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Basic Information

Rigging is a lightweight Python framework designed to make using large language models in production code simple and effective. It targets developers who need a structured way to call and manage LLMs, build chat or completion pipelines, and embed typed prompts directly in application code. The library emphasizes interchangeable structured Pydantic models and unstructured text, supports a wide array of model backends through a default LiteLLM generator as well as vLLM and transformers, and provides tools for prompt definition, tool invocation, tracing, and scaling. The README includes installation instructions via pip or building from source, examples for interactive chat, Jupyter code interpretation, RAG pipelines and agents, and guidance on configuring API keys and generators. The project is maintained by dreadnode and intended for production use in modern Python applications.

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Features
Rigging offers structured Pydantic parsing so functions can declare typed return types and receive parsed model outputs. Prompts are defined as Python functions with type hints and docstrings. The default LiteLLM generator gives access to many models and the library also supports vLLM and transformers. Generators and model configurations are stored as simple connection strings, and API keys can be passed in generator IDs or environment variables. The framework includes chat templating, continuations, generation parameter overloads, segment stripping, simple tool use even when the model API lacks tool support, integrated tracing with Logfire, async batching for scale, metadata and callbacks, and serialization utilities. The package is installable from PyPI and contains example scripts demonstrating common workflows.
Use Cases
Rigging simplifies building, testing, and running LLM-powered features by combining typed prompt definitions with flexible model backends and production-friendly utilities. Typed Pydantic parsing reduces brittle string parsing and makes downstream code safer. Connection-string style generator configuration and environment-based API key handling lower deployment friction. Built-in tracing and callbacks improve observability and debugging, while async batching and iteration support enable efficient large-scale generation. Tool integration and chat templating let developers augment model capabilities and control conversation structure. Provided examples for chat, Jupyter code execution, RAG, and agent-style tasks accelerate onboarding and prototyping. Overall, Rigging reduces engineering overhead for integrating LLMs into services, pipelines, and agent systems.

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