How to Make Your Open-Source Library Easier for AI Agents to Find, Trust, Use and Love
Give agents files they actually read, put documentation where the agent already is: your types, make the API self-explanatory.
The article provides a checklist for making an open-source library more discoverable and usable by AI agents. It uses markdown-regex as a case study, a TypeScript library of ready-made RegExp constants for parsing Markdown. The key points are:
1. Be findable and recommendable:
- Clearly state when the library is appropriate to use, and when it's not
- Write a concise description and topics that match real queries
- Use consistent naming, description, and README content across GitHub, npm, and other sources
2. Give agents files they actually read:
- Add an AGENTS.md file at the root of the repository, explaining how to build, test, lint, typecheck, and add features
- Consider smaller language-specific files like CLAUDE.md or llms.txt for concise summaries
- Leverage existing documentation for the AGENTS.md, types, and JSDoc comments
3. Make the API self-explanatory:
- Provide behavioral details for each export, including capture groups, flags, known failure modes, and real input/output examples
- Document patterns separately for matching Markdown syntax and cleaning up the results
By following these guidelines, maintainers can make their libraries more effective for AI agents to find, trust, use, and even love, without requiring human intervention.
Written by urgent.news from HackerNoon's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
