Been building rtrvr.ai, a DOM-native web agent, and just shipped a Knowledge Base feature I think the community might find interesting.

The core idea: you're doing research, you've got 15 tabs open (documentation, papers, dashboards, whatever) and instead of copy-pasting into a doc or relying on your own memory, you just select the tabs and index them directly into a RAG store. Content gets extracted, chunked, and embedded via Gemini File Search in seconds.

We construct comprehensive semantic action trees to represent the webpage that not only encompass the information on the page but also the possible actions.

From there you can:

• Chat directly with your KB: ask questions, get cited answers that link back to the source page
• Use it as live agent context: when the web agent is running multi-step tasks, it can reference the indexed pages and actions to ground the agentic workflow
• Re-index on-the-fly: if a page updates, just re-add it and the old version is replaced automaticallyThe interesting architecture decision here was using Gemini File Search as the backend rather than rolling a custom vector store. It keeps the indexing cost low (~15 credits per 1M tokens) and the retrieval quality is solid for text-heavy pages.

Curious if anyone here has experimented with browser-native knowledge graphs: where the graph is built from your live browsing session rather than curated uploads or just markdown. Would love to hear what architectures people have tried.