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Web Agent","maintainers":[{"name":"akshays","email":"akshay.shekhawat@gmail.com"},{"name":"a_harmless_pyro","email":"ninadsinhaa@gmail.com"},{"name":"leoscope","email":"nikhil@metlo.com"}],"readme":"<div align=\"center\">\n  <img src=\"assets/hyperagent-banner.png\" alt=\"Hyperagent Banner\" width=\"800\"/>\n\n  <p align=\"center\">\n    <strong>Intelligent Browser Automation with LLMs</strong>\n  </p>\n\n  <p align=\"center\">\n    <a href=\"https://www.npmjs.com/package/@hyperbrowser/agent\">\n      <img src=\"https://img.shields.io/npm/v/@hyperbrowser/agent?style=flat-square\" alt=\"npm version\" />\n    </a>\n    <a href=\"https://github.com/hyperbrowserai/hyperagent/blob/main/LICENSE\">\n      <img src=\"https://img.shields.io/npm/l/@hyperbrowser/agent?style=flat-square\" alt=\"license\" />\n    </a>\n    <a href=\"https://discord.gg/zsYzsgVRjh\" style=\"text-decoration:none;\">\n      <img alt=\"Discord\" src=\"https://img.shields.io/discord/1313014141165764619?style=flat-square&color=blue\">\n    </a>\n    <a href=\"https://x.com/AkshayShekhaw12\">\n      <img alt=\"X (formerly Twitter) Follow\" src=\"https://img.shields.io/twitter/follow/AkshayShekhaw12?style=social\">\n    </a>\n  </p>\n</div>\n\n## Overview\n\nHyperagent is Playwright supercharged with AI. No more brittle scripts, just powerful natural language commands.\nJust looking for scalable headless browsers or scraping infra? Go to [Hyperbrowser](https://app.hyperbrowser.ai/) to get started for free!\n\nView HyperAgent docs here: https://www.hyperbrowser.ai/docs/hyperagent/introduction\n\n### Features\n\n- 🤖 **AI Commands**: Simple APIs like `page.ai()`, `page.extract()` and `executeTask()` for any AI automation\n- ⚡ **Fallback to Regular Playwright**: Use regular Playwright when AI isn't needed\n- 🥷 **Stealth Mode** – Avoid detection with built-in anti-bot patches\n- ☁️ **Cloud Ready** – Instantly scale to hundreds of sessions via [Hyperbrowser](https://app.hyperbrowser.ai/)\n- 🔌 **MCP Client** – Connect to tools like Composio for full workflows (e.g. writing web data to Google Sheets)\n- 📼 **Action Caching** – Record and replay workflows deterministically without LLM calls\n\n## Quick Start\n\n### Installation\n\n```bash\n# Using npm\nnpm install @hyperbrowser/agent\n\n# Using yarn\nyarn add @hyperbrowser/agent\n```\n\n### CLI\n\n```bash\n$ npx @hyperbrowser/agent -c \"Find a route from Miami to New Orleans, and provide the detailed route information.\"\n```\n\n<p align=\"center\">\n  <img src=\"assets/flight-schedule.gif\" alt=\"Hyperagent Demo\"/>\n</p>\n\nThe CLI supports options for debugging or using hyperbrowser instead of a local browser\n\n```bash\n-d, --debug                       Enable debug mode\n-c, --command <task description>  Command to run\n--hyperbrowser                    Use Hyperbrowser for the browser provider\n```\n\n### Library\n\n```typescript\nimport { HyperAgent } from \"@hyperbrowser/agent\";\nimport { z } from \"zod\";\n\n// Initialize the agent\nconst agent = new HyperAgent({\n  llm: {\n    provider: \"openai\",\n    model: \"gpt-4o\",\n  },\n});\n\n// Execute a task\nconst result = await agent.executeTask(\n  \"Navigate to amazon.com, search for 'laptop', and extract the prices of the first 5 results\"\n);\nconsole.log(result.output);\n\n// Use page.ai, page.perform, and page.extract\nconst page = await agent.newPage();\nawait page.goto(\"https://flights.google.com\", { waitUntil: \"load\" });\nawait page.ai(\"search for flights from Rio to LAX from July 16 to July 22\");\nawait page.perform(\"click the search button\");\nconst res = await page.extract(\n  \"give me the flight options\",\n  z.object({\n    flights: z.array(\n      z.object({\n        price: z.number(),\n        departure: z.string(),\n        arrival: z.string(),\n      })\n    ),\n  })\n);\nconsole.log(res);\n\n// Clean up\nawait agent.closeAgent();\n```\n\n## Two Modes of Operation\n\nHyperAgent provides two complementary APIs optimized for different use cases:\n\n### 🎯 `page.perform()` - Single Granular Actions\n\n> `page.aiAction()` is deprecated and remains available as an alias; prefer `page.perform()` going forward.