |
 |-----------------|--------|----------------------|-----------------------------------|
 | BasicDeepSearch | ~2,174 | Yes (debug tracking) | YES - 6-stage autonomous pipeline |
 | BasicSession    | ~1,146 | Yes (full state)     | Foundation for agent state        |
 | BasicSummarizer | ~584   | Config only          | No                                |
 | BasicExtractor  | ~1,204 | Config only          | Partial (refine method)           |
 | BasicJudge      | ~776   | Config only          | No                                |

 BasicDeepSearch is essentially a ReAct-style agent:
 - Multi-stage autonomous pipeline (6 stages)
 - Reflexive refinement (self-improvement loops)
 - Parallel tool execution (ThreadPoolExecutor)
 - Dynamic decision making
 - State tracking (debug_info dict)

 This creates a tension: AbstractCore was designed to be "thin, stateless, horizontally scalable"
 but already contains a ~2,200 line stateful agent.

 ---
 SOTA Agent Framework Patterns (2025)

 Based on research from https://www.turing.com/resources/ai-agent-frameworks, https://softcery.com/l
 ab/top-14-ai-agent-frameworks-of-2025-a-founders-guide-to-building-smarter-systems, and https://ai.
 plainenglish.io/technical-comparison-of-autogen-crewai-langgraph-and-openai-swarm-1e4e9571d725:

 Framework Architecture Comparison

 | Framework        | Architecture                | LLM Coupling                | Package Separation
                      |
 |------------------|-----------------------------|-----------------------------|-------------------
 ---------------------|
 | LangGraph        | Graph-based state machines  | Part of LangChain ecosystem | langgraph separate
  from langchain-core |
 | AutoGen          | Multi-agent conversations   | LLM-agnostic (uses config)  | Standalone package
                      |
 | CrewAI           | Role-based teams            | Uses LiteLLM (separate pkg) | Standalone package
                      |
 | OpenAI Swarm     | Handoff-based orchestration | OpenAI SDK only             | Standalone package
                      |
 | Claude Agent SDK | Tool-use orchestration      | Anthropic SDK               | Standalone package
                      |

 Key SOTA Pattern: Separation

 Every major agent framework is SEPARATE from LLM provider libraries.

 - LangGraph ≠ LangChain (separate packages)
 - CrewAI uses LiteLLM (separate dependency)
 - AutoGen is provider-agnostic (config-based)
 - OpenAI Swarm is separate from openai SDK

 This separation exists because:
 1. Different scaling characteristics: Agents are stateful, LLM routing is stateless
 2. Different deployment targets: Agents can run on edge, LLM inference on GPU
 3. Clean dependencies: Don't want full LLM SDK just for agent orchestration
 4. Modularity: Mix and match agent frameworks with LLM providers

 ---
 The Two Components of an Agent

 Agents have fundamentally different components with different characteristics:

 ┌─────────────────────────────────────────────────────────────────────────┐
 │                         AGENT ANATOMY                                    │
 ├─────────────────────────────────────────────────────────────────────────┤
 │                                                                          │
 │  ┌────────────────────────────────────────────────────────────────────┐ │
 │  │  ORCHESTRATION LAYER (Stateful, Lightweight)                        │ │
 │  │  ────────────────────────────────────────────────────────────────── │ │
 │  │  • Decision making (what tool to call next)                        │ │
 │  │  • State management (scratchpad, history, iteration)               │ │
 │  │  • Tool coordination (execute, parse results)                       │ │
 │  │  • Human-in-the-loop (approvals, questions)                         │ │
 │  │  • Control flow (loops, conditionals, stopping)                     │ │
 │  │                                                                      │ │
 │  │  Characteristics:                                                    │ │
 │  │  - CPU-bound (no GPU required)                                      │ │
 │  │  - Memory: ~50-200MB                                                 │ │
 │  │  - Can run ANYWHERE (mobile, browser, edge, backend)               │ │
 │  └────────────────────────────────────────────────────────────────────┘ │
 │                               │                                          │
 │                               │ HTTP/SDK calls                          │
 │                               ▼                                          │
 │  ┌────────────────────────────────────────────────────────────────────┐ │
 │  │  INFERENCE LAYER (Stateless, Heavy)                                 │ │
 │  │  ────────────────────────────────────────────────────────────────── │ │
 │  │  • LLM generation (text, structured output)                        │ │
 │  │  • Token processing                                                 │ │
 │  │  • GPU memory management                                            │ │
 │  │  • Continuous batching                                              │ │
 │  │                                                                      │ │
 │  │  Characteristics:                                                    │ │
 │  │  - GPU-bound (requires NVIDIA/Apple Silicon)                        │ │
 │  │  - Memory: 8-128GB VRAM                                             │ │
 │  │  - Must run on GPU servers                                          │ │
 │  └────────────────────────────────────────────────────────────────────┘ │
 │                                                                          │
 └─────────────────────────────────────────────────────────────────────────┘

