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. Initially, we wanted to have Agents outside of AbstractCore - that was
 the primary reason from rewriting AbstractLLM into AbstractCore.. and we 
would then have AbstractMemory and AbstractAgent leveraging both 
AbstractCore and AbstractMemory... but now i am questioning that difference.
 in fact, i believe that integrating both ReAct and CodeAct agents in 
AbstractCore would now be fairly easy... so shouldn't we ? We have been 
depriving users of that functionality for some time, as we wanted to 
completely redesign the agentic memory.. but in reality, both ReAct and 
CodeAct do not need super advance memory, they already work with chat 
history, scratchpad and potentially a little bit of RAGs.. so i do not know 
what to do anymore. Please investigate the current code base, its 
complexity, its potentials and only then, make an informed recommendations 
that you must justify 

∴ Thinking…

  The user is asking me to investigate the AbstractCore codebase to understand 
  its current state, complexity, and potential for integrating agent 
  capabilities (ReAct and CodeAct). They're questioning whether to:
  1. Keep agents separate in a future AbstractAgent package
  2. Integrate ReAct and CodeAct agents directly into AbstractCore

  Let me start by exploring the codebase to understand:
  1. Current architecture and abstractions
  2. Existing agent-like functionality (if any)
  3. Tool system capabilities
  4. Session and memory management
  5. Complexity assessment

  I'll use the Explore agent to get a comprehensive understanding of the 
  codebase structure and then make an informed recommendation.

⏺ I'll investigate the AbstractCore codebase thoroughly to understand its
  architecture, complexity, and potential for agent integration. Let me
  explore multiple aspects in parallel.

⏺ Explore(Explore core architecture)
  ⎿  Prompt:                                                               
       Explore the AbstractCore codebase architecture comprehensively:

       1. Core abstractions: Examine abstractcore/core/,
       abstractcore/providers/ - understand the provider abstraction,
       session management, and how LLMs are created/used
       2. Tool system: Look at abstractcore/tools/ - understand how tools
       are defined, registered, and executed. This is critical for
       understanding agent potential.
       3. Processing capabilities: Examine abstractcore/processing/ - look
       for any existing agent-like patterns (BasicDeepSearch, summarizers,
       extractors, judges)
       4. Session/Memory: Check abstractcore/core/session.py and any
       memory-related code - understand conversation history management
       5. Architecture patterns: Identify design patterns used (factory,
       strategy, observer, etc.)

       Focus on understanding the complexity level and whether the codebase
       already has primitives that would make agent integration
       straightforward.

       Report: file counts, key abstractions, existing agent-like features,
       and overall architecture assessment.


  ⎿  > Explore the AbstractCore codebase architecture comprehensively:

     1. **Core abstractions**: Examine abstractcore/core/, 
     abstractcore/providers/ - understand the provider abstraction, session 
     management, and how LLMs are created/used

     2. **Tool system**: Look at abstractcore/tools/ - understand how tools are 
     defined, registered, and executed. This is critical for understanding agent 
     potential.

     3. **Processing capabilities**: Examine abstractcore/processing/ - look for 
     any existing agent-like patterns (BasicDeepSearch, summarizers, extractors, 
     judges)

     4. **Session/Memory**: Check abstractcore/core/session.py and any 
     memory-related code - understand conversation history management

     5. **Architecture patterns**: Identify design patterns used (factory, 
     strategy, observer, etc.)

     Focus on understanding the complexity level and whether the codebase already
      has primitives that would make agent integration straightforward.

     Report: file counts, key abstractions, existing agent-like features, and 
     overall architecture assessment. 
  ⎿  Bash(find /Users/albou/projects/abstractcore -type f -name "*.py" |
         head -100 | wc -l)
     Waiting…
  ⎿  Search(pattern: "**/*.py", path:
           "/Users/albou/projects/abstractcore/abstractcore")
  ⎿  Bash(ls -la /Users/albou/projects/abstractcore/abstractcore/)
     Waiting…
  ⎿  total 120                                                    
     drwxr-xr-x@ 23 albou  staff    736 Dec  2 00:25 .
     drwxr-xr-x@ 32 albou  staff   1024 Dec 10 16:54 ..
     -rw-r--r--@  1 albou  staff   6148 Nov  1 08:11 .DS_Store
     -rw-r--r--@  1 albou  staff  35689 Nov  8 14:44 README.md
     -rw-r--r--@  1 albou  staff   2476 Dec  2 00:25 __init__.py
     drwxr-xr-x@  4 albou  staff    128 Dec  9 20:20 __pycache__
     drwxr-xr-x@ 12 albou  staff    384 Dec 10 09:34 apps
     drwxr-xr-x@  7 albou  staff    224 Dec  2 00:25 architectures
     drwxr-xr-x@ 10 albou  staff    320 Nov  9 15:42 assets
     drwxr-xr-x@ 16 albou  staff    512 Nov  8 14:44 compression
     drwxr-xr-x@  8 albou  staff    256 Dec  2 00:25 config
     drwxr-xr-x@ 11 albou  staff    352 Dec 10 09:34 core
     -rw-r--r--@  1 albou  staff   8682 Dec  2 00:25 download.py
     drwxr-xr-x@  7 albou  staff    224 Dec  2 00:25 embeddings
     drwxr-xr-x@  5 albou  staff    160 Nov 27 11:26 events
     drwxr-xr-x@  5 albou  staff    160 Dec  2 00:25 exceptions
     drwxr-xr-x@ 13 albou  staff    416 Dec  2 00:25 media
     drwxr-xr-x@ 10 albou  staff    320 Dec 10 09:34 processing
     drwxr-xr-x@ 17 albou  staff    544 Dec 10 16:52 providers
     drwxr-xr-x@  6 albou  staff    192 Dec 10 16:52 server
     drwxr-xr-x@  7 albou  staff    224 Nov  8 14:44 structured
     drwxr-xr-x@ 12 albou  staff    384 Dec  2 00:25 tools
     drwxr-xr-x@ 14 albou  staff    448 Dec 10 16:52 utils
  ⎿  100

  ⎿    ⎿  Found 100 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/architectures/__init__
          .py
          /Users/albou/projects/abstractcore/abstractcore/architectures/enums.py
          /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/embeddings/__init__.py
          /Users/albou/projects/abstractcore/abstractcore/embeddings/models.py
          /Users/albou/projects/abstractcore/abstractcore/media/__init__.py
          /Users/albou/projects/abstractcore/abstractcore/media/base.py
          /Users/albou/projects/abstractcore/abstractcore/media/capabilities.py
          /Users/albou/projects/abstractcore/abstractcore/media/handlers/__init_
          _.py
          /Users/albou/projects/abstractcore/abstractcore/media/handlers/anthrop
          ic_handler.py
          /Users/albou/projects/abstractcore/abstractcore/media/processors/pdf_p
          rocessor.py
          /Users/albou/projects/abstractcore/abstractcore/media/utils/__init__.p
          y
          /Users/albou/projects/abstractcore/abstractcore/processing/basic_extra
          ctor.py
          /Users/albou/projects/abstractcore/abstractcore/processing/basic_summa
          rizer.py
          /Users/albou/projects/abstractcore/abstractcore/server/__init__.py
          /Users/albou/projects/abstractcore/abstractcore/structured/__init__.py
          /Users/albou/projects/abstractcore/abstractcore/structured/retry.py
          /Users/albou/projects/abstractcore/abstractcore/tools/__init__.py
          /Users/albou/projects/abstractcore/abstractcore/tools/core.py
          /Users/albou/projects/abstractcore/abstractcore/utils/message_preproce
          ssor.py
          /Users/albou/projects/abstractcore/abstractcore/utils/structured_loggi
          ng.py
          /Users/albou/projects/abstractcore/abstractcore/utils/token_utils.py
          /Users/albou/projects/abstractcore/abstractcore/apps/app_config_utils.
          py
          /Users/albou/projects/abstractcore/abstractcore/apps/intent.py
          /Users/albou/projects/abstractcore/abstractcore/processing/basic_inten
          t.py
          /Users/albou/projects/abstractcore/abstractcore/utils/cli.py
          /Users/albou/projects/abstractcore/abstractcore/structured/handler.py
          /Users/albou/projects/abstractcore/abstractcore/apps/__main__.py
          /Users/albou/projects/abstractcore/abstractcore/apps/deepsearch.py
          /Users/albou/projects/abstractcore/abstractcore/processing/__init__.py
          /Users/albou/projects/abstractcore/abstractcore/processing/basic_deeps
          earch.py
          /Users/albou/projects/abstractcore/abstractcore/compression/__init__.p
          y
          /Users/albou/projects/abstractcore/abstractcore/compression/analytics.
          py
          /Users/albou/projects/abstractcore/abstractcore/compression/cache.py
          /Users/albou/projects/abstractcore/abstractcore/compression/config.py
          /Users/albou/projects/abstractcore/abstractcore/compression/exceptions
          .py
          /Users/albou/projects/abstractcore/abstractcore/compression/glyph_proc
          essor.py
          /Users/albou/projects/abstractcore/abstractcore/compression/optimizer.
          py
          /Users/albou/projects/abstractcore/abstractcore/compression/orchestrat
          or.py
          /Users/albou/projects/abstractcore/abstractcore/compression/pil_text_r
          enderer.py
          /Users/albou/projects/abstractcore/abstractcore/compression/quality.py
          /Users/albou/projects/abstractcore/abstractcore/compression/text_forma
          tter.py
          /Users/albou/projects/abstractcore/abstractcore/compression/vision_com
          pressor.py
          /Users/albou/projects/abstractcore/abstractcore/config/main.py
          /Users/albou/projects/abstractcore/abstractcore/config/vision_config.p
          y
          /Users/albou/projects/abstractcore/abstractcore/media/auto_handler.py
          /Users/albou/projects/abstractcore/abstractcore/media/handlers/local_h
          andler.py
          /Users/albou/projects/abstractcore/abstractcore/media/handlers/openai_
          handler.py
          /Users/albou/projects/abstractcore/abstractcore/media/processors/__ini
          t__.py
          /Users/albou/projects/abstractcore/abstractcore/media/processors/direc
          t_pdf_processor.py
          /Users/albou/projects/abstractcore/abstractcore/media/processors/glyph
          _pdf_processor.py
          /Users/albou/projects/abstractcore/abstractcore/media/processors/image
          _processor.py
          /Users/albou/projects/abstractcore/abstractcore/media/processors/text_
          processor.py
          /Users/albou/projects/abstractcore/abstractcore/media/types.py
          /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/utils/vlm_token_calcul
          ator.py
          /Users/albou/projects/abstractcore/abstractcore/utils/trace_export.py
          /Users/albou/projects/abstractcore/abstractcore/utils/__init__.py
          /Users/albou/projects/abstractcore/abstractcore/events/__init__.py
          /Users/albou/projects/abstractcore/abstractcore/providers/base.py
          /Users/albou/projects/abstractcore/abstractcore/__init__.py
          /Users/albou/projects/abstractcore/abstractcore/config/__init__.py
          /Users/albou/projects/abstractcore/abstractcore/config/manager.py
          /Users/albou/projects/abstractcore/abstractcore/core/retry.py
          /Users/albou/projects/abstractcore/abstractcore/download.py
          /Users/albou/projects/abstractcore/abstractcore/embeddings/manager.py
          /Users/albou/projects/abstractcore/abstractcore/exceptions/__init__.py
          /Users/albou/projects/abstractcore/abstractcore/media/processors/offic
          e_processor.py
          /Users/albou/projects/abstractcore/abstractcore/media/utils/image_scal
          er.py
          /Users/albou/projects/abstractcore/abstractcore/media/vision_fallback.
          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/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
          /Users/albou/projects/abstractcore/abstractcore/utils/self_fixes.py
          /Users/albou/projects/abstractcore/abstractcore/architectures/detectio
          n.py
          /Users/albou/projects/abstractcore/abstractcore/apps/judge.py
          /Users/albou/projects/abstractcore/abstractcore/core/session.py
          /Users/albou/projects/abstractcore/abstractcore/processing/basic_judge
          .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
          /Users/albou/projects/abstractcore/abstractcore/server/app.py
  ⎿  Read(abstractcore/providers/base.py)

  ⎿  Read(abstractcore/core/factory.py)

  ⎿  Read(abstractcore/core/session.py)

  ⎿  Read 73 lines

  ⎿  Read 1146 lines

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  ⎿  Read(abstractcore/tools/__init__.py)

  ⎿  Read(abstractcore/tools/core.py)

  ⎿  Search(pattern: "**/*.py", path:
           "/Users/albou/projects/abstractcore/abstractcore/processing")
  ⎿  Read 101 lines

  ⎿  Read 170 lines

  ⎿    ⎿  Found 6 files                                                         
          /Users/albou/projects/abstractcore/abstractcore/processing/basic_extra
          ctor.py
          /Users/albou/projects/abstractcore/abstractcore/processing/basic_summa
          rizer.py
          /Users/albou/projects/abstractcore/abstractcore/processing/basic_inten
          t.py
          /Users/albou/projects/abstractcore/abstractcore/processing/__init__.py
          /Users/albou/projects/abstractcore/abstractcore/processing/basic_deeps
          earch.py
          /Users/albou/projects/abstractcore/abstractcore/processing/basic_judge
          .py
  ⎿  Read(abstractcore/processing/__init__.py)

  ⎿  Read(abstractcore/processing/basic_deepsearch.py)

  ⎿  Read(abstractcore/tools/registry.py)

  ⎿  Read 20 lines

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  ⎿  Read 2174 lines

  ⎿  Read(abstractcore/events/__init__.py)

  ⎿  Read(abstractcore/tools/handler.py)

  ⎿  Bash(find /Users/albou/projects/abstractcore -type f -name "*.py" | wc
         -l && find /Users/albou/projects/abstractcore/tests -type f -name
         "*.py" 2>/dev/null | wc -l)

     Waiting…
  ⎿  Read 431 lines

  ⎿  Read 308 lines

  ⎿  2798
     123
  ⎿  Read(abstractcore/core/interface.py)

  ⎿  Read(abstractcore/core/types.py)

  ⎿  Search(pattern: "**/*.py", path:
           "/Users/albou/projects/abstractcore/abstractcore/providers")
  ⎿  Read 357 lines

  ⎿  Read 138 lines

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

       ---
       AbstractCore Architecture Analysis Report

       Executive Summary

       AbstractCore is a production-ready, provider-agnostic LLM framework
       with approximately 100+ Python source files and 123 test files. The
       codebase demonstrates exceptional architectural maturity with
       well-established primitives that would make agent integration 
       straightforward.

       ---
       File Statistics

       | Category                 | Count
                                                  |
       |--------------------------|-----------------------------------------
       -------------------------------------------|
       | Total Python files       | ~2,798 (includes project root and
       subprojects)                                     |
       | Core package files       | ~100
                                                  |
       | Test files               | 123
                                                  |
       | Provider implementations | 8 (OpenAI, Anthropic, Ollama, LMStudio,
       MLX, HuggingFace, vLLM, OpenAI-Compatible) |

       ---
       Key Abstractions

       1. Provider Abstraction (abstractcore/providers/base.py)

       Core Class: BaseProvider(AbstractCoreInterface, ABC) (~1,600 lines)

       Key Features:
       - Abstract _generate_internal() for provider-specific implementations
       - Built-in telemetry via _track_generation() and _track_tool_call()
       - Native async support with agenerate() and _agenerate_internal()
       - Interaction tracing with ring buffer (_traces: deque)
       - Retry management with circuit breaker pattern
       - Automatic tool execution via _execute_tools_with_events()
       - Media processing pipeline
       - Structured output support with Pydantic models

       Agent-Relevant Methods:
       - generate() / agenerate() - Main generation interface
       - execute_tools=True/False parameter for tool execution control
       - _handle_tools_with_structured_output() - Hybrid tools + structured
       output

       2. Session Management (abstractcore/core/session.py)

       Core Class: BasicSession (~1,150 lines)

       Key Features:
       - Conversation history management with Message dataclass
       - Auto-compaction when token threshold exceeded
       - Persistence (save/load to JSON)
       - Built-in analytics: generate_summary(), generate_assessment(),
       extract_facts(), analyze_intents()
       - Native async support with agenerate() and _async_session_stream()
       - Tool registration with _register_tools()
       - Interaction tracing support (enable_tracing,
       get_interaction_history())

       Agent-Relevant Capabilities:
       - Full conversation context management
       - Session-level tool injection
       - Memory compaction for long-running sessions
       - Trace capture for observability

       3. Tool System (abstractcore/tools/)

       Core Components:

       | Component            | Purpose
                    |
       |----------------------|---------------------------------------------
       -------------|
       | ToolDefinition       | Tool schema with name, description,
       parameters, function |
       | ToolCall             | Represents LLM's request to execute a tool
                    |
       | ToolResult           | Result of tool execution (output, error,
       success)        |
       | ToolCallResponse     | Response containing content + tool calls
                    |
       | ToolRegistry         | Global registry for managing tools
                    |
       | UniversalToolHandler | Architecture-agnostic tool
       parsing/formatting            |

       Key Features:
       - @tool decorator for easy tool definition with enhanced metadata
       (tags, when_to_use, examples)
       - Automatic tool conversion from functions
       - Global registry pattern for tool discovery
       - Event emission for tool execution (TOOL_STARTED, TOOL_COMPLETED,
       TOOL_PROGRESS)
       - Architecture-specific parsing (OpenAI native, Qwen3, etc.)

       Tool Execution Flow:
       # Registration
       register_tool(my_function)

       # Execution
       results = execute_tools(tool_calls)  # Returns List[ToolResult]

       4. Event System (abstractcore/events/__init__.py)

       Core Components:
       - EventType enum with 13 event types
       - Event dataclass (type, timestamp, data, source)
       - EventEmitter mixin class
       - GlobalEventBus singleton

       Event Types:
       | Event                        | Purpose
       |
       |------------------------------|-------------------------------------
       |
       | GENERATION_STARTED           | LLM generation begins
       |
       | GENERATION_COMPLETED         | LLM generation finishes
       |
       | TOOL_STARTED                 | Tool execution begins
       |
       | TOOL_PROGRESS                | Real-time tool progress
       |
       | TOOL_COMPLETED               | Tool execution finishes
       |
       | ERROR                        | Any error occurred
       |
       | RETRY_ATTEMPTED              | Retry process starts
       |
       | RETRY_EXHAUSTED              | All retries failed
       |
       | VALIDATION_FAILED            | Structured output validation failed
       |
       | SESSION_CREATED/CLEARED      | Session lifecycle
       |
       | COMPACTION_STARTED/COMPLETED | Memory compaction
       |

       Agent-Relevant: Native async event emission with on_async() and
       emit_async() - perfect for agent progress tracking.

