crewAI/docs/edge/ko/observability/truefoundry.mdx

---
title: TrueFoundry Integration
icon: chart-line
mode: "wide"
---

TrueFoundry provides an enterprise-ready [AI Gateway](https://www.truefoundry.com/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

<Steps>
<Step title="Install CrewAI">
```bash
pip install crewai
```
</Step>

<Step title="Get TrueFoundry Access Token">
1. Sign up for a [TrueFoundry account](https://www.truefoundry.com/register)
2. Follow the steps here in [Quick start](https://docs.truefoundry.com/gateway/quick-start)
</Step>

<Step title="Configure CrewAI with TrueFoundry">
![TrueFoundry Code Configuration](/images/new-code-snippet.png)

```python
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
    )
```
</Step>
</Steps>

### Complete CrewAI Example

```python
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](/images/gateway-metrics.png)

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](https://docs.truefoundry.com/docs/tracing/tracing-getting-started).For tracing, you can add the Traceloop SDK:
For tracing, you can add the Traceloop SDK:

```bash
pip install traceloop-sdk
```

```python
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 CrewAI Tracing](/images/tracing_crewai.png)