Why RagView？
As RAG technology continues to evolve, there are now nearly 60 distinct approaches, reflecting a stage of diversity and rapid experimentation. Depending on the scenario, different RAG solutions may yield significantly different outcomes in terms of recall rate, accuracy, and F1 score. Beyond accuracy, enterprises and individual developers must also weigh factors such as computational cost, performance, framework maturity, and scalability. However, there is currently no unified platform that consolidates and compares these RAG technologies. Developers and enterprises are often forced to download open-source code, deploy systems independently, and run manual evaluations—an inefficient and costly process.
To address this gap, we are building RagView—a benchmarking and selection platform for RAG technologies, designed for both developers and enterprises. RagView provides standardized evaluation metrics, streamlined benchmarking workflows, intuitive visualization tools, and a modular plug-in architecture, enabling users to efficiently compare RAG solutions and select the approach best suited to their specific business needs.
We are a small, passion-driven team. While our technical expertise may not be exceptional, we are fueled by curiosity and commitment to learning. Through continuous exploration and iteration, we strive to grow and evolve—aiming to make RagView a truly valuable tool for developers and enterprises.
Here’s our GitHub repository: https://github.com/ragview
The project is still under development, and we look forward to your attention and support!
OUR YOUTUBE https://www.youtube.com/watch?v=OH5shXKnnsM
OUR DISCORD https://discord.gg/np5ejCuQ
OUR REDDIT https://www.reddit.com/r/Rag_View/
Legend:
✅ = Integrated | 🚧 = In Progress | ⏳ = Pending Integration
| No. | Name | GitHub Link | Features | Status |
|---|---|---|---|---|
| 0 | Langflow | langflow-ai/langflow | Build, scale, and deploy RAG and multi-agent AI apps.But we use it to build a naive RAG. | ✅ |
| 1 | R2R | SciPhi-AI/R2R | SoTA production-grade RAG system with Agentic RAG architecture and RESTful API support. | ✅ |
| 2 | KAG | OpenSPG/KAG | Retrieval framework combining OpenSPG engine and LLM, using logical forms for guided reasoning; overcomes traditional vector similarity limitations; supports domain-specific QA. | ⏳ |
| 3 | GraphRAG | microsoft/graphrag | Modular graph-based retrieval RAG system from Microsoft. | 🚧 |
| 4 | LightRAG | HKUDS/LightRAG | "Simple and Fast Retrieval-Augmented Generation," designed for simplicity and speed. | 🚧 |
| 5 | dsRAG | D-Star-AI/dsRAG | High-performance retrieval engine for unstructured data, suitable for complex queries and dense text. | 🚧 |
| 6 | paper-qa | Future-House/paper-qa | Scientific literature QA system with citation support and high accuracy. | ⏳ |
| 7 | cognee | topoteretes/cognee | Lightweight memory management for AI agents ("Memory for AI Agents in 5 lines of code"). | ⏳ |
| 8 | trustgraph | trustgraph-ai/trustgraph | Next-generation AI product creation platform with context engineering and LLM orchestration; supports API and private deployment. | ⏳ |
| 9 | graphiti | getzep/graphiti | Real-time knowledge graph builder for AI agents, supporting enterprise-grade applications. | ⏳ |
| 10 | DocsGPT | arc53/DocsGPT | Private AI platform supporting Agent building, deep research, document analysis, multi-model support, and API integration. | ✅ |
| 11 | youtu-graphrag | youtugraph/youtu-graphrag | Graph-based RAG framework from Tencent Youtu Lab, focusing on knowledge graph construction and reasoning for domain-specific applications. | ⏳ |
| 12 | Kiln | https://github.com/Kiln-AI/Kiln | Desktop app for zero-code fine-tuning, evals, synthetic data, and built-in RAG tools. | ⏳ |
| 13 | Quivr | https://github.com/QuivrHQ/quivr | a RAG that is opinionated, fast and efficient so you can focus on your product. | ⏳ |