       ---
       Existing Agent-Like Features

       1. BasicDeepSearch (abstractcore/processing/basic_deepsearch.py) - 
       2,174 lines

       This is the closest existing implementation to a full agent:

       Architecture:
       - Four-stage autonomous pipeline:
         a. Planning: Decompose query into structured research plan
         b. Question Development: Generate specific search queries
         c. Web Exploration: Execute parallel searches, gather evidence
         d. Report Generation: Synthesize findings into structured report
         e. (Optional) Verification: Fact-checking
         f. (Optional) Reflexive Refinement: Gap analysis and iterative
       improvement

       Key Components:
       - ResearchPlan, ResearchSubTask, ResearchFinding, ResearchReport -
       Structured data models
       - SourceManager - Source collection with deduplication and limits
       - CitationValidator - Citation validation and enhancement
       - Parallel web exploration with ThreadPoolExecutor
       - LLM-based relevance assessment
       - Structured output via Pydantic models

       Agent Patterns Demonstrated:
       - Multi-step autonomous workflow
       - Planning and decomposition
       - Tool usage (web_search, fetch_url)
       - Iterative refinement (reflexive mode)
       - Structured output generation

       2. Processing Components

       | Component           | Purpose                            | Agent
       Relevance          |
       |---------------------|------------------------------------|---------
       -----------------|
       | BasicSummarizer     | Chat history compaction            | Memory
       management        |
       | BasicExtractor      | Fact extraction (triples, JSON-LD) |
       Knowledge extraction     |
       | BasicJudge          | Quality assessment                 |
       Self-evaluation          |
       | BasicIntentAnalyzer | Intent detection                   |
       Understanding user goals |

       3. Common Tools (abstractcore/tools/common_tools.py)

       Pre-built tools ready for agent use:
       - web_search(query, num_results) - DuckDuckGo search
       - fetch_url(url, timeout) - Web page content extraction

       ---
       Design Patterns Identified

       | Pattern                 | Implementation
                   | Location                                 |
       |-------------------------|------------------------------------------
       ------------|------------------------------------------|
       | Factory                 | create_llm()
                   | core/factory.py                          |
       | Strategy                | Provider abstraction, media handlers
                   | providers/base.py                        |
       | Observer                | Event system
                   | events/__init__.py                       |
       | Registry                | Tool registry, Provider registry
                   | tools/registry.py, providers/registry.py |
       | Singleton               | GlobalEventBus, global tool registry
                   | events/__init__.py, tools/registry.py    |
       | Template Method         | generate_with_telemetry() calls
       _generate_internal() | providers/base.py                        |
       | Chain of Responsibility | Retry manager with circuit breaker
                   | core/retry.py                            |
       | Decorator               | @tool for tool definition
                   | tools/core.py                            |

       ---
       Architecture Assessment for Agent Integration

       Strengths (Agent-Ready Primitives)

       1. Complete Tool Infrastructure:
         - Tool definition, registration, execution, parsing all exist
         - Event-based progress tracking
         - Multi-format support (native API, prompted)
       2. Session Management:
         - Full conversation history
         - Auto-compaction for long sessions
         - Trace capture for debugging
       3. Async Support:
         - Native async for all providers (agenerate())
         - Async events (emit_async(), on_async())
         - 6-7.5x performance improvement for concurrent operations
       4. Event System:
         - TOOL_STARTED, TOOL_PROGRESS, TOOL_COMPLETED events
         - Perfect for agent progress reporting
         - Global and local event buses
       5. Existing Agent Example:
         - BasicDeepSearch demonstrates multi-step autonomous workflows
         - Patterns for planning, execution, and refinement

       Complexity Level: Medium-High (Well-Structured)

       The codebase is sophisticated (~100+ files, ~45,000 LOC) but
       extremely well-organized:
       - Clean separation of concerns
       - Consistent patterns across components
       - Comprehensive documentation in CLAUDE.md
       - 95% test-to-code ratio

       Recommendations for Agent Integration

       Option A: Extend BasicDeepSearch Pattern
       - Use BasicDeepSearch as template
       - Same multi-stage pipeline approach
       - Add custom tool sets

       Option B: Build on BasicSession
       - Session already handles tools, history, tracing
       - Add agent loop on top
       - Use existing event system for progress

       Option C: New Agent Module
       - Create abstractcore/agents/ module
       - BaseAgent class using existing primitives
       - Tool orchestration layer

       Key Primitives Available for Agents

       # Tool definition
       from abstractcore.tools import tool, ToolDefinition, register_tool

       # Tool execution
       from abstractcore.tools import execute_tools  # List[ToolCall] -> 
       List[ToolResult]

       # Event tracking
       from abstractcore.events import GlobalEventBus, EventType

       # Session management
       from abstractcore.core.session import BasicSession

       # LLM generation
       from abstractcore import create_llm

       ---
       Conclusion

       AbstractCore provides all the foundational primitives needed for
       agent implementation:

       1. Tool System: Complete with registration, execution, parsing, and
       events
       2. Session Management: Full conversation context with memory
       management
       3. Event System: Async-compatible progress tracking
       4. Provider Abstraction: Unified interface across 8 providers
       5. Existing Example: BasicDeepSearch demonstrates the pattern

       Agent integration would be straightforward - the architecture is
       designed for extensibility, and the existing BasicDeepSearch class
       serves as an excellent reference implementation for autonomous
       multi-step workflows with tool use.






























  ⎿  Done (18 tool uses · 155.4k tokens · 2m 0s)

⏺ Explore(Explore tool/agent patterns)
  ⎿  Prompt:                                                               
       Investigate the tool system and existing "agentic" patterns in
       AbstractCore:

       1. Tool definitions: Search for how tools are defined - look for
       ToolDefinition, @tool decorator, tool handlers
       2. Tool execution: How are tools called? Is there a tool execution
       loop? Look for patterns like "think -> act -> observe"
       3. BasicDeepSearch: According to CLAUDE.md, this implements ReAct +
       Tree of Thoughts patterns - examine
       abstractcore/processing/basic_deepsearch.py thoroughly
       4. Event system: Look at abstractcore/events/ - understand how
       events flow, which could be used for agent loops
       5. Existing agent patterns: Search for "ReAct", "CodeAct", "agent",
       "loop", "iteration" in the codebase

       Key question: Does AbstractCore already have agent-like behavior
       that just needs to be formalized?
  ⎿  > Investigate the tool system and existing "agentic" patterns in 
     AbstractCore:

     1. **Tool definitions**: Search for how tools are defined - look for 
     ToolDefinition, @tool decorator, tool handlers
     2. **Tool execution**: How are tools called? Is there a tool execution loop?
      Look for patterns like "think -> act -> observe"
     3. **BasicDeepSearch**: According to CLAUDE.md, this implements ReAct + Tree
      of Thoughts patterns - examine abstractcore/processing/basic_deepsearch.py 
     thoroughly
     4. **Event system**: Look at abstractcore/events/ - understand how events 
     flow, which could be used for agent loops
     5. **Existing agent patterns**: Search for "ReAct", "CodeAct", "agent", 
     "loop", "iteration" in the codebase

     Key question: Does AbstractCore already have agent-like behavior that just 
     needs to be formalized? 
  ⎿  Search(pattern: "**/*tool*.py", path:
           "/Users/albou/projects/abstractcore")
  ⎿  Search(pattern: "**/agent*.py", path:
           "/Users/albou/projects/abstractcore")
  ⎿  Search(pattern: "ToolDefinition|@tool|tool_handler", path:
           "/Users/albou/projects/abstractcore")
  ⎿  Search(pattern: "ReAct|CodeAct|agent.*loop|iteration.*loop", path:
           "/Users/albou/projects/abstractcore")
  ⎿    ⎿  Found 0 files

  ⎿    ⎿  Found 33 files                                             
          docs/backlog/completed/011-vllm-provider-implementation.md
          docs/prerequisites.md
          abstractcore/server/app.py
          docs/backlog/completed/011-provider-request-vllm.md
          abstractcore/tools/common_tools.py
          tests/embeddings/test_embeddings_llm_integration.py
          tests/embeddings/test_embeddings_matrix_operations.py
          tests/embeddings/test_embeddings_no_mock.py
          tests/embeddings/test_embeddings_real.py
          tests/embeddings/test_embeddings_semantic_validation.py
          abstractcore/tools/README.md
          abstractcore/utils/README.md
          abstractcore/processing/README.md
          abstractcore/embeddings/README.md
          docs/research/Agentic Deep Research Best Practices Guide.md
          tests/structured/test_comprehensive_native.py
          tests/structured/test_huggingface_structured.py
          docs/research/abstract-series.md
          docs/comparison.md
          docs/embeddings.md
          docs/apps/basic-intent.md
          tests/texts/intent2.json
          abstractcore/utils/cli.py
          abstractcore/processing/basic_intent.py
          examples/embeddings/simple_embeddings_examples.py
          examples/performance/matrix_operations_demo.py
          examples/rag/complete_rag_example.py
          examples/embeddings/embeddings_benchmark.py
          examples/embeddings/embeddings_demo.py
          docs/research/CODEX_GUIDE.md
          docs/research/Unified LLM Tool Call Handling.md
          docs/archive/proposals.md
          docs/archive/embeddings-endpoint.md
  ⎿    ⎿  Found 47 files                                                        
          llms-full.txt
          llms.txt
          abstractcore/providers/vllm_provider.py
          abstractcore/providers/openai_compatible_provider.py
          README.md
          CHANGELOG.md
          abstractcore/core/session.py
          abstractcore/tools/common_tools.py
          abstractcore/tools/handler.py
          abstractcore/tools/parser.py
          abstractcore/tools/registry.py
          abstractcore/providers/ollama_provider.py
          abstractcore/providers/openai_provider.py
          abstractcore/providers/anthropic_provider.py
          abstractcore/providers/lmstudio_provider.py
          abstractcore/providers/base.py
          docs/getting-started.md
          docs/backlog/008-mcp-integration.md
          tests/tools/test_tool_calling.py
          tests/tools/test_tool_execution_separation.py
          tests/streaming/test_streaming_enhancements.py
          tests/streaming/test_unified_streaming.py
          tests/streaming/test_critical_streaming_tool_fix.py
          abstractcore/tools/README.md
          abstractcore/providers/mlx_provider.py
          abstractcore/providers/huggingface_provider.py
          abstractcore/providers/README.md
          abstractcore/README.md
          docs/troubleshooting.md
          docs/chat-compaction.md
          tests/token_terminology/test_layer4_production.py
          tests/test_comprehensive_events.py
          tests/test_core_components.py
          tests/reports/2025-10-11_06-54-13_tool_execution_separation_validation
          .md
          tests/integration/test_system_integration.py
          examples/learning_path/03_tool_calling.py
          examples/learning_path/04_unified_streaming.py
          examples/learning_path/06_production_patterns.py
          examples/tools/tool_usage_advanced.py
          examples/tools/README.md
          examples/performance/README.md
          docs/tool-calling.md
          docs/research/Unified LLM Tool Call Handling.md
          docs/archive/proposals.md
          docs/backlog/multi-provider-requests.md
          abstractcore/tools/core.py
          abstractcore/tools/__init__.py
  ⎿    ⎿  Found 100 files                                                       
          /Users/albou/projects/abstractcore/.venv/lib/python3.12/site-packages/
          pip/_internal/utils/setuptools_build.py
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          pydantic/tools.py
          /Users/albou/projects/abstractcore/.venv/lib/python3.12/site-packages/
          pydantic/deprecated/tools.py
          /Users/albou/projects/abstractcore/.venv/lib/python3.12/site-packages/
          uvicorn/protocols/http/httptools_impl.py
          /Users/albou/projects/abstractcore/.venv/lib/python3.12/site-packages/
          tqdm/contrib/itertools.py
          /Users/albou/projects/abstractcore/.venv/lib/python3.12/site-packages/
          openai/lib/_tools.py
          /Users/albou/projects/abstractcore/.venv/lib/python3.12/site-packages/
          openai/types/beta/assistant_tool.py
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          openai/types/beta/assistant_tool_choice.py
          /Users/albou/projects/abstractcore/.venv/lib/python3.12/site-packages/
          openai/types/beta/assistant_tool_choice_function.py
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          openai/types/beta/assistant_tool_choice_function_param.py
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          openai/types/beta/assistant_tool_choice_option.py
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          openai/types/beta/assistant_tool_param.py
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          openai/types/beta/file_search_tool.py
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          openai/types/beta/file_search_tool_param.py
          /Users/albou/projects/abstractcore/.venv/lib/python3.12/site-packages/
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          openai/types/beta/threads/run_submit_tool_outputs_params.py
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          openai/types/beta/threads/runs/tool_call.py
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          openai/types/beta/threads/runs/tool_calls_step_details.py
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          openai/types/chat/chat_completion_allowed_tool_choice_param.py
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          openai/types/chat/chat_completion_custom_tool_param.py
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          openai/types/chat/chat_completion_function_tool.py
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          openai/types/chat/chat_completion_function_tool_param.py
          /Users/albou/projects/abstractcore/.venv/lib/python3.12/site-packages/
          openai/types/chat/chat_completion_message_custom_tool_call.py
          /Users/albou/projects/abstractcore/.venv/lib/python3.12/site-packages/
          openai/types/chat/chat_completion_message_custom_tool_call_param.py
          /Users/albou/projects/abstractcore/.venv/lib/python3.12/site-packages/
          openai/types/chat/chat_completion_message_function_tool_call.py
          /Users/albou/projects/abstractcore/.venv/lib/python3.12/site-packages/
          openai/types/chat/chat_completion_message_function_tool_call_param.py
          /Users/albou/projects/abstractcore/.venv/lib/python3.12/site-packages/
          openai/types/chat/chat_completion_message_tool_call.py
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          openai/types/chat/parsed_function_tool_call.py
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          openai/types/realtime/mcp_list_tools_failed.py
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          openai/types/responses/computer_tool.py
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          y
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          aram.py
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          abstractcore/providers/vllm_provider.py
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          abstractcore/providers/openai_compatible_provider.py
          abstractcore/tools/tag_rewriter.py
          abstractcore/tools/handler.py
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          abstractcore/utils/cli.py
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          abstractcore/tools/core.py
          abstractcore/tools/__init__.py
          abstractcore/core/types.py
          abstractcore/assets/session_schema.json
  ⎿  Read(abstractcore/providers/base.py · lines 1-400)

  ⎿  Read(abstractcore/core/session.py · lines 1-300)

  ⎿  Read 400 lines

  ⎿  Read 300 lines

  ⎿  Read(abstractcore/providers/base.py · lines 400-899)

  ⎿  Search(pattern: "tool.*loop|execute.*tools|_process_tool|tool_result|f
           inish_reason", path:
           "/Users/albou/projects/abstractcore/abstractcore", output_mode:
           "content", head_limit: 80)
  ⎿  Read 500 lines

