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Basic Information

Marvin is a Python framework for producing structured outputs and building agentic AI workflows. It provides a high-level API to define observable tasks, assign specialized AI agents, and compose tasks into threads to orchestrate multi-step behavior. The library includes structured-output utilities for extracting, casting, classifying, and generating typed values from unstructured input. Marvin integrates with Pydantic AI for LLM access and supports multiple model providers, with OpenAI as the default. It is designed for developers who want to create reproducible, type-safe interactions with language models, break complex objectives into discrete steps, and orchestrate collaboration between multiple agents, swarms, or teams while tracking progress and results.

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Features
Marvin exposes a task-centric architecture with Tasks, Agents, Threads, Swarms, and Teams to model and orchestrate work. It includes top-level convenience functions such as run, summarize, classify, extract, cast, generate, say, and plan, plus a decorator for writing AI-backed functions. Agents encapsulate model configuration, instructions, and tools and are portable and reusable. Tasks support type-safe results, custom tools, context sharing, and observability. Planning and orchestration let you auto-generate dependent tasks or compose them manually within a Thread context manager. The project uses Pydantic AI for LLM interactions, stores thread history in SQLite, and notes current limitations such as lack of streaming responses.
Use Cases
Marvin helps developers build reliable, maintainable AI-driven applications by enforcing type-safe outputs and breaking problems into observable, testable tasks. It simplifies creating specialized agents for roles like writing or research and enables teams or swarms of agents to collaborate and delegate. Built-in utilities accelerate common tasks like summarization, classification, and structured extraction while tooling hooks let agents interact with code and external systems. Thread management and progress tracking make debugging and monitoring easier. The framework streamlines prototyping and scaling agentic workflows, improves developer velocity by offering familiar high-level APIs, and integrates with existing codebases and LLM providers.

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