Top AI Repos — open-source AI, indexed and scored
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.
Talk to research papers like talking to authors - Python package with AI agent for arXiv papers
| Date | Stars |
|---|---|
| 2026-07-31 | 762 |
| 2026-08-06 | 767 |
| 2026-08-14 | 938 |
| 2026-08-18 | 771 |
| 2026-08-31 | 772 |
| 2026-09-01 | 778 |
| 2026-09-02 | 780 |
| 2026-09-04 | 779 |
| 2026-09-05 | 780 |
| 2026-09-07 | 781 |
| 2026-09-09 | 782 |
| 2026-09-12 | 783 |
| 2026-09-17 | 784 |
| 2026-09-18 | 785 |
| 2026-09-19 | 786 |
| 2026-09-20 | 789 |
| 2026-09-22 | 792 |
| 2026-09-24 | 794 |
| 2026-09-26 | 795 |
| 2026-09-27 | 796 |
| 2026-09-28 | 799 |
| 2026-09-29 | 800 |
| 2026-10-04 | 801 |
| 2026-10-05 | 802 |
| 2026-10-08 | 803 |
| 2026-10-09 | 804 |
| 2026-10-12 | 811 |
| 2026-10-13 | 812 |
| 2026-10-14 | 809 |
| 2026-10-16 | 810 |
| 2026-10-19 | 829 |
| 2026-10-20 | 834 |
| 2026-10-21 | 836 |
| 2026-10-22 | 837 |
| 2026-10-23 | 836 |
| 2026-10-24 | 837 |
| 2026-10-25 | 840 |
| 2026-10-26 | 839 |
| 2026-10-27 | 841 |
| 2026-10-28 | 842 |
| 2026-10-29 | 843 |
| 2026-11-02 | 845 |
| 2026-11-03 | 847 |
| 2026-11-04 | 845 |
Today
-2 stars today
This week
+3 stars this week
This month
+43 stars this month
Momentum
3.0
growth rate 0.36%/day
<h1 align="center">DeepXiv</h1> <p align="center"><em>The data layer agentic search is missing — full paper text, real citations, and the people behind them.</em></p> <p align="center"> <a href="https://1stauthor.com/assistant/">Live system</a> · <a href="USAGE.md">Full documentation</a> · <a href="https://data.rag.ac.cn/api/docs">API docs</a> · <a href="https://data.rag.ac.cn/status">Status</a> · <a href="https://arxiv.org/abs/2603.00084"><img src="https://img.shields.io/badge/arXiv-2603.00084-b31b1b" alt="arXiv"></a> · <a href="README.zh.md">中文</a> </p> <p align="center"> <img src="./assets/deepxiv_cover.jpg" width="100%" alt="DeepXiv — the best vertical-domain data service for agents"> </p> > [!IMPORTANT] > **Capacity: about 50 requests per second, shared by everyone.** DeepXiv's paper service and the 1stAuthor services run on small servers with limited compute and bandwidth. Donations to keep it running are very welcome 🙏. If you need large volumes, email `tommy[at]chien.io` and we'll work out a plan together. --- ## What it is A CLI and Python SDK over a service that has already read the literature. Papers arrive parsed into sections rather than PDFs, retrieval runs over full bodies rather than abstracts, and an agentic endpoint answers questions with citations that resolve to real arXiv IDs and URLs. Four things it does: | | Command | | |---|---|---| | **Ask the literature** | `deepxiv ask` | A question in, an answer out, cited with real arXiv IDs | | **Ask the web** | `deepxiv ask --web` | Same, over Google plus cached page bodies | | **Read a paper in layers** | `deepxiv search` / `paper` | Search, judge, then read only the section you need | | **Find the people** | `deepxiv fa … talent` | Who works on a topic, where, and what their record is | | **Watch US stocks** 🆕 | `deepxiv fa … stocks` | 8,000+ tickers: prices, per-ticker news, filings, financials, macro — point-in-time, and `fa ask -d stocks` for a cited analysis | | **Go beyond papers** | `deepxiv fa` | Statutes, judgments, trials, drugs, grants, filings, standards, CVEs — via 1stAuthor | ## What problem it solves An agent researching a topic has bad options. Search APIs return ten blue links and abstracts — enough to name a paper, never enough to answer "what speedup does it report on HumanEval". PDFs answer that, but cost 50k tokens each and arrive as a wall of text with no structure to navigate. DeepXiv removes the tradeoff. Papers are pre-parsed, so an agent can spend 300 tokens on a TLDR to decide whether to spend 5k on the Methods section. Questions that need evidence go to the agentic endpoint, which reads source text and hands back an answer with citations you can check. And because knowing *who* does the work is half of research, the same token searches scholars — plus law, clinical trials, drugs, grants and more through [1stAuthor](https://1stauthor.com)'s domains. ## DeepXiv × 1stAuthor DeepXiv works closely with [1stAuthor](https://1stauthor.com) to serve data beyond papers: researchers, US stocks, statutes, court judgments, clinical trials, drugs, grants, filings, standards and CVEs, all reached through `deepxiv fa` with the same token. **→ [The `deepxiv fa` guide](FA.md)** One CLI, fifteen domains, the same three verbs: `search` → `read` → `ask`. | Domain | What's in it | |---|---| | **`stocks`** 🆕 | **8,000+ US tickers: prices, per-ticker news and events, SEC filings, financials, macro series and news — all point-in-time** | | `talent` | AI researcher profiles: education, career, top papers, metrics | | `appraise` | What academicians and fellows said when they cited a paper | | `law` · `us_law` · `uk_eu_law` · `fr` | Chinese and 25 other jurisdictions' statutes; US statutes and regulations; UK and EU legislation; Federal Register | | `cases` | Chinese and US court judgments | | `trials` · `drugs` · `grants` | ClinicalTrials.gov, openFDA drug labels, NSF / NIH grants | | `filings` · `standards` | SEC and Chinese com
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Read on GitHub
Tommy Chien · BAAI · China
45
zhengliu · Microsoft · China
2
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:971b57070b983a4c, desc:ai agent