  ⎿    ⎿  Found 80 lines                                                        
          /Users/albou/projects/abstractcore/abstractcore/tools/registry.py-220-
          /Users/albou/projects/abstractcore/abstractcore/tools/registry.py-221-
                      return error_result
          /Users/albou/projects/abstractcore/abstractcore/tools/registry.py-222-
          abstractcore/tools/registry.py:223:    def execute_tools(self,
          tool_calls: List[ToolCall]) -> List[ToolResult]:
          /Users/albou/projects/abstractcore/abstractcore/tools/registry.py-224-
                  """
          /Users/albou/projects/abstractcore/abstractcore/tools/registry.py-225-
                  Execute multiple tool calls.
          /Users/albou/projects/abstractcore/abstractcore/tools/registry.py-226-
          --
          /Users/albou/projects/abstractcore/abstractcore/tools/registry.py-286-
              return _global_registry.execute_tool(tool_call)
          /Users/albou/projects/abstractcore/abstractcore/tools/registry.py-287-
          /Users/albou/projects/abstractcore/abstractcore/tools/registry.py-288-
          abstractcore/tools/registry.py:289:def execute_tools(tool_calls:
          List[ToolCall]) -> List[ToolResult]:
          /Users/albou/projects/abstractcore/abstractcore/tools/registry.py-290-
              """
          /Users/albou/projects/abstractcore/abstractcore/tools/registry.py-291-
              Execute multiple tool calls using the global registry.
          /Users/albou/projects/abstractcore/abstractcore/tools/registry.py-292-
          --
          abstractcore/tools/registry.py-296-    Returns:
          /Users/albou/projects/abstractcore/abstractcore/tools/registry.py-297-
                  List of ToolResults
          /Users/albou/projects/abstractcore/abstractcore/tools/registry.py-298-
              """
          abstractcore/tools/registry.py:299:    return
          _global_registry.execute_tools(tool_calls)
          /Users/albou/projects/abstractcore/abstractcore/tools/registry.py-300-
          /Users/albou/projects/abstractcore/abstractcore/tools/registry.py-301-
          abstractcore/tools/registry.py-302-def clear_registry():
          --
          /Users/albou/projects/abstractcore/abstractcore/tools/__init__.py-70-
             register_tool,
          /Users/albou/projects/abstractcore/abstractcore/tools/__init__.py-71-
             get_registry,
          /Users/albou/projects/abstractcore/abstractcore/tools/__init__.py-72-
             execute_tool,
          abstractcore/tools/__init__.py:73:    execute_tools,
          /Users/albou/projects/abstractcore/abstractcore/tools/__init__.py-74-
             clear_registry
          /Users/albou/projects/abstractcore/abstractcore/tools/__init__.py-75-)
          /Users/albou/projects/abstractcore/abstractcore/tools/__init__.py-76-
          --
          /Users/albou/projects/abstractcore/abstractcore/tools/__init__.py-96-
             "register_tool",
          /Users/albou/projects/abstractcore/abstractcore/tools/__init__.py-97-
             "get_registry",
          /Users/albou/projects/abstractcore/abstractcore/tools/__init__.py-98-
             "execute_tool",
          abstractcore/tools/__init__.py:99:    "execute_tools",
          /Users/albou/projects/abstractcore/abstractcore/tools/__init__.py-100-
              "clear_registry",
          /Users/albou/projects/abstractcore/abstractcore/tools/__init__.py-101-
          ]
          --
          /Users/albou/projects/abstractcore/abstractcore/tools/README.md-31-
          /Users/albou/projects/abstractcore/abstractcore/tools/README.md-32--
          **How do I define a tool?** → See [Tool Calling](../../tool-calling.md)
          /Users/albou/projects/abstractcore/abstractcore/tools/README.md-33--
          **How do I register a tool?** → See Registering Tools
          abstractcore/tools/README.md:34:- **How do I execute tools?** → See
          [Tool Calling](../../tool-calling.md)
          /Users/albou/projects/abstractcore/abstractcore/tools/README.md-35--
          **How do I use tools with an LLM?** → See Using with LLM
          /Users/albou/projects/abstractcore/abstractcore/tools/README.md-36--
          **How do I convert tool formats?** → See Syntax Rewriting
          /Users/albou/projects/abstractcore/abstractcore/tools/README.md-37--
          **How do I handle tool errors?** → See Error Handling
          --
          abstractcore/tools/README.md-356-    def execute_tool(self, tool_call:
           ToolCall) -> ToolResult:
          /Users/albou/projects/abstractcore/abstractcore/tools/README.md-357-
                """Execute a tool call with error handling"""
          /Users/albou/projects/abstractcore/abstractcore/tools/README.md-358-
          abstractcore/tools/README.md:359:    def execute_tools(self,
          tool_calls: List[ToolCall]) -> List[ToolResult]:
          /Users/albou/projects/abstractcore/abstractcore/tools/README.md-360-
                """Execute multiple tool calls"""
          /Users/albou/projects/abstractcore/abstractcore/tools/README.md-361-``
          `
          /Users/albou/projects/abstractcore/abstractcore/tools/README.md-362-
          --
          /Users/albou/projects/abstractcore/abstractcore/tools/README.md-833-
          abstractcore/tools/README.md-834-**Batch Execution**:
          /Users/albou/projects/abstractcore/abstractcore/tools/README.md-835-``
          `python
          abstractcore/tools/README.md:836:from abstractcore.tools import
          execute_tools
          /Users/albou/projects/abstractcore/abstractcore/tools/README.md-837-
          /Users/albou/projects/abstractcore/abstractcore/tools/README.md-838-to
          ol_calls = [
          abstractcore/tools/README.md-839-    ToolCall(name="add",
          arguments={"a": 5, "b": 3}),
          abstractcore/tools/README.md-840-    ToolCall(name="multiply",
          arguments={"x": 4, "y": 2})
          /Users/albou/projects/abstractcore/abstractcore/tools/README.md-841-]
          /Users/albou/projects/abstractcore/abstractcore/tools/README.md-842-
          abstractcore/tools/README.md:843:results = execute_tools(tool_calls)
          abstractcore/tools/README.md-844-for result in results:
          /Users/albou/projects/abstractcore/abstractcore/tools/README.md-845-
            print(result.output)
          /Users/albou/projects/abstractcore/abstractcore/tools/README.md-846-``
          `
          --
          abstractcore/tools/README.md-870-**With Tool Execution Loop**:
          /Users/albou/projects/abstractcore/abstractcore/tools/README.md-871-``
          `python
          /Users/albou/projects/abstractcore/abstractcore/tools/README.md-872-fr
          om abstractcore import create_llm
          abstractcore/tools/README.md:873:from abstractcore.tools import tool,
          execute_tools
          /Users/albou/projects/abstractcore/abstractcore/tools/README.md-874-
          /Users/albou/projects/abstractcore/abstractcore/tools/README.md-875-@t
          ool
          abstractcore/tools/README.md-876-def search_web(query: str) -> str:
          --
          /Users/albou/projects/abstractcore/abstractcore/tools/README.md-888-
          /Users/albou/projects/abstractcore/abstractcore/tools/README.md-889-#
          Execute tool calls if present
  ⎿  Bash(find /Users/albou/projects/abstractcore/abstractcore/processing
         -name "*.py" -type f | head -20)
     Waiting…
  ⎿  Search(pattern: "ReAct|Tree of
           Thoughts|Think.*Act.*Observe|thought_tree|research.*loop", path:
           "/Users/albou/projects/abstractcore", output_mode: "content",
           head_limit: 80)

  ⎿  /Users/albou/projects/abstractcore/abstractcore/processing/basic_summarizer
     .py
     /Users/albou/projects/abstractcore/abstractcore/processing/basic_extractor.
     py
     /Users/albou/projects/abstractcore/abstractcore/processing/basic_judge.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_intent.py
  ⎿    ⎿  Found 80 lines                                                        
          /Users/albou/projects/abstractcore/abstractcore/tools/common_tools.py-
          263-        {
          abstractcore/tools/common_tools.py-264-            "description":
          "Show content for specific patterns (default behavior)",
          abstractcore/tools/common_tools.py-265-            "arguments": {
          abstractcore/tools/common_tools.py:266:                "pattern":
          "generate.*tools|create_react_cycle",
          abstractcore/tools/common_tools.py-267-                "path":
          "abstractcore/session.py",
          abstractcore/tools/common_tools.py-268-                "head_limit": 5
          /Users/albou/projects/abstractcore/abstractcore/tools/common_tools.py-
          269-            }
          --
          /Users/albou/projects/abstractcore/abstractcore/tools/common_tools.py-
          300-        Search results in the specified format or error message
          /Users/albou/projects/abstractcore/abstractcore/tools/common_tools.py-
          301-
          abstractcore/tools/common_tools.py-302-    Examples:
          abstractcore/tools/common_tools.py:303:
          search_files("generate.*react|create_react_cycle",
          "abstractcore/session.py")  # Returns matching lines with content
          (default)
          /Users/albou/projects/abstractcore/abstractcore/tools/common_tools.py-
          304-        search_files("def.*search", ".", file_pattern="*.py")  #
          Search Python files only, show content
          /Users/albou/projects/abstractcore/abstractcore/tools/common_tools.py-
          305-        search_files("import.*re", ".", file_pattern="*.py|*.js")
           # Search Python and JavaScript files, show content
          /Users/albou/projects/abstractcore/abstractcore/tools/common_tools.py-
          306-        search_files("TODO|FIXME", ".",
          file_pattern="*.py|*.md|*.txt")  # Find TODO/FIXME in multiple file
          types, show content
          --
          abstractcore/tools/README.md-960-Agents leverage tools for autonomous
          operation:
          /Users/albou/projects/abstractcore/abstractcore/tools/README.md-961-
          /Users/albou/projects/abstractcore/abstractcore/tools/README.md-962-``
          `python
          abstractcore/tools/README.md:963:from abstractcore.agents import
          ReActAgent
          /Users/albou/projects/abstractcore/abstractcore/tools/README.md-964-fr
          om abstractcore.tools import register
          /Users/albou/projects/abstractcore/abstractcore/tools/README.md-965-
          /Users/albou/projects/abstractcore/abstractcore/tools/README.md-966-@r
          egister
          --
          /Users/albou/projects/abstractcore/abstractcore/tools/README.md-968-
            """Evaluate mathematical expressions"""
          /Users/albou/projects/abstractcore/abstractcore/tools/README.md-969-
            return eval(expression)
          /Users/albou/projects/abstractcore/abstractcore/tools/README.md-970-
          abstractcore/tools/README.md:971:agent = ReActAgent(
          /Users/albou/projects/abstractcore/abstractcore/tools/README.md-972-
            llm=llm,
          /Users/albou/projects/abstractcore/abstractcore/tools/README.md-973-
            tools=[calculator, web_search, read_file],
          /Users/albou/projects/abstractcore/abstractcore/tools/README.md-974-
            max_iterations=20
          --
          /Users/albou/projects/abstractcore/abstractcore/embeddings/README.md-4
          94-    "Python programming tutorial",
          /Users/albou/projects/abstractcore/abstractcore/embeddings/README.md-4
          95-    "Machine learning with Python",
          /Users/albou/projects/abstractcore/abstractcore/embeddings/README.md-4
          96-    "JavaScript web development",
          abstractcore/embeddings/README.md:497:    "React framework guide",
          /Users/albou/projects/abstractcore/abstractcore/embeddings/README.md-4
          98-    "Data science using Python",
          /Users/albou/projects/abstractcore/abstractcore/embeddings/README.md-4
          99-    "Vue.js tutorial",
          /Users/albou/projects/abstractcore/abstractcore/embeddings/README.md-5
          00-    "Python for data analysis"
          --
          /Users/albou/projects/abstractcore/abstractcore/embeddings/README.md-5
          20-#   - Python for data analysis
          abstractcore/embeddings/README.md-521-# Cluster 2:
          /Users/albou/projects/abstractcore/abstractcore/embeddings/README.md-5
          22-#   - JavaScript web development
          abstractcore/embeddings/README.md:523:#   - React framework guide
          /Users/albou/projects/abstractcore/abstractcore/embeddings/README.md-5
          24-#   - Vue.js tutorial
          /Users/albou/projects/abstractcore/abstractcore/embeddings/README.md-5
          25-```
          /Users/albou/projects/abstractcore/abstractcore/embeddings/README.md-5
          26-
          --
          /Users/albou/projects/abstractcore/abstractcore/processing/README.md-5
          52-- `DECISION_MAKING` - Evaluating options, making choices
          /Users/albou/projects/abstractcore/abstractcore/processing/README.md-5
          53-- `PERSUASION` - Convincing, influencing opinions
          /Users/albou/projects/abstractcore/abstractcore/processing/README.md-5
          54-- `CLARIFICATION` - Seeking or providing clarity
          abstractcore/processing/README.md:555:- `EMOTIONAL_EXPRESSION` -
          Expressing feelings, reactions
          /Users/albou/projects/abstractcore/abstractcore/processing/README.md-5
          56-- `RELATIONSHIP_BUILDING` - Social connection, rapport
          /Users/albou/projects/abstractcore/abstractcore/processing/README.md-5
          57-- `INSTRUCTION_GIVING` - Teaching, directing actions
          /Users/albou/projects/abstractcore/abstractcore/processing/README.md-5
          58-- `VALIDATION_SEEKING` - Seeking approval, confirmation
          --
          /Users/albou/projects/abstractcore/abstractcore/processing/basic_inten
          t.py-25-    DECISION_MAKING = "decision_making"              #
          Evaluating options, making choices
          /Users/albou/projects/abstractcore/abstractcore/processing/basic_inten
          t.py-26-    PERSUASION = "persuasion"                        #
          Convincing, influencing opinions
          /Users/albou/projects/abstractcore/abstractcore/processing/basic_inten
          t.py-27-    CLARIFICATION = "clarification"                  # Seeking
           or providing clarity
          abstractcore/processing/basic_intent.py:28:    EMOTIONAL_EXPRESSION =
          "emotional_expression"    # Expressing feelings, reactions
          /Users/albou/projects/abstractcore/abstractcore/processing/basic_inten
          t.py-29-    RELATIONSHIP_BUILDING = "relationship_building"  # Social
          connection, rapport
          /Users/albou/projects/abstractcore/abstractcore/processing/basic_inten
          t.py-30-    INSTRUCTION_GIVING = "instruction_giving"        #
          Teaching, directing actions
          /Users/albou/projects/abstractcore/abstractcore/processing/basic_inten
          t.py-31-    VALIDATION_SEEKING = "validation_seeking"        # Seeking
           approval, confirmation
          --
          /Users/albou/projects/abstractcore/abstractcore/server/app.py-11--
          OpenAI Vision API compatible format support
          /Users/albou/projects/abstractcore/abstractcore/server/app.py-12--
          Streaming responses with media attachments
          /Users/albou/projects/abstractcore/abstractcore/server/app.py-13--
          Clean delegation to AbstractCore
          abstractcore/server/app.py:14:- Proper ReAct loop support
          /Users/albou/projects/abstractcore/abstractcore/server/app.py-15--
          Comprehensive model listing from AbstractCore providers
          /Users/albou/projects/abstractcore/abstractcore/server/app.py-16-
          abstractcore/server/app.py-17-Media Support:
          --
          abstractcore/utils/README.md-180-**Tools**: `list_files()`,
          `search_files()`, `read_file()`, `write_file()`, `execute_command()`
          abstractcore/utils/README.md-181-**Attachments**: `"Analyze
          @screenshot.png"` (vision models), `"Read @file.txt"` (text
          processing)
          /Users/albou/projects/abstractcore/abstractcore/utils/README.md-182-
          abstractcore/utils/README.md:183:**Limitations**: Simple
          demonstrations only; no ReAct patterns, complex chains, or adaptive
          reasoning. Build custom solutions for production.
          /Users/albou/projects/abstractcore/abstractcore/utils/README.md-184-
          /Users/albou/projects/abstractcore/abstractcore/utils/README.md-185---
          -
          /Users/albou/projects/abstractcore/abstractcore/utils/README.md-186-
          --
  ⎿  Read(abstractcore/processing/basic_deepsearch.py)

  ⎿  Search(pattern: "**/agents/**/*.py", path:
           "/Users/albou/projects/abstractcore")
  ⎿  Search(pattern: "**/*agent*.py", path:
           "/Users/albou/projects/abstractcore")
  ⎿  Read 2174 lines

  ⎿    ⎿  Found 3 files                                                         
          /Users/albou/projects/abstractcore/examples/learning_path/05_server_age
          ntic_cli.py
          /Users/albou/projects/abstractcore/tests/test_agentic_cli_compatibilit
          y.py
          /Users/albou/projects/abstractcore/untracked/lm-agent.py
  ⎿    ⎿  Found 0 files

  ⎿  Read(examples/learning_path/05_server_agentic_cli.py)

  ⎿  Read(abstractcore/providers/streaming.py · lines 1-300)

  ⎿  Read(abstractcore/events/__init__.py)

  ⎿  Read 670 lines

  ⎿  Read 300 lines

  ⎿  Read 431 lines

  ⎿  Read(abstractcore/tools/README.md · lines 850-1049)

  ⎿  Read(abstractcore/utils/cli.py · lines 1-400)

  ⎿  Read 200 lines

  ⎿  Read 400 lines

  ⎿  Response:
       Now I have a comprehensive understanding of AbstractCore's tool
       system and agentic patterns. Let me compile my findings into a
       comprehensive report.

       ---
       AbstractCore Tool System & Agentic Patterns Investigation Report

       Executive Summary

       AbstractCore already has substantial agent-like behavior distributed
       across multiple components. The key finding is that agentic 
       capabilities exist but are not formalized into a unified agent 
       abstraction. The codebase shows clear patterns that could be
       consolidated.

       1. Tool Definitions System

       Location: /Users/albou/projects/abstractcore/abstractcore/tools/

       Key Components:

       1. core.py - Contains ToolDefinition class and @tool decorator
         - Tools are defined using the @tool decorator on Python functions
         - ToolDefinition dataclass stores tool metadata (name, description,
        parameters)
         - ToolDefinition.from_function() auto-generates definitions from
       Python functions using type hints and docstrings
       2. registry.py - Global tool registry with execution
         - ToolRegistry class manages registered tools
         - register_tool() / execute_tool() / execute_tools() functions
         - Global _global_registry singleton pattern
       3. handler.py - UniversalToolHandler for parsing/formatting
         - Handles both native and prompted tool calling modes
         - Supports multiple formats (Qwen, Llama, XML, Gemma)

       Tool Definition Pattern:

       @tool
       def get_weather(location: str) -> str:
           """Get current weather for a location"""
           return f"Weather in {location}: Sunny, 72°F"

       ---
       2. Tool Execution System

       Two Execution Modes in BaseProvider 
       (/Users/albou/projects/abstractcore/abstractcore/providers/base.py):

       1. execute_tools=True (Legacy/Internal mode)
         - AbstractCore executes tools automatically
         - Tools execute inside the provider, results returned inline
       2. execute_tools=False (Default/Pass-through mode)
         - Tool calls are parsed but NOT executed
         - External agent/CLI handles execution
         - This is the agentic mode - allows external control

       Tool Execution Flow (lines 844-898):

       def _execute_tools_with_events(self, response, tool_calls, 
       execute_tools_param):
           """Core tool execution with event emission"""
           should_execute = execute_tools_param if execute_tools_param is
       not None else self.execute_tools

           if not should_execute:
               # Tool execution disabled - return response with tool calls
               return response

           # Emit TOOL_STARTED event
           emit_global(EventType.TOOL_STARTED, ...)

           # Execute tools
           tool_results = execute_tools(tool_calls)

           # Emit TOOL_COMPLETED event
           emit_global(EventType.TOOL_COMPLETED, ...)

       ---
       3. BasicDeepSearch - The "Agent" That Exists

       Location: /Users/albou/projects/abstractcore/abstractcore/processing/
       basic_deepsearch.py

       This is the most "agentic" component in AbstractCore. It implements a
        multi-stage research pipeline with autonomous decision-making.

       Four-Stage Pipeline:

       1. Planning Stage - Decompose query into research plan using LLM
       2. Question Development - Generate specific search queries per
       sub-task
       3. Web Exploration - Execute searches in parallel, gather evidence
       4. Report Generation - Synthesize findings into structured report

       Agent-Like Patterns:

       - Autonomous loop: Iterates through sub-tasks until complete
       - Tool usage: Uses web_search() and fetch_url() tools
       - Parallel execution: ThreadPoolExecutor for concurrent searches
       - Reflexive mode: Can analyze gaps and refine (lines 2024-2173)
       - Structured output: Uses Pydantic models for validation

       Reflexive Refinement (Closest to ReAct):

       def _reflexive_refinement(self, initial_report, research_plan, 
       existing_findings):
           """Stage 6: Reflexive analysis and iterative improvement"""
           for iteration in range(self.max_reflexive_iterations):
               # Analyze gaps
               gaps = self._analyze_research_gaps(current_report,
       research_plan)

               if not gaps:
                   break  # No more gaps to address

               # Execute targeted searches for gaps
               new_findings = self._execute_gap_searches(gaps, query)

               # Regenerate report with enhanced findings
               current_report = self._generate_report(...)

       ---
       4. Event System for Agent Loops

       Location: 
       /Users/albou/projects/abstractcore/abstractcore/events/__init__.py

       Event Types for Agentic Behavior:
       - GENERATION_STARTED / GENERATION_COMPLETED - Track LLM calls
       - TOOL_STARTED / TOOL_PROGRESS / TOOL_COMPLETED - Tool execution
       lifecycle
       - RETRY_ATTEMPTED / RETRY_EXHAUSTED - Retry logic
       - ERROR - Error handling

       Async Event Support:
       GlobalEventBus.on_async(event_type, handler)  # Register async 
       handler
       await GlobalEventBus.emit_async(event_type, data)  # Emit async

       This event system is perfectly suited for agent observation and
       control.

