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Mem0 includes built-in support for various popular large language models. Memory can utilize the LLM provided by the user, ensuring efficient use for specific needs.

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Usage

To use a llm, you must provide a configuration to customize its usage. If no configuration is supplied, a default configuration will be applied, and OpenAI will be used as the llm.

For a comprehensive list of available parameters for llm configuration, please refer to Config.

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Supported LLMs

See the list of supported LLMs below.

All LLMs are supported in Python. The following LLMs are also supported in TypeScript: OpenAI, Anthropic, and Groq.

OpenAI

Ollama

Azure OpenAI

Anthropic

Together

Groq

Litellm

Mistral AI

Google AI

AWS bedrock

Gemini

DeepSeek

xAI

Sarvam AI

LM Studio

Langchain

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Structured vs Unstructured Outputs

Mem0 supports two types of OpenAI LLM formats, each with its own strengths and use cases:

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Structured Outputs

Structured outputs are LLMs that align with OpenAI’s structured outputs model:

  • Optimized for: Returning structured responses (e.g., JSON objects)
  • Benefits: Precise, easily parseable data
  • Ideal for: Data extraction, form filling, API responses
  • Learn more: OpenAI Structured Outputs Guide

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Unstructured Outputs

Unstructured outputs correspond to OpenAI’s standard, free-form text model:

  • Flexibility: Returns open-ended, natural language responses
  • Customization: Use the response_format parameter to guide output
  • Trade-off: Less efficient than structured outputs for specific data needs
  • Best for: Creative writing, explanations, general conversation

Choose the format that best suits your application’s requirements for optimal performance and usability.