I’ve been diving deep into RAG lately and ran into the same problem many of you probably have: there are *way* too many options. Naive RAG, GraphRAG, Self-RAG, LangChain, RAGFlow, DocGPT… just setting them up takes forever, let alone figuring out which one actually works best for my use case.

Then I stumbled on this little project that feels like a hidden gem:  
👉 [Github](https://github.com/RagView/RagView)

👉 [Ragview](https://ragview.ai/)

What it does is simple but super useful: it integrates multiple open-source RAG pipelines and runs the *same queries* across them, so you can directly compare:

* Answer accuracy
* Context precision / recall
* Overall score
* Token usage / latency

You can even test on your *own dataset*, which makes the results way more relevant. Instead of endless trial and error, you get a clear picture in just a few minutes of which setup fits your needs best.

The project is still early, but I think the idea is really practical. I tried it and it honestly saved me a ton of time.

If you’re struggling with choosing the “right” RAG flavor, definitely worth checking out. Maybe drop them a ⭐ if you find it useful.

Found a hidden gem! benchmark RAG frameworks side by side, pick the right one in minutes!