Context

Mem0 is a self-improving memory layer for LLM applications, enabling personalized AI experiences that save costs and delight users. It has amassed 27k stars on Github, funded by YCombinator, and has been adopted by other popular frameworks. For instance, Mem0 has been adopted by CrewAI framework for powering external memory. It already has integrations for PG, Elastic, Pinecone and others and MongoDB is a notable missing one. Having a high-quality and well-tested implementation for Mem0 is required, as it can be a gateway for other frameworks to build on MongoDB.

Definition of done

Enhance Mem0’s capabilities by integrating MongoDB Vector Search as a supported vector store provider. This integration will enable efficient storage, retrieval, and similarity search of vectorized data, aligning with Mem0’s mission to provide adaptable and cost-effective memory solutions for LLM applications.

•MongoDB Vector Search is fully integrated and functional within Mem0.

•Users can configure and use MongoDB as a vector store seamlessly.

•Comprehensive test coverage for all MongoDB-related operations.

•Updated documentation and usage guides for MongoDB integration.

References:{}

https://docs.mem0.ai/components/vectordbs/dbs/pgvector

•[Mem0|https://mem0.ai]

• Existing Mem0 Vector Store Integration Example{*}