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Introduction

Crew AI is a multi-agent framework that supports multiple LLM providers through LiteLLM integration. By using Helicone as a proxy, you can track and optimize your AI model usage across different providers through a unified dashboard.

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Quick Start

1

Create an Account & Generate an API Key

  1. Log into Helicone (or create a new account)
  2. Generate a write-only API key
Store your Helicone API key securely (e.g., in environment variables)
2

Set OPENAI_BASE_URL

Configure your environment to route API calls through Helicone:
import os

os.environ["OPENAI_BASE_URL"] = f"https://oai.helicone.ai/{HELICONE_API_KEY}/v1"
This points OpenAI API requests to Helicone’s proxy endpoint.
See Advanced Provider Configuration for other LLM providers.
3

Verify in Helicone

Run your CrewAI application and check the Helicone dashboard to confirm requests are being logged.

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Advanced Provider Configuration

CrewAI supports multiple LLM providers. Here’s how to configure different providers with Helicone:

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OpenAI (Alternative Method)

from crewai import LLM

llm = LLM(
    model="gpt-4o-mini",
    base_url="https://oai.helicone.ai/v1",
    api_key=os.environ.get("OPENAI_API_KEY"),
    extra_headers={
        "Helicone-Auth": f"Bearer {os.environ.get('HELICONE_API_KEY')}",
    }
)

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Anthropic

llm = LLM(
    model="anthropic/claude-3-sonnet-20240229-v1:0",
    base_url="https://anthropic.helicone.ai/v1",
    api_key=os.environ.get("ANTHROPIC_API_KEY"),
    extra_headers={
        "Helicone-Auth": f"Bearer {os.environ.get('HELICONE_API_KEY')}",
    }
)

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Gemini

llm = LLM(
    model="gemini/gemini-1.5-pro-latest",
    base_url="https://gateway.helicone.ai",
    api_key=os.environ.get("GEMINI_API_KEY"),
    extra_headers={
        "Helicone-Auth": f"Bearer {os.environ.get('HELICONE_API_KEY')}",
        "Helicone-Target-URL": "https://generativelanguage.googleapis.com",
    }
)

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Groq

llm = LLM(
    model="groq/llama-3.2-90b-text-preview",
    base_url="https://groq.helicone.ai/openai/v1",
    api_key=os.environ.get("GROQ_API_KEY"),
    extra_headers={
        "Helicone-Auth": f"Bearer {os.environ.get('HELICONE_API_KEY')}",
    }
)

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Other Providers

CrewAI supports many LLM providers through LiteLLM integration. If your preferred provider isn’t listed above but is supported by CrewAI, you can likely use it with Helicone. Simply:
  1. Check the dev integrations on the sidebar for your specific provider
  2. Configure your CrewAI LLM using the same base URL and headers structure shown in the provider’s Helicone documentation
For example, if a provider’s Helicone documentation shows: 
# Provider's Helicone documentation
base_url = "https://provider.helicone.ai"
headers = {
    "Helicone-Auth": "Bearer your-key",
    "Other-Required-Headers": "values"
}
You would configure your CrewAI LLM like this: 
llm = LLM(
    model="provider/model-name",
    base_url="https://provider.helicone.ai",
    api_key=os.environ.get("PROVIDER_API_KEY"),
    extra_headers={
        "Helicone-Auth": f"Bearer {os.environ.get('HELICONE_API_KEY')}",
        "Other-Required-Headers": "values"
    }
)

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Helicone Features

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Request Tracking

Add custom properties to track and filter requests: 
llm = LLM(
    model="your-model",
    base_url="your-helicone-endpoint",
    api_key="your-api-key",
    extra_headers={
        "Helicone-Auth": f"Bearer {helicone_api_key}",
        "Helicone-Property-Custom": "value",      # Custom properties
        "Helicone-User-Id": "user-abc",          # Track user-specific requests
        "Helicone-Session-Id": "session-123",     # Group requests by session
        "Helicone-Session-Name": "session-name",  # Group requests by session name
        "Helicone-Session-Path": "/session/path", # Group requests by session path
    }
)
Learn more about:

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Caching

Enable response caching to reduce costs and latency: 
llm = LLM(
    model="your-model",
    base_url="your-helicone-endpoint",
    api_key="your-api-key",
    extra_headers={
        "Helicone-Auth": f"Bearer {helicone_api_key}",
        "Helicone-Cache-Enabled": "true",
    }
)
Learn more about Caching

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Prompt Management

Track and version your prompts: 
llm = LLM(
    model="your-model",
    base_url="your-helicone-endpoint",
    api_key="your-api-key",
    extra_headers={
        "Helicone-Auth": f"Bearer {helicone_api_key}",
        "Helicone-Prompt-Name": "research-task",
        "Helicone-Prompt-Id": "uuid-of-prompt",
    }
)
Learn more about Prompts

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Multi-Agent Example

Create agents using different LLM providers: 
from crewai import Agent, Crew, Task

# Research agent using OpenAI
researcher = Agent(
    role="Research Specialist",
    goal="Analyze technical documentation",
    backstory="Expert in technical research",
    llm=openai_llm,
    verbose=True
)

# Writing agent using Anthropic
writer = Agent(
    role="Technical Writer",
    goal="Create documentation",
    backstory="Expert technical writer",
    llm=anthropic_llm,
    verbose=True
)

# Data processing agent using Gemini
analyst = Agent(
    role="Data Analyst",
    goal="Process research findings",
    backstory="Specialist in data interpretation",
    llm=gemini_llm,
    verbose=True
)

# Create crew with multiple agents
crew = Crew(
    agents=[researcher, writer, analyst],
    tasks=[...],  # Your tasks here
    verbose=True
)

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Additional Resources