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自定義工具集成

  • Client libraries for Spider framework offering Node.js, Python, and CLI interfaces to orchestrate AI agent workflows via API.
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    What is Spider Clients?
    Spider Clients are lightweight, language-specific SDKs that communicate with a Spider orchestration server to coordinate AI agent tasks. Using HTTP requests, clients enable users to open interactive sessions, dispatch multi-step chains, register custom tools, and retrieve streaming AI responses in real time. They handle authentication, serialization of prompt templates, and error recovery under the hood, while maintaining consistent APIs across Node.js and Python. Developers can configure retry policies, log metadata, and integrate custom middleware to intercept requests. The CLI client supports quick testing and workflow prototyping the terminal. Together, these clients accelerate the development of AI-powered agents by abstracting low-level network and protocol details, allowing teams to focus on prompt design and logic orchestration.
    Spider Clients Core Features
    • Node.js SDK
    • Python SDK
    • CLI interface
    • Session management
    • Multi-step chain execution
    • Custom tool registration
    • Streaming response support
    • Error handling and retries
    • Metadata logging
    • Middleware integration
    Spider Clients Pro & Cons

    The Cons

    The Pros

    Supports concurrent and streaming scraping.
    Integrates AI scraping and event-driven actions.
    Uses headless Chrome for versatile scraping.
    Supports HTTP proxies and cron jobs for scheduling.
    Includes blacklisting and budgeting depth features.
    Implements exponential backoff for reliability.
  • A Python framework to build and orchestrate autonomous AI agents with custom tools, memory, and multi-agent coordination.
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    What is Autonomys Agents?
    Autonomys Agents empowers developers to create autonomous AI agents capable of executing complex tasks without manual intervention. Built on Python, the framework provides tools for defining agent behaviors, integrating external APIs and custom functions, and maintaining conversational memory across interactions. Agents can collaborate in multi-agent setups, sharing knowledge and coordinating actions. Observability modules offer real-time logging, performance tracking, and debugging insights. With its modular architecture, teams can extend core components, incorporate new LLMs, and deploy agents across environments. Whether automating customer support, performing data analysis, or orchestrating research workflows, Autonomys Agents streamlines end-to-end development and management of intelligent autonomous systems.
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