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flujos de trabajo impulsados por AI

  • Capture webpage screenshots for AI analysis with ChatGPT integration.
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    What is ChatGPT Vision for ALL?
    Unlock the power of seamless integration with ChatGPT Vision for ALL, an all-in-one Chrome extension. Capture webpage screenshots or selected text for advanced AI-powered content analysis and text optimization. It automates text operations by directly inserting AI-generated outputs back into your webpages. This tool also offers functionalities like conversation history saving, prompt input management, and retry options for desired responses. Whether refining documents, composing emails, or analyzing data, this extension enhances your digital communication and productivity.
    ChatGPT Vision for ALL Core Features
    • Webpage screenshot capture
    • Selected text capture
    • Full automation for text operations
    • Conversation history saving
    • Prompt input and management
    • Seamless ChatGPT integration
    • Retry and refine options
    • User-friendly interface
  • Simulates an AI-powered taxi call center with GPT-based agents for booking, dispatch, driver coordination, and notifications.
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    What is Taxi Call Center Agents?
    This repository delivers a customizable multi-agent framework simulating a taxi call center. It defines distinct AI agents: CustomerAgent to request rides, DispatchAgent to select drivers based on proximity, DriverAgent to confirm assignments and update statuses, and NotificationAgent for billing and messages. Agents interact through an orchestrator loop using OpenAI GPT calls and memory, enabling asynchronous dialogue, error handling, and logging. Developers can extend or adapt agent prompts, integrate real-time systems, and prototype AI-driven customer service and dispatch workflows with ease.
  • AgentSmith is an open-source framework orchestrating autonomous multi-agent workflows using LLM-based assistants.
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    What is AgentSmith?
    AgentSmith is a modular agent orchestration framework built in Python that enables developers to define, configure, and run multiple AI agents collaboratively. Each agent can be assigned specialized roles—such as researcher, planner, coder, or reviewer—and communicate via an internal message bus. AgentSmith supports memory management through vector stores like FAISS or Pinecone, task decomposition into subtasks, and automated supervision to ensure goal completion. Agents and pipelines are configured via human-readable YAML files, and the framework integrates seamlessly with OpenAI APIs and custom LLMs. It includes built-in logging, monitoring, and error handling, making it ideal for automating software development workflows, data analysis, and decision support systems.
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