Describe your data needs in plain language and turn websites into a continuous source of fresh data.
Read the original at KDnuggets: BrowserAct AI Web Scraper in 2026: Build Once, Run Repeatedly
How OpenSmartRoute helps
Teams using OpenSmartRoute gain immediate control over their AI web scraping workflows. The router scores every candidate on quality, cost, speed, and safety before sending a request. A team sets the weights for each factor to match their specific data needs.
Hard rules ensure personal data stays on an on-premises model or never crosses a cost cap. An input guard spots prompt injection and personal data before a request leaves the system. This prevents leaks and keeps sensitive information secure during the scraping process.
The platform keeps a models catalogue with prices and public rankings built from real traffic. A savings ledger shows what each routed request cost next to what the most expensive model would have cost. This transparency lets managers decide exactly which models to keep in their catalogue.
New models are one catalogue entry and compete on the next request automatically. Nothing else changes in the app when a new scraper or agent is added. The system learns from outcomes so a model that answers well gets more traffic. A model that fails gets less traffic, and the team sees the results in the ledger.
The hosted platform allows teams to define their own rules for routing requests. They can set up text with personal data to stay on an on-premises model by default. This setup works with any OpenAI-compatible provider and open-weight models served locally.
The team can use MCP tools and A2A agents within their defined catalogue. An 'osr eval' measure routing accuracy on the team's own prompts and can fail a build when it drops. This ensures the scraper remains reliable before it runs repeatedly on live data.