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OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation

OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation

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OWL is a cutting-edge framework for multi-agent collaboration that pushes the boundaries of task automation, built on top of the CAMEL-AI Framework.

OWL is a framework designed to achieve task automation through multi-agent collaboration. It is built on the CAMEL-AI Framework and has the following key features:

Core Features

  • Real-time Information Retrieval: Leverage Wikipedia, Google Search, and other online sources for up-to-date information.
  • Multimodal Processing: Support for handling internet or local videos, images, and audio data.
  • Browser Automation: Utilize the Playwright framework for simulating browser interactions, including scrolling, clicking, input handling, downloading, navigation, and more.
  • Document Parsing: Extract content from Word, Excel, PDF, and PowerPoint files, converting them into text or Markdown format.
  • Code Execution: Write and execute Python code using interpreter.
  • Built-in Toolkits: Access to a comprehensive set of built-in toolkits including ArxivToolkit, AudioAnalysisToolkit, CodeExecutionToolkit, DalleToolkit, DataCommonsToolkit, ExcelToolkit, GitHubToolkit, GoogleMapsToolkit, GoogleScholarToolkit, ImageAnalysisToolkit, MathToolkit, NetworkXToolkit, NotionToolkit, OpenAPIToolkit, RedditToolkit, SearchToolkit, SemanticScholarToolkit, SymPyToolkit, VideoAnalysisToolkit, WeatherToolkit, WebToolkit, and many more for specialized tasks.

Summary

OWL aims to achieve more natural, efficient, and robust task automation through multi-agent collaboration. It provides rich features and tools to support various real-world applications.