Skip to main content
This guide provides instructions for integrating CrewAI with the Truefoundry AI Gateway.

​
What is CrewAI?

CrewAI is a framework for orchestrating role-playing, autonomous AI agents that collaborate to complete complex tasks. It enables you to create specialized agent crews where each agent has defined roles, responsibilities, and expertise areas, working together systematically to achieve shared objectives.

​
Key Features of CrewAI

  • Role-Playing Agents: Create specialized agents with distinct personas, skills, and responsibilities that can take on specific roles within your crew
  • Collaborative Crews: Orchestrate teams of agents that work together, share context, and coordinate their efforts to tackle complex multi-step workflows
  • Structured Tasks: Define clear, actionable tasks with specific objectives, expected outputs, and success criteria for systematic execution
  • Tool Integration: Equip your agents with powerful tools and capabilities to interact with external systems, APIs, and data sources

​
How TrueFoundry Integrates with CrewAI

​
Installation & Setup

1

Install CrewAI

pip install crewai
2

Get TrueFoundry Access Token

  1. Sign up for a TrueFoundry account
  2. Follow the steps here in Quick start
3

Configure CrewAI with TrueFoundry

TrueFoundry Code Configuration
from crewai import LLM

# Create an LLM instance with TrueFoundry AI Gateway
truefoundry_llm = LLM(
    model="openai-main/gpt-4o",  # Similarly you can call any model from any model provider
    base_url="your_truefoundry_gateway_base_url",
    api_key="your_truefoundry_api_key"
)

# Use in your CrewAI agents
from crewai import Agent

@agent
def researcher(self) -> Agent:
    return Agent(
        config=self.agents_config['researcher'],
        llm=truefoundry_llm,
        verbose=True
    )

​
Complete CrewAI Example

from crewai import Agent, Task, Crew, LLM

# Configure LLM with TrueFoundry
llm = LLM(
    model="openai-main/gpt-4o",
    base_url="your_truefoundry_gateway_base_url", 
    api_key="your_truefoundry_api_key"
)

# Create agents
researcher = Agent(
    role='Research Analyst',
    goal='Conduct detailed market research',
    backstory='Expert market analyst with attention to detail',
    llm=llm,
    verbose=True
)

writer = Agent(
    role='Content Writer', 
    goal='Create comprehensive reports',
    backstory='Experienced technical writer',
    llm=llm,
    verbose=True
)

# Create tasks
research_task = Task(
    description='Research AI market trends for 2024',
    agent=researcher,
    expected_output='Comprehensive research summary'
)

writing_task = Task(
    description='Create a market research report',
    agent=writer,
    expected_output='Well-structured report with insights',
    context=[research_task]
)

# Create and execute crew
crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, writing_task],
    verbose=True
)

result = crew.kickoff()

​
Observability and Governance

Monitor your CrewAI agents through TrueFoundry’s metrics tab: TrueFoundry metrics dashboard showing usage statistics, costs, and performance metrics for CrewAI agents With Truefoundry’s AI gateway, you can monitor and analyze:
  • Performance Metrics: Track key latency metrics like Request Latency, Time to First Token (TTFS), and Inter-Token Latency (ITL) with P99, P90, and P50 percentiles
  • Cost and Token Usage: Gain visibility into your application’s costs with detailed breakdowns of input/output tokens and the associated expenses for each model
  • Usage Patterns: Understand how your application is being used with detailed analytics on user activity, model distribution, and team-based usage
  • Rate limit and Load balancing: You can set up rate limiting, load balancing and fallback for your models

​
Tracing

For a more detailed understanding on tracing, please see getting-started-tracing.For tracing, you can add the Traceloop SDK: 
pip install traceloop-sdk

from traceloop.sdk import Traceloop

# Initialize enhanced tracing
Traceloop.init(
    api_endpoint="https://your-truefoundry-endpoint/api/tracing",
    headers={
        "Authorization": f"Bearer {your_truefoundry_pat_token}",
        "TFY-Tracing-Project": "your_project_name",
    },
)
This provides additional trace correlation across your entire CrewAI workflow. TrueFoundry tracing visualization showing CrewAI agent interactions and request flow