Tabstack is a powerful, Mozilla-backed web content extraction and transformation platform designed specifically for AI agent builders. It serves as a managed infrastructure layer that turns the chaotic web into clean, structured data for AI agents and applications, removing the need for users to maintain their own browsers, LLM orchestration pipelines, or complex scraping logic. By acting as an abstraction over browser infrastructure, Tabstack enables developers to focus on reasoning and application logic while offloading the technical debt associated with web navigation, rendering, and parsing.

Tabstack provides a suite of high-level API endpoints that allow users to interact with the live web through a simple REST interface. The platform supports structured data extraction where users define a schema and receive matching JSON, web research agents that synthesize information with inline citations from live sources, and browser automation capabilities that perform multi-step actions on websites, such as clicking, form submission, and navigating dynamic pages. These tools are powered by Pilo, an open-source browser execution engine that optimizes performance by using the accessibility tree and intelligent context compression to significantly reduce token usage compared to traditional screenshot-based agents.

Some of the key features are:

• Schema-Driven Extraction: Retrieve structured JSON from any URL by defining a target schema, ensuring consistent data formats even when webpage structures change.
• Autonomous Research: Generate synthesized answers from live web sources with inline citations, providing verifiable information for end users.
• Agentic Browser Automation: Automate multi-step web workflows including navigation, form filling, and interaction with JS-heavy or authenticated websites.
• Mozilla-Backed Privacy: Adheres to transparent, responsible data practices where customer requests and retrieved pages are used solely to complete the requested tasks and are never sold or used to train models.
• Pilo Execution Engine: Utilizes an accessibility-tree-based approach that achieves 60-80% fewer tokens per action than traditional screenshot-based browsers.
• Interactive Mode: Allows agents to pause and request human or parent-agent input when they encounter unknown or missing information during a task flow.
• Adaptive Effort Levels: Scale cost and performance dynamically based on the complexity of the targeted webpage.

Tabstack is used by passing instructions or tasks to its APIs, which handle the underlying orchestration—including rendering, model inference, and interaction logic. The platform returns clean, structured, and ready-to-use output, bypassing the need for users to provision infrastructure. It offers a variety of integration methods, including typed SDKs for TypeScript and Python, a CLI, and support for Model Context Protocol (MCP) to integrate directly into local development environments and coding assistants.

Some common use cases include:

• Competitive Intelligence: Tracking competitor pricing, inventory, and packaging on a recurring schedule to power live dashboards.
• Lead Enrichment: Transforming raw company domains into structured firmographic data, including headcount, funding status, and technical stacks.
• Research Agents: Powering in-product research features that provide synthesized, cited answers based on real-time web content rather than static datasets.
• Booking & Checkout Automation: Performing end-to-end web actions such as flight bookings or checkout flows on third-party sites without needing custom, brittle scripts.
• Workflow Automation: Automating back-office tasks by filling and submitting forms across various websites, with built-in hooks to pause for human intervention when judgment is required.