mcp-memory-service
Persistent Shared Memory for AI Agent Pipelines
Open-source memory backend for multi-agent systems.
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
🎬 See It in Action
Watch the Web Dashboard Walkthrough on YouTube — Semantic search, tag browser, document ingestion, analytics, quality scoring, and API docs in under 2 minutes.
🌐 Works with claude.ai (Browser)
Unlike desktop-only MCP servers, mcp-memory-service supports Remote MCP for native claude.ai integration.
What this means:
- ✅ Use persistent memory directly in your browser (no Claude Desktop required)
- ✅ Works on any device (laptop, tablet, phone)
- ✅ Enterprise-ready (OAuth 2.0 + HTTPS + CORS)
- ✅ Self-hosted OR cloud-hosted (your choice)
5-Minute Setup:
# 1. Start server with Remote MCP enabled
MCP_STREAMABLE_HTTP_MODE=1 \
MCP_SSE_HOST=0.0.0.0 \
MCP_SSE_PORT=8765 \
MCP_OAUTH_ENABLED=true \
python -m mcp_memory_service.server
# 2. Expose via Cloudflare Tunnel (or your own HTTPS setup)
cloudflared tunnel --url http://localhost:8765
# → Outputs: https://random-name.trycloudflare.com
# 3. In claude.ai: Settings → Connectors → Add Connector
# Paste the URL: https://random-name.trycloudflare.com/mcp
# OAuth flow will handle authentication automatically
Production Setup: See Remote MCP Setup Guide for Let's Encrypt, nginx, and firewall configuration.
Step-by-Step Tutorial: Blog: 5-Minute claude.ai Setup | Wiki Guide
Why Agents Need This
| 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 — 15 endpoints, no MCP client library needed
- Knowledge graph — agents share causal chains, not just facts
X-Agent-ID header — auto-tag memories by agent identity for scoped retrievalconversation_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
Agent Quick Start
pip install mcp-memory-service
MCP_ALLOW_ANONYMOUS_ACCESS=true memory server --http
# REST API running at http://localhost:8000
import httpx
BASE_URL = "http://localhost:8000"