于开发者而言，开源一个项目很简单，一个命令足矣，但维护一个项目，却意味着： 一边扛着本职工作，一边独自修复 Bug、优化文档； 深夜改着无人问津的 PR，独自面对着扎堆涌来的 Issue…… 看着冷清的仓库，一个问题在深夜里反复叩问你的内心： "开源容易，让项目活起来，怎么这么难？" 你肯定也曾仰望过那些 GitHub 上数万 Star 的项目，心中既有无限敬佩，又感到一丝遥远。你也渴 望自己的项目 Star 能从 1 增长到 Star 10000 …… 那么，如何才能穿越这段"至暗时刻"？ 为了回答这个问题，在 12 月 21 日（ 本周日 ）的 GOBI 2025 全球开源商业创新大会上，组委会 把那些真正戴上桂冠，并走得更远的人请到了现场。他们不是 理论家，而是从枪林弹雨中杀出来的 实战派。 万人 Star 的开源项目是如何炼成的？ 郑耀威 LLaMA Factory 作者 在「聚力·开源社区的进化与未来 聚拢微光，可成星河」Panel 上，来自 GitHub 60,000+ Star 的 LLaMA Factory 郑耀威、顶级推理框架 vLLM 社区核心贡献者张家驹、企业级 RAG 引擎新星 RAG ...

AI Agent Workflow Platforms - Sim AI is a user-friendly, open-source platform for building AI agent workflows, supporting major LLMs, MCP servers, and vectorDBs [1] - Transformer Lab offers tools like RAGFlow for deep document understanding and AutoAgent, a zero-code framework for building and deploying Agents [2] - Anything LLM is an all-in-one AI app for chatting with documents and using AI Agents, designed for multi-user environments and local operation [6] Open-Source LLM Tools - Llama Factory allows training and fine-tuning of open-source LLMs and VLMs without coding, supporting over 100 models [6] - RAGFlow is a RAG engine for building enterprise-grade RAG workflows on complex documents with citations, supporting multimodal data [2][4] - AutoAgent is a zero-code framework for building and deploying Agents using natural language, with universal LLM support and a native Vector DB [2][5] Key Features & Technologies - Sim AI's Finance Agent uses Firecrawl for web searches and Alpha Vantage's API for stock data via MCP servers [1] - RAGFlow supports multimodal data and deep research capabilities [2] - AutoAgent features function-calling and ReAct interaction modes [5] Community & Popularity - Sim AI is 100% open-source with 18 thousand stars [1] - Transformer Lab is 100% open-source with over 68 thousand stars [2] - LLaMA-Factory is 100% open-source with 62 thousand stars [6] - Anything LLM is 100% open-source with 48 thousand stars [6] - One project is 100% open-source with 8 thousand stars [3]

AI Tools & Platforms - RAGFlow is a linked resource [1] - Xpander is a linked resource [1] - Transformer Lab is a linked resource [1] - Llama Factory is a linked resource [1] - LangFlow is a linked resource [1] - AutoAgent is a linked resource [1]

Core Viewpoint - The article provides a comprehensive comparison of five mainstream LLM application platforms: Dify, Coze, n8n, FastGPT, and RAGFlow, emphasizing the importance of selecting the right platform based on individual needs and use cases [1][2]. Group 1: Overview of LLM Platforms - LLM application platforms significantly lower the development threshold for AI applications, accelerating the transition from concept to product [2]. - These platforms allow users to focus on business logic and user experience innovation rather than repetitive underlying technology construction [3]. Group 2: Platform Characteristics - **n8n**: Known for its powerful general workflow automation capabilities, it allows users to embed LLM nodes into complex automation processes [4]. - **Coze**: Launched by ByteDance, it emphasizes low-code/no-code AI agent development, enabling rapid construction and deployment of conversational AI applications [5]. - **FastGPT**: An open-source AI agent construction platform focused on knowledge base Q&A systems, offering data processing, model invocation, and visual workflow orchestration capabilities [6]. - **Dify**: An open-source LLM application development platform that integrates BaaS and LLMOps concepts, providing a one-stop solution for rapid AI application development and operation [7]. - **RAGFlow**: An open-source RAG engine focused on deep document understanding, specializing in knowledge extraction and high-quality Q&A from complex formatted documents [8][40]. Group 3: Detailed Platform Analysis - **Dify**: Described as a "Swiss Army Knife" of LLM platforms, it offers a comprehensive set of features including RAG pipelines, AI workflows, monitoring tools, and model management [8][10][12]. - **Coze**: Positioned as the "LEGO" of LLM platforms, it allows users to easily create and publish AI agents with a wide range of built-in tools and plugins [21][25]. - **FastGPT**: Recognized for its ability to quickly build high-quality knowledge bases, it supports various document formats and provides a user-friendly interface for creating AI Q&A assistants [33][35]. - **RAGFlow**: Distinguished by its deep document understanding capabilities, it supports extensive data preprocessing and knowledge graph functionalities [40][42]. - **n8n**: A low-code workflow automation tool that connects various applications and services, enhancing business process automation [46][49]. Group 4: User Suitability and Recommendations - For beginners in AI application development, Coze is recommended as the easiest platform to start with [61]. - For businesses requiring automation across multiple systems, n8n's robust workflow capabilities can save significant time [62]. - For building internal knowledge bases or Q&A systems, FastGPT and RAGFlow are suitable options, with FastGPT being lighter and RAGFlow offering higher performance [63]. - For teams with long-term plans to develop scalable enterprise-level AI applications, Dify's comprehensive ecosystem is advantageous [63]. Group 5: Key Considerations for Platform Selection - Budget considerations include the costs of self-hosting open-source platforms versus subscription fees for cloud services [68]. - Technical capabilities of the team should influence the choice of platform, with no-code options like Coze being suitable for those with limited technical skills [68]. - Deployment preferences, such as the need for local data privacy, should also be evaluated [69]. - Core functionality requirements must be clearly defined to select the platform that best meets specific needs [70]. - The sustainability of the platform, including update frequency and community support, is crucial for long-term viability [71]. - Data security and compliance are particularly important for enterprise users, with self-hosted solutions offering greater control over data [72].