Magentic is a Python library designed to seamlessly integrate Large Language Models (LLMs) as callable Python functions. It targets developers building agentic systems, offering structured outputs, streaming, and LLM-assisted retries for enhanced reliability.

How It Works

Magentic uses decorators like @prompt and @chatprompt to define LLM interactions. These decorators transform Python functions into LLM calls, automatically handling prompt templating, argument injection, and response parsing. It supports structured outputs via Pydantic models and integrates with various LLM providers (OpenAI, Anthropic, Ollama) through pluggable backends. The library also facilitates function calling and chaining, enabling complex agentic workflows.

Quick Start & Requirements

• Install via pip: pip install magentic or uv add magentic.
• Requires an LLM API key (e.g., OPENAI_API_KEY environment variable).
• Official documentation: https://github.com/jackmpcollins/magentic

Highlighted Details

• Supports structured outputs using Pydantic models and basic Python types.
• Enables streaming of text and structured outputs for real-time processing.
• Features LLM-assisted retries for improved adherence to complex output schemas.
• Integrates with OpenTelemetry for observability.
• Supports multiple LLM backends including OpenAI, Anthropic, Ollama, and LiteLLM.
• Facilitates function calling and prompt chaining for complex agentic workflows.

Maintenance & Community

• Actively maintained by jackmpcollins.
• Community links are not explicitly mentioned in the README.

Licensing & Compatibility

• The README does not specify a license.

Limitations & Caveats

• The library's license is not specified, which may impact commercial use.
• Type checkers might flag decorated functions due to their lack of implementation bodies, requiring specific configurations or # type: ignore comments.