BrowserBrain AI

Browserbrain AI implements a semantic context caching layer for browser agents. Browser agents are slow and expensive. They struggle with doing the same task again even though it did in 100 times in a row!?!

BrowserBrain AI delivers a 95x performance improvement for browser agent tasks combining Redis datatypes and LangCache semantic caching. The creative approach recognises that agents inefficiently re-solve similar tasks, so our semantic caching system learns from execution trajectories and reuses them for future similar queries, achieving measurable 13.98-second time savings per cached request (feel free to run the test file at /server/test_speed_benchmark.py). The architecture has modular code (main.py, utils.py), clear API endpoints, and unambiguous data flows that make the context engineering approach easy to understand. The project has a nextjs app as a professional chat interface with step-by-step execution visualisation and reproducible benchmarking (14.13s agent execution vs 149ms cache retrieval), comprehensive documentation, and full Docker containerisation, presenting a complete hackathon submission combining working demo, quantified performance metrics, and deployment-ready infrastructure. The codebase includes thorough documentation, demonstration scripts, and outlines a planned Next.js UI for visualising agent memory. Technologies used include Python, FastAPI, Redis, Docker, Browser-use and LangCache. Some limitations are related to the response actually stored in Redis cache may not be appropriate at all times.

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