Magentic is a Python library that lets you integrate large language models into your code as ordinary Python functions. You declare a function with type hints and a prompt, decorate it, and Magentic handles calling the model and returning a properly typed, validated result. The project has earned more than 2,400 stars on GitHub and was still actively updated in March 2026.
Working with LLM APIs directly means hand-writing request payloads, parsing free-text responses, and bolting on validation afterward. Magentic flips that around: the function’s signature and docstring define what you want, and the library constructs the prompt, constrains the output, and converts the response into the declared Python type using Pydantic. It supports chat-style prompting, function calling where the model can invoke your own Python functions as tools, streaming responses, and structured outputs such as lists and nested models. Because the interface is just decorated functions, LLM calls slot naturally into existing codebases, tests, and type checking. The library handles provider quirks, message formatting, and response handling behind the scenes, and a chat-style interface is available when a task needs multi-turn conversation rather than a single prompt and answer.
Magentic is free and open source under the MIT license. You only pay for the underlying model API usage from whichever provider you connect it to.
Magentic is for Python developers who want LLM capabilities inside applications without wrestling raw API payloads. It is a natural fit for data extraction, classification, summarization, and agent tooling where typed, validated outputs matter.