Open-source RAG infrastructure that survives production workloads. Upload documents, query via API, get answers with sources. Hybrid search, multimodal, complex reasoning - all included. Model-agnostic. Used by 1,500+ teams in medical AI, legal tech, enterprise search.

Hey Product Hunt 👋

Abdellatif here, founder of Agentset.

The story: Last year we needed RAG for 9M pages. Followed tutorials, got a prototype in a week. Tested on 100 docs - looked great.

Deployed to production - completely failed.

We spent 3 months debugging everything - query generation, reranking, custom chunking, metadata injection. My co-founder and I have built products for 5M+ users, but RAG humbled us hard.

After processing 5M+ documents across two enterprises, we finally cracked it. We thought: nobody else should waste 3 months like this.

So we built Agentset - open-source RAG that works in production, out of the box. All the painful lessons baked in: agentic reasoning, hybrid search + reranking, automatic citations, multimodal support.

Built with some amazing tools:

1,500+ teams are using it now. Feels incredible seeing others skip the pain we went through.

Try it: https://agentset.ai

Happy to answer anything about RAG, our journey, or what we learned in the trenches!

— Abdellatif

Built RAG pipelines where chunking strategy ended up mattering more than model choice. Does Agentset expose hooks for custom chunk boundaries, or does the 22-format parser handle semantic splits by default? That's usually where production accuracy lives or dies.