Large Language Models (LLMs) in CrewAI

Large Language Models (LLMs) are the backbone of intelligent agents in the CrewAI framework. This guide will help you understand, configure, and optimize LLM usage for your CrewAI projects.

Key Concepts

LLM: Large Language Model, the AI powering agent intelligence
Agent: A CrewAI entity that uses an LLM to perform tasks
Provider: A service that offers LLM capabilities (e.g., OpenAI, Anthropic, Ollama, more providers)

Configuring LLMs for Agents

CrewAI offers flexible options for setting up LLMs:

1. Default Configuration

By default, CrewAI uses the gpt-4o-mini model. It uses environment variables if no LLM is specified:

OPENAI_MODEL_NAME (defaults to “gpt-4o-mini” if not set)
OPENAI_API_BASE
OPENAI_API_KEY

2. Updating YAML files

You can update the agents.yml file to refer to the LLM you want to use:

Code
researcher:
    role: Research Specialist
    goal: Conduct comprehensive research and analysis to gather relevant information,
        synthesize findings, and produce well-documented insights.
    backstory: A dedicated research professional with years of experience in academic
        investigation, literature review, and data analysis, known for thorough and
        methodical approaches to complex research questions.
    verbose: true
    llm: openai/gpt-4o
    # llm: azure/gpt-4o-mini
    # llm: gemini/gemini-pro
    # llm: anthropic/claude-3-5-sonnet-20240620
    # llm: bedrock/anthropic.claude-3-sonnet-20240229-v1:0
    # llm: mistral/mistral-large-latest
    # llm: ollama/llama3:70b
    # llm: groq/llama-3.2-90b-vision-preview
    # llm: watsonx/meta-llama/llama-3-1-70b-instruct
    # llm: nvidia_nim/meta/llama3-70b-instruct
    # llm: sambanova/Meta-Llama-3.1-8B-Instruct
    # ...

Keep in mind that you will need to set certain ENV vars depending on the model you are using to account for the credentials or set a custom LLM object like described below. Here are some of the required ENV vars for some of the LLM integrations:

Code
OPENAI_API_KEY=<your-api-key>
OPENAI_API_BASE=<optional-custom-base-url>
OPENAI_MODEL_NAME=<openai-model-name>
OPENAI_ORGANIZATION=<your-org-id>       # OPTIONAL
OPENAI_API_BASE=<openaiai-api-base>     # OPTIONAL

Code
AZURE_API_KEY=<your-api-key>                    # "my-azure-api-key"
AZURE_API_BASE=<your-resource-url>              # "https://example-endpoint.openai.azure.com"
AZURE_API_VERSION=<api-version>                 # "2023-05-15"
AZURE_AD_TOKEN=<your-azure-ad-token>            # Optional
AZURE_API_TYPE=<your-azure-api-type>            # Optional

Code
WATSONX_URL=<your-url>                          # (required) Base URL of your WatsonX instance
WATSONX_APIKEY=<your-apikey>                    # (required) IBM cloud API key
WATSONX_TOKEN=<your-token>                      # (required) IAM auth token (alternative to APIKEY)
WATSONX_PROJECT_ID=<your-project-id>            # (optional) Project ID of your WatsonX instance
WATSONX_DEPLOYMENT_SPACE_ID=<your-space-id>     # (optional) ID of deployment space for deployed models

3. Custom LLM Objects

Pass a custom LLM implementation or object from another library.

See below for examples.

Code
agent = Agent(llm="gpt-4o", ...)

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:

