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.

​
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.

​
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

DeepSeek

xAI

Sarvam AI

LM Studio

Langchain

​
Structured vs Unstructured Outputs

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

​
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

​
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.