Mem0 MCP Server

Overview

The Mem0 MCP Server is a TypeScript‑based implementation of the Model Context Protocol that bridges AI assistants with the Mem0 memory platform. It gives developers a lightweight, plug‑in style server that exposes a set of tools and resources for creating, updating, querying, and deleting memory streams—all backed by Mem0’s persistent storage and semantic search capabilities. This solves the common pain point of managing conversational context across multiple sessions, users, or agents without building a custom database layer.

By exposing Mem0 operations as MCP tools, the server allows an AI assistant to treat memory like any other external capability. A client can create a new memory stream, append dialogue turns, search for relevant facts using vector embeddings, and read or delete streams—all through the same MCP interface that other tools use. This unified approach means developers can integrate persistent, searchable memory into their workflows without learning a new API or handling authentication separately; the server simply forwards requests to Mem0 using an API key supplied via environment variables.

Key capabilities include:

• Memory Stream Lifecycle: Create, read, append to, and delete streams with a clear resource URI (), enabling fine‑grained access control in MCP clients.
• Semantic Search: The tool leverages Mem0’s embedding‑based search, allowing assistants to retrieve contextually relevant information with a simple query string and optional relevance threshold.
• Role‑Aware Appends: When adding content, callers can tag entries as or , preserving conversational structure for downstream processing.
• Pagination Support: The tool accepts start and end indices, making it possible to stream large histories incrementally.
• Metadata Exposure: Creation responses return the stream ID and associated metadata, facilitating tracking and debugging.

Typical use cases span conversational agents that need to remember prior interactions (e.g., customer support bots), personal assistants that maintain user preferences, or collaborative agents that share knowledge across multiple team members. In each scenario, the server acts as a mediator: the AI writes to a stream, later queries it for context, and can even prune old data when necessary. Because the server follows MCP conventions, any Claude or OpenAI‑compatible assistant can consume these tools without modification.

The Mem0 MCP Server’s standout advantage is its tight coupling with a semantic memory backend while staying protocol‑agnostic. Developers benefit from persistent, searchable context without reinventing storage logic, and the MCP abstraction ensures that future memory providers can be swapped in with minimal changes to client code.