       ---
       5. Streaming Processor - Incremental Tool Detection

       Location: /Users/albou/projects/abstractcore/abstractcore/providers/s
       treaming.py

       IncrementalToolDetector - State machine for detecting tool calls in
       streaming responses:
       - SCANNING - Looking for tool call start
       - IN_TOOL_CALL - Inside a tool call
       - COMPLETE - Tool call finished

       This allows real-time tool detection during streaming without
       buffering the entire response.

       ---
       6. CLI "Agentic" Example

       Location: 
       /Users/albou/projects/abstractcore/abstractcore/utils/cli.py

       The CLI explicitly states its limitations (lines 1-20):
       """
       This is a simple CLI tool that demonstrates basic AbstractCore 
       functionality.
       ...
       - No ReAct pattern or complex reasoning chains
       - No adaptive actions or advanced reasoning patterns
       - Limited to basic demonstration purposes

       For production use cases requiring advanced reasoning, multi-step 
       tool chains,
       or complex agent behaviors, consider building custom solutions using 
       the
       AbstractCore framework directly.
       """

       Manual Tool Execution Loop Pattern (from tools README):

       # Initial request
       response = llm.generate(prompt, tools=[search_web])

       # Execute tool calls if present
       if response.tool_calls:
           tool_results = execute_tools(response.tool_calls)

           # Continue conversation with results
           final_response = llm.generate(
               prompt="Here are the search results: " +
       str(tool_results[0].output),
               conversation_history=[...]
           )

       ---
       7. Documentation References to Agents

       From tools README (lines 958-978):

       from abstractcore.agents import ReActAgent  # NOTE: This doesn't 
       exist!
       from abstractcore.tools import register

       @register
       def calculator(expression: str) -> float:
           """Evaluate mathematical expressions"""
           return eval(expression)

       agent = ReActAgent(
           llm=llm,
           tools=[calculator, web_search, read_file],
           max_iterations=20
       )

       result = agent.run("Calculate the GDP growth rate from the data in 
       report.csv")

       IMPORTANT: This ReActAgent class is referenced in documentation but
       does not exist in the codebase. It appears to be aspirational
       documentation.

       ---
       8. Key Finding: Agent-Like Patterns Already Exist

       | Pattern                  | Where It Exists
           | What's Missing                  |
       |--------------------------|-----------------------------------------
       ----|---------------------------------|
       | Think-Act-Observe        | BasicDeepSearch reflexive mode
           | Not formalized as ReAct         |
       | Tool execution loop      | Manual in CLI, automatic in
       BasicDeepSearch | No unified abstraction          |
       | Event-driven progress    | GlobalEventBus with async support
           | Not connected to agent loop     |
       | Streaming tool detection | IncrementalToolDetector
           | Works but no agent consuming it |
       | Parallel execution       | BasicDeepSearch ThreadPoolExecutor
           | Not generalized                 |
       | Retry/recovery           | RetryManager in base provider
           | Per-request, not per-task       |

       ---
       9. Answer to Key Question

       "Does AbstractCore already have agent-like behavior that just needs 
       to be formalized?"

       YES. The following components demonstrate agent-like behavior:

       1. BasicDeepSearch - Full autonomous research pipeline with:
         - Multi-stage planning
         - Parallel tool execution
         - Reflexive refinement (closest to ReAct)
         - Source quality assessment
       2. Tool System - Complete infrastructure for:
         - Tool definition via decorator
         - Global registry
         - Execution with events
         - Pass-through mode for external control
       3. Event System - Ready for agent observation:
         - TOOL_STARTED/PROGRESS/COMPLETED
         - Async handlers
         - Global bus pattern
       4. Streaming - Real-time tool detection:
         - State machine for incremental detection
         - Multiple format support

       What's Missing (to formalize):

       1. ReActAgent class - Referenced in docs but doesn't exist
       2. Unified agent abstraction - No base Agent class
       3. Agent loop primitive - think() -> act() -> observe() cycle not
       explicit
       4. Memory/State management - Beyond BasicSession's simple history
       5. CodeAct pattern - Generate and execute code as actions

       ---
       10. Recommendations

       To formalize agentic capabilities, AbstractCore could:

       1. Extract patterns from BasicDeepSearch into reusable agent
       primitives
       2. Create abstractcore.agents module with:
         - BaseAgent abstract class
         - ReActAgent implementation
         - CodeActAgent for code generation
       3. Leverage existing infrastructure:
         - Tool system (already complete)
         - Event system (already async-capable)
         - Streaming detector (already working)
       4. Unify the execution loop - Single place for think-act-observe
       cycle

       The foundation is solid; formalization would be relatively
       straightforward given the existing components.








  ⎿  Done (28 tool uses · 141.9k tokens · 2m 1s)

⏺ Explore(Research agent state)
  ⎿  Prompt:                                                               
       Research the state-of-the-art for agent frameworks to compare with
       AbstractCore:

       1. Search the codebase for any existing agent-related backlog items
       in docs/backlog/
       2. Look at llms.txt and llms-full.txt for current feature
       documentation
       3. Check README.md for stated goals and positioning
       4. Examine pyproject.toml for dependencies that might indicate agent
       support

       Also search for:
       - Any imports or references to LangChain, LlamaIndex, Pydantic-AI,
       CrewAI
       - Any existing agent classes or agent-like abstractions
       - Memory abstractions beyond simple chat history

       Report on: What agent capabilities are documented/planned vs what
       exists today.

  ⎿  > Research the state-of-the-art for agent frameworks to compare with 
     AbstractCore:

     1. **Search the codebase** for any existing agent-related backlog items in 
     docs/backlog/
     2. **Look at llms.txt and llms-full.txt** for current feature documentation
     3. **Check README.md** for stated goals and positioning
     4. **Examine pyproject.toml** for dependencies that might indicate agent 
     support

     Also search for:
     - Any imports or references to LangChain, LlamaIndex, Pydantic-AI, CrewAI
     - Any existing agent classes or agent-like abstractions
     - Memory abstractions beyond simple chat history

     Report on: What agent capabilities are documented/planned vs what exists 
     today. 
  ⎿  Search(pattern: "docs/backlog/**/*.md")

  ⎿  Read(llms.txt)

  ⎿  Read(llms-full.txt)

  ⎿  Read(README.md · lines 1-200)

  ⎿  Read(pyproject.toml)

  ⎿  Search(pattern: "langchain|llamaindex|llama_index|pydantic-ai|pydantic
           _ai|crewai|crew_ai|autogen")
  ⎿  Search(pattern: "class.*Agent|BaseAgent|AgentExecutor", glob:
           "**/*.py")
  ⎿  Search(pattern: "memory|Memory|conversation_history|chat_history",
           glob: "**/*.py")
  ⎿  Read 200 lines

  ⎿  Read 391 lines

  ⎿  Read 757 lines

  ⎿  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.
  ⎿    ⎿  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
  ⎿    ⎿  Found 0 files

  ⎿    ⎿  Found 14 files                                             
          CHANGELOG.md
          docs/apps/basic-judge.md
          docs/backlog/completed/structured_logging_audit_report.md
          docs/async-guide.md
          docs/backlog/async-mlx-hf.md
          docs/backlog/completed/002-async-await-support.md
          abstractcore/events/__init__.py
          docs/research/vision-first/glyph-complete-analysis.md
          docs/research/Agentic Deep Research Best Practices Guide.md
          docs/capabilities.md
          docs/archive/structured-response-keyword.md
          docs/comparison.md
          docs/research/Unified LLM Tool Call Handling.md
          docs/archive/chat-completions-endpoint.md
  ⎿    ⎿  Found 35 files                                       
          abstractcore/providers/vllm_provider.py
          abstractcore/providers/openai_compatible_provider.py
          abstractcore/core/session.py
          abstractcore/providers/ollama_provider.py
          abstractcore/providers/lmstudio_provider.py
          abstractcore/embeddings/manager.py
          abstractcore/providers/base.py
          tests/tools/test_tool_execution_separation.py
          tests/test_unload_memory.py
          tests/streaming/test_unified_streaming.py
          tests/providers/test_providers_comprehensive.py
          tests/providers/test_provider_basic_session.py
          tests/glyph/glyph_comparison_test.py
          tests/huggingface/test_gguf_models.py
          tests/embeddings/test_real_models_comprehensive.py
          tests/embeddings/test_embeddings_matrix_operations.py
          tests/embeddings/test_embeddings_no_mock.py
          tests/embeddings/test_embeddings_real.py
          tests/embeddings/test_embeddings_simple.py
          tests/embeddings/test_embeddings.py
          examples/media/glyph_compression_demo.py
          examples/media/glyph_complete_example.py
          abstractcore/providers/mlx_provider.py
          abstractcore/providers/huggingface_provider.py
          tests/test_integrated_functionality.py
          tests/test_final_comprehensive.py
          examples/learning_path/04_unified_streaming.py
          examples/learning_path/05_server_agentic_cli.py
          examples/embeddings/simple_embeddings_examples.py
          examples/performance/matrix_operations_demo.py
          examples/rag/complete_rag_example.py
          examples/embeddings/embeddings_demo.py
          abstractcore/processing/basic_summarizer.py
          abstractcore/media/processors/pdf_processor.py
          abstractcore/embeddings/__init__.py
  ⎿  Read(docs/backlog/008-mcp-integration.md)

  ⎿  Read(docs/backlog/abstract-processing.md)

  ⎿  Search(pattern: "agent|Agent|agentic|Agentic", glob: "docs/**/*.md")

  ⎿  Read(docs/comparison.md)

  ⎿  Search(pattern: "BasicDeepSearch|DeepResearcher|deep.*research", glob:
           "**/*.py")

  ⎿  Read 248 lines

  ⎿  Read 549 lines

  ⎿  Read 422 lines

  ⎿    ⎿  Found 3 files                              
          abstractcore/processing/basic_deepsearch.py
          abstractcore/apps/deepsearch.py
          abstractcore/processing/__init__.py
  ⎿    ⎿  Found 33 files                                                     
          docs/backlog/completed/011-vllm-provider-implementation.md
          docs/prerequisites.md
          docs/backlog/completed/011-provider-request-vllm.md
          docs/acore-cli.md
          docs/README.md
          docs/getting-started.md
          docs/backlog/008-mcp-integration.md
          docs/examples.md
          docs/research/vision-first/proposal.md
          docs/research/vision-first/Glyph_ Visual Text Compression Guide.md
          docs/research/Agentic Deep Research Best Practices Guide.md
          docs/apps/basic-deepsearch.md
          docs/capabilities.md
          docs/research/abstract-series.md
          docs/server.md
          docs/archive/structured-response-keyword.md
          docs/chat-compaction.md
          docs/comparison.md
          docs/research/Inferring Intent in Psychological Text Analysis.md
          docs/tool-calling.md
          docs/research/Unified LLM Tool Call Handling.md
          docs/reports/2025-10-10-report.md
          docs/reports/2025-10-11-sprint-documentation-improvements-report.md
          docs/archive/proposals.md
          docs/archive/server-quickstart.md
          docs/archive/server-troubleshooting.md
          docs/archive/universal-tool-conversion-fix.md
          docs/backlog/abstract-processing.md
          docs/archive/chat-completions-endpoint.md
          docs/archive/openai-format-fix.md
          docs/archive/server-configuration.md
          docs/architecture.md
          docs/archive/README.md
  ⎿  Read(abstractcore/processing/__init__.py)

  ⎿  Read(docs/research/Agentic Deep Research Best Practices Guide.md ·
         lines 1-150)
  ⎿  Search(pattern: "abstractcore/processing/*.py")

  ⎿  Read(docs/research/abstract-series.md)

  ⎿  Read 20 lines

  ⎿  Read 96 lines

  ⎿  Read 150 lines

  ⎿    ⎿  Found 6 files                                                         
          /Users/albou/projects/abstractcore/abstractcore/processing/basic_extra
          ctor.py
          /Users/albou/projects/abstractcore/abstractcore/processing/basic_summa
          rizer.py
          /Users/albou/projects/abstractcore/abstractcore/processing/basic_inten
          t.py
          /Users/albou/projects/abstractcore/abstractcore/processing/__init__.py
          /Users/albou/projects/abstractcore/abstractcore/processing/basic_deeps
          earch.py
          /Users/albou/projects/abstractcore/abstractcore/processing/basic_judge
          .py
  ⎿  Search(pattern: "class.*Session|memory_manager|MemoryManager|conversat
           ion_buffer|working_memory", glob: "abstractcore/**/*.py",
           output_mode: "content")
  ⎿  Read(abstractcore/core/session.py · lines 1-100)

  ⎿  Read(abstractcore/processing/basic_deepsearch.py · lines 1-100)

  ⎿  Read 100 lines

  ⎿  Read 100 lines

  ⎿    ⎿  Found 2 lines                                      
          abstractcore/core/session.py:19:class BasicSession:
          abstractcore/exceptions/__init__.py:55:class
          SessionError(AbstractCoreError):
  ⎿  Response:
       Now I have gathered comprehensive information. Let me compile my
       findings into a detailed report.

       ---
       AbstractCore Agent Capabilities Research Report

       Executive Summary

       AbstractCore is positioned as a lightweight, provider-agnostic LLM 
       infrastructure library - explicitly NOT as a full agent framework.
       The comparison documentation clearly states: "Agent Support: No
       (Single calls)" for AbstractCore vs full agent support in
       LangChain/LangGraph. However, the project has a broader vision
       documented in the "Abstract Series" roadmap that includes separate
       agent-focused packages.

       ---
       1. Current State: What Exists Today

       1.1 Core Infrastructure (Production-Ready)

       - 8 LLM Providers: OpenAI, Anthropic, Ollama, LMStudio, MLX,
       HuggingFace, vLLM, openai-compatible
       - Universal Tool Calling: @tool decorator works across ALL providers
       - Session Management: BasicSession with conversation history,
       auto-compaction, persistence
       - Interaction Tracing: Full LLM observability with prompts,
       responses, tokens, timing
       - Structured Output: Native and prompted strategies with retry logic
       - Async Support: Native async for all 4 network providers (3-10x
       batch performance)
       - Event System: Global event bus for monitoring and control

       1.2 Processing Capabilities (Built-in Applications)

       Located in abstractcore/processing/:
       - BasicSummarizer: Document summarization with styles and lengths
       - BasicExtractor: Knowledge extraction (JSON-LD output)
       - BasicJudge: Quality assessment with custom criteria
       - BasicIntentAnalyzer: Intent detection for conversations
       - BasicDeepSearch: Autonomous research agent (most agent-like
       component)

       1.3 BasicDeepSearch - The Closest to an Agent

       From abstractcore/processing/basic_deepsearch.py:
       - Four-stage pipeline: Planning, Question Development, Web
       Exploration, Report Generation
       - Parallel web exploration via ThreadPoolExecutor
       - Source management with deduplication
       - Citation validation
       - Structured report output with Pydantic models
       - Uses ReAct-like patterns (think/act/observe)

       1.4 Session Management

       From abstractcore/core/session.py:
       - Simple conversation history (list of messages)
       - Auto-compaction for long conversations
       - Save/load persistence to JSON
       - Interaction tracing for observability
       - NO: Long-term memory, knowledge graphs, semantic retrieval,
       episodic memory

       ---
       2. Planned/Documented Agent Capabilities

       2.1 Abstract Series Vision (from docs/research/abstract-series.md)

       The project envisions a complete ecosystem:

       | Component         | Status          | Description
                       |
       |-------------------|-----------------|------------------------------
       ----------------|
       | AbstractCore      | Production      | Unified LLM interface
       (current package)      |
       | AbstractServer    | In AbstractCore | OpenAI-compatible endpoints
                       |
       | AbstractMemory    | Planned         | Multi-layered memory with KG
       integration     |
       | AbstractAgent     | Planned         | ReAct loops, advanced
       reasoning, task queues |
       | AbstractToolForge | Planned         | Dynamic tool
       creation/evolution              |
       | AbstractFlow      | Planned         | (Not detailed)
                       |
       | AbstractTUI       | Planned         | (Not detailed)
                       |

       2.2 AbstractMemory (Planned) - Key Features

       From the abstract-series.md specification:
       - Multi-layered Memory:
         - Core (identity)
         - Working (short-term/current context)
         - Episodic (long-term events)
         - Semantic (knowledge)
         - Relational (people profiles)
         - Notes (subjective experiences)
         - Verbatims (100% accurate discussions)
         - Archive (subconscious, requires stimuli)
       - Triple Storage Layer:
         - Filesystem (markdown files)
         - LanceDB (vector embeddings with metadata filtering)
         - Knowledge Graph (entities and relationships)
       - Memory Tools:
         - reconstruct(), remember(), reflect(), alignment(), plan()
         - consolidate(), free(), resolve() for maintenance

       2.3 AbstractAgent (Planned) - Key Features

       - Advanced reasoning prompting
       - ReAct loops (think->act->observe with self-reflection)
       - Direct access to AbstractMemory
       - Task queue with priorities and completion evaluation
       - Start/Suspend/Stop task management

       2.4 MCP Integration (Backlog Item 008)

       From docs/backlog/008-mcp-integration.md:
       - Status: Proposed, Priority P2
       - Effort: 12-20 hours
       - Dependency: Requires async support (completed in v2.6.0)
       - Design: MCP as "tool source", NOT as a provider
       - Works with ANY existing provider
       - Target version: v2.7.0

       ---
       3. Comparison with SOTA Agent Frameworks

       3.1 Framework Positioning (from docs/comparison.md)

       | Feature         | AbstractCore             | LangChain          |
       LangGraph       | CrewAI            | LlamaIndex    |
       |-----------------|--------------------------|--------------------|--
       ---------------|-------------------|---------------|
       | Primary Focus   | Clean LLM infrastructure | Full LLM framework |
       Agent workflows | Role-based agents | RAG/search    |
       | Agent Support   | No (Single calls)        | Yes (chains)       |
       Yes (native)    | Yes (crews)       | Query engines |
       | Memory/Sessions | Simple sessions          | Advanced           |
       Advanced        | Advanced          | Advanced      |
       | Tool Calling    | Universal execution      | Via integrations   |
       Native          | Native            | Limited       |
       | Size            | ~8k LOC                  | 100k+ LOC          |
       ~30k LOC        | -                 | ~80k LOC      |

       3.2 Key Differences

       What AbstractCore Has:
       - Production-grade reliability (retry, circuit breakers)
       - Universal tool execution across ALL providers
       - Clean, simple API
       - Lightweight (3 core dependencies)

       What AbstractCore Lacks (compared to agent frameworks):
       - Multi-step agent reasoning
       - State graphs / workflow orchestration
       - Long-term memory / knowledge persistence
       - Human-in-the-loop workflows
       - Multi-agent coordination
       - Planning and task decomposition (except BasicDeepSearch)

       ---
       4. External Framework References in Codebase

       4.1 Mentions Found

       - LangChain: Referenced in comparison docs, research guides,
       changelog
       - LlamaIndex: Referenced in comparison docs
       - Pydantic-AI: Referenced in async research and implementation
       - CrewAI: Referenced in agentic research guide
       - AutoGen: Referenced in agentic research guide

       4.2 No Direct Dependencies

       From pyproject.toml:
       - NO imports of LangChain, LlamaIndex, CrewAI, AutoGen
       - Core dependencies: pydantic, httpx, tiktoken, requests
       - Provider-specific: openai, anthropic, transformers, torch, mlx,
       vllm, etc.

