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Magentic

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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

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
  • Vision
  • Formatting
  • Asyncio

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Magentic FAQ

What is Magentic?

Magentic is a Python library that allows developers to seamlessly integrate Large Language Models (LLMs) into their Python code. It uses decorators like @prompt and @chatprompt to define LLM interactions, enabling structured outputs and complex agentic behaviors.

How much does Magentic cost?

Magentic is a free and open-source Python library. However, using it will incur costs from the underlying Large Language Model providers (e.g., OpenAI, Anthropic, Ollama) based on their respective pricing models.

Is Magentic free?

Yes, Magentic itself is a free Python library. You can install and use it without any licensing fees. Costs would only come from the usage of the LLM APIs you integrate with.

Who is Magentic for?

Magentic is designed for Python developers who want to integrate LLMs into their applications, particularly those looking to create agentic systems, ensure structured LLM outputs, and streamline their LLM interaction code.

Source: magentic.dev

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