MolmoWeb: Open Visual Web Agent and Open Data for the Open Web

Web agents—autonomous systems that navigate and executetasks on the web on behalf of users—have the potential to transformhow people interact with the digital world. However, the most capableweb agents today rely on proprietary models with undisclosed trainingdata and recipes, limiting scientific understanding, reproducibility, andcommunity-driven progress. We believe agents for the open web shouldbe built in the open. To this end, we introduce (1) MolmoWebMix, alarge and diverse mixture of browser task demonstrations and web-GUIperception data and (2) MolmoWeb a family of fully open multimodalweb agents. Specifically, MolmoWebMix combines over 100K synthetictask trajectories from multiple complementary generation pipelines with30K+ human demonstrations, atomic web-skill trajectories, and GUIperception data, including referring expression grounding and screen-shot question answering. MolmoWeb agents operate as instruction-conditioned visual-language action policies: given a task instruction anda webpage screenshot, they predict the next browser action, requiring noaccess to HTML, accessibility trees, or specialized APIs. Available in 4Band 8B size, on browser-use benchmarks like WebVoyager, Online-Mind2Web, and DeepShop, MolmoWeb agents achieve state-of-the-art results outperforming similar scale open-weight-only models suchas Fara-7B, and Holo1-7B. MolmoWeb-8B also surpasses set-of-marks(SoM) agents built on much larger closed frontier models like GPT-4o.We further demonstrate consistent gains through test-time scaling viaparallel rollouts with best-of-N selection, achieving 94.7% and 60.5%pass@4 (compared to 78.2% and 35.3% pass@1) on WebVoyager andOnline-Mind2Web respectively. We release model checkpoints, train-ing data, code, and a unified evaluation harness to enable reproducibilityand accelerate open research on web agents (GitHub).