 Key Insight: The orchestration layer CAN and SHOULD be able to run separately from the inference
 layer.

 ---
 Deployment Scenarios Analysis

 Scenario A: Simple (Co-located on GPU) - Current

 ┌────────────────────────────────────────────────────────┐
 │  Single GPU Server                                      │
 │  ┌──────────┐ ┌─────────────┐ ┌────────┐ ┌──────────┐ │
 │  │ Frontend │→│ Agent Logic │→│Abstract│→│ LLM      │ │
 │  │          │ │             │ │ Core   │ │(Ollama)  │ │
 │  └──────────┘ └─────────────┘ └────────┘ └──────────┘ │
 │  Everything co-located - works fine                     │
 └────────────────────────────────────────────────────────┘

 Works with agents IN AbstractCore: ✅
 Works with separate AbstractAgent: ✅

 Scenario B: Mobile/Edge Agent

 ┌───────────────────┐         ┌─────────────────────────────┐
 │ Smartphone        │         │     GPU Server               │
 │ ┌───────────────┐ │  HTTP   │ ┌─────────────┐ ┌─────────┐ │
 │ │ Agent Logic   │ │────────▶│ │AbstractCore │→│ vLLM    │ │
 │ │ (thin client) │ │         │ │  (router)   │ │         │ │
 │ └───────────────┘ │         │ └─────────────┘ └─────────┘ │
 └───────────────────┘         └─────────────────────────────┘
 Agent on device, LLM on GPU

 Works with agents IN AbstractCore: ❌ (full AbstractCore on phone?)
 Works with separate AbstractAgent: ✅ (thin client)

 Scenario C: Frontend Agent (Browser/WASM)

 ┌──────────────────────────┐     ┌────────────────────────────┐
 │ Browser                  │     │ Backend (GPU)              │
 │ ┌──────────────────────┐ │     │ ┌─────────────┐ ┌────────┐ │
 │ │ Agent (JS/WASM)      │ │────▶│ │AbstractCore │→│  LLM   │ │
 │ │ orchestrates locally │ │HTTP │ │  (router)   │ │        │ │
 │ └──────────────────────┘ │     │ └─────────────┘ └────────┘ │
 └──────────────────────────┘     └────────────────────────────┘
 Agent in browser, LLM on backend

 Works with agents IN AbstractCore: ❌ (no Python in browser)
 Works with separate AbstractAgent: ✅ (could port to JS/WASM)

 Scenario D: Backend Agent Service (Production Scale)

 ┌─────────┐   ┌───────────────────┐   ┌───────────────────┐
 │Frontend │   │ Agent Service     │   │    GPU Fleet      │
 │  (CDN)  │──▶│ (stateful, scales │──▶│ AbstractCore+LLM  │
 │         │   │  with Redis/DB)   │   │ (per-server)      │
 └─────────┘   └───────────────────┘   └───────────────────┘
 Agents as separate microservice

 Works with agents IN AbstractCore: ⚠️ (forces coupling)
 Works with separate AbstractAgent: ✅ (clean microservice)

