MCP Memory Service
Self-hosted long-term memory for agents: semantic search, a knowledge graph and autonomous consolidation over MCP and a REST API
Medium risk
We rate an entry medium when the tool runs code, makes network calls or reads project files. Check what exactly it does before installing.
Why this level
- Runs a local service that reads and stores project data
- In remote mode it opens network access and needs authentication setup
Install
Manual install
pip install mcp-memory-serviceInstalls the package from PyPI. Then configure your MCP client.
This is third-party code. Review the repository files before installing.
What it does
The service stores an agent's memory across sessions so you stop re-explaining architecture and past decisions. It keeps notes in a local vector-search database, links them into a knowledge graph with typed edges and can compress older entries on its own. Access runs over MCP for agents such as Claude Code, Claude Desktop and Cursor, and over a REST API with many endpoints for pipelines built on LangGraph, CrewAI, AutoGen and any HTTP client. Embeddings are computed locally via ONNX, so data stays on your infrastructure and no external keys are needed for that. Several storage backends are available: SQLite for a single user, Cloudflare and a hybrid mode for multi-device sync, and Milvus as a dedicated vector database. A remote mode with OAuth and HTTPS enables use from the claude.ai browser.
Who it is for. For developers and devops engineers who need shared long-term memory for agents and pipelines on their own infrastructure.
Good fit when
- You want an agent to remember project context and decisions across sessions
- Several agents or pipelines need to share one memory store
- You want memory on your own infrastructure, without cloud lock-in or per-request cost
Not a fit when
- You want ready cloud memory without deploying and maintaining a service
- The agent's built-in memory is enough and a separate store is unnecessary
Example request
Save to memory that our API rate limit is 100 requests per minute and recall it next sessionLimitations
This is a separate service you install and maintain: SQLite is enough locally, while a team and sync need Cloudflare, Milvus or Docker. Browser remote access needs HTTPS and OAuth setup, and exposing it publicly needs a tunnel. Development has moved to GitHub, and the copy on the old host is no longer updated. Apache-2.0 licensed.
How to disable. Remove the memory block from the MCP client config, or for Claude Code run claude mcp remove memory. Stop the service process and, if you wish, remove the package: pip uninstall mcp-memory-service.
MCP
- Transport
- stdio, http, sse
- Authentication
- not required
| Environment variables | |
|---|---|
| MCP_ALLOW_ANONYMOUS_ACCESS | Allows anonymous access to the HTTP mode for local pipelines |
| MCP_OAUTH_ENABLED | Enables OAuth for remote access from the claude.ai browser |
Security check
- Runs a local service that reads and stores project data
- In remote mode it opens network access and needs authentication setup
README in short
The README presents an open-source memory backend for agents accessible via MCP, a REST API, OAuth, a CLI and a dashboard. Installation is pip install mcp-memory-service, then the client is configured: a config block for Claude Desktop, the claude mcp add command for Claude Code. For LangGraph, CrewAI, AutoGen and HTTP clients you run an HTTP mode with the REST API. Embeddings run locally via ONNX, and SQLite, Cloudflare, hybrid and Milvus backends are supported. A remote mode serves claude.ai through a tunnel and OAuth. Apache-2.0 licensed, published on PyPI.
FAQ
Does data go to the cloud?
Not by default. Embeddings run locally via ONNX and the database stays with you. Cloudflare and Milvus cloud backends are optional for sync.
Which agents does it work with?
Any MCP client: Claude Code, Claude Desktop, Cursor, Windsurf, VS Code, Gemini CLI, Codex CLI, OpenCode, Goose and others. Pipelines use the REST API.
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