Mem0 (pronounced as "mem-zero") enhances AI assistants and agents with an intelligent memory layer, enabling personalized AI interactions. Mem0 remembers user preferences, adapts to individual needs, and continuously improves over time, making it ideal for customer support chatbots, AI assistants, and autonomous systems.
New Feature: Introducing Graph Memory. Check out our documentation.
Mem0 leverages a hybrid database approach to manage and retrieve long-term memories for AI agents and assistants. Each memory is associated with a unique identifier, such as a user ID or agent ID, allowing Mem0 to organize and access memories specific to an individual or context.
When a message is added to the Mem0 using add() method, the system extracts relevant facts and preferences and stores it across data stores: a vector database, a key-value database, and a graph database. This hybrid approach ensures that different types of information are stored in the most efficient manner, making subsequent searches quick and effective.
When an AI agent or LLM needs to recall memories, it uses the search() method. Mem0 then performs search across these data stores, retrieving relevant information from each source. This information is then passed through a scoring layer, which evaluates their importance based on relevance, importance, and recency. This ensures that only the most personalized and useful context is surfaced.
The retrieved memories can then be appended to the LLM's prompt as needed, enhancing the personalization and relevance of its responses.
Mem0 empowers organizations and individuals to enhance:
The easiest way to set up Mem0 is through the managed Mem0 Platform. This hosted solution offers automatic updates, advanced analytics, and dedicated support. Sign up to get started.
If you prefer to self-host, use the open-source Mem0 package. Follow the installation instructions to get started.
Mem0 requires an LLM to function, with from OpenAI as the default. However, it supports a variety of LLMs; for details, refer to our Supported LLMs documentation.
To initialize Graph Memory you'll need to set up your configuration with graph store providers. Currently, we support Neo4j as a graph store provider. You can setup Neo4j locally or use the hosted Neo4j AuraDB. Moreover, you also need to set the version to (prior versions are not supported). Here's how you can do it:
For detailed usage instructions and API reference, visit our documentation at docs.mem0.ai. Here, you can find more information on both the open-source version and the hosted Mem0 Platform.
Join our community for support and discussions. If you have any questions, feel free to reach out to us using one of the following methods:
Join our Discord community to learn about memory management for AI agents and LLMs, and connect with Mem0 users and contributors. Share your ideas, questions, or feedback in our GitHub Issues.
We value and appreciate the contributions of our community. Special thanks to our contributors for helping us improve Mem0.
