## CrewAI - Frontegg AI for Python

The Frontegg AI Python SDK - [`frontegg-ai-sdk`](https://pypi.org/project/frontegg-ai-sdk/) -  connects your AI agent code to the Frontegg platform, allowing you to authenticate agents, set user context, and securely access tools and integrations like Slack, Google Workspace, or HubSpot.

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Prerequisites
**You’ll need:**

- Python 3.8+
- A [Frontegg account](https://www.frontegg.com)
- Your Frontegg Client ID, Client Secret, and Agent ID


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**Python dependencies:**

- `asyncio`
- `httpx`
- `anyio`
- `nest_asyncio` (for Jupyter or nested event loops)
- `pydantic`
- `modelcontextprotocol`
- `crewai` (optional, for CrewAI integration)


### Install the SDK


```bash
pip install frontegg-ai-sdk
```

### Environment configuration

Create a `.env` file and populate it with the credentials from your Frontegg dashboard:


```env
FRONTEGG_CLIENT_ID=your_client_id // Settings → Keys & domains
FRONTEGG_CLIENT_SECRET=your_client_secret // Settings → Domains tab (e.g., https://app-xxxx.frontegg.com)
FRONTEGG_AGENT_ID=your_agent_id // AI Agents → Agents → [Your Agent]
```

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#### Supported environments

The SDK supports multiple Frontegg environments. Use the appropriate environment enum based on your data residency needs:


```python
from frontegg_ai_python_sdk import Environment

# Available environments
Environment.EU  # European servers: '.frontegg.com'
Environment.US  # US servers: 'us.frontegg.com'
Environment.CA  # Canadian servers: 'ca.frontegg.com'
Environment.AU  # Australian servers: 'au.frontegg.com'
Environment.UK  # UK servers: 'uk.frontegg.com'
```

### Project setup

Use your environment variables to set up the Frontegg AI client:


```python
import asyncio
import os
from frontegg_ai_python_sdk import (
    Environment,
    FronteggAiClientConfig,
    FronteggAiClient
)

# Async usage
async def async_example():
    # Create client configuration
    config = FronteggAiClientConfig(
        environment=Environment.US,
        agent_id=os.environ.get("FRONTEGG_AGENT_ID"),
        client_id=os.environ.get("FRONTEGG_CLIENT_ID"),
        client_secret=os.environ.get("FRONTEGG_CLIENT_SECRET"),
    )

    # Create client
    client = FronteggAiClient(config)

    # Set Context manually
    tenant_id = os.getenv("FRONTEGG_TENANT_ID")
    user_id = os.getenv("FRONTEGG_USER_ID")
    client.set_context(tenant_id=tenant_id, user_id=user_id)

    # Or set the context using the user JWT
    user_jwt = "Bearer eyJ..."
    client.set_user_context_by_jwt(user_jwt)

    # List available tools
    tools = await client.list_tools()
    print(f"Available tools: {tools}")

    # Call a tool with arguments
    result = await client.call_tool(
        name="your_tool_name",
        arguments={"param1": "value1"},
    )
    print(f"Tool result: {result}")

if __name__ == "__main__":
    # Run async example
    asyncio.run(async_example())
```

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### CrewAI integration

You can integrate Frontegg AI tools directly into CrewAI agents for powerful multi-agent orchestration. Use the following example to get started:


```python
import os
import asyncio
from crewai import Agent, Crew, Task
from frontegg_ai_python_sdk import (
    Environment,
    FronteggAiClientConfig,
    FronteggAiClient
)

async def get_crewai_tools():
    # Configure Frontegg client
    config = FronteggAiClientConfig(
        environment=Environment.US,
        agent_id=os.environ.get("FRONTEGG_AGENT_ID"),
        client_id=os.environ.get("FRONTEGG_CLIENT_ID"),
        client_secret=os.environ.get("FRONTEGG_CLIENT_SECRET"),
    )

    # Create client
    client = FronteggAiClient(config)

    # Set context manually
    tenant_id = os.getenv("FRONTEGG_TENANT_ID")
    user_id = os.getenv("FRONTEGG_USER_ID")
    client.set_context(tenant_id=tenant_id, user_id=user_id)

    # Or use a JWT token
    # user_jwt = "Bearer eyJ..."
    # client.set_user_context_by_jwt(user_jwt)

    # Get tools in CrewAI-compatible format
    tools = await client.list_tools_as_crewai_tools()
    return tools

# Fetch tools for the agent
tools = asyncio.run(get_crewai_tools())

# Create a CrewAI agent with Frontegg tools
agent = Agent(
    role="Research Assistant",
    goal="Find and analyze information",
    backstory="I am an AI research assistant with access to various tools.",
    tools=tools,
    verbose=True
)

# Define the task for the agent
task = Task(
    description="Research topic X and provide insights",
    expected_output="A comprehensive report",
    agent=agent
)

# Launch the crew
crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
```

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### Custom logging

To add detailed logging to your Frontegg client, use the built-in `setup_logger` utility:


```python
from frontegg_ai_python_sdk import setup_logger
import logging

# Set up a custom logger
logger = setup_logger(
    name="my_custom_logger",
    level=logging.DEBUG,
    format_string="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
    file_handler="app.log"  # Optional: log to a file
)

# Initialize client with logger
client = FronteggAiClient(config, logger=logger)
```