Index – New Open Source browser agent

Hacker News

Hey HN, Robert from Laminar (lmnr.ai) here. We built Index - new SOTA Open Source browser agent. It reached 92% on WebVoyager with Claude 3.7 (extended thinking). o1 was used as a judge, also we manually double checked the judge. At the core is same old idea - run simple JS script in the browser to identify interactable elements -> draw bounding boxes around them on a screenshot of a browser window -> feed it to the LLM. What made Index so good: 1. We essentially created browser agent observability. We patched Playwright to record the entire browser session while the agent operates, simultaneously tracing all agent steps and LLM calls. Then we synchronized everything in the UI, creating an unparalleled debugging experience. This allowed us to pinpoint exactly where the agent fails by seeing what it "sees" in session replay alongside execution traces. 2. Our detection script is simple but extremely good. It's carefully crafted via trial and error. We also employed CV and OCR. 3. Agent is very simple, literally just a while loop. All power comes from carefully crafted prompt and ton of eval runs. Index is a simple python package. It also comes with a beautiful CLI. pip install lmnr-index playwright install chromium index run We've recently added o4-mini, Gemini 2.5 Pro and Flash. Pro is extremely good and fast . Give it a try via CLI. You can also use index via serverless API. ( https://docs.lmnr.ai/index-agent/api/getting-started ) Or via chat UI - https://lmnr.ai/chat . To learn more about browser agent observability and evals check out open-source repo ( https://github.com/lmnr-ai/lmnr ) and our docs ( https://docs.lmnr.ai/tracing/browser-agent-observability ).

Visit product View on Hacker NewsApr 23, 2025

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best fitHighest predicted score across all platforms for this description.

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48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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