Skip to main contentRAGLight
RAGLight is a lightweight, modular, and local-first Python framework designed to
rapidly prototype, test, and experiment with Retrieval-Augmented Generation (RAG) systems.
It is built for developers and researchers who want to iterate quickly on ideas,
run everything locally, and keep full visibility over how data is ingested,
retrieved, and injected into Large Language Models.
RAGLight favors clarity over abstraction and composition over hidden magic.
What RAGLight focuses on
RAGLight is intentionally minimal and explicit. It focuses on:
- Explicit and controllable data ingestion (files, folders, repositories)
- Pluggable embedding models and providers
- Interchangeable vector stores
- Simple, inspectable RAG pipelines
- Agentic and reasoning-oriented extensions (Agentic RAG, RAT)
- Local experimentation with LLMs (Ollama, LMStudio, vLLM)
Every step of the pipeline is configurable and observable, making it easy to debug,
benchmark, and iterate.
Who is RAGLight for?
RAGLight is designed for developers who want:
- Full control over their RAG stack
- A framework optimized for local prototyping and experimentation
- Minimal abstractions with predictable behavior
- The ability to swap components without refactoring everything
- A clean path from prototype to more advanced setups
It is particularly well suited for:
- Research and proof-of-concept work
- Testing new RAG or Agentic RAG ideas
- Local-first workflows and offline environments
- Developers who prefer explicit code over black-box solutions
What RAGLight is not
RAGLight does not try to be:
- A no-code or SaaS platform
- A fully managed production system
- An opinionated end-to-end product
Instead, it provides a solid, extensible foundation for building and exploring
RAG-based systems, without getting in your way.