Comprehensive 인터랙티브 채팅 인터페이스 Tools for Every Need

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인터랙티브 채팅 인터페이스

  • An open-source web platform enabling communities to deploy AI-powered chat assistants with personalized knowledge base and moderation.
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    What is Community AI Assistant?
    Community AI Assistant provides a ready-to-use framework for building and deploying AI-driven community chatbots. It leverages OpenAI embeddings to create a custom knowledge base from documentation, FAQs, and user guides. The assistant supports user management, secure authentication, and moderation workflows. It can be tailored via configuration files and environment variables, offering developers full control over prompts, UI, and integration into existing web applications or community platforms.
  • AutoGen UI is a React-based toolkit to build interactive UIs and dashboards for orchestrating multi-agent AI agent conversations.
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    What is AutoGen UI?
    AutoGen UI is a frontend toolkit designed to render and manage multi-agent conversational flows. It offers ready-made components such as chat windows, agent selectors, message timelines, and debugging panels. Developers can configure multiple AI agents, stream responses in real time, log each step of the conversation, and apply custom styling. It integrates easily with backend orchestration libraries to provide a complete end-to-end interface for building and monitoring AI agent interactions.
  • Open-source end-to-end chatbot using Chainlit framework for building interactive conversational AI with context management and multi-agent flows.
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    What is End-to-End Chainlit Chatbot?
    e2e-chainlit-chatbot is a sample project demonstrating the complete development lifecycle of a conversational AI agent using Chainlit. The repository includes end-to-end code for launching a local web server that hosts an interactive chat interface, integrating with large language models for responses, and managing conversation context across messages. It features customizable prompt templates, multi-agent workflows, and real-time streaming of responses. Developers can configure API keys, adjust model parameters, and extend the system with custom logic or integrations. With minimal dependencies and clear documentation, this project accelerates experimentation with AI-driven chatbots and provides a solid foundation for production-grade conversational assistants. It also includes examples for customizing front-end components, logging, and error handling. Designed for seamless integration with cloud platforms, it supports both prototype and production use cases.
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