Crew AI Integration - Helicone OSS LLM Observability

Warning

This integration method is maintained but no longer actively developed. For the best experience and latest features, use our new AI Gateway with unified API access to 100+ models.

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.

Quick Start#

Create an Account & Generate an API Key

Log into Helicone (or create a new account)
Generate a write-only API key

Note

Store your Helicone API key securely (e.g., in environment variables)

Set OPENAI_BASE_URL

Configure your environment to route API calls through Helicone:

import osos.environ["OPENAI_BASE_URL"] = f"https://oai.helicone.ai/{HELICONE_API_KEY}/v1"

This points OpenAI API requests to Helicone's proxy endpoint.

Note

See Advanced Provider Configuration for other LLM providers.

Verify in Helicone

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

Advanced Provider Configuration#

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

OpenAI (Alternative Method)#

from crewai import LLMllm = 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')}",    })

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')}",    })

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",    })

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')}",    })

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:

Check the dev integrations on the sidebar for your specific provider
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 documentationbase_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"    })

Helicone Features#

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:

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

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

Multi-Agent Example#

Create agents using different LLM providers:

from crewai import Agent, Crew, Task# Research agent using OpenAIresearcher = Agent(    role="Research Specialist",    goal="Analyze technical documentation",    backstory="Expert in technical research",    llm=openai_llm,    verbose=True)# Writing agent using Anthropicwriter = Agent(    role="Technical Writer",    goal="Create documentation",    backstory="Expert technical writer",    llm=anthropic_llm,    verbose=True)# Data processing agent using Geminianalyst = Agent(    role="Data Analyst",    goal="Process research findings",    backstory="Specialist in data interpretation",    llm=gemini_llm,    verbose=True)# Create crew with multiple agentscrew = Crew(    agents=[researcher, writer, analyst],    tasks=[...],  # Your tasks here    verbose=True)

Introduction#

Quick Start#

Advanced Provider Configuration#

OpenAI (Alternative Method)#

Anthropic#

Gemini#

Groq#

Other Providers#

Helicone Features#

Request Tracking#

Caching#

Prompt Management#

Multi-Agent Example#

Additional Resources#