TrueFoundry Integration

TrueFoundry provides an enterprise-ready AI Gateway which can integrate with agentic frameworks like CrewAI and provides governance and observability for your AI Applications. TrueFoundry AI Gateway serves as a unified interface for LLM access, providing:

• Unified API Access: Connect to 250+ LLMs (OpenAI, Claude, Gemini, Groq, Mistral) through one API
• Low Latency: Sub-3ms internal latency with intelligent routing and load balancing
• Enterprise Security: SOC 2, HIPAA, GDPR compliance with RBAC and audit logging
• Quota and cost management: Token-based quotas, rate limiting, and comprehensive usage tracking
• Observability: Full request/response logging, metrics, and traces with customizable retention

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

   

   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 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:

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: 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.