 Scenario E: Agent on Backend, LLM Fleet

 ┌─────────┐   ┌───────────────────────────────────────────┐
 │Frontend │   │ Backend Server (no GPU)                   │
 │  (CDN)  │──▶│ ┌─────────────┐ ┌───────────────────────┐ │
 │         │   │ │   Agent     │→│ AbstractCore (client) │ │
 └─────────┘   │ │   Logic     │ │ calls remote LLMs     │ │
               │ └─────────────┘ └───────────┬───────────┘ │
               └─────────────────────────────┼─────────────┘
                                             │ HTTP
               ┌─────────────────────────────▼─────────────┐
               │ GPU Fleet (vLLM, Ollama, etc.)            │
               │ ┌─────────────┐ ┌─────────────┐          │
               │ │ GPU Server 1│ │ GPU Server 2│  ...     │
               │ └─────────────┘ └─────────────┘          │
               └───────────────────────────────────────────┘
 Agent on backend without GPU, LLM on fleet

 Works with agents IN AbstractCore: ✅ (AbstractCore routes to fleet)
 Works with separate AbstractAgent: ✅ (depends on AbstractCore)

 ---
 Package Size & Dependency Analysis

 Current AbstractCore

 abstractcore/
 ├── core/          # ~3,500 lines (factory, session, retry)
 ├── providers/     # ~10,000 lines (8 providers)
 ├── processing/    # ~5,500 lines (summarizer, extractor, judge, deepsearch)
 ├── tools/         # ~1,500 lines (tool system)
 ├── media/         # ~3,000 lines (file handlers)
 ├── server/        # ~3,000 lines (FastAPI)
 ├── structured/    # ~1,500 lines (Pydantic integration)
 ├── events/        # ~500 lines
 ├── utils/         # ~2,000 lines (CLI, logging)
 └── apps/          # ~2,000 lines (CLI apps)

 Total: ~45,000 lines
 Dependencies: openai, anthropic, httpx, pydantic, rich, tenacity, ...
 Install size: ~50-100MB (with all providers)

 Proposed AbstractAgent

 abstractagent/
 ├── core/
 │   ├── state.py        # StateGraph, AgentState (~250 lines)
 │   ├── base.py         # BaseAgent ABC (~300 lines)
 │   └── scratchpad.py   # Memory management (~100 lines)
 ├── agents/
 │   ├── react.py        # ReActAgent (~400 lines)
 │   └── code_act.py     # CodeActAgent (~500 lines)
 ├── sandbox/
 │   └── executor.py     # Safe code execution (~300 lines)
 ├── tools/
 │   └── user.py         # Human-in-the-loop (~150 lines)
 └── client/
     └── remote.py       # Remote AbstractCore client (~200 lines)

 Total: ~2,200 lines (MUCH smaller)
 Dependencies: abstractcore OR httpx (client mode)
 Install size: ~5-10MB

 The Mobile/Edge Consideration

 On a smartphone or resource-constrained device:

 | Package                     | Size      | Dependencies                      | Viable?     |
 |-----------------------------|-----------|-----------------------------------|-------------|
 | Full AbstractCore           | ~50-100MB | Heavy (providers, media handlers) | ❌ Overkill |
 | AbstractAgent (client mode) | ~5-10MB   | Just httpx                        | ✅ Perfect  |
 | AbstractAgent (full mode)   | ~10-15MB  | + abstractcore-core               | ⚠️ Depends  |

 ---
 The Options

 Option 1: Agents IN AbstractCore

 from abstractcore.agents import ReActAgent, CodeActAgent
 from abstractcore import create_llm

 llm = create_llm("ollama", model="qwen3:4b")
 agent = ReActAgent(llm=llm, tools=[...])
 result = agent.run("Research quantum computing")

 Pros:
 - ✅ Single package install
 - ✅ Tighter integration with existing primitives
 - ✅ Easier for beginners
 - ✅ BasicDeepSearch precedent exists
 - ✅ Shared test infrastructure

 Cons:
 - ❌ Violates "thin, stateless" principle
 - ❌ Can't deploy agents separately
 - ❌ Full AbstractCore needed even for thin agent client
 - ❌ Not viable for mobile/browser scenarios
 - ❌ Package grows larger

 Option 2: Separate AbstractAgent Package

 from abstractagent import ReActAgent
 from abstractcore import create_llm

 # Option A: With local AbstractCore
 llm = create_llm("ollama", model="qwen3:4b")
 agent = ReActAgent(llm=llm, tools=[...])

