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

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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.

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

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🚀 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:

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⚡ 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

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✨ 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 and PPTX require optional parsing tools. Document ingestion guide

Capture and maintain knowledge

• ⚡ Automatic session capture — Configurable Claude Code hooks retrieve context and save session learnings; the local memory_harvest tool extracts decisions, bugs, conventions, and lessons from transcripts. Hooks setup · Harvest configuration
• 💡 Autonomous consolidation — Schedule clustering, compression, association discovery, and controlled forgetting. Insight cards surface patterns, trends, and knowledge gaps. Consolidation guide
• 💭 Beliefs and contradiction handling — Derive confidence-scored beliefs from observations. Heuristic contradiction detection can mark older memories as superseded, hiding them from default retrieval while retaining them for explicit lookup.
• 🤖 Agent bootstrap profiles — get_bootstrap_profile assembles behavioral context for a new agent within a token budget, with optional task-aware retrieval. Requires MCP_BOOTSTRAP_ENABLED=true; profiles are not isolated by project.
• 🧬 Knowledge graph and entities — Explore typed causal relationships in the interactive graph. 🔗 Entity extraction links @mentions, #tags, URLs, and file paths to a queryable entity graph. Knowledge graph guide
• 🏠 Quality scoring — Use your own OpenAI-compatible endpoint, including Ollama, LiteLLM, or vLLM. Configuration guide

Run locally and share across devices

• 🔒 Local-first storage — Local ONNX embeddings and SQLite storage keep core memory operations on your infrastructure. Optional LLM features use the providers you configure.
• ☁️ Cloud or self-hosted sync — Use Cloudflare or another MCP Memory Service instance as a Hybrid secondary. Separately enabled delta-sync exchanges changes between hosts; it is off by default. Self-hosted sync guide
• 📦 Import, export, and migration — Move memories through JSON import/export, including conversion of mem0 exports. Import/export tools · mem0 migration guide
• 💾 Database backups — Configure scheduled backups or trigger one manually through the HTTP API. Inspect backup status and available snapshots through the backup endpoints in the live API reference at /api/docs.
• 📊 Web dashboard — Search, browse, visualize, and manage memories at http://localhost:8000.

The dashboard has eight tabs — Dashboard, Search, Browse, Documents, Manage, Analytics, Quality, API Docs. Two-minute walkthrough on YouTube · Web Dashboard Guide

How it compares to Mem0, Zep and the MCP-native alternatives, benchmark results, and deployments people run in production: mcpmemory.services

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🛠️ Configuration Highlights

memory launch                  # Start HTTP server in background (127.0.0.1:8000)
memory launch --port 8192      # Custom port
memory info                    # Status and health
memory logs --lines 50         # Recent logs
memory stop                    # Stop server

These commands are optimized for fast startup and avoid loading heavy ML dependencies unless needed.

⚠️ Security note: the server binds to 127.0.0.1 (localhost only) by default. --host 0.0.0.0 / MCP_HTTP_HOST=0.0.0.0 exposes the API to your network — do that only in trusted environments with authentication and firewall rules, or behind TLS termination or a VPN overlay.

Backends, embedding models, quality scoring and every environment variable: Configuration Guide

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📚 Documentation

• Setup Guide – Decision tree and step-by-step paths
• Agent Integration Guides – LangGraph, CrewAI, AutoGen, HTTP generic
• Remote MCP Setup – claude.ai and ChatGPT via HTTPS + OAuth
• Configuration Guide – Backend options and customization
• Architecture Overview – How it works under the hood
• Knowledge Graph Dashboard – Interactive graph visualization
• Benchmarks – LongMemEval, DevBench, LoCoMo, with run commands
• Migration Guide – Upgrading between major versions
• Troubleshooting – Common issues and solutions
• Wiki – Long-form guides and API reference
• Full documentation index – Everything else

Also listed on Glama and Spark.

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📦 Releases

Every release, with upgrade notes: CHANGELOG.md · GitHub Releases · archived history

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🤝 Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines and SECURITY.md for reporting a vulnerability.

Who authors this project, who holds copyright, and what every change passes before it reaches main: AUTHORSHIP.md.

Quick Development Setup:

git clone https://github.com/doobidoo/mcp-memory-service.git
cd mcp-memory-service
pip install -e .  # Editable install
pytest tests/      # Run test suite

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Supporting the Project

MCP Memory Service is maintained by one person. If it saves you or your company time, you can support its development via Ko-fi, Buy Me a Coffee or PayPal: SPONSORS.md.