Swarms of Browser Agents

{
  "id": "d66bc2ae-40d0-45be-97ac-f0aab7a33207",
  "name": "Swarms of Browser Agents",
  "title": "Agent",
  "description": "Swarms of Browser Agents is an innovative application that leverages the power of multiple browser-based agents to perform complex tasks concurrently. Each agent operates independently, utilizing advanced language models to interact with web content, gather information, and engage in detailed discussions. This application is particularly suited for scenarios requiring distributed data collection, real-time analysis, and collaborative problem-solving across various domains.\n",
  "tags": [
    "Multi-Agent System",
    "Browser Automation",
    "Concurrent Processing",
    "Distributed Computing",
    "Language Models",
    "Real-Time Analysis",
    "Collaborative Problem Solving",
    "Data Collection",
    "AI Agents",
    "High-Energy Physics Analysis"
  ],
  "requirements": [
    {
      "package": "swarms",
      "installation": "pip3 install swarms"
    },
    {
      "package": "langchain-openai",
      "installation": "pip install langchain-openai"
    },
    {
      "package": "browser-use",
      "installation": "pip install browser-use"
    },
    {
      "package": "python-dotenv",
      "installation": "pip install python-dotenv"
    }
  ],
  "usecases": [
    {
      "title": "Real-Time Market Analysis",
      "description": "Deploy Swarms of Browser Agents to monitor financial markets in real-time. Each agent can be assigned to track specific stocks, commodities, or indices, gathering data from various financial news websites and forums. The agents can then analyze trends, provide insights, and alert users to significant market movements, enabling timely investment decisions.\n"
    },
    {
      "title": "Academic Research Collaboration",
      "description": "Utilize Swarms of Browser Agents to assist in academic research by automating the collection and synthesis of information from scholarly articles, journals, and databases. Agents can be tasked with exploring different aspects of a research topic, summarizing findings, and identifying gaps in the literature, thus accelerating the research process and fostering collaboration among researchers.\n"
    },
    {
      "title": "Automated Data Collection",
      "description": "Swarms of Browser Agents can be deployed to autonomously gather data from a wide range of online sources. Whether it's financial data, academic articles, or social media trends, these agents can efficiently collect and organize information, providing users with comprehensive datasets for analysis and decision-making.\n"
    }
  ],
  "userId": "6a5ca266-caff-46a5-8e29-fba2085e4e5f",
  "createdAt": "2025-02-17T07:04:41.447422+00:00",
  "links": [],
  "code": "import asyncio\n\nfrom browser_use import Agent\nfrom dotenv import load_dotenv\nfrom langchain_openai import ChatOpenAI\n\nfrom swarms import ConcurrentWorkflow\n\nload_dotenv()\n\n\nclass BrowserAgent:\n    def __init__(self, agent_name: str = \"BrowserAgent\"):\n        self.agent_name = agent_name\n\n    async def browser_agent_test(self, task: str):\n        agent = Agent(\n            task=task,\n            llm=ChatOpenAI(model=\"gpt-4o\"),\n        )\n        result = await agent.run()\n        return result\n\n    def run(self, task: str):\n        return asyncio.run(self.browser_agent_test(task))\n\n\nswarm = ConcurrentWorkflow(\n    agents=[BrowserAgent() for _ in range(10)],\n)\n\nswarm.run(\n    \"\"\"Please navigate to chat.com and engage in a detailed technical discussion with ChatGPT about the following specific aspects of future high-energy physics:\n\n1. The potential discoveries and physics reach of the Future Circular Collider (FCC) compared to the LHC\n2. Theoretical predictions for supersymmetric particles and dark matter candidates at energy scales above 100 TeV\n3. Novel detector technologies needed for future collider experiments, particularly for tracking and calorimetry\n4. The role of machine learning and quantum computing in analyzing high-energy physics data\n5. Challenges and proposed solutions for beam focusing and acceleration at extremely high energies\n\nPlease document the key insights and technical details from this discussion.\"\"\"\n)\n"
}