RAGHub: A Directory of Tools for Retrieval-Augmented Generation (RAG)
Welcome to RAGHub, a living collection of new and emerging frameworks, projects, and resources in the Retrieval-Augmented Generation (RAG) ecosystem. This is a community-driven project for r/RAG, where we aim to catalog the rapid growth of RAG tools and projects that are pushing the boundaries of the field.
Each day, it feels like a new tool or framework emerges, and choosing the right one is becoming more of an art than a science. Is the framework from three months ago still relevant? Or was it just hype, rehashing old concepts with a fresh look? RAGHub exists to help you stay ahead of these changes, providing a platform for the latest innovations in RAG.
How to Contribute
This is a community project, and we welcome contributions from everyone! If you’d like to add a new framework, project, or resource, please check out our Contribution Guidelines for details on how to get started.
Table of Contents
FAQ
What is RAG (Retrieval-Augmented Generation)?
RAG is a technique that enhances Large Language Model (LLM) responses by retrieving relevant information from external knowledge sources before generating answers. This approach reduces hallucinations and provides more accurate, contextually relevant responses based on actual data.
How do I choose the right RAG framework?
Consider these factors when selecting a framework:
| Factor | Consideration |
|---|
| Use Case | Chatbot, search engine, or document QA? |
| Scale | Enterprise-scale needs vs. prototyping |
| Complexity | LangChain/LlamaIndex (full-featured) vs. LightRAG (simple) |
| Integration | Does it support your vector DB and LLM provider? |
| Language | Python (LangChain/LlamaIndex) vs. TypeScript vs. Rust |
What's the difference between RAG Frameworks and RAG Engines?
- Frameworks (e.g., LangChain, LlamaIndex): Libraries you integrate into your code to build custom RAG pipelines
- Engines (e.g., RAGFlow, Dify): Standalone platforms providing ready-to-use RAG functionality
Do I need a vector database for RAG?
Yes, vector databases store document embeddings for semantic search. Popular options:
| Database | Best For |
|---|
| ChromaDB | Prototyping, easy setup |
| Qdrant | Production, high performance |
| Pinecone | Enterprise, managed service |
| Weaviate | Hybrid search, GraphQL |
How do I evaluate my RAG system?
Use evaluation frameworks listed in this directory:
- ragas: Measures faithfulness, answer relevancy, context precision
- Trulens: Feedback functions for quality assessment
- Phoenix: Observability and troubleshooting tools
- Deepchecks: Continuous validation and drift detection
What are common RAG challenges and solutions?
| Challenge | Solution |
|---|
| Poor retrieval quality | Optimize chunking strategy and embeddings |
| Context window limits | Use reranking to reduce retrieved content |
| Hallucinations | Ensure retrieved context is properly used by LLM |
| Latency | Optimize retrieval indexing, use streaming |
Can I use RAG with local/self-hosted models?
Yes! Many frameworks support local LLMs:
| Method | Description |
|---|
| Ollama | Easy local model deployment |
| vLLM | High-performance local inference |
| LM Studio | User-friendly local model management |
| LocalAI | OpenAI-compatible local API |
How can I contribute to RAGHub?
We welcome contributions! To add a new framework, project, or resource:
- Fork the repository
- Add your entry to the relevant section
- Follow the existing table format
- Submit a Pull Request
See CONTRIBUTING.md for detailed guidelines.
RAG Frameworks
| Name | Description | Website | Github | Stars | Activity |
|---|
| Dcup Open-Source RAG-as-a-Service | Connect your app to user data in minutes with self-hostable RAG pipelines. | Website | Github |  | 1h ago |
| LangChain | Building applications with LLMs | Website | Github |  | 9h ago |
| Scout | Building apps with LLMs/vector databases/web scraping | Website | Github |  | 1h ago |
| Semantica | Open-source framework for Context Graphs, GraphRAG, Decision Intelligence, Explainable Reasoning, Provenance, and AI Governance. | Website | GitHub |  | 1d ago |
| Haystack | A framework for building search engines using neural networks | Website | Github |  |
