MCP Memory Service：让智能体共享记忆

Open-source persistent memory for AI agent pipelines (LangGraph, CrewAI, AutoGen) and Claude. REST API + knowledge graph + autonomous con…

mcp-memory-service Persistent Shared Memory for AI Agent Pipelines Open-source memory backend for AI agents — REST API, MCP, OAuth, CLI, dashboard. One self-hosted service, every transport. Agents store decisions, share causal knowledge graphs, and retrieve context in 5ms — without cloud lock-in or API costs. Works with LangGraph · CrewAI · AutoGen · any HTTP client · Claude Desktop · OpenCode ▶ The 3D knowledge graph in motion — every memory a glowing node, every relationship a curved edge. Why Agents Need This Your AI assistant forgets everything when you start a new chat. You spend 10 minutes re-explaining your architecture. Again. MCP Memory Service captures project context, architecture decisions, and code patterns automatically — new sessions start with everything already known. Without mcp-memory-service With mcp-memory-service Each agent run starts from zero Agents retrieve prior decisions in 5ms Memory is local to one graph/run Memory is shared across all agents and runs You manage Redis + Pinecone + glue code One self-hosted service, zero cloud cost No causal relationships between facts Knowledge graph with typed edges (causes, fixes, contradicts) Context window limits create amnesia Autonomous consolidation compresses old memories Key capabilities for agent pipelines: Framework-agnostic REST API — no MCP client library needed; the live endpoint list is at /api/docs Knowledge graph — agents share causal chains, not just facts X-Agent-ID header — auto-tag memories by agent identity for scoped retrieval conversation_id — bypass deduplication for incremental conversation storage SSE events — real-time notifications when any agent stores or deletes a memory Embeddings run locally via ONNX — memory never leaves your infrastructure 🚀 Get Started in 60 Seconds Not sure which setup fits? The Setup Guide walks you to the right path in under a minute. 1. Install: pip install mcp-memory-service 2. Configure your AI client: Claude Desktop Add to your config file: macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json Linux: ~/.config/Claude/claude_desktop_config.json { "mcpServers": { "memory": { "command": "memory", "args": ["server"] } } } Restart Claude Desktop. Your AI now remembers everything across sessions. Claude Code claude mcp add memory -- memory server Restart Claude Code. Memory tools will appear automatically. Agent pipelines (REST API — LangGraph, CrewAI, AutoGen, any HTTP client) MCP_ALLOW_ANONYMOUS_ACCESS=true memory server --http # REST API running at http://localhost:8000 Store a memory with POST /api/memories, search with POST /api/search, retrieve by tag with POST /api/search/by-tag. Send X-Agent-ID: <id> on a store request and the server tags the memory agent:<id>, which a tag search then scopes retrieval by. Worked examples per framework, the tag conventions and the async patterns: docs/agents/ OpenCode MCP_ALLOW_ANONYMOUS_ACCESS=true memory server --http The plugin ships as repository files for the local plugin directory, so clone the repository once even if you installed from PyPI. Install steps, the /memory slash command and the endpoint override: opencode/README.md Codex (automatic memory hooks) Start the Memory Service HTTP API, then install the hooks from this repository: python3 codex/install_hooks.py Restart Codex and review the hooks with /hooks. They retrieve project context, capture new learnings, and checkpoint outcomes before compaction and at session end. See Codex hook setup for prerequisites, authentication, and project-local installation. claude.ai and ChatGPT (browser — Remote MCP) Remote MCP puts persistent memory in the browser on any device, no desktop app required: OAuth 2.0 over HTTPS, self-hosted or cloud-hosted. Both claude.ai and ChatGPT (Developer Mode) connect to the same endpoint. Cloudflare Tunnel quick start, Let's Encrypt, nginx, Caddy and Docker production setups: Remote MCP Setup · 5-minute tutorial Advanced: custom backends and team setup git clone https://github.com/doobidoo/mcp-memory-service.git cd mcp-memory-service python scripts/installation/install.py Choose from SQLite (local, fast, single-user), Cloudflare (cloud, multi-device sync), Hybrid (5ms local reads with background cloud sync — recommended for production) or Milvus (dedicated vector DB: Lite file, self-hosted, or Zilliz Cloud). For self-hosted team setups, the Hybrid backend can sync to another HTTP MCP Memory Service instead of Cloudflare using MCP_HYBRID_SECONDARY_BACKEND=http. For long-lived services, prefer Docker Milvus or Zilliz Cloud over Milvus Lite — why. ⚡ Works With Your Favorite AI Tools Agent frameworks (REST): LangGraph · CrewAI · AutoGen · OpenClaw/Nanobot · any HTTP client CLI and terminal (MCP): Claude Code · Gemini CLI · OpenCode · Codex CLI · Goose · Aider · Amp Desktop and IDE (MCP): Claude Desktop · VS Code · Cursor · Windsurf · Raycast · JetBrains · Zed Chat (MCP): ChatGPT (Developer Mode) · claude.ai (Remote MCP over HTTPS) Full list, plus clients without OAuth such as Home Assistant: docs/integrations.md ✨ Features Store and retrieve context 🧠 Persistent memory — Keep decisions, notes, and project context across sessions and agents. 🔍 Semantic and keyword search — Combine vector search with BM25; use mode="ranked" to weigh recency, access frequency, and quality. An optional fallback cascade tries exact and tag matching when semantic results are sparse. 🏷️ Scoped retrieval — Filter by tags with AND/OR matching, natural-language time ranges, and store partitions (default, docs, or all for cross-store search). 📝 Optional search summaries — Request summarize=true for query-aware summaries with validated source references. Requires MCP_SEARCH_SUMMARIZE_ENABLED=true and a configured LLM provider; unavailable summaries fall back to raw results. Search API and examples 📄 Document ingestion — Import PDF, text, and Markdown with chunking for retrieval. Additional formats such as DOCX