 # Option B: With remote AbstractCore (thin client)
 from abstractagent.client import RemoteClient
 client = RemoteClient("https://abstractcore.yourdomain.com")
 agent = ReActAgent(client=client, tools=[...])

 Pros:
 - ✅ Clean separation of concerns
 - ✅ AbstractCore stays thin/stateless
 - ✅ Agents can run anywhere (mobile, browser via port, backend)
 - ✅ Follows SOTA pattern (LangGraph, CrewAI, AutoGen)
 - ✅ Remote client mode for edge/mobile
 - ✅ Smaller footprint for orchestration-only installs

 Cons:
 - ❌ Two packages to manage
 - ❌ Dependency coordination
 - ❌ More initial setup
 - ❌ Need to move BasicDeepSearch (breaking change)

 Option 3: Hybrid - Agents in AbstractCore with Remote Mode

 from abstractcore.agents import ReActAgent

 # Local mode
 agent = ReActAgent(provider="ollama", model="qwen3:4b")

 # Remote mode (doesn't load full providers)
 agent = ReActAgent.remote("https://abstractcore.yourdomain.com")

 Pros:
 - ✅ Single package conceptually
 - ✅ Remote mode reduces footprint
 - ✅ Gradual migration path

 Cons:
 - ❌ Complexity in one package
 - ❌ Still pulls AbstractCore deps even in remote mode
 - ❌ Confusing API

 ---
 Beyond SOTA: Optimal Architecture

 If I were designing from scratch with no constraints:

 ┌─────────────────────────────────────────────────────────────────────────┐
 │                    IDEAL LAYERED ARCHITECTURE                            │
 ├─────────────────────────────────────────────────────────────────────────┤
 │                                                                          │
 │  Layer 4: Applications                                                   │
 │  ┌──────────────────────────────────────────────────────────────────┐   │
 │  │  Digital Article, DeepSearch CLI, Custom Apps                     │   │
 │  │  (Use AbstractAgent + AbstractCore + domain-specific logic)       │   │
 │  └──────────────────────────────────────────────────────────────────┘   │
 │                               │                                          │
 │  Layer 3: Agent Orchestration (AbstractAgent)                           │
 │  ┌──────────────────────────────────────────────────────────────────┐   │
 │  │  ReActAgent, CodeActAgent, CustomAgent                            │   │
 │  │  • State machine / graph execution                                │   │
 │  │  • Tool coordination                                              │   │
 │  │  • Human-in-the-loop                                              │   │
 │  │  • Scratchpad / memory                                            │   │
 │  │  • Can run anywhere (edge, browser, backend)                      │   │
 │  │  Dependencies: abstractcore-client OR abstractcore (full)         │   │
 │  └──────────────────────────────────────────────────────────────────┘   │
 │                               │                                          │
 │  Layer 2: LLM Abstraction (AbstractCore)                                │
 │  ┌──────────────────────────────────────────────────────────────────┐   │
 │  │  Provider abstraction, Tool system, Sessions, Structured output   │   │
 │  │  • Stateless routing layer                                        │   │
 │  │  • OpenAI-compatible API                                          │   │
 │  │  • Horizontally scalable                                          │   │
 │  │  Dependencies: provider SDKs (openai, anthropic, httpx, etc.)     │   │
 │  └──────────────────────────────────────────────────────────────────┘   │
 │                               │                                          │
 │  Layer 1: Inference (vLLM, Ollama, Cloud APIs)                          │
 │  ┌──────────────────────────────────────────────────────────────────┐   │
 │  │  GPU servers, cloud providers                                     │   │
 │  │  • Actual LLM inference                                           │   │
 │  │  • Token generation                                               │   │
 │  │  • Vertically scaled (GPU memory)                                 │   │
 │  └──────────────────────────────────────────────────────────────────┘   │
 │                                                                          │
 └─────────────────────────────────────────────────────────────────────────┘

 Key Design Principles

 1. Separate orchestration from inference: Agent logic doesn't need GPU
 2. Protocol-based communication: Agents use HTTP/SDK, not direct coupling
 3. State externalization: Agent state in Redis/DB for scalability
 4. Client mode: AbstractAgent can work with remote AbstractCore
 5. Modular dependencies: Don't force full stack for simple use cases

 ---
 Migration Path for BasicDeepSearch

 If we go with Option 2 (separate AbstractAgent), what happens to BasicDeepSearch?

