AutoSearch

Research that gets smarter every time you use it.

34 search channels. Self-evolving queries. Cited reports. Zero API keys.

Website • Documentation • Quick Start • What Makes It Different • Self-Evolution • How It Works • Channels • Contributing

---

Quick Start

npm install -g @0xmariowu/autosearch

Then start a new Claude Code session and run:

/autosearch "compare vector databases for RAG applications"

Alternative install (no npm)

curl -fsSL https://raw.githubusercontent.com/0xmariowu/autosearch/main/scripts/install.sh | bash

AutoSearch asks two questions — how deep and what format — then searches, evaluates, and delivers a cited report with real-time progress:

[Phase 1/6] Recall     — 25 rubrics, 47 items recalled, 15 queries planned
[Phase 2/6] Search     — 62 results from 12 channels
[Phase 3/6] Evaluate   — 54 relevant, 3 gap queries
[Phase 4/6] Synthesize — report ready (38 citations)
[Phase 5/6] Rubrics    — 23/25 rubrics passed
[Phase 6/6] Evolve     — 4 patterns saved

What Makes It Different

	AutoSearch	Perplexity	Native Claude
Search channels	34 dedicated connectors	~3 web engines	1 (WebSearch)
Chinese sources	12 native (zhihu, bilibili, 36kr, csdn...)	0	0
Academic sources	6 (arXiv, Semantic Scholar, OpenReview, Papers with Code...)	1	0

...