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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

Set Your API Key

Get your API key from the dashboard and set it as an environment variable:
Or use a .env file:

Your First Request

Let’s build this incrementally.

1) Chat with a model

2) Add an MCP server

Here we connect a well-known MCP server and let the model use it.

3) Add a local tool

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.

4) Stream output

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
Connect these docs programmatically to Claude, VSCode, and more via MCP for real-time answers.
Last modified on June 30, 2026