 Option A: Keep in AbstractCore (Pragmatic)

 - BasicDeepSearch stays where it is
 - New agents (ReAct, CodeAct) go to AbstractAgent
 - Document that BasicDeepSearch is a "processing pipeline" not an "agent"
 - Future: Deprecate and migrate in major version

 Option B: Move to AbstractAgent (Clean)

 - Create AbstractAgent package
 - Move BasicDeepSearch to abstractagent.pipelines.deepsearch
 - AbstractCore processing/basic_deepsearch.py becomes thin wrapper
 - Allows clean "agents are in AbstractAgent" story

 Option C: Dual-Home (Transitional)

 - Create AbstractAgent with new agents
 - BasicDeepSearch stays in AbstractCore for now
 - Gradually encourage users to use AbstractAgent for agent workloads
 - In v3.0, move BasicDeepSearch to AbstractAgent

 Recommendation: Option C - Pragmatic transition without breaking users

 ---
 CodeAct Sandbox Consideration

 You mentioned wanting sandbox=True by default but with an option to disable:

 class CodeActAgent:
     def __init__(
         self,
         llm,
         sandbox: bool = True,  # Safe by default
         sandbox_config: Optional[SandboxConfig] = None,
         allow_system_access: bool = False,  # Explicit opt-in
         ...
     ):
         if sandbox:
             self.executor = SandboxedExecutor(sandbox_config)
         elif allow_system_access:
             self.executor = DirectExecutor()  # Unrestricted
         else:
             raise ValueError(
                 "Must enable sandbox=True OR explicitly set allow_system_access=True"
             )

 This is the right pattern - safe defaults with explicit opt-in for power users.

 ---
 Final Decision: Separate AbstractAgent Repository

 Based on user requirements:
 - ✅ Dual deployment: Agents must run on BOTH frontend/client AND backend
 - ✅ Clean slate: Can break BasicDeepSearch (few users), just improve it
 - ✅ Separate repo: /Users/albou/projects/abstractagent/
 - ✅ Architecture first: Get it right, don't rush

 ---
 Finalized Architecture

 Repository Structure

 /Users/albou/projects/abstractagent/    # NEW REPO
 ├── .git/                               # Own git history
 ├── pyproject.toml                      # Modern Python packaging
 ├── README.md
 ├── CLAUDE.md                           # Project instructions
 ├── abstractagent/
 │   ├── __init__.py                     # Package exports
 │   ├── core/
 │   │   ├── __init__.py
 │   │   ├── state.py                    # StateGraph, AgentState, Node, Edge
 │   │   ├── base.py                     # BaseAgent ABC
 │   │   └── scratchpad.py               # Serializable memory/working memory
 │   ├── agents/
 │   │   ├── __init__.py
 │   │   ├── react.py                    # ReActAgent (Think-Act-Observe)
 │   │   ├── code_act.py                 # CodeActAgent (Python code execution)
 │   │   └── deepsearch.py               # DeepSearchAgent (migrated from AC)
 │   ├── sandbox/
 │   │   ├── __init__.py
 │   │   └── executor.py                 # Safe code execution (subprocess)
 │   ├── tools/
 │   │   ├── __init__.py
 │   │   └── user.py                     # ask_user, approve_action (HITL)
 │   └── client/
 │       ├── __init__.py
 │       └── remote.py                   # RemoteClient for thin/edge deployment
 └── tests/
     ├── __init__.py
     ├── agents/
     │   ├── test_react.py
     │   ├── test_code_act.py
     │   └── test_deepsearch.py
     └── client/
         └── test_remote.py