\n\n**Best for**: Single, specific actions like \"click login\", \"fill email with test@example.com\"\n\n**Advantages**:\n- ⚡ **Fast** - Uses accessibility tree (no screenshots)\n- 💰 **Cheap** - Single LLM call per action\n- 🎯 **Reliable** - Direct element finding and execution\n- 📊 **Efficient** - Text-based DOM analysis with automatic ad-frame filtering\n\n**Example**:\n```typescript\nconst page = await agent.newPage();\nawait page.goto(\"https://example.com/login\");\n\n// Fast, reliable single actions\nawait page.perform(\"fill email with user@example.com\");\nawait page.perform(\"fill password with mypassword\");\nawait page.perform(\"click the login button\");\n```\n\n### 🧠 `page.ai()` - Complex Multi-Step Tasks\n\n**Best for**: Complex workflows requiring multiple steps and visual context\n\n**Advantages**:\n\n- 🖼️ **Visual Understanding** - Can use screenshots with element overlays\n- 🎭 **Complex Tasks** - Handles multi-step workflows automatically\n- 🧠 **Context-Aware** - Better at understanding page layout and relationships\n- 🔄 **Adaptive** - Can adjust strategy based on page state\n\n**Parameters**:\n\n- `useDomCache` (boolean): Reuse DOM snapshots for speed\n- `enableVisualMode` (boolean): Enable screenshots and overlays (default: false)\n\n**Example**:\n\n```typescript\nconst page = await agent.newPage();\nawait page.goto(\"https://flights.google.com\");\n\n// Complex task with multiple steps handled automatically\nawait page.ai(\"search for flights from Miami to New Orleans on July 16\", {\n  useDomCache: true,\n});\n```\n\n### 🎨 Mix and Match\n\nCombine both APIs for optimal performance:\n\n```typescript\n// Use perform for fast, reliable individual actions\nawait page.perform(\"click the search button\");\nawait page.perform(\"type laptop into search\");\n\n// Use ai() for complex, multi-step workflows\nawait page.ai(\"filter results by price under $1000 and sort by rating\");\n\n// Extract structured data\nconst products = await page.extract(\n  \"get the top 5 products\",\n  z.object({\n    products: z.array(z.object({ name: z.string(), price: z.number() }))\n  })\n);\n```\n\n## ☁️ Cloud\n\nYou can scale HyperAgent with cloud headless browsers using Hyperbrowser\n\n1. Get a free api key from [Hyperbrowser](https://app.hyperbrowser.ai/)\n2. Add it to your env as `HYPERBROWSER_API_KEY`\n3. Set your `browserProvider` to `\"Hyperbrowser\"`\n\n```typescript\nconst agent = new HyperAgent({\n  browserProvider: \"Hyperbrowser\",\n});\n\nconst response = await agent.executeTask(\n  \"Go to hackernews, and list me the 5 most recent article titles\"\n);\n\nconsole.log(response);\nawait agent.closeAgent();\n```\n\n## Usage Guide\n\n### Multi-Page Management\n\n```typescript\n// Create and manage multiple pages\nconst page1 = await agent.newPage();\nconst page2 = await agent.newPage();\n\n// Execute tasks on specific pages\nconst page1Response = await page1.ai(\n  \"Go to google.com/travel/explore and set the starting location to New York. Then, return to me the first recommended destination that shows up. Return to me only the name of the location.\"\n);\nconst page2Response = await page2.ai(\n  `I want to plan a trip to ${page1Response.output}. Recommend me places to visit there.`\n);\n\nconsole.log(page2Response.output);\n\n// Get all active pages\nconst pages = await agent.getPages();\nawait agent.closeAgent();\n```\n\n## Customization\n\n### Output Schema Definition\n\nHyperAgent can extract data in a specified schema. The schema can be passed in at a per-task level\n\n```typescript\nimport { z } from \"zod\";\n\nconst agent = new HyperAgent();\nconst agentResponse = await agent.executeTask(\n  \"Navigate to imdb.com, search for 'The Matrix', and extract the director, release year, and rating\",\n  {\n    outputSchema: z.object({\n      director: z.string().describe(\"The name of the movie director\"),\n      releaseYear: z.number().describe(\"The year the movie was released\"),\n      rating: z.string().describe(\"The IMDb rating of the movie\"),\n    }),\n  }\n);\nconsole.log(agentResponse.output);\nawait agent.closeAgent();\n```\n\n```bash\n{\n  \"director\": \"Lana Wachowski, Lilly Wachowski\",\n  \"releaseYear\": 1999,\n  \"rating\": \"8.7/10\"\n}\n```\n\n### Using Different LLM Providers\n\nHyperagent supports multiple LLM providers with native SDKs for better performance and reliability.