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Mem0 includes built-in support for various popular databases. Memory can utilize the database provided by the user, ensuring efficient use for specific needs.

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Supported Vector Databases

See the list of supported vector databases below.
The following vector databases are supported in the Python implementation. The TypeScript implementation currently only supports Qdrant, Redis, Valkey, Vectorize and in-memory vector database.

Qdrant

Chroma

PGVector

Upstash Vector

Milvus

Pinecone

MongoDB

Azure

Redis

Valkey

Elasticsearch

OpenSearch

Supabase

Vertex AI

Weaviate

FAISS

LangChain

Amazon S3 Vectors

Databricks

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Usage

To utilize a vector database, you must provide a configuration to customize its usage. If no configuration is supplied, a default configuration will be applied, and Qdrant will be used as the vector database. For a comprehensive list of available parameters for vector database configuration, please refer to Config.

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Common issues

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Using Model with Different Dimensions

If you are using a customized model with different dimensions other than 1536 (for example, 768), you may encounter the following error: ValueError: shapes (0,1536) and (768,) not aligned: 1536 (dim 1) != 768 (dim 0) You can add "embedding_model_dims": 768, to the config of the vector_store to resolve this issue.
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