 Dependency Strategy

 # pyproject.toml
 [project]
 name = "abstractagent"
 dependencies = [
     "pydantic>=2.0",
     "httpx>=0.25.0",         # Always needed for HTTP
 ]

 [project.optional-dependencies]
 # Full mode: local LLM execution via AbstractCore
 full = [
     "abstractcore>=2.6.0",   # Full AbstractCore
 ]

 # Client mode: thin client for edge/frontend
 client = [
     # No abstractcore - just httpx (already in base deps)
 ]

 # Development
 dev = [
     "pytest>=7.0",
     "abstractcore>=2.6.0",   # Needed for testing
 ]

 Usage Modes

 # ═══════════════════════════════════════════════════════════════════════════
 # MODE 1: Full Mode (Backend with Local LLM)
 # ═══════════════════════════════════════════════════════════════════════════
 # pip install abstractagent[full]

 from abstractagent import ReActAgent, CodeActAgent
 from abstractcore import create_llm

 llm = create_llm("ollama", model="qwen3:4b")
 agent = ReActAgent(
     llm=llm,
     tools=[my_tool1, my_tool2],
     max_iterations=20
 )
 result = agent.run("Research quantum computing advances in 2025")

 # ═══════════════════════════════════════════════════════════════════════════
 # MODE 2: Client Mode (Frontend/Edge/Mobile)
 # ═══════════════════════════════════════════════════════════════════════════
 # pip install abstractagent  (base install, no abstractcore)

 from abstractagent import ReActAgent
 from abstractagent.client import RemoteClient

 # Connect to remote AbstractCore server (GPU fleet)
 client = RemoteClient(
     base_url="https://abstractcore.yourdomain.com",
     api_key="your-api-key"  # Optional auth
 )

 agent = ReActAgent(
     client=client,                    # Uses HTTP, not local LLM
     model="ollama/qwen3:4b",          # Model selection
     tools=[my_tool1, my_tool2],
     max_iterations=20
 )
 result = agent.run("Research quantum computing advances in 2025")

 # ═══════════════════════════════════════════════════════════════════════════
 # MODE 3: CodeAct with Sandbox Control
 # ═══════════════════════════════════════════════════════════════════════════

 from abstractagent import CodeActAgent
 from abstractagent.sandbox import SandboxConfig

 # Default: Sandboxed (safe)
 safe_agent = CodeActAgent(
     llm=llm,
     sandbox=True,  # DEFAULT
     sandbox_config=SandboxConfig(
         timeout=30,
         max_memory_mb=512,
         allowed_imports=["math", "json", "re", "datetime"]
     )
 )

 # Power user: Direct system access
 power_agent = CodeActAgent(
     llm=llm,
     sandbox=False,
     allow_system_access=True  # EXPLICIT opt-in required
 )

 Deployment Topologies Enabled

 ═══════════════════════════════════════════════════════════════════════════════
 TOPOLOGY 1: Simple Backend (Co-located)
 ═══════════════════════════════════════════════════════════════════════════════
 ┌─────────────────────────────────────────────────────────────────┐
 │  GPU Server                                                      │
 │  ┌──────────┐  ┌─────────────┐  ┌─────────────┐  ┌────────────┐ │
 │  │ Frontend │→ │ AbstractAgent│→ │ AbstractCore│→ │ Ollama/LLM │ │
 │  │  (React) │  │  (Full Mode) │  │  (router)   │  │            │ │
 │  └──────────┘  └─────────────┘  └─────────────┘  └────────────┘ │
 └─────────────────────────────────────────────────────────────────┘

