What problem does it solve?
Agent-browser removes manual, error-prone web interactions by letting AI agents navigate pages, discover and interact with UI elements, capture visual evidence, and extract structured content without hand-crafting selectors for every target page. It reduces context bloat for LLMs by using compact snapshot refs and enforces a consistent capture convention so results are organized and reproducible.
Core Features & Use Cases
- Ref-based Interaction: Snapshot-driven refs (e.g., @e1) let agents click, fill, select, and type without sending full DOM blobs to the model.
- End-to-end Workflows: Navigate, authenticate, persist session state, run interactions, and verify changes with snapshot diffs and visual screenshots.
- Capture & Testing: Produce screenshots, PDFs, recordings, visual diffs, and performance profiles for debugging, regression testing, or documentation.
- Security & Policy: Domain allowlists, action policies, content boundaries, and optional encryption for saved state mitigate unsafe or unexpected actions.
- Use Cases: Automate login flows and authenticated scraping, run E2E tests and visual regressions, capture pages for archival, or generate evidence for audits and support tickets.
Quick Start
Open a page, snapshot interactive refs, perform interactions using those refs, and save screenshots to the ./screenshots/ directory.