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Comprehensive 로그 기능 Tools for Every Need

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

로그 기능

  • Autogen Studio Research

    An experimental low-code studio for designing, orchestrating, and visualizing multi-agent AI workflows with interactive UI and customizable agent templates.
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    What is Autogen Studio Research?
    Autogen Studio Research is a GitHub-hosted research prototype for building, visualizing, and iterating on multi-agent AI applications. It offers a web-based UI that lets you drag and drop agent components, define communication channels, and configure execution pipelines. Under the hood, it uses a Python SDK to connect to various LLM backends (OpenAI, Azure, local models) and provides real-time logging, metrics, and debugging tools. The platform is designed for rapid prototyping of collaborative agent systems, decision-making workflows, and automated task orchestration.

  • LLMFlow

    LLMFlow is an open-source framework enabling the orchestration of LLM-based workflows with tool integration and flexible routing.
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    What is LLMFlow?
    LLMFlow provides a declarative way to design, test, and deploy complex language model workflows. Developers create Nodes which represent prompts or actions, then chain them into Flows that can branch based on conditions or external tool outputs. Built-in memory management tracks context between steps, while adapters enable seamless integration with OpenAI, Hugging Face, and others. Extend functionality via plugins for custom tools or data sources. Execute Flows locally, in containers, or as serverless functions. Use cases include creating conversational agents, automated report generation, and data extraction pipelines—all with transparent execution and logging.

  • Kin Kernel

    Kin Kernel is a modular AI agent framework enabling automated workflows through LLM orchestration, memory management, and tool integrations.
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    What is Kin Kernel?
    Kin Kernel is a lightweight, open-source kernel framework for constructing AI-powered digital workers. It provides a unified system for orchestrating large language models, managing contextual memory, and integrating custom tools or APIs. With an event-driven architecture, Kin Kernel supports asynchronous task execution, session tracking, and extensible plugins. Developers define agent behaviors, register external functions, and configure multi-LLM routing to automate workflows ranging from data extraction to customer support. The framework also includes built-in logging and error handling to facilitate monitoring and debugging. Designed for flexibility, Kin Kernel can be integrated into web services, microservices, or standalone Python applications, enabling organizations to deploy robust AI agents at scale.
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