Open-source Deep Research
Searching GitHub from your AI Agent
Let agents inspect repositories, issues, and examples before they recommend technical paths.
01
Repository evidence in workflow
AutoSearch lets agents search GitHub as part of a broader research task. They can collect repositories, issues, examples, and maintenance signals before deciding whether a package, pattern, or integration is worth using.
02
Connects code with discussion
GitHub rarely tells the whole story alone. AutoSearch can pair repository evidence with docs, Hacker News, Reddit, arXiv, YouTube, Twitter, and Chinese sources, giving agents a fuller view of adoption and risk.
03
Cited technical recommendations
When an agent recommends a library or architecture, users need to inspect why. AutoSearch returns cited GitHub and external sources, making recommendations easier to review before code changes begin.
How it fits
AutoSearch sits in the research step before technical decisions. An agent can ask it to search GitHub for examples, competing libraries, issue patterns, and maintenance signals, then compare those findings with other channels. The cited result can guide implementation planning, migration risk assessment, or package selection while the coding host remains responsible for edits and tests.
Try this prompt
Search GitHub for active MCP server examples in TypeScript.
Compare stars, recent commits, issue patterns, and cited docs.