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CLAUDE.md: lean — commands, key conventions, fast tool guide, @ARCHITECTURE.md import routecheck/CLAUDE.md: purpose, access paths, env vars, gotchas openmemory/CLAUDE.md: tools, two Ollama instances, prompts, notes Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
27 lines
1.3 KiB
Markdown
27 lines
1.3 KiB
Markdown
# openmemory
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FastMCP server wrapping mem0 for persistent per-session memory, backed by Qdrant + nomic-embed-text.
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## Tools exposed (MCP)
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- `add_memory(text, user_id)` — extract facts from a conversation turn and store in Qdrant
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- `search_memory(query, user_id)` — semantic search, returns results with score ≥ 0.5
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- `get_all_memories(user_id)` — dump all stored memories for a session
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These are called directly by `agent.py` (outside the agent loop), never exposed to the LLM as tools.
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## Two Ollama instances
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- **GPU** (`OLLAMA_GPU_URL`, port 11436) — extraction model (`qwen2.5:1.5b`): pulls facts from conversation text
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- **CPU** (`OLLAMA_CPU_URL`, port 11435) — embedding model (`nomic-embed-text`): 50–150 ms per query
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## Prompts
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Custom `EXTRACTION_PROMPT` starts with `/no_think` to suppress qwen3 chain-of-thought and get clean JSON output. Custom `UPDATE_MEMORY_PROMPT` handles deduplication — mem0 merges new facts with existing ones rather than creating duplicates.
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## Notes
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- Qdrant collection is created automatically on first use
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- Memory is keyed by `user_id` which equals `session_id` in `agent.py`
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- Extraction runs after the reply is sent (background task) — GPU contention with medium model is avoided since the semaphore is released before `_store_memory()` is scheduled
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