# RAGHub: a community index of RAG frameworks, engines and evaluation tools > RAGHub is a README-first directory of Retrieval-Augmented Generation projects maintained for r/RAG. It helps you shortlist tools, but it does not install, run or benchmark any of them. **Andrew-Jang/RAGHub** — A community-driven collection of RAG (Retrieval-Augmented Generation) frameworks, projects, and resources. Contribute and explore the evolving RAG ecosystem. - Repository: https://github.com/Andrew-Jang/RAGHub - Stars: 2,006 · Forks: 186 - Language: Unknown - License: MIT - Published: 2026-09-10 · Updated: 2026-09-10 · Language: en - Canonical page: https://hysenlabs.com/projects/andrew-jang-raghub ## What RAGHub actually is, and who it is for RAGHub is a Markdown directory. The repository's top level holds three files: CONTRIBUTING.md, LICENSE and README.md. There is no package manifest, no source directory and no build step described anywhere in the README. The project describes itself as "a living collection of new and emerging frameworks, projects, and resources" in the Retrieval-Augmented Generation ecosystem, run as "a community-driven project for r/RAG". That framing tells you who it is for. If you are an engineer who already knows what retrieval does and needs to compare LangChain against Haystack, or RAGFlow against Dify, the tables save you a round of searching. If you have never built a retrieval pipeline, the FAQ section carries the basic vocabulary: what RAG is, why a vector database is involved, and how frameworks differ from engines. The README answers that last one directly, calling frameworks libraries you integrate into your code and engines standalone platforms providing ready-to-use RAG functionality. The audience is narrow on purpose. RAGHub is a map, not a vehicle. Nothing in the repository executes. ## How the directory is organised: tables, sections and a hand-written FAQ The README is a single long document with a table of contents linking to sections: RAG Frameworks, RAG Evaluation and Optimization Frameworks, RAG Engines, FAQ, RAG Resources and Sites, Model LeaderBoards, License and Join the Conversation. Each tool section is a Markdown table. The RAG Frameworks table uses the columns Name, Description, Website, Github, Stars and Activity. The Stars column is filled with shields.io badge images rather than plain numbers, and the Activity column in the visible rows contains values like "1h ago" and "9h ago". That Activity column is the weakest part of the design. A relative timestamp baked into a README goes stale the moment the file is committed, and nothing in the repository suggests it is regenerated automatically. Treat it as a hint that a maintainer looked at the project recently, not as a measurement. The same caution applies to the badge column: RAGHub itself offers no quality signal behind it. The FAQ is more useful than the tables for newcomers, because it is written as prose and comparison. It answers whether you need a vector database (yes, the README says, for storing embeddings used in semantic search) and lists ChromaDB for prototyping, Qdrant for production, Pinecone as a managed service and Weaviate for hybrid search. It also lists local model options: Ollama, vLLM, LM Studio and LocalAI. These are opinions with names attached, which is more than the tables give you. ## Installing RAGHub: there is nothing to install The README documents no installation, because there is no software to install. There is no package on any registry, no Docker image and no CLI. The homepage field is empty, so there is no separate documentation site to send you to either. The only way to use RAGHub is to read the README on the repository, or to clone it if you want the file locally. If you want a local copy of the directory to search and annotate, cloning is the whole procedure: ```bash git clone https://github.com/Andrew-Jang/RAGHub.git cd RAGHub ``` After that you have CONTRIBUTING.md, LICENSE and README.md. The README is the directory. There is no command that renders it, indexes it or checks its links. The other documented workflow is contribution, and it is the closest thing to a first real use. The README's FAQ gives four steps: fork the repository, add your entry to the relevant section, follow the existing table format, and submit a pull request. The contribution guidelines live in CONTRIBUTING.md. Note the constraint hidden in step three: the existing table format includes a Stars badge column and an Activity column, so a new row has to match that shape or it will look out of place. If you were looking for a RAGHub tutorial, this is the honest answer: the tutorial is reading the tables and then going to the linked project. RAGHub does not run retrieval for you. ## What RAGHub cannot tell you The directory cannot tell you whether a listed tool works. It records a name, a one-line description, a website and a GitHub link. It does not record versions, supported Python or TypeScript runtimes, dependency weight, licence compatibility with your product, or whether the project has a release process at all. Two tools in the same table can be a maintained library and an abandoned experiment, and the table will not say so. The Activity column is not a substitute. A relative timestamp does not distinguish a documentation typo from a feature release, and because it is static text in a Markdown file, it can be months out of date while still reading as recent. The README does not document how or when those values are refreshed. RAGHub