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MCP Memory Service

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

Install

$claude mcp add memory -- memory server

Install command taken from the project's README or registry entry. Always review code before running it.

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

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