自托管记忆管理服务

claude mcp add --scope user --transport stdio mem0 \
  --env MEM0_USER_ID=your-user-id \
  -- uvx --from git+https://github.com/elvismdev/mem0-mcp-selfhosted.git mem0-mcp-selfhosted

claude mcp add --scope user --transport stdio mem0 \
  --env MEM0_PROVIDER=ollama \
  --env MEM0_LLM_MODEL=qwen3:14b \
  --env MEM0_USER_ID=your-user-id \
  -- uvx --from git+https://github.com/elvismdev/mem0-mcp-selfhosted.git mem0-mcp-selfhosted

{
  "mcpServers": {
    "mem0": {
      "command": "uvx",
      "args": ["--from", "git+https://github.com/elvismdev/mem0-mcp-selfhosted.git", "mem0-mcp-selfhosted"],
      "env": {
        "MEM0_PROVIDER": "ollama",
        "MEM0_LLM_MODEL": "qwen3:14b",
        "MEM0_USER_ID": "your-user-id"
      }
    }
  }
}

> Search my memories for TypeScript preferences
> Remember that I prefer Hatch for Python packaging
> Show me all entities in my knowledge graph

# MCP Servers

- **mem0**: Persistent memory across sessions. At the start of each session, `search_memories` for relevant context before asking the user to re-explain anything. Use `add_memory` whenever you discover project architecture, coding conventions, debugging insights, key decisions, or user preferences. Use `update_memory` when prior context changes. Save information like: "This project uses PostgreSQL with Prisma", "Tests run with pytest -v", "Auth uses JWT validated in middleware". When in doubt, save it, future sessions benefit from over-remembering.

mem0-install-hooks

mem0-install-hooks --global

Claude Code
  |
  ├── MCP stdio/SSE/streamable-http
  │     |
  │     ├── env.py               ← Centralized env var readers (whitespace-safe)
  │     ├── auth.py              ← Hybrid token fallback chain + OAT self-refresh
  │     ├── llm_anthropic.py     ← Custom Anthropic LLM provider (OAT + structured outputs)
  │     ├── llm_ollama.py        ← Custom Ollama LLM provider (restored tool-calling)
  │     ├── config.py            ← Env vars → MemoryConfig dict (provider + URL cascades)
  │     ├── helpers.py           ← Error wrapper, concurrency lock, safe bulk-delete, monkey-patches
  │     ├── graph_tools.py       ← Direct Neo4j Cypher queries (lazy driver)
  │     ├── llm_router.py        ← Split-model graph LLM router (gemini_split)
  │     ├── __init__.py          ← Telemetry suppression (before any mem0 import)
  │     └── server.py            ← FastMCP orchestrator (11 tools + prompt)
  │           |
  │           ├── mem0ai Memory class
  │           │     ├── Vector: LLM fact extraction → Ollama embed → Qdrant
  │           │     └── Graph: LLM entity extraction (tool calls) → Neo4j
  │           |
  │           └── Infrastructure
  │                 ├── Qdrant          ← Vector store
  │                 ├── Ollama          ← Embeddings
  │                 ├── Neo4j           ← Knowledge graph (optional)
  │                 └── Anthropic/Ollama ← Main LLM (configurable)
  |
  └── Session Hooks (subprocess, not MCP)
        |
        └── hooks.py             ← Cross-session memory (SessionStart + Stop hooks)
              ├── context_main()   → Injects memories as additionalContext on startup/compact
              ├── stop_main()      → Saves session summary to mem0 on exit
              └── install_main()   → CLI to patch .claude/settings.json

MEM0_ENABLE_GRAPH=true
MEM0_GRAPH_LLM_PROVIDER=ollama
MEM0_GRAPH_LLM_MODEL=qwen3:14b

MEM0_ENABLE_GRAPH=true
MEM0_GRAPH_LLM_PROVIDER=gemini
MEM0_GRAPH_LLM_MODEL=gemini-2.5-flash-lite
GOOGLE_API_KEY=your-google-api-key

MEM0_ENABLE_GRAPH=true
MEM0_GRAPH_LLM_PROVIDER=gemini_split
GOOGLE_API_KEY=your-google-api-key
MEM0_GRAPH_CONTRADICTION_LLM_PROVIDER=anthropic
MEM0_GRAPH_CONTRADICTION_LLM_MODEL=claude-opus-4-6

# Install with dev dependencies
pip install -e ".[dev]"

# Run unit tests
python3 -m pytest tests/unit/ -v

# Run contract tests (validates mem0ai internal API assumptions)
python3 -m pytest tests/contract/ -v

# Run integration tests (requires live Qdrant + Neo4j + Ollama)
python3 -m pytest tests/integration/ -v

# Run all tests
python3 -m pytest tests/ -v