is also the wrong tool if you want a recommendation. The FAQ lists factors for choosing a framework (use case, scale, complexity, integration, language) and names LangChain and LlamaIndex as full-featured versus LightRAG as simple, but it does not score anything or declare a winner. If your decision hinges on retrieval quality, latency or cost, none of that is in this repository. You will have to read the linked projects' own documentation and run your own evaluation, which is exactly what the evaluation section of the directory points at. ## Alternatives: a curated index versus a framework you actually run The natural alternative is not another directory. It is picking one framework from the list and building against it. LangChain and LlamaIndex appear in the RAG Frameworks table; the README's FAQ describes both as full-featured options for building custom pipelines, with LightRAG positioned as the simpler choice. The difference in approach is fundamental: RAGHub is a static Markdown file you read, while those are libraries you add to a project and call from code. One gives you a shortlist; the other gives you chunking, embedding, retrieval and generation primitives. A second alternative is a RAG engine. The README draws the line itself: frameworks are libraries integrated into your code, engines are standalone platforms providing ready-to-use functionality, and it names RAGFlow and Dify as examples. If you would rather run a service and point it at documents than write a pipeline, the engine column of the directory is the relevant one, and the framework table is not. A third alternative is the evaluation tooling the directory also lists: ragas, Trulens, Phoenix and Deepchecks. These do not replace a framework either. They measure one. The README says ragas covers faithfulness, answer relevancy and context precision, which is the kind of number RAGHub itself never provides. ## Maintenance, licence and the cost of keeping a directory current The repository is not archived, and the last push was on 2026-07-28. That is roughly seven weeks before the date of this article, so the project has seen recent activity, but a single push date says nothing about whether the tables were updated in that push or whether the Activity values were refreshed. There are no releases. The README documents no versioning scheme for the directory itself, which is consistent with a project whose deliverable is prose. Upgrading RAGHub means pulling the latest README and re-reading the tables; there is no migration path because there is no interface to migrate. The real maintenance cost sits with the contributors. Every row carries a Stars badge and an Activity string, both of which decay. The README's contribution steps do not mention a script that regenerates them, so the work is manual and falls on whoever opens the next pull request. That is the structural weakness of a hand-maintained index in a field the README itself describes as producing a new tool every day. The repository is MIT licensed. Under that licence you can copy, modify and redistribute the contents, including the tables, provided you keep the copyright and permission notice. That matters if you want to fork the directory internally. It says nothing about the licences of the projects listed, which are separate and not recorded in the README. Check each linked repository's own licence before you build on it; the MIT file in RAGHub covers RAGHub only. ## Conclusion Use RAGHub when you need a shortlist of RAG frameworks, engines and evaluation tools, and read it as a starting point rather than a verdict. Skip it if you want installable code, benchmarks or a hosted service; there is no package, no CLI and no login. Before relying on any entry, open the linked repository and check its own README, licence and last commit, because RAGHub's tables are maintained by hand and the repository itself only ships CONTRIBUTING.md, LICENSE and README.md. ## FAQ ### What exactly is RAG? The RAGHub README defines RAG as a technique that enhances Large Language Model responses by retrieving relevant information from external knowledge sources before generating answers, which it says reduces hallucinations and grounds responses in actual data. ### What is the difference between RAG and an LLM? RAGHub treats the LLM as the component that generates the answer and RAG as the retrieval step that supplies context first. The README's FAQ frames the goal as reducing hallucinations by giving the model retrieved data instead of relying on its parameters alone. ### Is ChatGPT a RAG LLM? The RAGHub README does not discuss ChatGPT or any specific commercial model. It defines RAG as a technique layered on top of an LLM, so the distinction it draws is between the retrieval step and the model, not between named products. ### What is RAG used for? The README's FAQ lists chatbots, search engines and document question answering as the use cases behind framework selection, and describes RAG generally as producing answers based on external knowledge sources rather than model memory. ## Sources - [Andrew-Jang/RAGHub on GitHub](https://github.com/Andrew-Jang/RAGHub) - [Issues](https://github.com/Andrew-Jang/RAGHub/issues) - [License: MIT](https://github.com/Andrew-Jang/RAGHub/blob/main/LICENSE) - [README](https://github.com/Andrew-Jang/RAGHub/blob/main/README.md) --- Hysen Labs editorial analysis, written from the project's own repository and release notes. Cite the canonical page: https://hysenlabs.com/projects/andrew-jang-raghub