Not every page needs a full browser. 🌐
We’ve added the "effort" parameter to Tabstack! Now you can choose how hard we work to fetch a page:
🏎️ min: <1s for static sites
⚖️ standard: Smart default
🛠️ max: Full JS rendering
Optimize your speed: bit.ly/4cTabmc
As we began building Tabstack, we recognized early on that agent native search was critical for our automation layer.
Parallel's ability to consistently surface authoritative, up-to-date information in clean, structured formats that integrate smoothly into our agentic loop.
Mozilla built @tabstack to let AI agents browse the web the way humans do: by clicking, scrolling, form-filling, and extracting page information.
They use Parallel’s Search API to handle what comes before all of that.
Web automation is broken. Scripts are brittle. The web is chaotic. 🌪️
Meet Pilo: an open-source agentic engine that gives LLMs a steering wheel. 🚗
It plans, observes the accessibility tree, executes, and heals itself.
And today, it's yours to use. ⚡️
bit.ly/4rBnxbe
Extracting data from a URL is an infrastructure problem. Answering "Compare Slack vs Teams retention" is a reasoning problem
We’re launching Tabstack Research to bridge this gap. Multistep discovery, gap detection, and cited synthesis 🚀
tabstack.ai/blog/tabstack-re…
#LLMs #DevTools
🚀 Tabstack: Browsing Infrastructure for AI Agents
You built a brilliant agent. Then you connected it to the internet, and everything broke. 💥
Stop paying to tokenize noise. We fixed the infrastructure so you can focus on the intelligence 🧠
mzl.la/4qm92aD
