Alternatives
Products that do what Super RAG does
Super performant RAG pipelines for AI apps
- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9
- 10
- 11
- 12SW
Hey HN, We’re Basia, Fokke, and Geno from Liquidmetal AI, and we built something we wish we had a long time ago: SmartBuckets. We’ve spent a lot of time building RAG and AI systems, and honestly, the infrastructure side has always been a pain. Every project turned into a mess of vector databases, graph databases, and endless custom pipelines before you could even get to the AI part. SmartBuckets is our take on fixing that. It works like an object store, but under the hood it handles the messy stuff — vector search, graph relationships, metadata indexing — the kind of infrastructure you'd…
2025
- 13
- 14
- 15
Smarter RAG with Agentic Retrieval & Context-Aware MCP
Sep 2025
- 16
- 17VA
2023 · github.com
- 18PA
We're excited to release PaperQA2, an open source RAG library specialized to work with the scientific literature. We've seen some really compelling results with it (https://paper.wikicrow.ai), like superhuman performance at question answering and summarization when compared with expert scientists. PaperQA2 is a major overhaul of our prior PaperQA system, it includes automatically obtained rich metadata for each paper, a CLI to work with local papers directly, a local full-text search engine for keywords searches over PDF files, a state-of-the-art algorithm for LLM-based re-ranking…
2024 · github.com
- 19PR
Hi HN, While building RAG agents, I noticed a lot of token budget was wasted on formatting overhead (HTML tags, JSON structure, whitespace). Existing solutions felt too heavy (often requiring torch/transformers), so I wrote this lightweight, zero-dependency library to solve it. It includes strategies for context packing, PII redaction, and tool output compression. Benchmarks show it can save ~15% of tokens with negligible latency overhead (<0.5ms). Happy to answer any questions!
Dec 2025 · github.com
- 20AE
Hi all, Sharing a repo I was working on for a while. It’s open-source and includes many different strategies for RAG (currently 17), including tutorials, and visualizations. This is great learning and reference material. Open issues, suggest more strategies, and use as needed. Enjoy!
2024 · github.com
- 21AO
Hi HN, We built one of the largest RAG set-ups that exist toady with Usul.ai (6B tokens). We started by using langchain and llamaindex, they were able to get us to a prototype in a couple of days, but took 3 months of taking pieces apart and optimizing them to make it perform well at such large scale. We put all of these learning into an MIT licensed open-source project — Agentset. Our goal to let people get production quality RAG w/o having to understand or optimize the underlying pieces. It supports 22 file formats, agentic search, deep research, citations, and a UI out of the box.…
Oct 2025 · github.com
- 22BL
Hello everyone! I am Jan, CTO and one of the creators of Pathway, the real-time data processing framework. I’m excited to share Pathway’s ready-to-use AI Pipelines, configurable with just YAML! These frameworks offer out-of-the-box solutions for AI search, RAG, and more—optimized for real-time indexing and in-memory processing. What makes it simple? YAML templates! The pipeline templates are fully customizable using YAMLs to fit your needs, from changing the data sources to the choice of the LLM model, all without touching Pathway’s Python code. Thanks to the Pathway data processing engine,…
2024 · pathway.com
- 23RA
2024 · github.com
- 24
Ranked by how close each launch is in meaning, then by votes. Refine with a description →