RAGLight
A modular framework for Retrieval-Augmented Generation that supports integration with various LLMs and external tools.
GraphCanon updated 3d · GitHub synced 3d
Decision brief
RAGLight emerges as an adaptable framework for Retrieval-Augmented Generation, offering integration flexibility with multiple LLMs and external tools through MCP.
Good fit when
- When you require seamless integration with various Language Models (LLMs) like Hugging Face or OpenAI models, making RAGLight a suitable choice for diverse model environments.
- If your project necessitates flexible connection to third-party tools and data sources via MCP, allowing for extended functionalities beyond standard Retrieval-Augmented Generation capabilities.
Avoid when
- Avoid using RAGLight if your workflow strictly demands proprietary integration methods that are not supported by its modular framework structure.
- If the project focuses on a specific LLM without the need for flexibility or interchangeability, the overhead of configuring diverse integrations in RAGLight might be unnecessary.
Observed Jul 14, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Steady (57d since push)
- As of 3d
- Provenance
- Not a fork · Personal account
- As of 3d
- Security (OSV)
- No MCP manifest
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install RAGLightSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
RAGLight offers a flexible way to integrate different LLMs, embeddings, vector stores, and third-party tools using MCP.
Capability facts
- CLI
- CLI entrypoint
Source: pyproject.toml:[project.scripts] · Aug 22, 2026
- Languages
- python
Source: github.language+pyproject.toml · Aug 22, 2026
Categories
Tags
README
Deploy with Docker Compose
The quickest way to deploy in production :
cd examples/serve_example
cp .env.example .env # edit values as needed
docker-compose up
The docker-compose.yml uses extra_hosts: host.docker.internal:host-gateway so the container can reach an Ollama instance running on the host machine.
Use RAGLight with Docker
You can use RAGLight inside a Docker container easily. Find Dockerfile example here : examples/Dockerfile.example
For agents
This page has a .md twin and JSON over the API.