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Seamlessly integrate Large Language Models into Python code with structured outputs.

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Tracked since2026
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The Bottom Line

Entry price

Free, no paid tier

Biggest pro

Simplifies LLM integration into Python with decorators

Biggest con

Requires familiarity with Python decorators and type hinting

TL;DR - Magentic

  • Integrate LLMs into Python using decorators for structured outputs.
  • Supports multiple LLM providers and advanced features like function calling and streaming.
  • Simplifies building complex AI agentic systems with Python code.
Pricing: Free forever
Best for: Individuals & startups

What is Magentic?

Editorial review
Magentic is a Python library designed to simplify the integration of Large Language Models (LLMs) into Python applications. It allows developers to define LLM prompts as Python functions using decorators like `@prompt` and `@chatprompt`, enabling the LLM to generate structured outputs based on type annotations (including Pydantic models). This tool is ideal for Python developers looking to build complex agentic systems by combining LLM queries and tool use with traditional Python code. It streamlines the process of interacting with LLMs, ensuring that outputs are well-defined and easily consumable within Python workflows. Magentic supports various LLM providers, including OpenAI, Anthropic, and Ollama, and offers features like streaming, LLM-assisted retries, and observability. Key benefits include improved code readability and maintainability when working with LLMs, automatic handling of structured output parsing, and the ability to chain LLM calls with function execution for more sophisticated AI agents. It reduces the boilerplate typically associated with LLM integrations, allowing developers to focus on application logic.

Pros & Cons

Pros

  • Simplifies LLM integration into Python with decorators
  • Ensures structured and type-safe LLM outputs
  • Supports various LLM providers, offering flexibility
  • Enhances observability for LLM interactions
  • Facilitates building complex agentic systems with function chaining

Cons

  • Requires familiarity with Python decorators and type hinting
  • Reliance on external LLM providers for core functionality

Key Features

Structured Outputs using Pydantic models and built-in Python typesStreaming of structured outputs and function callsLLM-Assisted Retries for complex output schemasObservability using OpenTelemetry with Pydantic Logfire integrationType Annotations for linters and IDEsConfiguration options for multiple LLM providers (OpenAI, Anthropic, Ollama)Chat Prompting with system and few-shot messagesParallel Function Calling

Pricing Plans

Pricing checked Jul 13, 2026

Free

Free

  • Structured Outputs using pydantic models and built-in python types
  • Streaming of structured outputs and function calls
  • LLM-Assisted Retries
  • Observability using OpenTelemetry
  • Type Annotations
  • Configuration options for multiple LLM providers (OpenAI, Anthropic, Ollama)
  • Chat Prompting
  • Parallel Function Calling

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Magentic FAQ

How does Magentic help developers integrate Large Language Models into Python?

Magentic simplifies LLM integration by allowing developers to define LLM prompts as Python functions using decorators like @prompt and @chatprompt. This approach enables the LLM to generate structured outputs based on type annotations, streamlining the process of incorporating LLMs into Python applications.

Which teams would benefit most from using Magentic?

Teams of Python developers focused on building complex agentic systems will find Magentic particularly useful. It is designed for those who need to combine LLM queries and tool use with traditional Python code, enhancing code readability and maintainability for AI agents.

How is Magentic priced?

Magentic is free to use, meaning there is no paid plan required to access its features. Users only need to account for the costs associated with the external LLM providers they choose to integrate.

What kind of outputs can Magentic generate from LLMs?

Magentic ensures that LLMs generate structured outputs, which can include Pydantic models, based on Python type annotations. This capability allows for automatic handling of output parsing, making LLM responses easily consumable within Python workflows.

Can Magentic be used with different Large Language Model providers?

Yes, Magentic supports various LLM providers, including OpenAI, Anthropic, and Ollama. This flexibility allows developers to choose their preferred LLM backend while maintaining a consistent integration approach within their Python applications.

What are the main trade-offs when adopting Magentic for LLM integration?

A primary trade-off when using Magentic is the requirement for developers to be familiar with Python decorators and type hinting. Additionally, its core functionality relies on external LLM providers, meaning users are dependent on those services for LLM processing.

How does Magentic compare to a tool like Aider for AI agent development?

Magentic focuses on simplifying the integration of LLMs into Python code with structured outputs and decorator-based prompt definitions, supporting various LLM providers. While Aider also assists with AI agent development, Magentic specifically streamlines the process of ensuring type-safe and easily consumable LLM outputs within Python workflows.

Source: magentic.dev

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