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Basic Information

Magentic is a Python library designed to integrate large language models directly into Python code by turning prompts into callable functions. It provides decorators such as @prompt, @chatprompt and @prompt_chain so developers can define prompt templates as function signatures and receive typed, structured outputs. The project supports function calling where the LLM can return a FunctionCall object that is executed by local functions, and it can automatically resolve multi-step prompt chains. Magentic also supports streaming of text and structured objects, asyncio for concurrent generation, and configurable backends for OpenAI, Anthropic, LiteLLM, Mistral and Ollama-compatible APIs. The library emphasizes type annotations, pydantic models for structured outputs, configurable model contexts, and environment-variable configuration to make LLM-driven functionality testable and composable within standard Python projects.

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Features
Magentic exposes decorator-based prompt functions that respect Python type annotations and pydantic schemas to produce structured outputs. It supports chat-style prompting via @chatprompt and multi-step resolution with @prompt_chain. Streaming is available for raw text using StreamedStr and for structured objects via iterable return types, enabling incremental processing. Function calling lets models return FunctionCall objects that invoke local functions and magentic can automatically execute these and feed results back to the model. Asyncio support enables concurrent generation. Observability features include OpenTelemetry integration and pydantic logfire support. LLM backends are configurable and include OpenAI, Anthropic, LiteLLM, Mistral and Ollama-compatible endpoints. Additional conveniences include LLM-assisted retries for schema adherence, annotated parameter metadata and environment-variable based configuration.
Use Cases
Magentic helps developers embed LLM behavior into Python applications with predictable, typed outputs so those outputs can be validated, parsed and used by normal program logic. The decorator model makes prompts reusable and testable as functions, while structured outputs via pydantic reduce parsing error and simplify downstream processing. Streaming and object streaming let applications consume partial results for better responsiveness and parallel throughput. Function calling and prompt chains enable complex agent-like workflows where models can call tools and refine results iteratively. Asyncio support and multiple backend options make it adaptable to performance and deployment constraints. Observability and retry mechanisms aid debugging and reliability when integrating LLMs into production code.

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