       ---
       5. Gap Analysis: What's Needed for Agent Capabilities

       5.1 Missing Components for Full Agent Support

       | Capability               | Current State               | Gap
                                |
       |--------------------------|-----------------------------|-----------
       -------------------------|
       | Multi-step reasoning     | BasicDeepSearch only        | Need
       general-purpose ReAct loop    |
       | Long-term memory         | None                        | Need
       AbstractMemory implementation |
       | Knowledge persistence    | Session save/load only      | Need
       vector store + KG integration |
       | Task orchestration       | None                        | Need task
       queue with priorities    |
       | Multi-agent coordination | None                        | Need
       orchestrator-worker pattern   |
       | State management         | Simple conversation history | Need
       explicit state graphs         |
       | Human-in-the-loop        | None                        | Need
       approval/checkpoint system    |

       5.2 Existing Foundations to Build On

       | Foundation        | Available                  | Can Support
                 |
       |-------------------|----------------------------|-------------------
       ----------|
       | Tool system       | @tool decorator, execution | Agent tool use
                 |
       | Event system      | GlobalEventBus             | Agent monitoring,
       callbacks |
       | Session           | BasicSession               | Agent context
                 |
       | Async             | Native for all providers   | Parallel agent
       execution    |
       | Structured output | Pydantic models            | Structured agent
       reasoning  |
       | Tracing           | Full interaction history   | Agent
       observability         |

       ---
       6. Recommendations

       6.1 Short-term (Within AbstractCore)

       1. Enhance BasicDeepSearch as the "reference agent implementation"
       2. Implement MCP Integration (backlog item 008) for expanded tool
       access
       3. Add planning utilities as optional processing components

       6.2 Medium-term (Separate Packages)

       1. AbstractMemory: Implement the documented multi-layered memory
       system
       2. AbstractAgent: Build on AbstractCore foundations with ReAct loops

       6.3 Philosophy Alignment

       AbstractCore's explicit design philosophy is "Clean infrastructure 
       you build on" vs "Full framework with everything included". Agent
       capabilities should remain in separate packages (AbstractAgent,
       AbstractMemory) to maintain:
       - Lightweight core
       - Minimal dependencies
       - Clear separation of concerns
       - User choice in complexity level

       ---
       7. Summary

       Documented/Planned:
       - Full agent ecosystem in "Abstract Series" vision
       - AbstractMemory with multi-layered cognitive architecture
       - AbstractAgent with ReAct loops and task management
       - MCP integration for external tool access

       Exists Today:
       - BasicDeepSearch (autonomous research agent)
       - BasicSession (simple conversation history)
       - Universal tool calling infrastructure
       - Event system for monitoring
       - Async support for parallel execution

       Gap:
       - No long-term memory implementation
       - No general-purpose agent loop
       - No multi-agent coordination
       - No state graph / workflow orchestration
       - No knowledge persistence beyond session save/load

       AbstractCore is intentionally positioned as infrastructure for 
       building agents, not as an agent framework itself. The documented
       vision shows a clear roadmap (AbstractMemory, AbstractAgent) for
       agent capabilities, but these are planned as separate packages in the
        Abstract Series ecosystem.











  ⎿  Done (20 tool uses · 105.6k tokens · 1m 25s)

∴ Thinking…

  The three exploration agents have provided comprehensive information. Let me 
  synthesize the key findings:

  Key Findings:

  1. Current Architecture

  - 100+ Python source files, ~45,000 LOC
  - 8 providers (OpenAI, Anthropic, Ollama, LMStudio, MLX, HuggingFace, vLLM, 
  openai-compatible)
  - Complete tool system with @tool decorator, registry, execution, event 
  emission
  - Session management with conversation history, auto-compaction, persistence
  - Native async support for all network providers (6-7.5x performance)
  - Event system ready for agent observation (TOOL_STARTED, TOOL_PROGRESS, 
  TOOL_COMPLETED)

  2. Existing Agent-Like Components

  - BasicDeepSearch (2,174 lines) - The closest to a full agent:
    - Four-stage autonomous pipeline
    - ReAct-like reflexive refinement
    - Parallel execution via ThreadPoolExecutor
    - Structured output with Pydantic
    - Uses web_search and fetch_url tools

  3. What's Missing for Agents

  - No formalized ReActAgent or CodeActAgent classes
  - No unified agent abstraction (BaseAgent)
  - No explicit think->act->observe loop
  - Long-term memory beyond simple chat history
  - Multi-agent coordination

  4. Original Vision

  - "Abstract Series" planned AbstractMemory and AbstractAgent as separate 
  packages
  - Clear philosophy: "Clean infrastructure you build on" vs "Full framework"
  - AbstractCore intended to remain lightweight

  5. The Key Question

  User is questioning whether to:
  A) Keep agents separate (AbstractAgent, AbstractMemory)
  B) Integrate ReAct/CodeAct into AbstractCore

  Their reasoning:
  - ReAct and CodeAct don't need super advanced memory
  - They work with chat history, scratchpad, and potentially light RAG

  - The redesign of agentic memory is blocking users from having agent 
  functionality

  I recognize the critical bottleneck preventing agent implementation. The 
  memory redesign has become a significant roadblock, hindering users from 
  accessing core agent capabilities. Immediate action is needed to unblock this 
  development path and provide a streamlined approach to agent functionality.

⏺ Now I have a comprehensive understanding. Let me examine a few critical
  files to form my final recommendation.

⏺ Read(abstractcore/processing/basic_deepsearch.py · lines 1-200)
  ⎿  Read 200 lines

⏺ Read(abstractcore/tools/core.py · lines 1-150)
  ⎿  Read 150 lines

⏺ Read(abstractcore/core/session.py · lines 1-150)
  ⎿  Read 150 lines

∴ Thinking…

  Now I have a comprehensive understanding of the codebase. Let me analyze the 
  situation:

  Key Facts:

  What AbstractCore Already Has:

  1. Complete Tool System (core.py, registry.py)
    - @tool decorator with metadata
    - ToolDefinition, ToolCall, ToolResult, ToolCallResponse
    - Tool registry with global registration
    - Event emission for tool lifecycle
  2. Session Management (session.py)
    - BasicSession with conversation history
    - Auto-compaction for long conversations
    - Tool registration
    - Interaction tracing
  3. BasicDeepSearch (2,174 lines)
    - Four-stage autonomous pipeline
    - Planning, Question Development, Web Exploration, Report Generation
    - Parallel execution (ThreadPoolExecutor)
    - Structured output (Pydantic models)
    - This is already an agent - just not formalized
  4. Event System
    - TOOL_STARTED, TOOL_PROGRESS, TOOL_COMPLETED
    - Async event handlers
    - GlobalEventBus singleton
  5. Native Async Support
    - All 4 network providers have native async
    - 6-7.5x performance for concurrent operations

  What's Missing for ReAct/CodeAct:

  1. No formalized agent loop (think → act → observe)
  2. No BaseAgent abstraction
  3. No CodeAct pattern (generate and execute code)

  User's Key Insight:

  "ReAct and CodeAct do not need super advanced memory, they already work with 
  chat history, scratchpad and potentially a little bit of RAGs"

  This is absolutely correct! The original papers on ReAct show it working with 
  simple conversation history + scratchpad.

  Analysis:

  The Original Vision vs Reality:

  - Original Plan: AbstractMemory (complex multi-layered memory) → AbstractAgent
   (depends on AbstractMemory)

  - Reality: Users are stalled waiting for an "advanced memory" system that 
  ReAct/CodeAct fundamentally don't require

  What BasicDeepSearch Proves:

  BasicDeepSearch reveals that existing infrastructure already supports 
  sophisticated autonomous workflows:
  1. Fully functional autonomous agents can leverage AbstractCore's current 
  primitives
  2. Complex multi-stage pipelines are entirely feasible
  3. Existing tool, session, and event systems provide robust foundation

  Strategic Considerations:

  The current architecture offers clear pathways for agent development without 
  overcomplicating the core system. By focusing on lightweight, modular 
  components, we can enable rapid agent prototyping while maintaining system 
  flexibility.

  The key is recognizing that simplicity often trumps complexity in agent 
  design. The existing primitives are more powerful than initially anticipated.

  Recommended Approach:

  Implementing lightweight ReAct/CodeAct directly in AbstractCore provides 
  immediate user value. This strategy leverages existing infrastructure, 
  requires minimal additional code, and maintains the system's core philosophy 
  of elegant, efficient design.

  The implementation would be straightforward: a clean, focused agent pattern 
  built on ~300-500 lines of code using current session and tool frameworks.

  Key advantages include rapid deployment, minimal complexity, and alignment 
  with the existing architectural patterns. By building on BasicSession and 
  current processing tools, we create a natural extension that doesn't 
  compromise the system's lightweight nature.

  The proposed file structure offers a modular, clean approach to agent 
  implementation, with base classes and specific agent types organized 
  logically. This design allows for easy expansion and maintains clear 
  separation of concerns.

  Critically, this approach delivers functional agents now, without waiting for 
  more complex memory systems. Users get immediate utility while keeping the 
  core system lean and adaptable.

⏺ Now I have a comprehensive understanding of the codebase. Let me
  synthesize my findings and provide a well-justified recommendation.

  Summary of Findings

  What AbstractCore Already Has (Agent-Ready Primitives)

  | Component       | Lines  | Status      | Agent Relevance
                      |
  |-----------------|--------|-------------|--------------------------------
  --------------------|
  | Tool System     | ~400   | Complete    | @tool decorator, registry,
  execution, events       |
  | BasicSession    | ~1,150 | Complete    | Conversation history,
  auto-compact, persistence    |
  | Event System    | ~300   | Complete    |
  TOOL_STARTED/PROGRESS/COMPLETED, async handlers    |
  | BasicDeepSearch | 2,174  | IS an agent | Four-stage autonomous pipeline,
   proves the pattern |
  | Native Async    | —      | Complete    | 6-7.5x batch performance for
  all network providers |

  The Critical Insight

  BasicDeepSearch IS ALREADY a full agent — it just isn't formalized as
  such:
  - Four-stage autonomous pipeline (Planning → Search → Execute → Report)
  - Uses tools (web_search, fetch_url)
  - Parallel execution via ThreadPoolExecutor
  - Reflexive refinement (gap analysis → iterate)
  - Structured output via Pydantic

  Your intuition is correct: ReAct and CodeAct don't need complex memory
  systems. The original papers use simple conversation history + scratchpad.

  ---
  My Recommendation: Integrate Simple Agents into AbstractCore

  Three Options Evaluated

  | Option              | Description                               | Pros
                | Cons                       |
  |---------------------|-------------------------------------------|-------
  --------------|----------------------------|
  | A. Keep Separate    | Wait for AbstractMemory + AbstractAgent   | Clean
  separation    | Users blocked indefinitely |
  | B. Full Framework   | Build comprehensive agent framework       |
  All-in-one          | Bloats core, mission drift |
  | C. Simple Agents ✅ | ReAct + CodeAct using existing primitives | Users
  get value NOW | Slight scope expansion     |

  Why Option C is Right

  1. Users get functionality NOW — No more waiting for AbstractMemory
  vaporware
  2. Builds on proven patterns — BasicDeepSearch already demonstrates
  multi-stage agents work
  3. Minimal code — ~400-600 lines total for both agents
  4. Uses existing primitives — BasicSession, tools, events (zero new
  infrastructure)
  5. Natural fit — abstractcore/agents/ parallels abstractcore/processing/
  6. Maintains philosophy — Still lightweight, still "infrastructure you
  build on"

  Proposed Architecture

  abstractcore/agents/
  ├── __init__.py           # Exports BaseAgent, ReActAgent, CodeActAgent
  ├── base.py               # BaseAgent ABC (~150 lines)
  ├── react.py              # ReActAgent (~350 lines)
  ├── code_act.py           # CodeActAgent (~450 lines)
  └── scratchpad.py         # Simple in-memory scratchpad (~80 lines)

  Key Design Principles

  1. Use BasicSession for history (no new memory system)
  2. Use existing tool system (@tool, execute_tools())
  3. Use existing events (TOOL_STARTED, TOOL_COMPLETED)
  4. Simple scratchpad as instance variable (no persistence overhead)
  5. Max iterations to prevent runaway loops
  6. Optional callbacks for human-in-the-loop
  7. Async-native via existing agenerate()

  ---
  Before I write my final plan, I have a few clarifying questions:
                                                                                  
⏺ User answered Claude's questions:
  ⎿  · For CodeAct agent, should code execution use a sandboxed subprocess or trust
      the user's environment? → """Sandboxed subprocess (Recommended)
          Execute code in subprocess with timeout and resource limits. Safer but 
     requires 
          more infrastructure.""" : this option, but make sure to enable perfect 
     observability with structured_logging. you must also ensure a complete 
     serialization of not only the chat history, but also the scratchpad. you must 
     use graph to represent the states and not chains (eg more lang graph 
     concepts).

     · Should agents support human-in-the-loop approval for tool calls? → """Yes, 
     optional callback
          Add optional approve_action callback. If provided, agent pauses for 
     approval 
          before tool execution.""" . in fact, we also need to have a tool where 
     the LLM can ask questions to the users; the user can then answer by selecting 
     one of the choices proposed by the LLM or answer with free text. this is for 
     both react and codeact

     · Where should agents live in the package structure? → """abstractcore/agents/
          New top-level module. Clear separation, discoverable, signals agents are 
          first-class citizens.""" : but be sure to register it properly as a 
     module in the toml and in the various installation aliases. in addition, once 
     you have your plan, also create a docs/backlog/update-apps-agents.md where you
      will explain how we could update our various basic_x.py and associated to 
     leverage these agents. especially for the deepsearch





∴ Thinking…

  The user has provided valuable additional requirements:

  1. CodeAct Safety: Sandboxed subprocess with:
    - Perfect observability via structured_logging
    - Complete serialization of chat history AND scratchpad
    - Graph-based state representation (like LangGraph), NOT chains
  2. Human-in-the-loop: 
    - Optional approve_action callback
    - Plus: A tool for LLM to ask questions to users (with choices or free text)
    - Both ReAct and CodeAct need this
  3. Location: 
    - abstractcore/agents/ as new top-level module
    - Properly registered in pyproject.toml and installation aliases
    - Create backlog doc for updating BasicDeepSearch and other apps to use agents

  This is more sophisticated than I initially anticipated. The user wants:
  - Graph-based state machine (not simple loop) - like LangGraph
  - Full serialization for resume/replay
  - User interaction tool - bidirectional communication
  - Proper package registration
  - Migration path for existing apps

  Let me design this properly. This changes the architecture somewhat:

  abstractcore/agents/
  ├── __init__.py
  ├── base.py               # BaseAgent ABC with state graph
  ├── state.py              # AgentState, StateGraph for transitions
  ├── react.py              # ReActAgent
  ├── code_act.py           # CodeActAgent  
  ├── scratchpad.py         # Serializable scratchpad
  ├── sandbox.py            # Sandboxed code execution
  └── tools/
      └── user_interaction.py  # ask_user tool

  The graph-based approach is important. Instead of a simple while loop:
  - Each state has explicit transitions
  - State can be serialized/deserialized
  - Can resume from any state
  - Visual representation possible

  Let me also think about the user interaction tool. The user wants the LLM to be able 
  to ask questions with:

  - Multiple choice options
  - Free text input

  I'll design a flexible user interaction mechanism that supports different input 
  types. The tool will allow dynamic questioning during agent execution, enabling more 
  interactive and adaptive workflows. This approach provides a standardized way for 
  agents to request user input, with built-in flexibility for various interaction 
  scenarios.

  The proposed implementation supports optional choice lists and free text, making it 
  versatile for different interaction needs. Runtime-specific implementations can 
  handle the actual user interaction, ensuring broad compatibility across different 
  environments.

⏺ Excellent! Your requirements add important sophistication. Let me update my
  understanding and write the plan.

  Key additions from your feedback:
  1. Graph-based state machine (LangGraph-style, not simple chains)
  2. Full serialization of chat history + scratchpad for resume/replay
  3. User interaction tool for bidirectional LLM-user communication
  4. Structured logging for perfect observability
  5. Migration backlog for existing apps like BasicDeepSearch

  Let me write the comprehensive plan:

⏺ Updated plan
  ⎿  Wrote 505 lines to /Users/albou/.claude/plans/velvety-puzzling-breeze.md     
     # Agent Integration Plan for AbstractCore

     ## Executive Summary

     Integrate ReAct and CodeAct agents into AbstractCore using a **graph-based 
     state machine** architecture (LangGraph-style). This enables users to have
     working agents NOW without waiting for the separate
     AbstractMemory/AbstractAgent packages.

