My team and I are building Tabstack to handle the "web layer" for AI agents. Launch Post: https://tabstack.ai/blog/intro-browsing-infrastructure-ai-ag...
Maintaining a complex infrastructure stack for web browsing is one of the biggest bottlenecks in building reliable agents. You start with a simple fetch, but quickly end up managing a complex stack of proxies, handling client-side hydration, and debugging brittle selectors. and writing custom parsing logic for every site.
Tabstack is an API that abstracts that infrastructure. You send a URL and an intent; we handle the rendering and return clean, structured data for the LLM.
How it works under the hood:
- Escalation Logic: We don't spin up a full browser instance for every request (which is slow and expensive). We attempt lightweight fetches first, escalating to full browser automation only when the site requires JS execution/hydration.
- Token Optimization: Raw HTML is noisy and burns context window tokens. We process the DOM to strip non-content elements and return a markdown-friendly structure that is optimized for LLM consumption.
- Infrastructure Stability: Scaling headless browsers is notoriously hard (zombie processes, memory leaks, crashing instances). We manage the fleet lifecycle and orchestration so you can run thousands of concurrent requests without maintaining the underlying grid.
On Ethics: Since we are backed by Mozilla, we are strict about how this interacts with the open web.
- We respect robots.txt rules.
- We identify our User Agent.
- We do not use requests/content to train models.
- Data is ephemeral and discarded after the task.
The linked post goes into more detail on the infrastructure and why we think browsing needs to be a distinct layer in the AI stack.
This is obviously a very new space and we're all learning together. There are plenty of known unknowns (and likely even more unknown unknowns) when it comes to agentic browsing, so we’d genuinely appreciate your feedback, questions, and tips.
Happy to answer questions about the stack, our architecture, or the challenges of building browser infrastructure.
Diti•2h ago
I also wanted to see how/if it handled semantic data (schema.org and Wikidata ontologies), but the hidden pricing threw me off.
MrTravisB•1h ago
We are simply in an early stage and still finalizing our long-term subscription tiers. Currently, we use a simple credit model which is $1 per 10,000 credits. However, every account receives 50,000 credits for free every month ($5 value). We will have a dedicated public pricing page up as soon as our monthly plans are finalized.
Regarding semantic data, our JSON extraction endpoint is designed to extract any data on the page. That said, we would love to know your specific use cases for those ontologies to see if we can further improve our support for them.