> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mem0.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Overview

> Overview of all supported LLM providers in Mem0, including OpenAI, Anthropic, Groq, Ollama, and more.

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](./config).

## Supported LLMs

See the list of supported LLMs below.

<Note>
  All LLMs are supported in Python. The following LLMs are also supported in TypeScript: **OpenAI**, **Anthropic**, **Groq**, **Azure OpenAI**, **DeepSeek**, **Google AI**, **Langchain**, **LM Studio**, **Mistral AI**, and **Ollama**.
</Note>

<CardGroup cols={4}>
  <Card title="OpenAI" href="/components/llms/models/openai" />

  <Card title="Ollama" href="/components/llms/models/ollama" />

  <Card title="Azure OpenAI" href="/components/llms/models/azure_openai" />

  <Card title="Anthropic" href="/components/llms/models/anthropic" />

  <Card title="Together" href="/components/llms/models/together" />

  <Card title="Groq" href="/components/llms/models/groq" />

  <Card title="Litellm" href="/components/llms/models/litellm" />

  <Card title="Mistral AI" href="/components/llms/models/mistral_AI" />

  <Card title="Google AI" href="/components/llms/models/google_AI" />

  <Card title="AWS bedrock" href="/components/llms/models/aws_bedrock" />

  <Card title="DeepSeek" href="/components/llms/models/deepseek" />

  <Card title="MiniMax" href="/components/llms/models/minimax" />

  <Card title="xAI" href="/components/llms/models/xAI" />

  <Card title="Sarvam AI" href="/components/llms/models/sarvam" />

  <Card title="LM Studio" href="/components/llms/models/lmstudio" />

  <Card title="Langchain" href="/components/llms/models/langchain" />
</CardGroup>

## 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.