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Setup and Integration

Get Your API Key

To use Dedalus Labs, you’ll need an API key. To get one:
  1. Create an account at dedaluslabs.ai.
  2. Navigate to your dashboard.
  3. Generate a new API key in the settings section.
  4. Add your API key to your environment as DEDALUS_API_KEY. For local development, create a .env file with:
DEDALUS_API_KEY=your_api_key_here
Keep your API key secure and never share it publicly.HTTP API Authentication: When making direct HTTP requests, pass your API key using either:
  • Authorization: Bearer <your_api_key> header, or
  • x-api-key: <your_api_key> header

Install Our SDK

We provide SDKs for multiple programming languages to make integration seamless:
pip install dedalus-labs

Hello World

This example demonstrates the most basic usage of the Dedalus SDK - making a simple chat completion call.
import asyncio
from dedalus_labs import AsyncDedalus, DedalusRunner
from dotenv import load_dotenv
from dedalus_labs.utils.stream import stream_async

load_dotenv()

async def main():
    client = AsyncDedalus()
    runner = DedalusRunner(client)

    response = await runner.run(
        input="What was the score of the 2025 Wimbledon final?",
        model="openai/gpt-5-mini",
        mcp_servers=["windsor/exa-search-mcp"]
    )

    print(response.final_output)

if __name__ == "__main__":
    asyncio.run(main())

Basic Tools

This example demonstrates basic tool execution using the Dedalus Runner with simple mathematical tools.
GPT 5 or 4.1 (openai/gpt-5 or openai/gpt-4.1) are strong tool-calling models. In general, older models may struggle with tool calling.
import asyncio
from dedalus_labs import AsyncDedalus, DedalusRunner
from dotenv import load_dotenv
from dedalus_labs.utils.stream import stream_async

load_dotenv()

def add(a: int, b: int) -> int:
    """Add two numbers."""
    return a + b

def multiply(a: int, b: int) -> int:
    """Multiply two numbers."""
    return a * b

async def main():
    client = AsyncDedalus()
    runner = DedalusRunner(client)

    result = await runner.run(
        input="Calculate (15 + 27) * 2",
        model="openai/gpt-5",
        tools=[add, multiply]
    )

    print(f"Result: {result.final_output}")

if __name__ == "__main__":
    asyncio.run(main())

Streaming

MCP Integration

Model Handoffs

Tool Chaining

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Next Steps

View the Examples

See detailed examples for your use-case

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