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Basic Information

HyperAgent is a developer-focused library that supercharges Playwright with large language models to enable intelligent browser automation via natural language commands. It provides a programmable agent interface and simple APIs such as page.ai(), page.extract(), and executeTask() so developers can direct browsers with descriptive task prompts instead of brittle scripted steps. The project includes a CLI for one-off commands, a library for embedding agents in applications, multi-page management, and built-in support for schema-validated extraction using zod. It also acts as an MCP client for connecting to external tools and workflows. HyperAgent supports multiple LLM providers that extend LangChain's BaseChatModel class, can run locally or scale to cloud headless browsers via Hyperbrowser, and includes stealth and debug options for robust, production-oriented automation.

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Features
HyperAgent exposes AI-first browser APIs including page.ai(), page.extract(), and executeTask() for natural language automation. It falls back to regular Playwright when AI control is not required. The library supports output schema definition with zod for structured extraction and validation. Multi-page management lets you create and coordinate several browser pages. Cloud-ready operation integrates with Hyperbrowser for scalable headless sessions. It functions as an MCP client, enabling tool integrations like writing web data to Google Sheets via Composio. The repo provides a CLI with debug and provider options, examples for common tasks, support for multiple LLM providers (OpenAI, Anthropic via LangChain adapters), stealth anti-bot patches, and extensibility through custom action definitions.
Use Cases
HyperAgent reduces the maintenance cost of brittle browser scripts by letting developers express tasks in natural language, which simplifies web scraping, data extraction, and workflow automation. It helps extract structured data with schema validation, making results reliable for downstream processes. Multi-page coordination and cloud provider integration enable scalable sessions and parallel tasks. MCP support and custom actions allow connecting browser tasks to external services such as Google Sheets, enabling end-to-end automation pipelines. The CLI and debug options speed up iteration and testing. Support for multiple LLMs and fallback to Playwright give flexibility in provider choice and robustness. Example use cases in the README include flight searches, scraping product prices, extracting movie metadata, and populating spreadsheets.

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