This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
Product upvotes vs the next 3
Waiting for data. Loading
Product comments vs the next 3
Waiting for data. Loading
Product upvote speed vs the next 3
Waiting for data. Loading
Product upvotes and comments
Waiting for data. Loading
Product vs the next 3
Loading
RagLeap
No CUDA, no 2GB torch. 23MB reranker. 6 vector DBs.
The open-source core of RagLeap — a self-hosted RAG chat engine (document ingestion, retrieval, citations) powering RagLeap's full AI business manager: Voice AI, WhatsApp/Telegram/Discord bots, database & CRM integrations, workflow automation, and a private executive assistant. Foundation layer — hosted platform at ragleap.com - antonyrag/ragleap-core
I built this because every RAG library I tried assumed I had a GPU.
RagLeap's reranker runs on ONNX Runtime — a 23MB quantized model instead of the 2GB+ torch/CUDA stack most RAG frameworks pull in even for CPU-only use.
It supports 6 vector backends (pgvector, FAISS, Pinecone, Weaviate, Qdrant, Milvus), 8 embedding providers, and 12+ generation providers with automatic fallback if one goes down. Real per-call cost tracking with monthly budgets. 238 tests, real CI on every PR.
Everything in the docs is either live-verified against real infrastructure or explicitly labeled unverified — no marketing claims, no numbers we haven't actually measured.
pip install ragleap-rag
Contributions welcome — the roadmap, and what we've deliberately said no to, are both documented in the open: github.com/antonyrag/ragleap-core
Would love feedback, especially from anyone who's fought GPU dependency bloat before.
About RagLeap on Product Hunt
“No CUDA, no 2GB torch. 23MB reranker. 6 vector DBs.”
RagLeap was submitted on Product Hunt and earned 4 upvotes and 3 comments, placing #90 on the daily leaderboard. The open-source core of RagLeap — a self-hosted RAG chat engine (document ingestion, retrieval, citations) powering RagLeap's full AI business manager: Voice AI, WhatsApp/Telegram/Discord bots, database & CRM integrations, workflow automation, and a private executive assistant. Foundation layer — hosted platform at ragleap.com - antonyrag/ragleap-core
On the analytics side, RagLeap competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how RagLeap performed against the three products that launched closest to it on the same day.
Who hunted RagLeap?
RagLeap was hunted by TC Antony. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of RagLeap including community comment highlights and product details, visit the product overview.
TC Antony