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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
AutoRAG: Now your agent can find anything in your computer. It gets smarter if you are using it frequently.
| Date | Stars |
|---|---|
| 2026-07-24 | 4938 |
| 2026-07-25 | 4939 |
| 2026-07-28 | 4939 |
| 2026-07-30 | 4939 |
| 2026-07-31 | 4961 |
| 2026-08-06 | 4961 |
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# AutoRAG **A self-evolving librarian agent for document collections.** > [!IMPORTANT] > **Looking for the original AutoRAG (RAG AutoML / pipeline optimization tool)?** > This repository now hosts **AutoRAG 2.0**, a complete reimagining of AutoRAG as a self-evolving librarian agent. The original Python-based AutoRAG — the RAG AutoML tool for automatically finding an optimal RAG pipeline for your data — now lives in the [`legacy/`](legacy/) directory of this repository. > > **The legacy AutoRAG is NOT abandoned.** It continues to be maintained (bug fixes, dependency updates, and PyPI releases via `pip install AutoRAG`) in maintenance mode. Existing users can keep using it exactly as before — see the [legacy README](legacy/README.md) for its documentation, and file issues in this repository as usual. New feature development is focused on AutoRAG 2.0. AutoRAG searches your PDFs, wikis, notes, research papers, and knowledge bases — then curates the results into clean, numbered knowledge units. No raw grep dumps. Just answers. AutoRAG is a customized [Pi](https://github.com/earendil-works/pi-mono) agent — the Pi agent loop configured into a librarian. Searches use a two-tier workflow: a parent orchestrator delegates exploration to explorer agents. The roles and providers are independently configured from the models available in the user's authenticated runtime; AutoRAG does not ship a private provider default. ## Why AutoRAG ### The problem with search tools Every search tool gives you the same thing: a list of file paths and matching lines. Then *you* have to: - Open each file - Read the surrounding context - Decide what's relevant - Synthesize an answer - Remember what worked for next time That's the human doing all the hard work. The tool just points. ### AutoRAG does the hard work AutoRAG is not a search tool. It's a **librarian** — it searches, reads, thinks, and reports back: ``` You ask: "What were the key findings in the Q3 report?" AutoRAG: [1] Revenue grew 23% YoY to $4.2M, driven by enterprise contracts. (pages 3-5) [2] Three new risk factors: supply chain, regulatory, talent retention. (pages 12-14) [3] Headcount target missed by 12 — engineering hiring bottleneck. (page 8) ``` No file paths. No line numbers. Just curated knowledge you can act on. ### It gets smarter over time AutoRAG has a **self-evolving memory system**. Every search teaches it something: - Which retrieval methods work for which types of queries - Which document areas are most productive - What the caller found useful (via explicit feedback) A fresh AutoRAG tries everything. A seasoned one knows exactly where to look. This is not a static configuration — it's learned behavior from real usage. ### Multiple retrieval methods, one interface Different documents need different search strategies: | Your documents | Best method | Why | |---|---|---| | Plain text, config files | grep (pattern matching) | Fast, precise, literal | | Research papers, dense prose | Vector search (semantic) | Understands meaning, not just keywords | | Legal documents, specifications | BM25 (keyword ranking) | Handles domain terminology well | | Mixed collections | Hybrid (vector + BM25) | Combines precision and recall | AutoRAG supports **pluggable retrieval methods**. It ships with lexical **BM25** and semantic **MinSync** methods wired through the `RetrievalMethodRegistry`, and the architecture is ready for additional vector and hybrid backends. The parent orchestrator owns process-bound retrieval tools and gives bounded seed packs to explorers; explorers use read-only `read`/`grep`/`find`/`ls` tools to inspect the underlying documents. The `ResultMerger` handles cross-method score normalization and deduplication — you get one unified result set regardless of how many methods contributed. BM25 and MinSync are **enabled by default** — no explicit configuration is needed for standard lexical + semantic retrieval. Both can be disabled by setting `"bm25": fals
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:74b103dbf3f28920, topic:rag, topic:retrieval-augmented-generation, readme:rag pipeline
matched fp:74b103dbf3f28920, topic:llm
matched fp:74b103dbf3f28920, topic:embeddings
matched fp:74b103dbf3f28920, topic:evaluation, topic:llm-evaluation