pilo

ilo captures the browser's accessibility tree (via Playwright's _snapshotForAI). This gives the LLM a semantic, stable view of the page (buttons, links, inputs) rather than div hell. - Context Compression: We pipe that tree through a compression engine. We map verbose tags (like listitem -> li), shorten reference IDs, and deduplicate repetitive text. This reduces token usage by 60-80% without losing interactive elements, allowing for much longer agent loops. - Layered Error Handling: The web is flaky. Pilo treats navigation failures as distinct from interaction failures. It uses timeout escalation for network issues (doubling wait times) and will automatically restart the browser instance if it detects a "stuck" state or DNS failure. - Agentic Loop: It follows a strict Plan -> Observe -> Act -> Validate loop. It even includes a separate validation step where a second LLM "grades" the final output against the original success criteria before returning it. The "Cool" Part (Browser Extension) Since the core logic is decoupled from the runtime, we packaged it into a browser extension. You can install it, type a prompt, and literally watch the agent drive your local browser tab in real-time. It’s a great way to debug how the LLM "sees" the page. Why Open Source? We sell the managed infrastructure (scaling browsers, persistent sessions, etc.) at Tabstack. But the execution engine itself, the thing that decides "click here" or "scroll there", should be open. You can run Pilo entirely on your own machine with your own API keys without paying us a dime. You can read more about it on our blog https://tabstack.ai/blog/introducing-pilo-browser-automation. Or check out the repo, install it, and give it a try - https://github.com/mozilla/pilo We’d love to hear your feedback on the compression pipeline or how you’re handling agent state in your own projects. Happy to answer any questions!