 ═══════════════════════════════════════════════════════════════════════════════
 TOPOLOGY 2: Split Frontend/Backend
 ═══════════════════════════════════════════════════════════════════════════════
 ┌─────────────────────────────────┐      ┌───────────────────────────────────┐
 │  Frontend Server (no GPU)       │      │  GPU Fleet                         │
 │  ┌───────────────────────────┐  │      │  ┌─────────────┐  ┌─────────────┐ │
 │  │  AbstractAgent            │  │ HTTP │  │AbstractCore │→ │ vLLM        │ │
 │  │  (Client Mode)            │─────────│─▶│  (router)   │  │             │ │
 │  │  • Orchestrates locally   │  │      │  └─────────────┘  └─────────────┘ │
 │  │  • LLM calls via HTTP     │  │      │                                   │
 │  └───────────────────────────┘  │      │  ┌─────────────┐  ┌─────────────┐ │
 └─────────────────────────────────┘      │  │AbstractCore │→ │ Ollama      │ │
                                          │  │  (replica)  │  │             │ │
                                          │  └─────────────┘  └─────────────┘ │
                                          └───────────────────────────────────┘

 ═══════════════════════════════════════════════════════════════════════════════
 TOPOLOGY 3: Edge/Mobile Agent
 ═══════════════════════════════════════════════════════════════════════════════
 ┌───────────────────────┐         ┌─────────────────────────────────────────┐
 │  Mobile Device        │         │  Cloud GPU                               │
 │  ┌─────────────────┐  │  HTTPS  │  ┌─────────────┐  ┌───────────────────┐ │
 │  │ AbstractAgent   │  │─────────│─▶│AbstractCore │→ │ LLM Fleet         │ │
 │  │ (Client Mode)   │  │         │  │  (API)      │  │ (vLLM, Ollama)    │ │
 │  │ ~5MB footprint  │  │         │  └─────────────┘  └───────────────────┘ │
 │  └─────────────────┘  │         │                                         │
 └───────────────────────┘         └─────────────────────────────────────────┘

 ═══════════════════════════════════════════════════════════════════════════════
 TOPOLOGY 4: Browser Agent (Future - TypeScript port)
 ═══════════════════════════════════════════════════════════════════════════════
 ┌──────────────────────────────┐     ┌────────────────────────────────────────┐
 │  Browser                     │     │  Backend                                │
 │  ┌────────────────────────┐  │     │  ┌─────────────┐  ┌──────────────────┐ │
 │  │ AbstractAgent.js       │  │ HTTP│  │AbstractCore │→ │ LLM              │ │
 │  │ (TypeScript port)      │──│─────│─▶│  (API)      │  │                  │ │
 │  │ • Agent loop in browser│  │     │  └─────────────┘  └──────────────────┘ │
 │  │ • Tools execute locally│  │     │                                        │
 │  └────────────────────────┘  │     │                                        │
 └──────────────────────────────┘     └────────────────────────────────────────┘

 ---
 Implementation Plan

 Phase 1: Repository Setup (1 hour)

 1. Create /Users/albou/projects/abstractagent/
 2. git init
 3. Create pyproject.toml with dependency strategy
 4. Create CLAUDE.md with project instructions
 5. Setup basic package structure

 Phase 2: Core Infrastructure (4-6 hours)

 1. abstractagent/core/state.py - StateGraph, AgentState, Node, Edge
 2. abstractagent/core/base.py - BaseAgent ABC with common lifecycle
 3. abstractagent/core/scratchpad.py - Serializable working memory

 Phase 3: Remote Client (3-4 hours)

 1. abstractagent/client/remote.py - HTTP client for AbstractCore server
 2. OpenAI-compatible API calls
 3. Streaming support
 4. Authentication handling

 Phase 4: ReActAgent (4-5 hours)

 1. abstractagent/agents/react.py
 2. Think-Act-Observe loop
 3. Tool execution
 4. State serialization (save/load)
 5. Human-in-the-loop hooks

 Phase 5: CodeActAgent (5-6 hours)

 1. abstractagent/agents/code_act.py
 2. Python code generation
 3. abstractagent/sandbox/executor.py - Sandboxed execution
 4. allow_system_access=True option for power users
 5. Output capture and integration

 Phase 6: DeepSearchAgent Migration (4-5 hours)

 1. abstractagent/agents/deepsearch.py - Port from AbstractCore
 2. Improve/refactor during migration
 3. Update AbstractCore to deprecate original (or remove)

 Phase 7: Human-in-the-Loop (2-3 hours)