     **Key Insight**: BasicDeepSearch (2,174 lines) already proves AbstractCore can
      support autonomous multi-stage agents. We formalize this pattern.

     ---

     ## Architecture Decision

     ### Why Graph-Based State Machine (Not Chains)

     | Aspect | Chain-Based | Graph-Based (Chosen) |
     |--------|------------|---------------------|
     | State transitions | Linear (A→B→C) | Any-to-any (A↔B↔C) |
     | Serialization | Complex (stack unwinding) | Simple (state + context) |
     | Resume/replay | Difficult | Natural |
     | Debugging | Trace logs only | Visual graph + state snapshots |
     | Extensibility | Hard (modify chain) | Easy (add nodes/edges) |

     ### Design Principles

     1. **Graph-first**: Every agent is a state graph with explicit transitions
     2. **Full serialization**: AgentState captures everything needed to resume
     3. **Observable**: Structured logging at every state transition
     4. **Interactive**: Built-in `ask_user` tool for bidirectional communication
     5. **Safe execution**: Sandboxed subprocess for CodeAct with resource limits
     6. **Existing primitives**: Uses BasicSession, tools, events (minimal new
     code)

     ---

     ## Module Structure

     ```
     abstractcore/agents/
     ├── __init__.py              # Exports: BaseAgent, ReActAgent, CodeActAgent,
     AgentState
     ├── state.py                 # AgentState, StateGraph, Node, Edge (~250 lines)
     ├── base.py                  # BaseAgent ABC with graph execution (~300 lines)
     ├── react.py                 # ReActAgent implementation (~400 lines)
     ├── code_act.py              # CodeActAgent implementation (~500 lines)
     ├── scratchpad.py            # Serializable scratchpad (~100 lines)
     ├── sandbox.py               # Sandboxed code execution (~300 lines)
     └── tools/
         ├── __init__.py
         └── user_interaction.py  # ask_user, confirm_action tools (~150 lines)
     ```

     **Total new code**: ~2,000 lines (comparable to BasicDeepSearch)

     ---

     ## Critical Files to Create

     ### 1. `abstractcore/agents/state.py` - State Graph Infrastructure

     ```python
     # Core classes for graph-based agent execution

     @dataclass
     class AgentState:
         """Fully serializable agent state for resume/replay"""
         id: str
         current_node: str
         session_history: List[Dict]      # Full chat history
         scratchpad: Dict[str, Any]       # Working memory
         tool_results: List[Dict]         # Tool execution history
         iteration: int
         max_iterations: int
         created_at: datetime
         updated_at: datetime
         status: str  # "running", "paused", "completed", "failed", "waiting_user"

         def serialize(self) -> str:
             """JSON serialization for persistence"""

         @classmethod
         def deserialize(cls, data: str) -> 'AgentState':
             """Resume from serialized state"""

     @dataclass
     class Node:
         """Graph node representing an agent state"""
         name: str
         handler: Callable[[AgentState], AgentState]

     @dataclass
     class Edge:
         """Conditional transition between nodes"""
         from_node: str
         to_node: str
         condition: Callable[[AgentState], bool]

     class StateGraph:
         """LangGraph-style state graph 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, handler: Callable) -> 'StateGraph':
             """Add a state node"""

         def add_edge(self, from_node: str, to_node: str, condition: Callable =
     None) -> 'StateGraph':
             """Add transition edge (unconditional or conditional)"""

         def set_entry_point(self, node: str) -> 'StateGraph':
             """Set the starting node"""

         def compile(self) -> 'CompiledGraph':
             """Validate and compile graph for execution"""
     ```

     ### 2. `abstractcore/agents/base.py` - BaseAgent ABC

     ```python
     class BaseAgent(ABC):
         """Base class for graph-based agents"""

         def __init__(
             self,
             llm: AbstractCoreInterface,
             tools: List[Callable] = None,
             max_iterations: int = 20,
             approve_action: Optional[Callable[[str, Dict], bool]] = None,
             on_state_change: Optional[Callable[[AgentState], None]] = None,
         ):
             self.llm = llm
             self.session = BasicSession(provider=llm, tools=tools)
             self.tools = tools or []
             self.max_iterations = max_iterations
             self.approve_action = approve_action  # HITL callback
             self.on_state_change = on_state_change  # Observability hook
             self.graph = self._build_graph()
             self.state: Optional[AgentState] = None
             self.logger = get_logger(__name__)

         @abstractmethod
         def _build_graph(self) -> StateGraph:
             """Subclasses define their state graph"""

         def run(self, task: str) -> AgentState:
             """Execute agent synchronously"""

         async def arun(self, task: str) -> AgentState:
             """Execute agent asynchronously"""

         def resume(self, state: AgentState) -> AgentState:
             """Resume from serialized state"""

         def save_state(self, path: Path) -> None:
             """Persist current state to file"""

         @classmethod
         def load_state(cls, path: Path, llm: AbstractCoreInterface) ->
     'BaseAgent':
             """Load agent from saved state"""
     ```

     ### 3. `abstractcore/agents/react.py` - ReActAgent

     ```python
     class ReActAgent(BaseAgent):
         """
         ReAct (Reasoning + Acting) agent implementation.

         State Graph:
             START → THINK → DECIDE → [ACT | ASK_USER | COMPLETE]
                       ↑       ↓
                       ←── OBSERVE
         """

         def _build_graph(self) -> StateGraph:
             graph = StateGraph()

             # Define nodes
             graph.add_node("think", self._think)      # Generate reasoning
             graph.add_node("decide", self._decide)    # Choose action or finish
             graph.add_node("act", self._act)          # Execute tool
             graph.add_node("observe", self._observe)  # Process tool result
             graph.add_node("ask_user", self._ask_user)  # Request user input
             graph.add_node("complete", self._complete)  # Final answer

             # Define edges
             graph.add_edge("think", "decide")
             graph.add_edge("decide", "act", lambda s: s.scratchpad.get("action")
     == "tool")
             graph.add_edge("decide", "ask_user", lambda s:
     s.scratchpad.get("action") == "ask")
             graph.add_edge("decide", "complete", lambda s:
     s.scratchpad.get("action") == "finish")
             graph.add_edge("act", "observe")
             graph.add_edge("observe", "think")  # Loop back
             graph.add_edge("ask_user", "think")  # After user responds, continue
     thinking

             graph.set_entry_point("think")
             return graph.compile()
     ```

     ### 4. `abstractcore/agents/code_act.py` - CodeActAgent

     ```python
     class CodeActAgent(BaseAgent):
         """
         CodeAct agent - generates and executes Python code as actions.

         State Graph:
             START → PLAN → GENERATE_CODE → [APPROVE] → EXECUTE → OBSERVE → [PLAN |
      COMPLETE]
         """

         def __init__(
             self,
             llm: AbstractCoreInterface,
             allowed_imports: List[str] = None,  # Whitelist
             timeout: int = 30,  # Execution timeout
             memory_limit_mb: int = 512,
             **kwargs
         ):
             self.sandbox = Sandbox(
                 allowed_imports=allowed_imports,
                 timeout=timeout,
                 memory_limit_mb=memory_limit_mb
             )
             super().__init__(llm, **kwargs)
     ```

     ### 5. `abstractcore/agents/sandbox.py` - Sandboxed Execution

     ```python
     class Sandbox:
         """Sandboxed Python execution environment"""

         def __init__(
             self,
             timeout: int = 30,
             memory_limit_mb: int = 512,
             allowed_imports: List[str] = None,
         ):
             self.timeout = timeout
             self.memory_limit_mb = memory_limit_mb
             self.allowed_imports = allowed_imports or ["math", "json", "datetime",
      "re"]
             self.logger = get_logger(__name__)

         def execute(self, code: str, context: Dict[str, Any] = None) ->
     SandboxResult:
             """
             Execute code in sandboxed subprocess.

             Features:
             - Timeout enforcement
             - Memory limits (via resource module on Unix)
             - Import whitelist
             - Capture stdout/stderr
             - Serialize results
             """

         async def aexecute(self, code: str, context: Dict[str, Any] = None) ->
     SandboxResult:
             """Async sandboxed execution"""

     @dataclass
     class SandboxResult:
         """Result of sandboxed code execution"""
         success: bool
         output: Any
         stdout: str
         stderr: str
         execution_time_ms: float
         error: Optional[str] = None
     ```

     ### 6. `abstractcore/agents/tools/user_interaction.py` - User Tools

     ```python
     @tool(
         name="ask_user",
         description="Ask the user a question and wait for their response",
         when_to_use="When you need clarification, approval, or input from the
     user"
     )
     def ask_user(
         question: str,
         choices: Optional[List[str]] = None,
         allow_free_text: bool = True,
         default: Optional[str] = None
     ) -> str:
         """
         Ask the user a question during agent execution.

         Args:
             question: The question to ask
             choices: Optional list of predefined choices
             allow_free_text: If True, user can provide custom text input
             default: Default answer if user provides no input

         Returns:
             User's response as string
         """
         # Implementation varies by runtime (CLI, web, API)
         # Uses callback mechanism set by agent runtime

     @tool(
         name="confirm_action",
         description="Request user confirmation before proceeding",
         when_to_use="Before executing potentially dangerous or irreversible
     actions"
     )
     def confirm_action(
         action_description: str,
         details: Optional[str] = None
     ) -> bool:
         """Request yes/no confirmation from user"""
     ```

     ---

     ## Integration Points

     ### 1. Package Registration (`pyproject.toml`)

     ```toml
     [project.optional-dependencies]
     agents = [
         "abstractcore[all]",  # Includes all provider dependencies
     ]

     # Update 'all' to include agents
     all = [
         # ... existing deps ...
     ]
     ```

     ### 2. Module Exports (`abstractcore/__init__.py`)

     ```python
     # Add to exports
     from abstractcore.agents import BaseAgent, ReActAgent, CodeActAgent,
     AgentState
     ```

     ### 3. Structured Logging Integration

     Every state transition logs via `get_logger()`:
     ```python
     self.logger.info(
         "state_transition",
         from_node=prev_node,
         to_node=next_node,
         iteration=state.iteration,
         state_id=state.id,
         scratchpad_keys=list(state.scratchpad.keys())
     )
     ```

     ---

     ## Files to Modify

     | File | Changes |
     |------|---------|
     | `pyproject.toml` | Add `agents` optional dependency, update `all` |
     | `abstractcore/__init__.py` | Export agent classes |
     | `abstractcore/tools/__init__.py` | Export `ask_user`, `confirm_action` |
     | `docs/README.md` | Add agents section |

     ---

     ## Testing Strategy

     ### Test Files to Create

     ```
     tests/agents/
     ├── __init__.py
     ├── test_state_graph.py       # State machine tests
     ├── test_react_agent.py       # ReAct functionality
     ├── test_code_act_agent.py    # CodeAct functionality
     ├── test_sandbox.py           # Sandboxed execution
     ├── test_serialization.py     # State save/load/resume
     └── test_user_interaction.py  # ask_user tool
     ```

     ### Test Approach (Per CLAUDE.md: NO MOCKING)
     - Real LLM calls using Ollama (qwen3:4b for speed)
     - Real tool execution
     - Real sandbox subprocess execution
     - State serialization roundtrip tests

     ---

     ## Usage Examples

     ### ReActAgent

     ```python
     from abstractcore import create_llm
     from abstractcore.agents import ReActAgent
     from abstractcore.tools import web_search, fetch_url

     llm = create_llm("ollama", model="qwen3:4b")

     agent = ReActAgent(
         llm=llm,
         tools=[web_search, fetch_url],
         max_iterations=20,
         approve_action=lambda action, args: input(f"Allow {action}? [y/n]:
     ").lower() == "y"
     )

     # Run agent
     result = agent.run("What is the current price of Bitcoin and why did it change
      recently?")

     print(result.scratchpad["final_answer"])

     # Save state for later resume
     agent.save_state(Path("agent_state.json"))
     ```

     ### CodeActAgent

     ```python
     from abstractcore.agents import CodeActAgent

     agent = CodeActAgent(
         llm=llm,
         allowed_imports=["pandas", "numpy", "matplotlib"],
         timeout=60,
         memory_limit_mb=1024,
         approve_action=lambda action, args: True  # Auto-approve (dangerous!)
     )

     result = agent.run(
         "Load the CSV file 'sales.csv', calculate monthly totals, and create a bar
      chart"
     )

     # Access generated artifacts
     print(result.scratchpad["generated_code"])
     print(result.scratchpad["execution_results"])
     ```

     ---

     ## Migration Path for Existing Apps

     After implementation, create `docs/backlog/update-apps-agents.md` documenting
     how to:

     1. **BasicDeepSearch** → Could become `ReActAgent` with specialized research
     tools
     2. **BasicSummarizer** → Could use agents for iterative refinement
     3. **BasicExtractor** → Could use CodeAct for complex extraction logic
     4. **BasicJudge** → Could use ReAct for multi-criteria evaluation

     ---

     ## Implementation Order

     ### Phase 1: Core Infrastructure (~4 hours)
     1. `state.py` - AgentState, StateGraph, Node, Edge
     2. `scratchpad.py` - Serializable working memory
     3. `base.py` - BaseAgent ABC

     ### Phase 2: ReActAgent (~3 hours)
     1. `react.py` - Full implementation
     2. `tools/user_interaction.py` - ask_user, confirm_action
     3. Tests for ReActAgent

     ### Phase 3: CodeActAgent (~4 hours)
     1. `sandbox.py` - Sandboxed execution
     2. `code_act.py` - Full implementation
     3. Tests for CodeActAgent

     ### Phase 4: Integration (~2 hours)
     1. Package exports and pyproject.toml
     2. Documentation updates
     3. Create migration backlog doc

     **Total estimated time**: 13-16 hours

     ---

     ## Success Criteria

     1. ReActAgent completes multi-step web research task
     2. CodeActAgent generates and executes Python code safely
     3. Full state serialization/deserialization works
     4. Human-in-the-loop approval callback works
     5. ask_user tool allows LLM to request user input
     6. All tests pass with real implementations (no mocking)
     7. Structured logs capture all state transitions

     ---

     ## Risks and Mitigations

     | Risk | Mitigation |
     |------|------------|
     | Sandbox escape | Import whitelist, subprocess isolation, no network by 
     default |
     | Runaway loops | max_iterations with hard stop |
     | State corruption | Immutable state updates, validation on deserialize |
     | LLM hallucination | Tool result validation, error handling |
     | Performance | Async execution, parallel tool calls where safe |

     ---

     ## Summary

     This plan integrates ReAct and CodeAct agents into AbstractCore using:
     - **Graph-based state machine** for flexibility and observability
     - **Full serialization** for resume/replay capability
     - **Sandboxed execution** for safe code execution
     - **Human-in-the-loop** via optional callbacks and `ask_user` tool
     - **Existing primitives** (BasicSession, tools, events) to minimize new code

     Users get working agents NOW without waiting for AbstractMemory. Advanced
     memory features remain a separate concern for the future AbstractMemory
     package.





∴ Thinking…

  I've written a comprehensive plan. Now I need to:
  1. Consider if there are any remaining questions for the user2. Exit plan mode when 
  ready

  The user provided detailed requirements. Let me make sure I've addressed all of them:

  1. ✅ CodeAct with sandboxed subprocess - covered in sandbox.py
  2. ✅ Perfect observability with structured_logging - covered in logging integration 
  section
  3. ✅ Complete serialization of chat history and scratchpad - covered in AgentState
  4. ✅ Graph-based state representation - entire architecture is built on StateGraph
  5. ✅ HITL with approve_action callback - covered in BaseAgent
  6. ✅ LLM ask_user tool - covered in user_interaction.py
  7. ✅ abstractcore/agents/ location - covered
  8. ✅ Proper registration in toml - covered in pyproject.toml section
  9. ✅ Migration backlog doc - mentioned in "Migration Path" section

  I think the plan is comprehensive. Let me exit plan mode so the user can review.

⏺ 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

  ---
  Before finalizing the plan, I need to understand your architectural preferences:
                                                                                                
⏺ User answered Claude's questions:
  ⎿  · Given the deployment scenarios above, where do you PRIMARILY expect agents to run? → Both 
     equally important

     · Should BasicDeepSearch (2,174 lines, already in AbstractCore) stay in AbstractCore or move
      to AbstractAgent? → Move to AbstractAgent
     · How lightweight must the edge/mobile agent client be? → Configurable (thin or full)
     · Should we create AbstractAgent as a separate repo/package NOW, or add agents to 
     AbstractCore and potentially extract later? → AbstractAgent must be its separate 
     repo/package and the local folder is /Users/albou/projects/abstractagent/ . it will require 
     a git init, it's empty. I do not know what to do with AbstractMemory. It is a very complex 
     architecture for handling autonomous long term memory. I always thought it would be 
     AbstractCore -> powering AbstractMemory -> powering AbstractAgent, but I am now wondering if
      AbstractMemory is not gonna be powered by AbstractAgent... does it make sense ? think and 
     examine the idea and why it could be good or bad


∴ Thinking…

  Excellent! The user has made clear decisions:

  1. Agent Location: Both equally important (edge/client AND backend)
  2. BasicDeepSearch: Move to AbstractAgent (clean separation)
  3. Agent Size: Configurable (thin or full mode)
  4. Timing: Create AbstractAgent as separate repo NOW at /Users/albou/projects/abstractagent/

  And the user raises an interesting philosophical question about AbstractMemory:
  - Original thought: AbstractCore → AbstractMemory → AbstractAgent
  - New thought: Could AbstractAgent power AbstractMemory?

  Let me think deeply about the AbstractMemory dependency direction...

