crewAI/docs/how-to/LLM-Connections.md

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Connect CrewAI to LLMs	Guide on integrating CrewAI with various Large Language Models (LLMs).

Connect CrewAI to LLMs

!!! note "Default LLM" By default, crewAI uses OpenAI's GPT-4 model for language processing. However, you can configure your agents to use a different model or API. This guide will show you how to connect your agents to different LLMs.

CrewAI offers flexibility in connecting to various LLMs, including local models via Ollama and different APIs like Azure. It's compatible with all LangChain LLM components, enabling diverse integrations for tailored AI solutions.

Ollama Integration

Ollama is preferred for local LLM integration, offering customization and privacy benefits. It requires installation and configuration, including model adjustments via a Modelfile to optimize performance.

Setting Up Ollama

• Installation: Follow Ollama's guide for setup.
• Configuration: Adjust your local model with a Modelfile, considering adding Observation as a stop word and playing with parameters like top_p and temperature.

Integrating Ollama with CrewAI

Instantiate Ollama and pass it to your agents within CrewAI, enhancing them with the local model's capabilities.

from langchain_community.llms import Ollama

# Assuming you have Ollama installed and downloaded the openhermes model
ollama_openhermes = Ollama(model="openhermes")

local_expert = Agent(
  role='Local Expert',
  goal='Provide insights about the city',
  backstory="A knowledgeable local guide.",
  tools=[SearchTools.search_internet, BrowserTools.scrape_and_summarize_website],
  llm=ollama_openhermes,
  verbose=True
)

OpenAI Compatible API Endpoints

You can use environment variables for easy switch between APIs and models, supporting diverse platforms like FastChat, LM Studio, and Mistral AI.

Configuration Examples

FastChat

# Required
OPENAI_API_BASE="http://localhost:8001/v1"
OPENAI_API_KEY=NA
MODEL_NAME='oh-2.5m7b-q51' # Depending on the model you have available

LM Studio

# Required
OPENAI_API_BASE="http://localhost:8000/v1"
OPENAI_API_KEY=NA
MODEL_NAME=NA

Mistral API

OPENAI_API_KEY=your-mistral-api-key
OPENAI_API_BASE=https://api.mistral.ai/v1
MODEL_NAME="mistral-small" # Check documentation for available models

text-gen-web-ui

# Required
API_BASE_URL=http://localhost:5000
OPENAI_API_KEY=NA
MODEL_NAME=NA

Azure Open AI

Azure's OpenAI API needs a distinct setup, utilizing the langchain_openai component for Azure-specific configurations.

Configuration settings:

AZURE_OPENAI_VERSION="2022-12-01"
AZURE_OPENAI_DEPLOYMENT=""
AZURE_OPENAI_ENDPOINT=""
AZURE_OPENAI_KEY=""

from dotenv import load_dotenv
from langchain_openai import AzureChatOpenAI

load_dotenv()

default_llm = AzureChatOpenAI(
    azure_endpoint=os.environ.get("AZURE_OPENAI_ENDPOINT"),
    api_key=os.environ.get("AZURE_OPENAI_KEY")
)

example_agent = Agent(
  role='Example Agent',
  goal='Demonstrate custom LLM configuration',
  backstory='A diligent explorer of GitHub docs.',
  llm=default_llm
)

Conclusion

Integrating CrewAI with different LLMs expands the framework's versatility, allowing for customized, efficient AI solutions across various domains and platforms.