Open-source Deep Research
Open-source Deep Research for Research Teams
Give analysts a repeatable way to gather cited, multilingual source packets with agents.
01
Repeatable source collection process
Research teams need consistency across analysts and projects. AutoSearch gives agents a repeatable way to query 40 channels, return citations, and preserve source context, making handoffs and quality review easier.
02
Works across research domains
A team may research markets one week and technical infrastructure the next. AutoSearch covers papers, repositories, social discussion, developer forums, videos, and Chinese platforms, making it useful across many open-source intelligence tasks.
03
Review stays with the team
AutoSearch collects evidence, while analysts decide what matters. Its LLM-decoupled design lets each team keep its preferred host, review process, and synthesis model without rebuilding channel access for every workflow.
How it fits
AutoSearch sits at the collection stage for research teams using AI agents. The agent asks for a source packet, AutoSearch gathers cited material across 40 channels, and the team reviews, tags, and synthesizes the findings. It works well for recurring briefs, competitor watches, literature scans, and multilingual monitoring because the research behavior is consistent even when the final report format changes.
Try this prompt
Build a cited research packet on the agent browser market.
Cover GitHub, docs, Reddit, Hacker News, YouTube, Twitter, and Chinese sources.