Magentic – Use LLMs as simple Python functions
This is a Python package that allows you to write function signatures to define LLM queries. This makes it easy to mix regular code with calls to LLMs, which enables you to use the LLM for its creativity and reasoning while also enforcing structure/logic as necessary. LLM output is parsed for you according to the return type annotation of the function, including complex return types such as streaming an array of structured objects. I built this to show that we can think about using LLMs more fluidly than just chains and chats, i.e. more interchangeably with regular code, and to make it…
In plain words
Magentic is a Python package that lets developers use large language models as standard functions within code. Users define function signatures to create LLM queries, automatically parsing responses according to return type annotations. This approach integrates LLM capabilities like reasoning and creativity directly into regular code logic, supporting complex outputs such as streaming structured objects. It's designed for developers who want to combine LLM functionality with traditional programming more flexibly than chat or chain-based approaches.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
This is a Python package that allows you to write function signatures to define LLM queries. This makes it easy to mix regular code with calls to LLMs, which enables you to use the LLM for its creativity and reasoning while also enforcing structure/logic as necessary. LLM output is parsed for you according to the return type annotation of the function, including complex return types such as streaming an array of structured objects. I built this to show that we can think about using LLMs more fluidly than just chains and chats, i.e. more interchangeably with regular code, and to make it easy to do that. Please let me know what you think! Contributions welcome. https://github.com/jackmpcollins/magentic
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