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

# Connect to any LLM

> Comprehensive guide on integrating CrewAI with various Large Language Models (LLMs) using LiteLLM, including supported providers and configuration options.

## Connect CrewAI to LLMs

CrewAI connects to LLMs through native SDK integrations for the most popular providers (OpenAI, Anthropic, Google Gemini, Azure, and AWS Bedrock), and uses LiteLLM as a flexible fallback for all other providers.

> [!NOTE]
>
> By default, CrewAI uses the `gpt-4o-mini` model. This is determined by the `OPENAI_MODEL_NAME` environment variable, which defaults to "gpt-4o-mini" if not set.
> You can easily configure your agents to use a different model or provider as described in this guide.

## Supported Providers

LiteLLM supports a wide range of providers, including but not limited to:

* OpenAI
* Anthropic
* Google (Vertex AI, Gemini)
* Azure OpenAI
* AWS (Bedrock, SageMaker)
* Cohere
* VoyageAI
* Hugging Face
* Ollama
* Mistral AI
* Replicate
* Together AI
* AI21
* Cloudflare Workers AI
* DeepInfra
* Groq
* SambaNova
* Nebius AI Studio
* [NVIDIA NIMs](https://docs.api.nvidia.com/nim/reference/models-1)
* And many more!

For a complete and up-to-date list of supported providers, please refer to the [LiteLLM Providers documentation](https://docs.litellm.ai/docs/providers).

> [!NOTE]
>
> To use any provider not covered by a native integration, add LiteLLM as a dependency to your project:
>
> ```bash theme={null}
> uv add 'crewai[litellm]'
> ```
>
> Native providers (OpenAI, Anthropic, Google Gemini, Azure, AWS Bedrock) use their own SDK extras — see the [Provider Configuration Examples](https://docs.crewai.com/en/concepts/llms.md#provider-configuration-examples).

## Changing the LLM

To use a different LLM with your CrewAI agents, you have several options:

**Using a String Identifier**

Pass the model name as a string when initializing the agent:

```python Code theme={null}
from crewai import Agent

# Using OpenAI's GPT-4
openai_agent = Agent(
    role='OpenAI Expert',
    goal='Provide insights using GPT-4',
    backstory="An AI assistant powered by OpenAI's latest model.",
    llm='gpt-4'
)

# Using Anthropic's Claude
claude_agent = Agent(
    role='Anthropic Expert',
    goal='Analyze data using Claude',
    backstory="An AI assistant leveraging Anthropic's language model.",
    llm='claude-2'
)
```

**Using the LLM Class**

For more detailed configuration, use the LLM class:

```python Code theme={null}
from crewai import Agent, LLM

llm = LLM(
    model="gpt-4",
    temperature=0.7,
    base_url="https://api.openai.com/v1",
    api_key="your-api-key-here"
)

agent = Agent(
    role='Customized LLM Expert',
    goal='Provide tailored responses',
    backstory="An AI assistant with custom LLM settings.",
    llm=llm
)
```

## Configuration Options

When configuring an LLM for your agent, you have access to a wide range of parameters:

| Parameter | Type | Description |
| :- | :-: | :- |
| **model** | `str` | The name of the model to use (e.g., "gpt-4", "claude-2") |
| **temperature** | `float` | Controls randomness in output (0.0 to 1.0) |
| **max\_tokens** | `int` | Maximum number of tokens to generate |
| **top\_p** | `float` | Controls diversity of output (0.0 to 1.0) |
| **frequency\_penalty** | `float` | Penalizes new tokens based on their frequency in the text so far |
| **presence\_penalty** | `float` | Penalizes new tokens based on their presence in the text so far |
| **stop** | `str`, `List[str]` | Sequence(s) to stop generation |
| **base\_url** | `str` | The base URL for the API endpoint |
| **api\_key** | `str` | Your API key for authentication |

For a complete list of parameters and their descriptions, refer to the LLM class documentation.

## Connecting to OpenAI-Compatible LLMs

You can connect to OpenAI-compatible LLMs using either environment variables or by setting specific attributes on the LLM class:

**Using Environment Variables**

```python Generic theme={null}
import os

os.environ["OPENAI_API_KEY"] = "your-api-key"
os.environ["OPENAI_API_BASE"] = "https://api.your-provider.com/v1"
os.environ["OPENAI_MODEL_NAME"] = "your-model-name"
```

```python Google theme={null}
import os

# Example using Gemini's OpenAI-compatible API.
os.environ["OPENAI_API_KEY"] = "your-gemini-key"  # Should start with AIza...
os.environ["OPENAI_API_BASE"] = "https://generativelanguage.googleapis.com/v1beta/openai/"
os.environ["OPENAI_MODEL_NAME"] = "openai/gemini-3.7-flash"  # Add your Gemini model here, under openai/
```

**Using LLM Class Attributes**

```python Generic theme={null}
llm = LLM(
    model="custom-model-name",
    api_key="your-api-key",
    base_url="https://api.your-provider.com/v1"
)
agent = Agent(llm=llm, ...)
```

```python Google theme={null}
# Example using Gemini's OpenAI-compatible API
llm = LLM(
    model="openai/gemini-3.7-flash",
    base_url="https://generativelanguage.googleapis.com/v1beta/openai/",
    api_key="your-gemini-key",  # Should start with AIza...
)
agent = Agent(llm=llm, ...)
```

## Using Local Models with Ollama

For local models like those provided by Ollama:

**1. Download and install Ollama**

[Click here to download and install Ollama](https://ollama.com/download)

**2. Pull the desired model**

For example, run `ollama pull llama3.2` to download the model.

**3. Configure your agent**

```python Code theme={null}
    agent = Agent(
        role='Local AI Expert',
        goal='Process information using a local model',
        backstory="An AI assistant running on local hardware.",
        llm=LLM(model="ollama/llama3.2", base_url="http://localhost:11434")
    )
```

## Changing the Base API URL

You can change the base API URL for any LLM provider by setting the `base_url` parameter:

```python Code theme={null}
llm = LLM(
    model="custom-model-name",
    base_url="https://api.your-provider.com/v1",
    api_key="your-api-key"
)
agent = Agent(llm=llm, ...)
```

This is particularly useful when working with OpenAI-compatible APIs or when you need to specify a different endpoint for your chosen provider.

## Conclusion

By leveraging LiteLLM, CrewAI offers seamless integration with a vast array of LLMs. This flexibility allows you to choose the most suitable model for your specific needs, whether you prioritize performance, cost-efficiency, or local deployment. Remember to consult the [LiteLLM documentation](https://docs.litellm.ai/docs/) for the most up-to-date information on supported models and configuration options.


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