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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🎬 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.

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🌐 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

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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 — 76 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 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

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"

# Store — auto-tag with X-Agent-ID header
async with httpx.AsyncClient() as client:
    await client.post(f"{BASE_URL}/api/memories", json={
        "content": "API rate limit is 100 req/min",
        "tags": ["api", "limits"],
    }, headers={"X-Agent-ID": "researcher"})
    # Stored with tags: ["api", "limits", "agent:researcher"]

# Search — scope to a specific agent
    results = await client.post(f"{BASE_URL}/api/memories/search", json={
        "query": "API rate limits",
        "tags": ["agent:researcher"],
    })
    print(results.json()["memories"])

Framework-specific guides: docs/agents/

Real-World: Multi-Agent Cluster with Shared Memory

"After I work with one of the cluster agents on something I want my local agent to know about, the cluster agent adds a special tag to the memory entry that my local agent recognizes as a message from a cluster agent. So they end up using it as a comms bridge — and it's pretty delightful." — @jeremykoerber, issue #591

A 5-agent openclaw cluster uses mcp-memory-service as shared state and as an inter-agent messaging bus — without any custom protocol. Cluster agents tag memories with a sentinel like msg:cluster, and the local agent filters on that tag to receive cross-cluster signals. The memory service becomes the coordination layer with zero additional infrastructure.

# Cluster agent stores a learning and flags it for the local agent
await client.post(f"{BASE_URL}/api/memories", json={
    "content": "Rate limit on provider X is 50 RPM — switch to provider Y after 40",
    "tags": ["api", "limits", "msg:cluster"],       # sentinel tag
}, headers={"X-Agent-ID": "cluster-agent-3"})

# Local agent polls for cluster messages
results = await client.post(f"{BASE_URL}/api/memories/search", json={
    "query": "messages from cluster",
    "tags": ["msg:cluster"],
})

This pattern — tags as inter-agent signals — emerges naturally from the tagging system and requires no additional infrastructure.

Real-World: Self-Hosted Docker Stack with Cloudflare Tunnel

"The quality of life that session-independent memory adds to AI workflows is immense. File-based memory demands constant discipline. Semantic recall from a live database doesn't. Storing data on my own hardware while making it remotely accessible across platforms turned out to be a feature I didn't know I needed." — @PL-Peter, discussion #602

A production-tested self-hosted deployment using Docker containers behind a Cloudflare tunnel, with AuthMCP Gateway handling authentication:

| Layer | Role | |-------|------| | Cloudflare Tunnel | Name-based routing, subnet-based access control, authentication before hitting self-hosted resources | | AuthMCP Gateway | Auth/aggregation with locally managed users, admin UI, per-user MCP server access control, bearer token auth | | mcp-memory-service | Two Docker containers sharing one SQLite backend — one for MCP, one for the web UI (document ingestion) |

Security best practices for this setup:

• Use Cloudflare ZeroTrust with subnet-based access control (e.g., allow Anthropic subnets + your own IPs)
• Add Client IP Address Filtering to all Cloudflare API tokens (Dashboard → My Profile → API Tokens → Edit → Client IP Address Filtering) to limit abuse if a token leaks
• If using IPv6, include your IPv6 /64 network in the allowlist (Python prefers IPv6 by default)
• For long-running browser sessions, request the offline_access scope during authorization to receive a rotating refresh_token (lifetime via MCP_OAUTH_REFRESH_TOKEN_EXPIRE_DAYS, default 30 days). Without this scope, access tokens are the only credential — extend MCP_OAUTH_ACCESS_TOKEN_EXPIRE_MINUTES up to 1440 (24h) if you need longer single-shot sessions.
• Consider an auth proxy like AuthMCP or mcp-auth-proxy for robust session management

Comparison with Alternatives

vs. Commercial Memory APIs

| | Mem0 | Zep | DIY Redis+Pinecone | mcp-memory-service | |---|---|---|---|---| | License | Proprietary | Enterprise | — | Apache 2.0 | | Cost | Per-call API | Enterprise | Infra costs | $0 | | 🌐 claude.ai Browser | ❌ Desktop only | ❌ Desktop only | ❌ | ✅ Remote MCP | | OAuth 2.0 + DCR | ❓ Unknown | ❓ Unknown | ❌ | ✅ Enterprise-ready | | Streamable HTTP | ❌ | ❌ | ❌ | ✅ (SSE also supported) | | Framework integration | SDK | SDK | Manual | REST API (any HTTP client) | | Knowledge graph | No | Limited | No | Yes (typed edges) | | Auto consolidation | No | No | No | Yes (decay + compression) | | On-premise embeddings | No | No | Manual | Yes (ONNX, local) | | Privacy | Cloud | Cloud | Partial | 100% local | | Hybrid search | No | Yes | Manual | Yes (BM25 + vector) | | MCP protocol | No | No | No | Yes | | REST API | Yes | Yes | Manual | Yes (76 endpoints) |

vs. MCP-Native Alternatives

MemPalace is an MCP-native alternative that went viral in April 2026 with strong LongMemEval claims. A community code review (Issue #27) subsequently showed that the headline numbers reflect the underlying vector store rather than the advertised Palace architecture, and the maintainers acknowledged most points. We keep the comparison here for transparency, but readers should interpret the scores with that context in mind.

| | MemPalace | mcp-memory-service | |---|---|---| | LongMemEval R@5 (raw ChromaDB, zero LLM) | 96.6%¹ | 86.0% (session) / 80.4% (turn) | | LongMemEval R@5 (with reranking) | 100%² | — | | Storage granularity | Session-level | Turn-level + session-level | | Team / multi-device sync | ❌ Local only | ✅ Cloudflare sync | | REST API / Web dashboard | ❌ | ✅ | | OAuth 2.1 + multi-user | ❌ | ✅ | | Knowledge graph | ❌ | ✅ (typed edges) | | Auto consolidation | ❌ | ✅ (decay + compression) | | Compatible AI tools | Claude-focused | 25+ tools | | License | MIT | Apache 2.0 |

Why the benchmark gap? Two independent factors:

1. Ingestion granularity. MemPalace stores each conversation as a single unit (session-level). LongMemEval asks "which session contains the answer?" — a question that session-level storage answers structurally. mcp-memory-service defaults to turn-level storage (one entry per message), which enables fine-grained retrieval ("what exactly did the user say about X?") but spreads a session's signal across many entries. Using memory_store_session (added in v10.35.0) brings our score to 86.0% R@5.
2. What the 96.6% actually measures. Per Issue #27, MemPalace's headline numbe