主权AI系统

Hardware Requirements

The orchestrator runs on CPU. GPU VRAM is only needed on inference nodes (Ollama). The moe-codex Enterprise Data Stack (NiFi 4 GB, lakeFS 512 MB, Marquez 1.5 GB + 2× Postgres) adds significant overhead — plan a dedicated host or at least 8 GB additional RAM.

---

One-Line Install

curl -sSL https://moe-sovereign.org/install.sh | bash

Manual Setup

git clone https://github.com/h3rb3rn/moe-sovereign.git
cd moe-sovereign
cp .env.example .env
nano .env                      # Set credentials and inference server URLs
sudo docker compose up -d
curl http://localhost:8002/v1/models

---

Deployment Targets

All targets use the same OCI image --- no code forks, no feature loss.

---

Quick Start

Pipeline Stages

Module Structure

The orchestrator codebase is organised into focused packages. main.py is a thin entry point (~1 500 LOC) holding the FastAPI app, lifespan, middleware, and graph wiring. All domain logic lives in dedicated packages:

moe-infra/
├── main.py                    # FastAPI app, lifespan, middleware, graph wiring (~1 500 LOC)
├── config.py                  # All os.getenv() — typed config constants
├── state.py                   # Shared mutable globals (redis_client, _userdb_pool, …)
├── prompts.py                 # Static prompt text + routing detection regexes
├── metrics.py                 # Single Prometheus registry
├── parsing.py                 # Stateless parsers: JSON extraction, confidence, history truncation
├── context_budget.py          # Per-model context-window estimation
│
├── routes/                    # FastAPI APIRouters (one per concern)
│   ├── health.py              # /health, /metrics
│   ├── watchdog.py            # /api/watchdog/*, Starfleet feature toggles
│   ├── mission_context.py     # /api/mission-context
│   ├── graph.py               # /graph/*
│   ├── feedback.py            # /v1/feedback, /v1/memory/ingest
│   ├── admin_*.py             # Benchmark, ontology, stats admin endpoints
│   ├── models.py              # /v1/models
│   ├── ollama_compat.py       # /api/* (Ollama protocol)
│   └── anthropic_compat.py    # /v1/messages, /v1/responses, /v1/chat/completions
│
├── services/                  # Business logic — no FastAPI imports
│   ├── auth.py                # OIDC + API key validation + budget enforcement
│   ├── tracking.py            # Usage logging, request lifecycle, budget counters
│   ├── routing.py             # Expert template + per-template prompt resolution
│   ├── templates.py           # Expert template + Claude Code profile loading
│   ├── llm_instances.py       # ChatOpenAI singletons (judge, planner, ingest, search)
│   ├── inference.py           # Node selection, fallback chain, Thompson sampling
│   ├── helpers.py             # Progress reports, semantic memory, self-evaluation
│   ├── skills.py              # Server-side skill resolution + ADMIN_APPROVED hard-lock
│   ├── healer.py              # Ontology gap-healer (one-shot + dedicated subprocess)
│   ├── kafka.py               # Fire-and-forget Kafka publish helper
│   └── pipeline/              # OpenAI / Anthropic / Ollama / Responses API handlers
│       ├── chat.py            # OpenAI chat completions
│       ├── anthropic.py       # Anthropic Messages API + tool/MoE/reasoning handlers
│       ├── ollama.py          # Ollama-protocol streaming wrappers
│       └── responses.py       # OpenAI Responses API
│
├── graph/                     # LangGraph node implementations
│   ├── router_nodes.py        # cache_lookup, semantic_router, fuzzy_router, _route_cache
│   ├── tool_nodes.py          # mcp_node, graph_rag_node, math_node_wrapper
│   ├── planner.py             # planner_node + plan sanitization + topological levels
│   ├── expert.py              # expert_worker (parallel expert execution)
│   ├── research.py            # research_node + research_fallback + domain extraction
│   └── synthesis.py           # merger_node, thinking_node, resolve_conflicts_node, critic_node
│
├── pipeline/
│   ├── __init__.py            # LangGraph graph builder — assembles nodes into the pipeline DAG
│   └── state.py               # AgentState TypedDict (67 fields across 3 categories)
│
├── web_search.py              # SearXNG integration with domain-reliability scoring
├── math_node.py               # SymPy-backed math node (solve, integrate, differentiate)
├── graph_rag/                 # GraphRAG query, entity linking, ontology, corrections
├── federation/                # Push / pull federation client to MoE Libris hubs
├── mcp_server/                # 28 MCP precision tools (AST-whitelisted)
├── admin_ui/                  # Admin backend: experts, users, budgets, cleanup manager
├── prompts/systemprompt/      # 15 expert system prompts (English, "Respond in German.")
├── tests/                     # 195 unit + integration + smoke tests (all green)
└── benchmarks/                # Overnight benchmark suite, GAIA runner, result injection

The orchestrator started as an 11 190-line monolith in main.py. A 14-phase split (Q2 2026) decomposed it into the structure above without a single behavioural change — every phase ended with the full test suite green. See docs/ARCHITECTURE.md for the detailed module map.

---

C) Enterprise Data Management (`moe-codex` Extension)

This feature group requires the optional moe-codex enterprise stack (Apache NiFi, Marquez/OpenLineage, lakeFS). It is not part of the moe-sovereign core and is deployed as a separate compose stack. See the moe-codex repository for setup instructions.

---

Agent Integration

---