 1. abstractagent/tools/user.py
 2. ask_user() tool for agent questions
 3. approve_action() callback for sensitive operations
 4. State persistence during user wait

 Phase 8: Testing & Documentation (4-5 hours)

 1. Comprehensive tests (real implementations, no mocking)
 2. README with usage examples
 3. Architecture documentation

 Total Estimate: 27-35 hours

 ---
 Key Design Decisions

 1. State Graph Architecture (LangGraph-inspired)

 # abstractagent/core/state.py

 @dataclass
 class AgentState:
     """Fully serializable agent state for persistence and resume."""
     state_id: str
     current_node: str
     iteration: int
     messages: List[Dict]           # Conversation history
     scratchpad: Dict[str, Any]     # Working memory
     tool_results: List[Dict]       # Tool execution results
     status: str                    # "running", "waiting_user", "completed", "failed"
     created_at: datetime
     updated_at: datetime

     def save(self, path: str) -> None: ...
     def load(cls, path: str) -> "AgentState": ...
     def to_dict(self) -> Dict: ...

 class StateGraph:
     """Graph-based state machine for agent execution."""
     def __init__(self):
         self.nodes: Dict[str, Node] = {}
         self.edges: List[Edge] = []
         self.entry_point: str = None

     def add_node(self, name: str, action: Callable) -> None: ...
     def add_edge(self, from_node: str, to_node: str, condition: Optional[Callable] = None) -> None:
  ...
     def compile(self) -> "CompiledGraph": ...

 2. Dual-Mode LLM Access

 # abstractagent/core/base.py

 class BaseAgent(ABC):
     def __init__(
         self,
         llm: Optional["AbstractCoreInterface"] = None,  # Full mode
         client: Optional["RemoteClient"] = None,         # Client mode
         model: Optional[str] = None,                     # For client mode
         **kwargs
     ):
         if llm is not None:
             self._llm = llm
             self._mode = "full"
         elif client is not None:
             self._llm = ClientLLMWrapper(client, model)
             self._mode = "client"
         else:
             raise ValueError("Must provide either 'llm' or 'client'")

 3. Sandbox Configuration

 # abstractagent/sandbox/executor.py

 @dataclass
 class SandboxConfig:
     timeout: int = 30                           # Seconds
     max_memory_mb: int = 512                    # Memory limit
     allowed_imports: List[str] = field(default_factory=lambda: [
         "math", "json", "re", "datetime", "itertools", "collections"
     ])
     allow_network: bool = False                 # Block network by default
     allow_file_write: bool = False              # Block writes by default

 class SandboxedExecutor:
     """Execute Python code in isolated subprocess."""

 class DirectExecutor:
     """Execute Python code directly (power user mode)."""

 4. Human-in-the-Loop Pattern

 # abstractagent/tools/user.py

 async def ask_user(
     question: str,
     options: Optional[List[str]] = None,
     allow_free_text: bool = True
 ) -> str:
     """Pause agent and ask user for input."""

 def approve_action(
     action: str,
     details: Dict[str, Any],
     callback: Callable[[bool], None]
 ) -> None:
     """Request user approval for sensitive action."""

 ---
 Migration from AbstractCore BasicDeepSearch

 Since breaking changes are acceptable:

 1. Create abstractagent/agents/deepsearch.py with improved implementation
 2. Deprecate abstractcore/processing/basic_deepsearch.py with warning
 3. Update abstractcore/apps/deepsearch.py to use AbstractAgent
 4. Document migration path in CHANGELOG
 5. Remove original in AbstractCore v3.0 (or immediately if preferred)

 ---
 Sources

 - https://www.turing.com/resources/ai-agent-frameworks
 - https://softcery.com/lab/top-14-ai-agent-frameworks-of-2025-a-founders-guide-to-building-smarter-
 systems
 - https://ai.plainenglish.io/technical-comparison-of-autogen-crewai-langgraph-and-openai-swarm-1e4e
 9571d725
 - https://latenode.com/blog/langgraph-vs-autogen-vs-crewai-complete-ai-agent-framework-comparison-a
 rchitecture-analysis-2025
