Mem0 MCP

> [!CAUTION] > ## This project has been archived > > **mem0-mcp-server** is no longer actively maintained and this repository is now a public archive. > > Thank you to the 640+ stargazers, 140+ forkers, and every contributor who helped shape this project. Your support and feedback meant the world to us. > > **Looking for Mem0 MCP?** We now offer an official cloud-hosted MCP server. Check out the [docs](https://docs.mem0.ai/platform/mem0-mcp) to get started. > > **Quick install across all major clients:** > ```bash > npx mcp-add \ > --name mem0-mcp \ > --type http \ > --url "https://mcp.mem0.ai/mcp" \ > --clients "claude,claude code,cursor,windsurf,vscode,opencode" > ``` # Mem0 MCP Server [](https://pypi.org/project/mem0-mcp-server/) [](LICENSE) [](https://smithery.ai/server/@mem0ai/mem0-memory-mcp) `mem0-mcp-server` wraps the official [Mem0](https://mem0.ai) Memory API as a Model Context Protocol (MCP) server so any MCP-compatible client (Claude Desktop, Cursor, custom agents) can add, search, update, and delete long-term memories. ## Tools The server exposes the following tools to your LLM: | Tool | Description | | --------------------- | --------------------------------------------------------------------------------- | | `add_memory` | Save text or conversation history (or explicit message objects) for a user/agent. | | `search_memories` | Semantic search across existing memories (filters + limit supported). | | `get_memories` | List memories with structured filters and pagination. | | `get_memory` | Retrieve one memory by its `memory_id`. | | `update_memory` | Overwrite a memory's text once the user confirms the `memory_id`. | | `delete_memory` | Delete a single memory by `memory_id`. | | `delete_all_memories` | Bulk delete all memories in the confirmed scope (user/agent/app/run). | | `delete_entities` | Delete a user/agent/app/run entity (and its memories). | | `list_entities` | Enumerate users/agents/apps/runs stored in Mem0. | All responses are JSON strings returned directly from the Mem0 API. ## Usage Options There are three ways to use the Mem0 MCP Server: 1. **Python Package** - Install and run locally using `uvx` with any MCP client 2. **Docker** - Containerized deployment that creates an `/mcp` HTTP endpoint 3. **Smithery** - Remote hosted service for managed deployments ## Quick Start ### Installation ```bash uv pip install mem0-mcp-server ``` Or with pip: ```bash pip install mem0-mcp-server ``` ### Client Configuration Add this configuration to your MCP client: ```json { "mcpServers": { "mem0": { "command": "uvx", "args": ["mem0-mcp-server"], "env": { "MEM0_API_KEY": "m0-...", "MEM0_DEFAULT_USER_ID": "your-handle" } } } } ``` ### Test with the Python Agent <details> <summary><strong>Click to expand: Test with the Python Agent</strong></summary> To test the server immediately, use the included Pydantic AI agent: ```bash # Install the package pip install mem0-mcp-server # Or with uv uv pip install mem0-mcp-server # Set your API keys export MEM0_API_KEY="m0-..." export OPENAI_API_KEY="sk-openai-..." # Clone and test with the agent git clone https://github.com/mem0ai/mem0-mcp.git cd mem0-mcp-server python example/pydantic_ai_repl.py ``` **Using different server configurations:** ```bash # Use with Docker container export MEM0_MCP_CONFIG_PATH=example/docker-config.json export MEM0_MCP_CONFIG_SERVER=mem0-docker python example/pydantic_ai_repl.py # Use with Smithery remote server export MEM0_MCP_CONFIG_PATH=example/config-smithery.json export MEM0_MCP_CONFIG_SERVER=mem0-memory-mcp python example/pydantic_ai_repl.py ``` </details> ## What You Can Do The Mem0 MCP server enables powerful memory capabilities for your AI applications: - Remember that I'm allergic to peanuts and shellfish - Add new health information to memory - Store these trial parameters: 200 participants, double-blind, placebo-controlled study - Save research data - What do you know about my dietary preferences? - Search and retrieve all food-related memories - Update my project status: the mobile app is now 80% complete - Modify existing memory with new info - Delete all memories from 2023, I need a fresh start - Bulk remove outdated memories - Show me everything I've saved about the Phoenix project - List all memories for a specific topic ## Configuration ### Environment Variables - `MEM0_API_KEY` (required) – Mem0 platform API key. - `MEM0_DEFAULT_USER_ID` (optional) – default `user_id` injected into filters and write requests (defaults to `mem0-mcp`). - `MEM0_ENABLE_GRAPH_DEFAULT` (optional) – Enable graph memories by default (defaults to `false`). - `MEM0_MCP_AGENT_MODEL` (optional) – default LLM for the bundled agent example (defaults to `openai:gpt-4o-mini`). ## Advanced Setup <details> <summary><strong>Click to expand: Docker, Smithery, and Development</strong></summary> ### Docker Deployment To run with Docker: 1. Build the image: ```bash docker build -t mem0-mcp-server . ``` 2. Run the container: ```bash docker run --rm -d \ --name mem0-mcp \ -e MEM0_API_KEY=m0-... \ -p 8080:8081 \ mem0-mcp-server ``` 3. Monitor the container: ```bash # View logs docker logs -f mem0-mcp # Check status docker ps ``` ### Running with Smithery Remote Server To connect to a Smithery-hosted server: 1. Install the MCP server (Smithery dependencies are now bundled): ```bash pip install mem0-mcp-server ``` 2. Configure MCP client with Smithery: ```json { "mcpServers": { "mem0-memory-mcp": { "command": "npx", "args": [ "-y", "@smithery/cli@latest", "run", "@mem0ai/mem0-memory-mcp", "--key", "your-smithery-key", "--profile", "your-profile-name" ], "env": { "MEM0_API_KEY": "m0-..." } } } } ``` ### Development Setup Clone and run from source: ```bash git clone https://github.com/mem0ai/mem0-mcp.git cd mem0-mcp-server pip install -e ".[dev]" # Run locally mem0-mcp-server # Or with uv uv sync uv run mem0-mcp-server ``` </details> ## License [Apache License 2.0](https://github.com/mem0ai/mem0-mcp/blob/main/LICENSE)