\n\n```typescript\n// Using OpenAI\nconst agent = new HyperAgent({\n  llm: {\n    provider: \"openai\",\n    model: \"gpt-4o\",\n  },\n});\n\n// Using Anthropic's Claude\nconst agent = new HyperAgent({\n  llm: {\n    provider: \"anthropic\",\n    model: \"claude-sonnet-4-0\",\n  },\n});\n\n// Using Google Gemini\nconst agent = new HyperAgent({\n  llm: {\n    provider: \"gemini\",\n    model: \"gemini-2.5-flash\",\n  },\n});\n\n// Using DeepSeek\nconst agent = new HyperAgent({\n  llm: {\n    provider: \"deepseek\",\n    model: \"deepseek-chat\",\n  },\n});\n```\n\n### MCP Support\n\nHyperAgent functions as a fully functional MCP client. For best results, we recommend using\n`gpt-4o` as your LLM.\n\nHere is an example which reads from wikipedia, and inserts information into a google sheet using the composio Google Sheet MCP. For the full example, see [here](https://github.com/hyperbrowserai/HyperAgent/tree/main/examples/mcp/google-sheets/most-populated-states.ts)\n\n```typescript\nconst agent = new HyperAgent({\n  llm: llm,\n  debug: true,\n});\n\nawait agent.initializeMCPClient({\n  servers: [\n    {\n      command: \"npx\",\n      args: [\n        \"@composio/mcp@latest\",\n        \"start\",\n        \"--url\",\n        \"https://mcp.composio.dev/googlesheets/...\",\n      ],\n      env: {\n        npm_config_yes: \"true\",\n      },\n    },\n  ],\n});\n\nconst response = await agent.executeTask(\n  \"Go to https://en.wikipedia.org/wiki/List_of_U.S._states_and_territories_by_population and get the data on the top 5 most populous states from the table. Then insert that data into a google sheet. You may need to first check if there is an active connection to google sheet, and if there isn't connect to it and present me with the link to sign in. \"\n);\n\nconsole.log(response);\nawait agent.closeAgent();\n```\n\n### Custom Actions\n\nHyperAgent's capabilities can be extended with custom actions. Custom actions require 3 things:\n\n- type: Name of the action. Should be something descriptive about the action.\n- actionParams: A zod object describing the parameters that the action may consume.\n- run: A function that takes in a context, and the params for the action and produces a result based on the params.\n\nHere is an example that performs a search using Exa\n\n```typescript\nconst exaInstance = new Exa(process.env.EXA_API_KEY);\n\nexport const RunSearchActionDefinition: AgentActionDefinition = {\n  type: \"perform_search\",\n  actionParams: z.object({\n    search: z\n      .string()\n      .describe(\n        \"The search query for something you want to search about. Keep the search query concise and to-the-point.\"\n      ),\n  }).describe(\"Search and return the results for a given query.\"),\n  run: async function (\n    ctx: ActionContext,\n    params: { search: string }\n  ): Promise<ActionOutput> {\n    const results = (await exaInstance.search(params.search, {})).results\n      .map(\n        (res) =>\n          `title: ${res.title} || url: ${res.url} || relevance: ${res.score}`\n      )\n      .join(\"\\n\");\n\n    return {\n      success: true,\n      message: `Successfully performed search for query ${params.search}. Got results: \\n${results}`,\n    };\n  },\n};\n\nconst agent = new HyperAgent({\n  customActions: [RunSearchActionDefinition],\n});\n\nconst result = await agent.executeTask(\n  \"Search about the news for today in New York\"\n);\n```\n\n## 📼 Action Caching\n\nHyperAgent automatically records every action during `page.ai()` runs, capturing XPaths, frame indices, and execution details. This enables deterministic replay without LLM calls—perfect for regression testing, CI pipelines, and cost optimization.