Why I built an open learning map for LLM agents
Sep 15, 2026
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1 min read
When I started working on agents for geospatial AI, most of my time went into finding out what to read next. Blog posts, courses and papers were plentiful, but the order in which they made sense was not obvious, and many resources were only available in one language.
The LLM Agent Roadmap is my attempt to write that order down. It groups the field into stages, links each stage to resources I found useful, and keeps a timeline of papers that shifted the direction of the field. Keeping the content in plain JSON makes it easy for others to correct or extend it, and a monthly update keeps it from going stale.
It is a personal map rather than a complete survey, so suggestions are very welcome on GitHub.
Authors
PhD Student in Geospatial AI
I am a PhD student at the Chair of Cartography and Visual Analytics
at the Technical University of Munich, supervised by Prof. Liqiu Meng and
mentored by Prof. Yu Feng. I came to AI from maps. I studied geodesy,
cartography and GIS at Wuhan University, where I first used language
models to make sense of geospatial code, and I now spend most of my time
on the AI side of GeoAI. Besides research, I supervise Master’s students
and maintain open learning resources on LLM agents and web scraping.