● PHANTOM
🇮🇳 IN
✕
Skip to main content

​
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.
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
  • And many more!
For a complete and up-to-date list of supported providers, please refer to the LiteLLM Providers documentation.
To use any provider not covered by a native integration, add LiteLLM as a dependency to your project:
uv add 'crewai[litellm]'
Native providers (OpenAI, Anthropic, Google Gemini, Azure, AWS Bedrock) use their own SDK extras — see the Provider Configuration Examples.

​
Changing the LLM

To use a different LLM with your CrewAI agents, you have several options:
Pass the model name as a string when initializing the agent:
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'
)

​
Configuration Options

When configuring an LLM for your agent, you have access to a wide range of parameters:
ParameterTypeDescription
modelstrThe name of the model to use (e.g., “gpt-4”, “claude-2”)
temperaturefloatControls randomness in output (0.0 to 1.0)
max_tokensintMaximum number of tokens to generate
top_pfloatControls diversity of output (0.0 to 1.0)
frequency_penaltyfloatPenalizes new tokens based on their frequency in the text so far
presence_penaltyfloatPenalizes new tokens based on their presence in the text so far
stopstr, List[str]Sequence(s) to stop generation
base_urlstrThe base URL for the API endpoint
api_keystrYour 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: 
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"

​
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
2

Pull the desired model

For example, run ollama pull llama3.2 to download the model.
3

Configure your agent

    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:
Code
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 for the most up-to-date information on supported models and configuration options.