Overview - Mem0

Documentation Index

Fetch the complete documentation index at: /llms.txt

Use this file to discover all available pages before exploring further.

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 an 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, AWS Bedrock, Groq, Azure OpenAI, DeepSeek, Google AI, Langchain, LM Studio, Mistral AI, and Ollama.

OpenAI](https://docs.mem0.ai/components/llms/models/openai)

Ollama](https://docs.mem0.ai/components/llms/models/ollama)

Azure OpenAI](https://docs.mem0.ai/components/llms/models/azure_openai)

Anthropic](https://docs.mem0.ai/components/llms/models/anthropic)

Together](https://docs.mem0.ai/components/llms/models/together)

Groq](https://docs.mem0.ai/components/llms/models/groq)

Litellm

Mistral AI](https://docs.mem0.ai/components/llms/models/mistral_AI)

Google AI](https://docs.mem0.ai/components/llms/models/google_AI)

AWS bedrock](https://docs.mem0.ai/components/llms/models/aws_bedrock)

DeepSeek](https://docs.mem0.ai/components/llms/models/deepseek)

MiniMax](https://docs.mem0.ai/components/llms/models/minimax)

xAI](https://docs.mem0.ai/components/llms/models/xAI)

Sarvam AI](https://docs.mem0.ai/components/llms/models/sarvam)

LM Studio](https://docs.mem0.ai/components/llms/models/lmstudio)

Langchain](https://docs.mem0.ai/components/llms/models/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.