  AbstractMemory Dependency Analysis

  Option A: AbstractCore → AbstractMemory → AbstractAgent (Original)

  AbstractCore (LLM routing)
      ↓
  AbstractMemory (memory persistence, KG, vector stores)
      ↓
  AbstractAgent (orchestration using memory)

  Logic: Agents NEED memory, so memory comes first
  Problem: 
  - Memory is COMPLEX (multi-layered, KG, vector stores)
  - Agents are blocked waiting for memory
  - ReAct/CodeAct don't NEED complex memory

  Option B: AbstractCore → AbstractAgent → AbstractMemory (Flipped)

  AbstractCore (LLM routing)
      ↓
  AbstractAgent (orchestration with simple scratchpad)
      ↓
  AbstractMemory (advanced memory as EXTENSION)

  Logic: 
  - Agents work with simple scratchpad/history NOW
  - Memory is an ENHANCEMENT, not a prerequisite
  - Memory can be injected INTO agents as optional component

  This is actually BRILLIANT because:
  1. Users get working agents NOW
  2. Memory is optional enhancement
  3. Agents define the INTERFACE for memory
  4. Memory implementations can be swapped

  Option C: Peer Dependencies (Both build on AbstractCore)

             AbstractCore
            /           \


  AbstractAgent     AbstractMemory
           \           /
            \ (optional) /
             (can connect)

  Logic:
  - Both build on AbstractCore independently
  - AbstractAgent can optionally use AbstractMemory
  - AbstractMemory can work without agents (for RAG, etc.)

  This is most flexible because:
  1. Neither blocks the other
  2. Memory useful beyond agents (RAG, search)
  3. Agents work without memory (simple history)
  4. When both present, agents can USE memory

  My Analysis: Option C is Best

  I see a flexible architecture that decouples core components while maintaining powerful 
  interconnectivity. The peer dependency model allows maximum adaptability, letting each system 
  evolve independently while providing optional integration points. This approach prioritizes 
  modularity and extensibility, enabling diverse use cases from simple agent interactions to complex 
  memory-augmented systems.

  The key insight is creating an optional, interchangeable memory layer that doesn't constrain agent 
  functionality but can dramatically enhance capabilities when needed. The architecture reveals a 
  sophisticated, modular design with layered abstractions. At its core, the system provides flexible 
  components for agent and memory interactions, enabling dynamic tool integration and knowledge 
  management.

  The dependency graph illustrates a robust framework where AbstractAgent and AbstractMemory can 
  interact independently. Key implementations like ReActAgent, CodeActAgent, EpisodicMemory, and 
  SemanticMemory demonstrate the system's versatility in handling complex computational tasks.

  Structural highlights include optional memory connections, diverse agent types, and a pluggable 
  architecture that supports various computational paradigms without rigid constraints. I'll complete
   the partial thought from the previous thinking by copying over the next thinking, and then 
  summarize:

  (self, query: str, k: int = 5) -> List[Dict]: ...

  @abstractmethod
  def reflect(self) -> str: ...
  The interface defines key memory interactions: storing information, retrieving relevant memories, 
  and enabling reflection. By creating an abstract base class, we establish a flexible contract for 
  memory systems that agents can easily integrate.

  The adapter pattern allows seamless connection between different memory implementations. Agents can
   work with any memory system that adheres to the core interface, promoting modularity and 
  extensibility. This design enables developers to swap or upgrade memory components without 
  disrupting the core agent functionality.

⏺ This is a profound architectural question. Let me analyze the AbstractMemory dependency
  direction deeply.

  AbstractMemory Dependency Analysis

  The Three Options

  Option A: Original                Option B: Flipped                Option C: Peers
  (RECOMMENDED)
  ─────────────────                 ─────────────────
  ─────────────────────────────
  AbstractCore                      AbstractCore                              AbstractCore
       │                                 │                                   /           \
       ▼                                 ▼                                  /             \
  AbstractMemory                    AbstractAgent                   AbstractAgent
  AbstractMemory
       │                                 │                                  \             /
       ▼                                 ▼                                   \           /
  AbstractAgent                     AbstractMemory                         (optional link)

  Why Option C (Peers) is Architecturally Superior

  | Consideration                 | Original (A)                       | Flipped (B) | Peers (C)
              |
  |-------------------------------|------------------------------------|-------------|-----------
  ------------|
  | Agents blocked by memory?     | YES - must wait for complex memory | No          | No
              |
  | Memory useful without agents? | Awkward                            | No          | YES (RAG,
  search, KG) |
  | Testing complexity            | High (cascading deps)              | Medium      | Low
  (isolated)        |
  | User flexibility              | Low                                | Medium      | HIGH
              |
  | Evolution independence        | Low                                | Medium      | HIGH
              |

  Key Insight: Memory Has Multiple Consumers

  AbstractMemory is NOT just for agents:
  - RAG pipelines need semantic search
  - Knowledge management needs KG
  - Document Q&A needs retrieval
  - Chat applications need conversation history

  Making AbstractMemory dependent on AbstractAgent would be wrong because many memory use cases
  don't involve agents.

  The Interface Pattern Solution

  AbstractAgent defines an INTERFACE that memory systems can implement:

  # abstractagent/interfaces/memory.py
  class AgentMemoryInterface(ABC):
      """Interface for agent memory - AbstractMemory can implement this"""

      @abstractmethod
      def store(self, key: str, value: Any, metadata: Dict = None) -> None: ...

      @abstractmethod
      def recall(self, query: str, k: int = 5) -> List[MemoryItem]: ...

      @abstractmethod
      def get_context(self, task: str) -> str: ...

  # In AbstractMemory (when it exists):
  class MemoryManagerAgentAdapter(AgentMemoryInterface):
      """Adapts MemoryManager to work with AbstractAgent"""
      def __init__(self, memory_manager): ...

  This means:
  1. Agents work standalone with simple scratchpad
  2. Memory is pluggable when AbstractMemory exists
  3. Other memory implementations can be used (Redis, custom)

  Proposed Dependency Graph

  ┌─────────────────────────────────────────────────────────────────────────────┐
  │                        ABSTRACT SERIES ARCHITECTURE                          │
  ├─────────────────────────────────────────────────────────────────────────────┤
  │                                                                             │
  │                         ┌────────────────────┐                              │
  │                         │    AbstractCore    │                              │
  │                         │  ┌──────────────┐  │                              │
  │                         │  │ Provider API │  │                              │
  │                         │  │ Tool System  │  │                              │
  │                         │  │ Events       │  │                              │
  │                         │  │ BasicSession │  │                              │
  │                         │  └──────────────┘  │                              │
  │                         └─────────┬──────────┘                              │
  │                                   │                                         │
  │                    ┌──────────────┼──────────────┐                          │
  │                    │              │              │                          │
  │                    ▼              │              ▼                          │
  │         ┌─────────────────┐      │     ┌─────────────────┐                  │
  │         │  AbstractAgent  │      │     │ AbstractMemory  │                  │
  │         │ ┌─────────────┐ │      │     │ ┌─────────────┐ │                  │
  │         │ │ ReActAgent  │ │      │     │ │ VectorStore │ │                  │
  │         │ │ CodeActAgent│ │      │     │ │ KnowledgeGr │ │                  │
  │         │ │ StateGraph  │ │      │     │ │ Episodic    │ │                  │
  │         │ │ Scratchpad  │ │◄─────┼────▶│ │ Semantic    │ │                  │
  │         │ └─────────────┘ │ opt  │     │ └─────────────┘ │                  │
  │         └─────────────────┘      │     └─────────────────┘                  │
  │                                  │                                          │
  │              Optional integration via AgentMemoryInterface                  │
  │                                                                             │
  └─────────────────────────────────────────────────────────────────────────────┘

  Revised Architecture Summary

  | Package        | Depends On      | Provides                   | Status    |
  |----------------|-----------------|----------------------------|-----------|
  | AbstractCore   | pydantic, httpx | LLM routing, tools, events | EXISTS    |
  | AbstractAgent  | AbstractCore    | ReAct, CodeAct, StateGraph | TO CREATE |
  | AbstractMemory | AbstractCore    | VectorStore, KG, Episodic  | FUTURE    |

  Connection Pattern:
  # Using agent WITHOUT memory (simple)
  agent = ReActAgent(llm=create_llm("ollama", model="qwen3"))
  result = agent.run("Research quantum computing")

  # Using agent WITH memory (when AbstractMemory exists)
  from abstractmemory import MemoryManager
  agent = ReActAgent(
      llm=create_llm("ollama", model="qwen3"),
      memory=MemoryManager(...)  # Optional enhancement
  )

  ---
  Now let me update the plan to reflect this architecture and the decision to create
  AbstractAgent as a separate package:

⏺ Updated plan
  ⎿  Updated /Users/albou/.claude/plans/velvety-puzzling-breeze.md with 43 additions and 6      
     removals
        1 -  # Agent Integration Plan for AbstractCore
        1 +  # AbstractAgent - Separate Package Implementation Plan
        2    
        3    ## Executive Summary
        4    
        5 -  Integrate ReAct and CodeAct agents into AbstractCore using a **graph-based state 
          - machine** architecture (LangGraph-style). This enables users to have working 
          - agents NOW without waiting for the separate AbstractMemory/AbstractAgent packages.
        5 +  Create **AbstractAgent** as a separate package at 
          + `/Users/albou/projects/abstractagent/` that provides ReAct and CodeAct agents 
          + using a **graph-based state machine** architecture (LangGraph-style).
        6    
        7 -  **Key Insight**: BasicDeepSearch (2,174 lines) already proves AbstractCore can 
          - support autonomous multi-stage agents. We formalize this pattern.
        7 +  **Key Decisions Made**:
        8 +  1. **Separate package**: AbstractAgent is its own repo, not part of AbstractCore
        9 +  2. **BasicDeepSearch moves**: Will migrate from AbstractCore to AbstractAgent
       10 +  3. **Configurable client**: Thin mode (HTTP only) or full mode (local providers)
       11 +  4. **Memory is optional**: AbstractMemory will be a PEER package, not a 
          + dependency
       12    
       13    ---
       14    
       15 -  ## Architecture Decision
       15 +  ## Abstract Series Architecture
       16    
       17 +  ```
       18 +  ┌─────────────────────────────────────────────────────────────────────────────┐
       19 +  │                        ABSTRACT SERIES DEPENDENCY GRAPH                      │
       20 +  ├─────────────────────────────────────────────────────────────────────────────┤
       21 +  │                                                                             │
       22 +  │                         ┌────────────────────┐                              │
       23 +  │                         │    AbstractCore    │  (LLM routing, tools)        │
       24 +  │                         └─────────┬──────────┘                              │
       25 +  │                                   │                                         │
       26 +  │                    ┌──────────────┴──────────────┐                          │
       27 +  │                    │                             │                          │
       28 +  │                    ▼                             ▼                          │
       29 +  │         ┌─────────────────┐           ┌─────────────────┐                   │
       30 +  │         │  AbstractAgent  │◄─────────▶│ AbstractMemory  │                   │
       31 +  │         │  (orchestration)│  optional │  (persistence)  │                   │
       32 +  │         └─────────────────┘           └─────────────────┘                   │
       33 +  │                                                                             │
       34 +  │         Both are PEERS - neither depends on the other                       │
       35 +  │         Connection via AgentMemoryInterface (optional)                      │
       36 +  │                                                                             │
       37 +  └─────────────────────────────────────────────────────────────────────────────┘
       38 +  ```
       39 +  
       40 +  **Why Peers (not sequential)**:
       41 +  - AbstractMemory is useful WITHOUT agents (RAG, KG, search)
       42 +  - AbstractAgent works WITHOUT memory (simple scratchpad)
       43 +  - Users choose complexity level
       44 +  - No blocking - both can develop independently
       45 +  
       46 +  ---
       47 +  
       48 +  ## AbstractAgent Architecture
       49 +  
       50    ### Why Graph-Based State Machine (Not Chains)
       51    
       52    | Aspect | Chain-Based | Graph-Based (Chosen) |
     ...
       26    2. **Full serialization**: AgentState captures everything needed to resume
       27    3. **Observable**: Structured logging at every state transition
       28    4. **Interactive**: Built-in `ask_user` tool for bidirectional communication
       29 -  5. **Safe execution**: Sandboxed subprocess for CodeAct with resource limits
       30 -  6. **Existing primitives**: Uses BasicSession, tools, events (minimal new code)
       29 +  5. **Safe by default**: Sandboxed subprocess for CodeAct (with bypass option)
       30 +  6. **Thin or Full**: Can run as HTTP client or with full AbstractCore
       31    
       32    ---
       33    

∴ Thinking…

  I need to continue updating the plan file to reflect the new architecture. Let me update the module
   structure and other sections to reflect that this is now AbstractAgent as a separate package.

⏺ Updated plan
  ⎿  Updated /Users/albou/.claude/plans/velvety-puzzling-breeze.md with 39 additions and 12     
     removals
        68    
        69    ---
        70    
        71 -  ## Module Structure
        71 +  ## Package Structure
        72    
        73 +  **Location**: `/Users/albou/projects/abstractagent/`
        74 +  
        75    ```
        76 -  abstractcore/agents/
        77 -  ├── __init__.py              # Exports: BaseAgent, ReActAgent, CodeActAgent, 
           - AgentState
        78 -  ├── state.py                 # AgentState, StateGraph, Node, Edge (~250 lines)
        79 -  ├── base.py                  # BaseAgent ABC with graph execution (~300 lines)
        80 -  ├── react.py                 # ReActAgent implementation (~400 lines)
        81 -  ├── code_act.py              # CodeActAgent implementation (~500 lines)
        82 -  ├── scratchpad.py            # Serializable scratchpad (~100 lines)
        83 -  ├── sandbox.py               # Sandboxed code execution (~300 lines)
        84 -  └── tools/
        76 +  abstractagent/
        77 +  ├── pyproject.toml                # Package config, dependencies
        78 +  ├── README.md
        79 +  ├── abstractagent/
        80 +  │   ├── __init__.py               # Exports: ReActAgent, CodeActAgent, 
           + AgentState
        81 +  │   ├── core/
        82 +  │   │   ├── __init__.py
        83 +  │   │   ├── state.py              # AgentState, StateGraph, Node, Edge (~300 
           + lines)
        84 +  │   │   ├── base.py               # BaseAgent ABC (~350 lines)
        85 +  │   │   └── scratchpad.py         # Serializable working memory (~120 lines)
        86 +  │   ├── agents/
        87 +  │   │   ├── __init__.py
        88 +  │   │   ├── react.py              # ReActAgent (~450 lines)
        89 +  │   │   ├── code_act.py           # CodeActAgent (~550 lines)
        90 +  │   │   └── deep_search.py        # BasicDeepSearch (migrated, ~2,200 lines)
        91 +  │   ├── execution/
        92 +  │   │   ├── __init__.py
        93 +  │   │   ├── sandbox.py            # Sandboxed code execution (~350 lines)
        94 +  │   │   └── direct.py             # Direct execution (no sandbox) (~100 lines)
        95 +  │   ├── client/
        96 +  │   │   ├── __init__.py
        97 +  │   │   ├── http.py               # HTTP client for remote AbstractCore (~200 
           + lines)
        98 +  │   │   └── local.py              # Local AbstractCore wrapper (~100 lines)
        99 +  │   ├── tools/
       100 +  │   │   ├── __init__.py
       101 +  │   │   └── user_interaction.py   # ask_user, confirm_action (~180 lines)
       102 +  │   └── interfaces/
       103 +  │       ├── __init__.py
       104 +  │       └── memory.py             # AgentMemoryInterface for AbstractMemory (~80
           +  lines)
       105 +  └── tests/
       106        ├── __init__.py
       107 -      └── user_interaction.py  # ask_user, confirm_action tools (~150 lines)
       107 +      ├── test_state_graph.py
       108 +      ├── test_react_agent.py
       109 +      ├── test_code_act_agent.py
       110 +      ├── test_sandbox.py
       111 +      └── test_http_client.py
       112    ```
       113    
       114 -  **Total new code**: ~2,000 lines (comparable to BasicDeepSearch)
       114 +  **Total estimated code**: ~4,500 lines (includes migrated BasicDeepSearch)
       115    
       116    ---
       117    

∴ Thinking…

  I need to continue updating the plan. Let me update the critical files section to reflect 
  AbstractAgent being its own package, and update the paths accordingly. I should also add sections 
  about:1. Thin vs Full client mode
  2. CodeAct sandbox bypass option
  3. Migration plan for BasicDeepSearch4. Changes needed in AbstractCoreLet me continue editing the 
  plan.

⏺ Updated plan
  ⎿  Updated /Users/albou/.claude/plans/velvety-puzzling-breeze.md with 67 additions and 1      
     removal
       115    
       116    ---
       117    
       118 +  ## Deployment Modes
       119 +  
       120 +  ### Thin Mode (HTTP Client)
       121 +  For edge devices, mobile, browser (via WASM/JS wrapper):
       122 +  
       123 +  ```python
       124 +  from abstractagent import ReActAgent
       125 +  from abstractagent.client import HTTPClient
       126 +  
       127 +  # Connect to remote AbstractCore server
       128 +  client = HTTPClient(base_url="https://api.example.com")
       129 +  
       130 +  agent = ReActAgent(
       131 +      client=client,  # Uses HTTP, no local providers
       132 +      tools=[web_search, calculator]
       133 +  )
       134 +  result = agent.run("Research quantum computing")
       135 +  ```
       136 +  
       137 +  **Package size in thin mode**: Minimal - no torch, transformers, etc.
       138 +  
       139 +  ### Full Mode (Local AbstractCore)
       140 +  For backend servers with direct LLM access:
       141 +  
       142 +  ```python
       143 +  from abstractagent import ReActAgent
       144 +  from abstractcore import create_llm
       145 +  
       146 +  # Use local AbstractCore directly
       147 +  llm = create_llm("ollama", model="qwen3:4b")
       148 +  
       149 +  agent = ReActAgent(
       150 +      llm=llm,  # Direct provider access
       151 +      tools=[web_search, calculator]
       152 +  )
       153 +  result = agent.run("Research quantum computing")
       154 +  ```
       155 +  
       156 +  ---
       157 +  
       158 +  ## CodeAct Execution Modes
       159 +  
       160 +  ### Sandboxed (Default - Safe)
       161 +  ```python
       162 +  agent = CodeActAgent(
       163 +      llm=llm,
       164 +      sandbox=True,  # DEFAULT
       165 +      allowed_imports=["math", "json", "datetime"],
       166 +      timeout=30,
       167 +      memory_limit_mb=512
       168 +  )
       169 +  ```
       170 +  
       171 +  ### Direct (Bypass Sandbox - Power Users)
       172 +  ```python
       173 +  agent = CodeActAgent(
       174 +      llm=llm,
       175 +      sandbox=False,  # DANGEROUS - full system access
       176 +      # No restrictions - code runs in main process
       177 +  )
       178 +  ```
       179 +  
       180 +  **Warning**: Direct mode gives LLM full system access. Use only in trusted 
           + environments.
       181 +  
       182 +  ---
       183 +  
       184    ## Critical Files to Create
       185    
       186 -  ### 1. `abstractcore/agents/state.py` - State Graph Infrastructure
       186 +  ### 1. `abstractagent/core/state.py` - State Graph Infrastructure
       187    
       188    ```python
       189    # Core classes for graph-based agent execution

∴ Thinking…

  I need to continue updating the file paths in the rest of the document. Let me continue editing.

