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Seamlessly integrate LLMs as Python functions
| Date | Stars |
|---|---|
| 2026-07-24 | 2413 |
| 2026-07-25 | 2413 |
| 2026-07-28 | 2413 |
| 2026-07-30 | 2413 |
| 2026-07-31 | 2414 |
| 2026-08-06 | 2414 |
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# magentic
Seamlessly integrate Large Language Models into Python code. Use the `@prompt` and `@chatprompt` decorators to create functions that return structured output from an LLM. Combine LLM queries and tool use with traditional Python code to build complex agentic systems.
## Features
- [Structured Outputs] using pydantic models and built-in python types.
- [Streaming] of structured outputs and function calls, to use them while being generated.
- [LLM-Assisted Retries] to improve LLM adherence to complex output schemas.
- [Observability] using OpenTelemetry, with native [Pydantic Logfire integration].
- [Type Annotations] to work nicely with linters and IDEs.
- [Configuration] options for multiple LLM providers including OpenAI, Anthropic, and Ollama.
- Many more features: [Chat Prompting], [Parallel Function Calling], [Vision], [Formatting], [Asyncio]...
## Installation
```sh
pip install magentic
```
or using uv
```sh
uv add magentic
```
Configure your OpenAI API key by setting the `OPENAI_API_KEY` environment variable. To configure a different LLM provider see [Configuration] for more.
## Usage
### @prompt
The `@prompt` decorator allows you to define a template for a Large Language Model (LLM) prompt as a Python function. When this function is called, the arguments are inserted into the template, then this prompt is sent to an LLM which generates the function output.
```python
from magentic import prompt
@prompt('Add more "dude"ness to: {phrase}')
def dudeify(phrase: str) -> str: ... # No function body as this is never executed
dudeify("Hello, how are you?")
# "Hey, dude! What's up? How's it going, my man?"
```
The `@prompt` decorator will respect the return type annotation of the decorated function. This can be [any type supported by pydantic](https://docs.pydantic.dev/latest/usage/types/types/) including a `pydantic` model.
```python
from magentic import prompt
from pydantic import BaseModel
class Superhero(BaseModel):
name: str
age: int
power: str
enemies: list[str]
@prompt("Create a Superhero named {name}.")
def create_superhero(name: str) -> Superhero: ...
create_superhero("Garden Man")
# Superhero(name='Garden Man', age=30, power='Control over plants', enemies=['Pollution Man', 'Concrete Woman'])
```
See [Structured Outputs] for more.
### @chatprompt
The `@chatprompt` decorator works just like `@prompt` but allows you to pass chat messages as a template rather than a single text prompt. This can be used to provide a system message or for few-shot prompting where you provide example responses to guide the model's output. Format fields denoted by curly braces `{example}` will be filled in all messages (except `FunctionResultMessage`).
```python
from magentic import chatprompt, AssistantMessage, SystemMessage, UserMessage
from pydantic import BaseModel
class Quote(BaseModel):
quote: str
character: str
@chatprompt(
SystemMessage("You are a movie buff."),
UserMessage("What is your favorite quote from Harry Potter?"),
AssistantMessage(
Quote(
quote="It does not do to dwell on dreams and forget to live.",
character="Albus Dumbledore",
)
),
UserMessage("What is your favorite quote from {movie}?"),
)
def get_movie_quote(movie: str) -> Quote: ...
get_movie_quote("Iron Man")
# Quote(quote='I am Iron Man.', character='Tony Stark')
```
See [Chat Prompting] for more.
### FunctionCall
An LLM can also decide to call functions. In this case the `@prompt`-decorated function returns a `FunctionCall` object which can be called to execute the function using the arguments provided by the LLM.
```python
from typing import Literal
from magentic import prompt, FunctionCall
def search_twitter(query: str, category: Literal["latest", "people"]) -> str:
"""Searches Twitter for a query."""
print(f"Searching Twitter for {query!r} in category {category!r}")
return "<twitter results>"
def search_youtube(query:Excerpt of 20,731 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:7f415f428dc7412a, topic:llm, topic:gpt
matched fp:7f415f428dc7412a, topic:agentic, readme:agentic, readme:tool use
matched fp:7f415f428dc7412a, topic:prompt, readme:structured output
matched fp:7f415f428dc7412a, topic:chatbot, topic:chatgpt