## Documentation Index

Fetch the complete documentation index at: [/llms.txt](https://docs.mem0.ai/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](https://docs.mem0.ai/components/llms/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**](https://docs.mem0.ai/components/llms/models/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](https://platform.openai.com/docs/guides/structured-outputs/introduction)

### 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.