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Comprehensive 도커 지원 Tools for Every Need

Get access to 도커 지원 solutions that address multiple requirements. One-stop resources for streamlined workflows.

도커 지원

  • Llama Deploy

    Deploy LlamaIndex-powered AI agents as scalable, serverless chat APIs across AWS Lambda, Vercel, or Docker.
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    What is Llama Deploy?
    Llama Deploy enables you to transform your LlamaIndex data indexes into production-ready AI agents. By configuring deployment targets such as AWS Lambda, Vercel Functions, or Docker containers, you get secure, auto-scaled chat APIs that serve responses from your custom index. It handles endpoint creation, request routing, token-based authentication, and performance monitoring out of the box. Llama Deploy streamlines the end-to-end process of deploying conversational AI, from local testing to production, ensuring low-latency and high availability.
    Llama Deploy Core Features
    • Serverless chat API provisioning
    • Multi-provider support (AWS Lambda, Vercel, Docker)
    • Automatic endpoint and routing setup
    • Token-based authentication
    • Built-in logging and monitoring
    Llama Deploy Pro & Cons

    The Cons

    Lacks publicly available pricing information.
    May require familiarity with microservices and async programming for effective use.
    Documentation may require additional details on troubleshooting and advanced use cases.

    The Pros

    Facilitates seamless deployment from development to production with minimal code changes.
    Microservices architecture supports easy scalability and component flexibility.
    Built-in fault tolerance with retry mechanisms for robust production use.
    State management simplifies coordination of complex multi-step workflows.
    Async-first design fits high concurrency and real-time application needs.

  • BeeAI Framework

    An open-source framework for developers to build, customize, and deploy autonomous AI agents with plugin support.
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    What is BeeAI Framework?
    BeeAI Framework provides a fully modular architecture for building intelligent agents that can perform tasks, manage state, and interact with external tools. It includes a memory manager for long-term context retention, a plugin system for custom skill integration, and built-in support for API chaining and multi-agent coordination. The framework offers Python and JavaScript SDKs, a command-line interface for scaffolding projects, and deployment scripts for cloud, Docker, or edge devices. Monitoring dashboards and logging utilities help track agent performance and troubleshoot issues in real time.
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