\n\n### How It Works\n\nEvery `page.ai()` run produces an `actionCache` containing the exact sequence of actions performed:\n\n```typescript\nconst page = await agent.newPage();\nconst { actionCache } = await page.ai(\n  \"Go to flights.google.com and search for flights from Rio to LAX\"\n);\n\n// actionCache contains the recorded steps\nconsole.log(actionCache);\n```\n\nExample cache entry:\n```json\n{\n  \"taskId\": \"86d13abe-b9f3-4ca3-a9bb-bdeddf234cd1\",\n  \"createdAt\": \"2025-12-06T05:44:52.257Z\",\n  \"status\": \"completed\",\n  \"steps\": [\n    {\n      \"stepIndex\": 0,\n      \"instruction\": \"Click on the departure field\",\n      \"elementId\": \"0-138\",\n      \"method\": \"click\",\n      \"arguments\": [],\n      \"frameIndex\": 0,\n      \"xpath\": \"/html[1]/body[1]/div[1]/input[1]\",\n      \"actionType\": \"actElement\",\n      \"success\": true\n    }\n  ]\n}\n```\n\n### Replaying Cached Actions\n\nReplay a recorded session using `runFromActionCache()`. It attempts XPath-based execution first (no LLM calls), falling back to LLM only if the page structure has changed:\n\n```typescript\nimport { ActionCacheOutput } from \"@hyperbrowser/agent\";\nimport fs from \"fs\";\n\n// Load a previously saved action cache\nconst cache: ActionCacheOutput = JSON.parse(\n  fs.readFileSync(\"action-cache.json\", \"utf-8\")\n);\n\nconst agent = new HyperAgent();\nconst page = await agent.newPage();\n\n// Replay the cached actions\nconst replay = await page.runFromActionCache(cache, {\n  maxXPathRetries: 3,  // Retry XPath resolution up to 3 times before LLM fallback\n  debug: true,\n});\n\nconsole.log(replay);\n// {\n//   replayId: \"...\",\n//   sourceTaskId: \"86d13abe-...\",\n//   status: \"completed\",\n//   steps: [{ stepIndex: 0, usedXPath: true, fallbackUsed: false, success: true }]\n// }\n\nawait agent.closeAgent();\n```\n\n### Generating Replay Scripts\n\nGenerate a standalone TypeScript script from an action cache for easy integration into your test suite:\n\n```typescript\nconst { actionCache } = await page.ai(\"search for flights from Miami to NYC\");\n\n// Generate a replay script\nconst script = agent.createScriptFromActionCache(actionCache.steps);\nconsole.log(script);\n```\n\nThis produces a script using typed helper methods like `performClick()`, `performType()`, etc., that can be run independently.\n\n### Use Cases\n\n- **Regression Testing**: Record a workflow once, replay it in CI without LLM costs\n- **Flaky Test Debugging**: Compare XPath-based replay vs LLM-driven execution\n- **Cost Optimization**: Cache expensive multi-step workflows and replay deterministically\n- **Workflow Templates**: Save common flows (login, checkout) and replay across environments\n\n## CDP First\n\nHyperAgent speaks Chrome DevTools Protocol natively. Element lookup, scrolling, typing, frame management, and screenshots all go through CDP so every action has exact coordinates, execution contexts, and browser events. This allows for more custom commands and deep iframe tracking.\n\nHyperAgent integrates seamlessly with Playwright, so you can still use familiar commands, while the actions take full advantage of native CDP protocol with fast locators and advanced iframe tracking.\n\n**Key Features:**\n\n- **Auto-Ad Filtering**: Automatically filters out ad and tracking iframes to keep context clean\n- **Deep Iframe Support**: Tracking across nested and cross-origin iframes (OOPIFs)\n- **Exact Coordinates**: Actions use precise CDP coordinates for reliability\n\nKeep in mind that CDP is still experimental, and stability is not guaranteed. If you'd like the agent to use Playwright's native locators/actions instead, set `cdpActions: false` when you create the agent and it will fall back automatically.\n\nThe CDP layer is still evolving—expect rapid polish (and the occasional sharp edge). If you hit something quirky you can toggle CDP off for that workflow and drop us a bug report.\n\n## Contributing\n\nWe welcome contributions to Hyperagent! Here's how you can help:\n\n1. Fork the repository\n2. Create your feature branch (`git checkout -b feature/AmazingFeature`)\n3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)\n4. Push to the branch (`git push origin feature/AmazingFeature`)\n5. Open a Pull Request\n\n## Support\n\n- 📚 [Documentation](https://docs.hyperbrowser.ai/hyperagent/introduction)\n- 💬 [Discord Community](https://discord.gg/zsYzsgVRjh)\n- 🐛 [Issue Tracker](https://github.com/hyperbrowserai/HyperAgent/issues)\n- 📧 [Email Support](mailto:info@hyperbrowser.ai)\n","readmeFilename":"README.md"}