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stagehand

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Basic Information

Stagehand is an AI browser automation framework for developers who need reliable, production-ready web automation that mixes traditional code with language‚Äëdriven AI. It provides primitives to run Playwright actions alongside AI agents so teams can choose where to write explicit code and where to delegate navigation or interpretation to models. The repo contains examples, build and run instructions, and integration points for LLM providers such as OpenAI and Anthropic. It includes utilities to preview and cache AI actions, examples showing page.act, page.extract with zod schemas, and a one-line CLI quickstart. The project is intended to be used with an LLM API key and Browserbase credentials, has documentation at docs.stagehand.dev, a Python implementation in a separate repo, and community support via Slack. It is MIT licensed.

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16348
App URL
https://github.com/browserbase/stagehand
Github Repository
https://github.com/browserbase/stagehand/blob/main/README.md

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App Details

Features
Stagehand combines Playwright-based browser control with AI agents and computer-use models. It offers a mixed code-and-language workflow so developers can use low-level Playwright calls when precise control is needed and AI agents when pages are unfamiliar. Features include previewing AI actions before execution, caching repeatable actions to save tokens, easy integration with OpenAI and Anthropic models, an agent API (agent.execute) and page-level helpers (page.act, page.extract with zod validation). The repo provides examples (including a 2048 demo), build instructions using pnpm, a create-browser-app quickstart via npx, and links to full documentation. The project emphasizes reliability, speed, and cost management and includes community and contribution guidance.
Use Cases
Stagehand reduces the need to write brittle, low-level automation code for every web task by letting developers delegate ambiguous or novel interactions to AI while retaining deterministic code where required. Preview and caching features make AI-driven steps inspectable and repeatable, lowering risk and token costs. Built on Playwright, it leverages a mature browser automation engine for resilience and integrates SOTA computer-use models with minimal setup to handle navigation, extraction, and high‚Äëlevel goals. The provided examples, CLI quickstart, and documentation shorten onboarding. The framework is intended to improve automation reliability, speed up development of scrapers or bots, and give teams control over where AI is used in production workflows.

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