Tabstack
The Web Access Challenge for Agentic AI
Every AI engineer eventually hits the same infrastructure wall. You build an agent that reasons perfectly in a sandbox, but the moment you connect it to the open web to perform a complex task, you are forced to stop building AI and start managing the massive overhead of browser orchestration.
What starts as a simple script to fetch a URL quickly spirals into a full-time job. You find yourself managing headless browsers just to handle client-side rendering. You spend hours writing brittle selectors to parse raw HTML, only to watch them break the moment a site updates its layout. Instead of getting clean data, you are stuck debugging a chaotic mess of tags and attributes.
This is the hidden tax of autonomous systems. The web is messy, unpredictable, and designed for human eyes rather than software agents. When teams try to bring browsing infrastructure to production themselves, they end up spending more cycles handling session reliability and patching browser fleets than they do improving their agent’s intelligence.
Suddenly, you are not building AI anymore. You are maintaining a fragile, expensive web layer. We built Tabstack to turn that chaotic infrastructure into a solved problem.
Web Browsing as Infrastructure
Today, we are publicly launching Tabstack, the web execution layer for AI systems.
Tabstack is a developer-focused API that enables AI agents to extract, generate, and automate web content. It acts as a single abstraction layer that collapses the complexity of web automation. Tabstack gives your agents the ability to navigate pages, interact with real user flows, and turn the chaotic web into clean, actionable inputs.
We treat browsing as a distinct infrastructure problem. Developers should not have to manually assemble network layers, proxies, and rendering engines just to reliably interact with a website.
Whether you need to render complex single-page applications or navigate multi-step user workflows, Tabstack handles the heavy lifting of orchestration and reliability. We provide the stability needed to operate at scale so you can focus on building the intelligence that drives your agents.
A Continuum of Execution
Under the hood, Tabstack dynamically routes every request to the most efficient extraction method. It does not just blindly fire up a heavy browser for every request. Instead, it intelligently selects the lightest viable method by starting with raw HTTP fetches and escalating to full browser sessions only when edge cases or complex interactions demand it.
This adaptive approach delivers:
- Speed: Process simple pages instantly via lightweight fetches.
- Reliability: Handle complex single-page apps (SPAs) with a robust automation engine that manages scrolling, rendering, and wait times.
- Resilience: Maintain high success rates with intelligent routing and retry logic, without your team needing to manage the underlying infrastructure.
Turning the Web into Data
Tabstack provides a high-level interface for both understanding and acting on web content. By filtering out the noise before it reaches your model, Tabstack prevents you from burning your budget on parsing raw HTML. We ensure every token in your context window is spent on reasoning, not rendering.
Whether you are feeding a RAG (Retrieval-Augmented Generation) pipeline or building an autonomous agent, we have an endpoint for the job:
- /markdown: Instantly convert any URL’s page content into clean Markdown, perfect for feeding into LLM context windows.
- /json: Transform unstructured web pages into structured JSON objects using a schema you provide. This is ideal for turning product listings or news articles into database-ready records.
- /automate: Instruct Tabstack to navigate, click, type, and handle complex interactions to complete multi-step workflows. This allows your agent to execute tasks just like a human user.
Giving Your Agent Eyes
We designed our API to be dead simple. Just import the SDK and tell Tabstack what to do.
Here is how you can automate a complex task, like researching the top posts on Hacker News, in just a few lines of Python:
import os from tabstack import Tabstack
# Initialize the client
tabs = Tabstack(api_key=os.getenv('TABSTACK_API_KEY'))
# Run an autonomous task
result = await tabs.agent.automate(
url="https://news.ycombinator.com",
task="Go through 5 pages of the top posts. For each post, determine the website it is from and group all the posts by website. Return the list of the 10 websites with the most posts."
)
print(result)
In the background, Tabstack’s engine navigates pagination, interprets the content, aggregates the results, and returns the final answer. Your agent stays focused on reasoning while we handle the browsing.
Built for Production
Tabstack is designed for teams building AI systems that need to work reliably beyond a prototype. You can start quickly with a simple API and SDKs, then scale without re-architecting your stack or taking on new operational burden. There is no need to manage browser fleets, network layers, or constantly shifting edge cases.
We are seeing developers use Tabstack to build incredible things that go far beyond simple data retrieval:
- Autonomous Research: Agents that "read" the news or documentation and synthesize answers rather than hallucinating from outdated training sets.
- Action-Oriented Agents: Systems that can book reservations, check flight statuses, or fill out forms on your behalf.
- Live Market Analysis: Tools that navigate e-commerce sites to track pricing and availability in real-time.
Privacy You Can Trust (Backed by Mozilla)
We know that giving an external service access to your web traffic requires trust. Tabstack is built by Mozilla.
In an era where data is often harvested without consent, we are building this capability in a way that reflects Mozilla's values.
- No Training on Your Data: Mozilla does not train AI systems on the data that is collected by Tabstack.
- Ephemeral by Default: We practice strict data minimization. Internally, we use the returned data solely to execute the requested task. Customer data is treated as ephemeral.
- Secure by Design: We utilize end-to-end TLS and scoped API keys to support the secure handling of sensitive information, giving you control and peace of mind.
We are building a tool for developers to access the web responsibly, rather than a mass data harvester for model training.
Get Started
The era of static, isolated AI is over. It is time to let your agents browse.
Tabstack is now in public early access. We want to see what you can build when the entire web is an API.
- Website: https://tabstack.ai
- Documentation: https://docs.tabstack.ai
- Twitter: @tabstack
Let’s see what you build.