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

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

🚀 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:

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 mcp add memory -- memory server

Restart Claude Code. Memory tools will appear automatically.

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/

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

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

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

🧠 Persistent Memory – Context survives across sessions with semantic search 🔍 Smart Retrieval – Finds relevant context automatically using AI embeddings ⚡ 5ms Speed – Instant context injection, no latency ☁️ Cloud Sync – Optional Cloudflare backend for team collaboration 🔒 Privacy-First – Local-first, you control your data 📊 Web Dashboard – Visualize and manage memories at http://localhost:8000 🧬 Knowledge Graph – Interactive D3.js visualization of memory relationships 🏠 Homelab Quality Scoring – Point scoring at any OpenAI-compatible endpoint (Ollama, LiteLLM, vLLM) 🔗 Entity Extraction – Auto-links @mentions, #tags, URLs, and file paths to a queryable entity graph 💡 Insight Cards – Consolidation surfaces patterns, trends, and knowledge gaps as structured insights 🏷️ Tag Match Filtering – tag_match=AND/OR on memory_search for precise multi-tag queries

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

🛠️ 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

📚 Document