Rigging

Overview

Rigging is a lightweight Python framework for interacting with large language models. It provides a simple, chainable API for generating text, managing prompts, and handling model outputs.

Best for

Best for
Python developers who want a simple, no-frills way to call LLMs in scripts or small applications.

Use cases

• Building quick prototypes that call LLMs from Python scripts
• Chaining multiple model calls with prompt templates
• Integrating LLM responses into existing Python applications

Notes

Rigging is a lightweight Python framework for interacting with large language models. It provides a simple, chainable API for generating text, managing prompts, and handling model outputs.

411 stars on GitHub. Last updated 2026-05-13. Licensed MIT.

Use cases

• Building quick prototypes that call LLMs from Python scripts
• Chaining multiple model calls with prompt templates
• Integrating LLM responses into existing Python applications

Pros

• Minimal dependencies and small codebase make it easy to adopt
• Clean, chainable API reduces boilerplate for common LLM tasks
• Actively maintained community project with growing adoption

Cons

• Limited to Python, not usable from other languages
• Smaller ecosystem and fewer integrations than larger frameworks
• May lack advanced features needed for production-scale orchestration

Indexed from awesome-langchain and enriched against its public facts.