Documentation Index Fetch the complete documentation index at: https://docs.dedaluslabs.ai/llms.txt
Use this file to discover all available pages before exploring further.
Dedalus helps you ship agent workflows that are:
Provider-agnostic : Use OpenAI, Anthropic, Google, xAI, DeepSeek, and more with one API.Tool- and MCP-native : Let models call local functions and hosted MCP servers.Production-ready : Streaming, structured outputs, routing/handoffs, and runtime policies.
What are you trying to build?
Chat with a model Send a prompt and get a response from any provider/model.
Equip a model with tools Let the model call typed Python/TS functions that you implement.
Stream agent output Print responses as they’re generated (great for UIs/CLIs).
Get reliable JSON Validate model output against schemas (Pydantic/Zod).
Route across models Provide multiple models; the agent can route/handoff by phase.
Generate images DALL-E generation, edits, variations, and vision analysis.
Installation uv pip install dedalus-labs
Set Your API Key Get your API key from the dashboard and set it as an environment variable:
export DEDALUS_API_KEY = "your-api-key"
Or use a .env file:
DEDALUS_API_KEY = your-api-key
Your First Request Let’s build this incrementally.
1) Chat with a model import asyncio from dedalus_labs import AsyncDedalus, DedalusRunner from dotenv import load_dotenv load_dotenv() async def main (): client = AsyncDedalus() runner = DedalusRunner(client) response = await runner.run( input = "What are the key factors that influence weather patterns?" , model = "openai/gpt-5.4" , ) print (response.final_output) if __name__ == "__main__" : asyncio.run(main())
2) Add an MCP server Here we connect a well-known MCP server and let the model use it.
import asyncio from dedalus_labs import AsyncDedalus, DedalusRunner from dotenv import load_dotenv load_dotenv() async def main (): client = AsyncDedalus() runner = DedalusRunner(client) response = await runner.run( input = "What's the weather forecast for San Francisco this week?" , model = "openai/gpt-5.4" , mcp_servers = [ "windsornguyen/open-meteo-mcp" ], # Weather forecasts via Open-Meteo ) print (response.final_output) if __name__ == "__main__" : asyncio.run(main())
Define a function with type hints and a docstring. Pass it to runner.run(). The SDK extracts the schema automatically and handles execution when the model decides to use it.
import asyncio from dedalus_labs import AsyncDedalus, DedalusRunner from dotenv import load_dotenv load_dotenv() def as_bullets (items: list[ str ]) -> str : """Format items as a bulleted list.""" return " \n " .join( f "• { item } " for item in items) async def main (): client = AsyncDedalus() runner = DedalusRunner(client) response = await runner.run( input = ( "Get the 7-day weather forecast for San Francisco " "and format the daily conditions as bullets using as_bullets." ), model = "openai/gpt-5.4" , mcp_servers = [ "windsornguyen/open-meteo-mcp" ], tools = [as_bullets], ) print (response.final_output) if __name__ == "__main__" : asyncio.run(main())
4) Stream output import asyncio from dedalus_labs import AsyncDedalus, DedalusRunner from dedalus_labs.utils.stream import stream_async from dotenv import load_dotenv load_dotenv() async def main (): client = AsyncDedalus() runner = DedalusRunner(client) stream = runner.run( input = "Explain how weather forecasting works in one paragraph, streaming as you write." , model = "openai/gpt-5.4" , stream = True , ) await stream_async(stream) if __name__ == "__main__" : asyncio.run(main())
Next steps
Use Cases Start from common agent patterns and templates.
Cookbook End-to-end implementations and working recipes.
Go deeper : Tools · MCP Servers · Structured Outputs · Streaming Get the latest SDKs
Python SDK dedalus-labs/dedalus-sdk-python
TypeScript SDK dedalus-labs/dedalus-sdk-typescript
