@gustavoserafim
Basic information
No overview available yet
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"mem0-mcp-server": {
"command": "uv",
"args": [
"pip",
"install",
"-e",
"."
]
}
}
}Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
MCP-Mem0 is a reference implementation of an MCP (Model Context Protocol) server integrated with the Mem0 memory platform, providing AI agents with persistent, searchable long-term memory. It is designed for developers building MCP-compatible agents that need to store, retrieve, and semantically search information across sessions.
Clone the repository, install dependencies with uv or build the Docker image, configure environment variables (transport, LLM provider, database URL), and run the server via uv run src/main.py or docker run. Connect any MCP client by pointing it to the SSE endpoint or by configuring stdio transport in the client's MCP settings.
Python 3.12+, a PostgreSQL database (with Supabase supported) for vector storage, and API keys for your chosen LLM provider (OpenAI, OpenRouter, or Ollama). Docker is recommended for containerized deployments.
SSE (Server-Sent Events) and stdio (standard input/output) transport protocols are both supported, configurable via the `TRANSPORT` environment variable.
OpenAI, OpenRouter, and Ollama are supported. The provider, base URL, API key, model, and embedding model are all configurable through environment variables.
Memories are stored with semantic indexing using embedding vectors in a PostgreSQL database. The server offers three tools: `save_memory` to store information, `get_all_memories` to retrieve everything, and `search_memories` to find relevant memories via semantic search.
For SSE transport, use the URL `http://localhost:8050/sse` (adjust host/port as needed). For stdio transport, configure the client to run the Python script or Docker container with the required environment variables.
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