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Mimir: Unified local-first memory for AI agents with hybrid search

Mimir provides unified local memory for AI agents, using SQLite for memories, docs, and code, with hybrid BM25+vector search and an MCP server.

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Mimir: Unified Local Memory for AI Agents

Mimir is a powerful tool designed to provide AI agents with unified, local-first memory capabilities. It leverages SQLite as its core storage engine, enabling efficient management of memories, documents, and code. This approach ensures that your AI agents have persistent and readily accessible data without relying on external cloud services for basic memory functions. Mimir integrates seamlessly into agent workflows, offering a robust foundation for complex AI applications.

What Mimir Does

Mimir acts as a central memory hub for your AI agents. It stores and retrieves information using a hybrid search mechanism that combines the strengths of BM25 keyword search with vector embeddings. This allows for both precise keyword matching and semantic understanding of your data. By utilizing SQLite, Mimir offers a local-first solution, meaning data is stored and processed on your machine, enhancing privacy and reducing latency. It also includes an MCP server, facilitating communication and data exchange between different agent components.

Key Features

Who Mimir is For

Mimir is an essential tool for AI developers and builders creating sophisticated AI agents. If you are working on projects that require persistent memory, efficient data retrieval, and local data control, Mimir is an ideal solution. It is particularly beneficial for those developing agents that need to recall past interactions, access and process large document sets, or manage code snippets effectively. Developers seeking to build robust, self-contained AI systems will find Mimir's capabilities invaluable.