Code
import os

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

LLM Configuration Options

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

Parameter	Type	Description
model	`str`	Name of the model to use (e.g., “gpt-4”, “gpt-3.5-turbo”, “ollama/llama3.1”). For more options, visit the providers documentation.
timeout	`float, int`	Maximum time (in seconds) to wait for a response.
temperature	`float`	Controls randomness in output (0.0 to 1.0).
top_p	`float`	Controls diversity of output (0.0 to 1.0).
n	`int`	Number of completions to generate.
stop	`str, List[str]`	Sequence(s) where generation should stop.
max_tokens	`int`	Maximum number of tokens to generate.
presence_penalty	`float`	Penalizes new tokens based on their presence in prior text.
frequency_penalty	`float`	Penalizes new tokens based on their frequency in prior text.
logit_bias	`Dict[int, float]`	Modifies likelihood of specified tokens appearing.
response_format	`Dict[str, Any]`	Specifies the format of the response (e.g., JSON object).
seed	`int`	Sets a random seed for deterministic results.
logprobs	`bool`	Returns log probabilities of output tokens if enabled.
top_logprobs	`int`	Number of most likely tokens for which to return log probabilities.
base_url	`str`	The base URL for the API endpoint.
api_version	`str`	Version of the API to use.
api_key	`str`	Your API key for authentication.

These are examples of how to configure LLMs for your agent.

Code
from crewai import LLM

llm = LLM(
    model="gpt-4",
    temperature=0.8,
    max_tokens=150,
    top_p=0.9,
    frequency_penalty=0.1,
    presence_penalty=0.1,
    stop=["END"],
    seed=42,
    base_url="https://api.openai.com/v1",
    api_key="your-api-key-here"
)
agent = Agent(llm=llm, ...)

CrewAI supports using Ollama for running open-source models locally:

Install Ollama: ollama.ai
Run a model: ollama run llama2
Configure agent:

Code
from crewai import LLM

agent = Agent(
    llm=LLM(
        model="ollama/llama3.1",
        base_url="http://localhost:11434"
    ),
    ...
)

You can use IBM Watson by seeting the following ENV vars:

Code
WATSONX_URL=<your-url>
WATSONX_APIKEY=<your-apikey>
WATSONX_PROJECT_ID=<your-project-id>

You can then define your agents llms by updating the agents.yml

Code
researcher:
    role: Research Specialist
    goal: Conduct comprehensive research and analysis to gather relevant information,
        synthesize findings, and produce well-documented insights.
    backstory: A dedicated research professional with years of experience in academic
        investigation, literature review, and data analysis, known for thorough and
        methodical approaches to complex research questions.
    verbose: true
    llm: watsonx/meta-llama/llama-3-1-70b-instruct

You can also set up agents more dynamically as a base level LLM instance, like bellow:

Code
from crewai import LLM

llm = LLM(
    model="watsonx/ibm/granite-13b-chat-v2",
    base_url="https://api.watsonx.ai/v1",
    api_key="your-api-key-here"
)
agent = Agent(llm=llm, ...)

Changing the Base API URL

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

Code
from crewai import LLM

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.

Best Practices

Choose the right model: Balance capability and cost.
Optimize prompts: Clear, concise instructions improve output.
Manage tokens: Monitor and limit token usage for efficiency.
Use appropriate temperature: Lower for factual tasks, higher for creative ones.
Implement error handling: Gracefully manage API errors and rate limits.

Troubleshooting

API Errors: Check your API key, network connection, and rate limits.
Unexpected Outputs: Refine your prompts and adjust temperature or top_p.
Performance Issues: Consider using a more powerful model or optimizing your queries.
Timeout Errors: Increase the timeout parameter or optimize your input.

Get Started

Core Concepts

How to Guides

Tools

Telemetry

LLMs

Large Language Models (LLMs) in CrewAI

Key Concepts

Configuring LLMs for Agents

1. Default Configuration

2. Updating YAML files

3. Custom LLM Objects

Connecting to OpenAI-Compatible LLMs

LLM Configuration Options

Changing the Base API URL

Best Practices

Troubleshooting

Get Started

Core Concepts

How to Guides

Tools

Telemetry

​Large Language Models (LLMs) in CrewAI

​Key Concepts

​Configuring LLMs for Agents

​1. Default Configuration

​2. Updating YAML files

​3. Custom LLM Objects

​Connecting to OpenAI-Compatible LLMs

​LLM Configuration Options

​Changing the Base API URL

​Best Practices

​Troubleshooting

Large Language Models (LLMs) in CrewAI

Key Concepts

Configuring LLMs for Agents

1. Default Configuration

2. Updating YAML files

3. Custom LLM Objects

Connecting to OpenAI-Compatible LLMs

LLM Configuration Options

Changing the Base API URL

Best Practices

Troubleshooting