Magentic

Free

Seamlessly integrate LLMs as Python functions ![GitHub Repo stars](https://img.shields.io/github/stars/jackmpcollins/magentic?style=social)

FreeFree tier

Type

Open Source

Visit Website GitHub (40 stars)

About Magentic

Magentic is an open-source Python library that seamlessly integrates Large Language Models (LLMs) into Python code using decorators like @prompt and @chatprompt. It enables developers to create functions that return structured output from an LLM, supporting pydantic models and built-in Python types. Key features include structured outputs, streaming, LLM-assisted retries, observability via OpenTelemetry, type annotations, and support for multiple LLM providers such as OpenAI, Anthropic, and Ollama. Magentic also offers chat prompting, parallel function calling, vision, formatting, and asyncio capabilities, making it suitable for building complex agentic systems.

Key Features

Structured Outputs using pydantic models and built-in Python types

Streaming of structured outputs and function calls

LLM-Assisted Retries for complex output schemas

Observability with OpenTelemetry and native Pydantic Logfire integration

Type Annotations for compatibility with linters and IDEs

Multi-Provider Support (OpenAI, Anthropic, Ollama)

Chat Prompting and few-shot prompting

Parallel Function Calling

Vision support

Formatting and Asyncio capabilities

Pros & Cons

Pros

Simple decorator-based API that integrates naturally into Python code
Supports multiple LLM providers (OpenAI, Anthropic, Ollama) with easy configuration
Streaming of structured outputs allows real-time use while generating
Observability built-in with OpenTelemetry for monitoring and debugging
Open-source and free to use with no vendor lock-in
Strong typing and pydantic support ensure reliable structured outputs

Cons

Limited to Python; not available for other programming languages
Requires API keys and internet access for LLM providers
May have a learning curve for developers unfamiliar with decorators or pydantic
Performance depends on external LLM latency and costs
Documentation is primarily on GitHub; comprehensive guides or tutorials could be more extensive

Best For

Building LLM-powered Python applications with structured outputsData extraction and transformation using natural languageCreating chatbots with tool integrationGenerating structured data from user queries (e.g., JSON, pydantic models)Rapid prototyping of AI features with minimal boilerplateBuilding agentic systems combining LLM calls and traditional Python logic

FAQ

How do I install Magentic?

You can install Magentic using pip: `pip install magentic` or with uv: `uv add magentic`.

Which LLM providers does Magentic support?

Magentic supports multiple providers including OpenAI, Anthropic, and Ollama. Configuration is handled via environment variables or settings.

Can Magentic return structured data?

Yes, Magentic uses pydantic models and built-in Python types to return structured outputs from LLM calls. The return type annotation of the decorated function determines the output schema.

Does Magentic support streaming?

Yes, Magentic supports streaming of structured outputs and function calls, allowing you to process data as it is generated.

Is Magentic free to use?

Yes, Magentic is open-source and free to use. You only pay for the API usage of the underlying LLM provider.