⏺ Updated plan
  ⎿  Updated /Users/albou/.claude/plans/velvety-puzzling-breeze.md with 1 addition and 1 removal
       242            """Validate and compile graph for execution"""
       243    ```
       244    
       245 -  ### 2. `abstractcore/agents/base.py` - BaseAgent ABC
       245 +  ### 2. `abstractagent/core/base.py` - BaseAgent ABC
       246    
       247    ```python
       248    class BaseAgent(ABC):

∴ Thinking…

  Let me continue updating the remaining file paths.

⏺ Updated plan
  ⎿  Updated /Users/albou/.claude/plans/velvety-puzzling-breeze.md with 1 addition and 1 removal
       287            """Load agent from saved state"""
       288    ```
       289    
       290 -  ### 3. `abstractcore/agents/react.py` - ReActAgent
       290 +  ### 3. `abstractagent/agents/react.py` - ReActAgent
       291    
       292    ```python
       293    class ReActAgent(BaseAgent):

∴ Thinking…

  Let me continue updating the file paths for the remaining sections.

⏺ Updated plan
  ⎿  Updated /Users/albou/.claude/plans/velvety-puzzling-breeze.md with 1 addition and 1 removal
       324            return graph.compile()
       325    ```
       326    
       327 -  ### 4. `abstractcore/agents/code_act.py` - CodeActAgent
       327 +  ### 4. `abstractagent/agents/code_act.py` - CodeActAgent
       328    
       329    ```python
       330    class CodeActAgent(BaseAgent):

∴ Thinking…

  Continue updating file paths...

⏺ Updated plan
  ⎿  Updated /Users/albou/.claude/plans/velvety-puzzling-breeze.md with 1 addition and 1 removal
       351            super().__init__(llm, **kwargs)
       352    ```
       353    
       354 -  ### 5. `abstractcore/agents/sandbox.py` - Sandboxed Execution
       354 +  ### 5. `abstractagent/execution/sandbox.py` - Sandboxed Execution
       355    
       356    ```python
       357    class Sandbox:

∴ Thinking…

  Continue updating file paths...

⏺ Updated plan
  ⎿  Updated /Users/albou/.claude/plans/velvety-puzzling-breeze.md with 1 addition and 1 removal
       394        error: Optional[str] = None
       395    ```
       396    
       397 -  ### 6. `abstractcore/agents/tools/user_interaction.py` - User Tools
       397 +  ### 6. `abstractagent/tools/user_interaction.py` - User Tools
       398    
       399    ```python
       400    @tool(

∴ Thinking…

  Now I need to update the Integration Points section and other sections to reflect that this is a 
  separate package. Let me continue.

⏺ Updated plan
  ⎿  Updated /Users/albou/.claude/plans/velvety-puzzling-breeze.md with 38 additions and 10     
     removals
       437    
       438    ---
       439    
       440 -  ## Integration Points
       440 +  ## Package Configuration
       441    
       442 -  ### 1. Package Registration (`pyproject.toml`)
       442 +  ### 1. `abstractagent/pyproject.toml`
       443    
       444    ```toml
       445 +  [project]
       446 +  name = "abstractagent"
       447 +  version = "0.1.0"
       448 +  description = "Graph-based ReAct and CodeAct agents for AbstractCore"
       449 +  dependencies = [
       450 +      "pydantic>=2.0",
       451 +      "httpx>=0.24",  # For thin client mode
       452 +  ]
       453 +  
       454    [project.optional-dependencies]
       455 -  agents = [
       456 -      "abstractcore[all]",  # Includes all provider dependencies
       455 +  # Full mode with local AbstractCore
       456 +  full = [
       457 +      "abstractcore>=2.6.0",
       458    ]
       459    
       460 -  # Update 'all' to include agents
       460 +  # All features
       461    all = [
       462 -      # ... existing deps ...
       462 +      "abstractagent[full]",
       463    ]
       464 +  
       465 +  [project.urls]
       466 +  Homepage = "https://github.com/yourorg/abstractagent"
       467    ```
       468    
       469 -  ### 2. Module Exports (`abstractcore/__init__.py`)
       469 +  ### 2. Module Exports (`abstractagent/__init__.py`)
       470    
       471    ```python
       472 -  # Add to exports
       473 -  from abstractcore.agents import BaseAgent, ReActAgent, CodeActAgent, AgentState
       472 +  from abstractagent.agents import ReActAgent, CodeActAgent
       473 +  from abstractagent.core.state import AgentState, StateGraph
       474 +  from abstractagent.core.base import BaseAgent
       475 +  from abstractagent.client import HTTPClient
       476 +  
       477 +  __all__ = [
       478 +      "ReActAgent", "CodeActAgent", "AgentState",
       479 +      "StateGraph", "BaseAgent", "HTTPClient"
       480 +  ]
       481    ```
       482    
       483    ### 3. Structured Logging Integration
       484    
       485 -  Every state transition logs via `get_logger()`:
       485 +  Every state transition logs via AbstractCore's `get_logger()`
           +   (when available) or stdlib logging:
       486 +  
       487    ```python
       488 +  # abstractagent/utils/logging.py
       489 +  try:
       490 +      from abstractcore.utils.structured_logging import get_logger
       491 +  except ImportError:
       492 +      import logging
       493 +      get_logger = logging.getLogger
       494 +  
       495    self.logger.info(
       496        "state_transition",
       497        from_node=prev_node,

∴ Thinking…

  Now I need to update the "Files to Modify" section to reflect changes in AbstractCore (like adding 
  deprecation warnings to BasicDeepSearch, etc.). Let me continue.

⏺ Updated plan
  ⎿  Updated /Users/albou/.claude/plans/velvety-puzzling-breeze.md with 29 additions and 5      
     removals
       504    
       505    ---
       506    
       507 -  ## Files to Modify
       507 +  ## Changes to AbstractCore
       508    
       509 +  After AbstractAgent is established, these changes to AbstractCore are needed:
       510 +  
       511    | File | Changes |
       512    |------|---------|
       513 -  | `pyproject.toml` | Add `agents` optional dependency, update `all` |
       514 -  | `abstractcore/__init__.py` | Export agent classes |
       515 -  | `abstractcore/tools/__init__.py` | Export `ask_user`, `confirm_action` |
       516 -  | `docs/README.md` | Add agents section |
       513 +  | `abstractcore/processing/basic_deepsearch.py` | Add deprecation warning 
           + pointing to AbstractAgent |
       514 +  | `abstractcore/__init__.py` | (Optional) Add deprecation warning for 
           + BasicDeepSearch import |
       515 +  | `README.md` | Document AbstractAgent as the agent solution |
       516 +  | `docs/backlog/update-apps-agents.md` | Migration guide for apps to use 
           + AbstractAgent |
       517    
       518 +  ### Deprecation Strategy for BasicDeepSearch
       519 +  
       520 +  ```python
       521 +  # abstractcore/processing/basic_deepsearch.py
       522 +  import warnings
       523 +  
       524 +  class BasicDeepSearch:
       525 +      def __init__(self, *args, **kwargs):
       526 +          warnings.warn(
       527 +              "BasicDeepSearch in abstractcore is deprecated. "
       528 +              "Use `from abstractagent.agents import DeepSearchAgent` instead. "
       529 +              "See: https://github.com/yourorg/abstractagent",
       530 +              DeprecationWarning,
       531 +              stacklevel=2
       532 +          )
       533 +          # ... existing init ...
       534 +  ```
       535 +  
       536 +  **Timeline**:
       537 +  1. v2.7.0: Add deprecation warning (keep working)
       538 +  2. v3.0.0: Remove BasicDeepSearch from AbstractCore
       539 +  
       540    ---
       541    
       542    ## Testing Strategy

∴ Thinking…

  Let me also update the Testing Strategy section to reflect that tests are in AbstractAgent package 
  now.

⏺ Updated plan
  ⎿  Updated /Users/albou/.claude/plans/velvety-puzzling-breeze.md with 12 additions and 9      
     removals
       541    
       542    ## Testing Strategy
       543    
       544 -  ### Test Files to Create
       544 +  ### Test Files to Create (in AbstractAgent)
       545    
       546    ```
       547 -  tests/agents/
       547 +  abstractagent/tests/
       548    ├── __init__.py
       549    ├── test_state_graph.py       # State machine tests
       550 -  ├── test_react_agent.py       # ReAct functionality
       551 -  ├── test_code_act_agent.py    # CodeAct functionality
       552 -  ├── test_sandbox.py           # Sandboxed execution
       550 +  ├── test_react_agent.py       # ReAct functionality (requires LLM)
       551 +  ├── test_code_act_agent.py    # CodeAct functionality (requires LLM)
       552 +  ├── test_sandbox.py           # Sandboxed execution (no LLM needed)
       553    ├── test_serialization.py     # State save/load/resume
       554 -  └── test_user_interaction.py  # ask_user tool
       554 +  ├── test_user_interaction.py  # ask_user tool callbacks
       555 +  ├── test_http_client.py       # Thin client mode
       556 +  └── test_memory_interface.py  # AgentMemoryInterface contract
       557    ```
       558    
       559 -  ### Test Approach (Per CLAUDE.md: NO MOCKING)
       560 -  - Real LLM calls using Ollama (qwen3:4b for speed)
       561 -  - Real tool execution
       559 +  ### Test Approach (Following AbstractCore's Philosophy: NO MOCKING)
       560 +  - Real LLM calls using Ollama (qwen3:4b for speed) when testing full mode
       561 +  - Real HTTP calls to AbstractCore server when testing thin mode
       562    - Real sandbox subprocess execution
       563    - State serialization roundtrip tests
       564 +  - Tests gracefully skip when LLM not available
       565    
       566    ---
       567    

∴ Thinking…

  Now let me update the Implementation Order section to reflect the new package structure and the 
  additional work needed.

⏺ Updated plan
  ⎿  Updated /Users/albou/.claude/plans/velvety-puzzling-breeze.md with 37 additions and 16     
     removals
       629    
       630    ## Implementation Order
       631    
       632 -  ### Phase 1: Core Infrastructure (~4 hours)
       633 -  1. `state.py` - AgentState, StateGraph, Node, Edge
       634 -  2. `scratchpad.py` - Serializable working memory
       635 -  3. `base.py` - BaseAgent ABC
       632 +  ### Phase 0: Project Setup (~1 hour)
       633 +  1. `git init` at `/Users/albou/projects/abstractagent/`
       634 +  2. Create `pyproject.toml` with dependencies
       635 +  3. Create package structure directories
       636 +  4. Set up basic README.md
       637    
       638 -  ### Phase 2: ReActAgent (~3 hours)
       639 -  1. `react.py` - Full implementation
       638 +  ### Phase 1: Core Infrastructure (~5 hours)
       639 +  1. `core/state.py` - AgentState, StateGraph, Node, Edge
       640 +  2. `core/scratchpad.py` - Serializable working memory
       641 +  3. `core/base.py` - BaseAgent ABC with graph execution
       642 +  4. `interfaces/memory.py` - AgentMemoryInterface
       643 +  5. `utils/logging.py` - Logging with AbstractCore fallback
       644 +  6. Unit tests for state graph
       645 +  
       646 +  ### Phase 2: Client Modes (~3 hours)
       647 +  1. `client/local.py` - Local AbstractCore wrapper
       648 +  2. `client/http.py` - HTTP client for remote AbstractCore
       649 +  3. Tests for both client modes
       650 +  
       651 +  ### Phase 3: ReActAgent (~4 hours)
       652 +  1. `agents/react.py` - Full implementation
       653    2. `tools/user_interaction.py` - ask_user, confirm_action
       654 -  3. Tests for ReActAgent
       654 +  3. Integration tests for ReActAgent
       655    
       656 -  ### Phase 3: CodeActAgent (~4 hours)
       657 -  1. `sandbox.py` - Sandboxed execution
       658 -  2. `code_act.py` - Full implementation
       659 -  3. Tests for CodeActAgent
       656 +  ### Phase 4: CodeActAgent (~5 hours)
       657 +  1. `execution/sandbox.py` - Sandboxed subprocess execution
       658 +  2. `execution/direct.py` - Direct execution (no sandbox)
       659 +  3. `agents/code_act.py` - Full implementation
       660 +  4. Integration tests for CodeActAgent
       661    
       662 -  ### Phase 4: Integration (~2 hours)
       663 -  1. Package exports and pyproject.toml
       664 -  2. Documentation updates
       665 -  3. Create migration backlog doc
       662 +  ### Phase 5: Migration (~4 hours)
       663 +  1. Copy and adapt `BasicDeepSearch` from AbstractCore
       664 +  2. Refactor to use StateGraph infrastructure
       665 +  3. Tests for migrated DeepSearchAgent
       666    
       667 -  **Total estimated time**: 13-16 hours
       667 +  ### Phase 6: Documentation & Release (~2 hours)
       668 +  1. Complete README with examples
       669 +  2. Create CHANGELOG.md
       670 +  3. Update AbstractCore docs to point to AbstractAgent
       671 +  4. Create `docs/backlog/update-apps-agents.md` in AbstractCore
       672    
       673 +  **Total estimated time**: 24-28 hours
       674 +  
       675    ---
       676    
       677    ## Success Criteria

∴ Thinking…

  Now let me update the Summary section at the end of the plan to reflect the new architecture 
  decisions.

⏺ Updated plan
  ⎿  Updated /Users/albou/.claude/plans/velvety-puzzling-breeze.md with 28 additions and 7      
     removals
       700    
       701    ## Summary
       702    
       703 -  This plan integrates ReAct and CodeAct agents into AbstractCore using:
       704 -  - **Graph-based state machine** for flexibility and observability
       705 -  - **Full serialization** for resume/replay capability
       706 -  - **Sandboxed execution** for safe code execution
       707 -  - **Human-in-the-loop** via optional callbacks and `ask_user` tool
       708 -  - **Existing primitives** (BasicSession, tools, events) to minimize new code
       703 +  This plan creates **AbstractAgent** as a new, separate package that provides:
       704    
       705 -  Users get working agents NOW without waiting for AbstractMemory. Advanced memory
           -  features remain a separate concern for the future AbstractMemory package.
       705 +  ### Architecture
       706 +  - **Separate repo**: `/Users/albou/projects/abstractagent/` (requires `git 
           + init`)
       707 +  - **Peer to AbstractMemory**: Neither depends on the other, both build on 
           + AbstractCore
       708 +  - **Graph-based state machine**: LangGraph-style for flexibility and 
           + observability
       709 +  - **Full serialization**: Resume/replay capability for long-running tasks
       710 +  
       711 +  ### Key Features
       712 +  - **ReActAgent**: Think → Act → Observe loop with tool calling
       713 +  - **CodeActAgent**: Generate and execute Python code safely
       714 +  - **Sandboxed execution**: Default safe mode with bypass option
       715 +  - **Thin/Full client**: HTTP-only for edge, full AbstractCore for backend
       716 +  - **Human-in-the-loop**: Optional callbacks and `ask_user` tool
       717 +  - **AgentMemoryInterface**: Ready for AbstractMemory when it exists
       718 +  
       719 +  ### Migration
       720 +  - **BasicDeepSearch** moves from AbstractCore to AbstractAgent
       721 +  - Deprecation warnings in AbstractCore v2.7.0
       722 +  - Full removal in AbstractCore v3.0.0
       723 +  
       724 +  ### Why This Architecture
       725 +  1. **Users get agents NOW** - No waiting for AbstractMemory
       726 +  2. **AbstractCore stays thin** - Maintains stateless routing philosophy
       727 +  3. **Maximum flexibility** - Agents can run anywhere (edge, browser, backend)
       728 +  4. **Clean separation** - Each package has single responsibility
       729 +  5. **Natural evolution** - AbstractMemory can enhance agents when ready
       730 +  
       731 +  **Estimated effort**: 24-28 hours for full implementation

∴ Thinking…

  The plan is now comprehensive. Let me read the final plan to make sure everything is in order 
  before asking the user if they have any final questions.

⏺ Reading Plan(~/.claude/plans/velvety-puzzling-breeze.md · lines 1-100)
  ⎿  Read 100 lines

∴ Thinking…

  The plan is comprehensive. Let me now present a summary to the user and ask if they have any final 
  questions before exiting plan mode.

⏺ The plan is now comprehensive. Let me present the key architectural decisions for your final
  review:

  Final Architecture Summary

  1. Package Separation

  - AbstractAgent → New package at /Users/albou/projects/abstractagent/ (requires git init)
  - AbstractCore → Stays as-is, with deprecation warnings added to BasicDeepSearch
  - AbstractMemory → Future PEER package (not dependency)

  2. AbstractMemory Position (Your Question)

  After analysis, AbstractMemory should be a PEER to AbstractAgent, not powered by it:

  AbstractCore (foundation)
      ├── AbstractAgent (orchestration) ←── optional ──→ AbstractMemory (persistence)

  Rationale:
  - AbstractMemory is useful WITHOUT agents (RAG, document search, KG)
  - AbstractAgent works WITHOUT memory (simple scratchpad)
  - Neither blocks the other's development
  - Connection via AgentMemoryInterface when both exist

  3. Deployment Flexibility

  - Thin mode: HTTP client only (~200KB) - for edge/mobile/browser
  - Full mode: Direct AbstractCore import - for backend servers

  4. CodeAct Safety

  - Default: Sandboxed subprocess (safe)
  - Bypass: sandbox=False for power users who need full system access

  5. Estimated Effort

  24-28 hours total for full implementation including:
  - Core infrastructure (StateGraph, BaseAgent)
  - ReActAgent and CodeActAgent
  - HTTP and local clients
  - BasicDeepSearch migration
  - Tests and documentation
