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Production-ready platform for agentic workflow development.
Git-Stars 是独立产品，不隶属于 GitHub 或该项目。 分析可能由 AI 辅助生成，依据公开仓库元数据和 README 的短摘要。 我们不镜像完整 README、文档、Issue 或社媒评论。
langgenius/dify 被追踪为 TypeScript 项目，主要属于 Automation, LLM Tool, AI Agent, MCP Server 方向。这个评估结合公开 GitHub 元数据、分类信号、短来源摘要和 Git-Stars 编辑规则，而不是复制项目文档。
增长检查：该仓库目前有 149k Star，今日 +0，本周 +0，本月 +0。这些窗口用于区分持续采用信号和短期曝光峰值。
维护检查：当前活跃度为 活跃；最近一次推送距今 1 天，未关闭 Issue 为 1.1k，约占总 Star 的 0.71%。这只是采用信号，不替代工程尽调。
采用检查：24k Fork 和 149k Watcher 反映项目被复用和关注的程度。许可证信号：NOASSERTION。商业或内部使用前请核验许可证兼容性。
适用判断：当你需要「AI 原型、LLM 工作流和 Agent 类应用」时，这个项目更值得评估；如果「需要法律审查、安全审计或生产 SLA 保证」，则需要谨慎。
来源检查：Git-Stars 当前为这份报告保留了 1 个明确来源引用，近期增长信号为 3.4k。最终安装、安全和版本信息仍应以原始 GitHub 仓库为准。
热度
149k 星标
复用
24k 复刻
关注
149k 关注者
维护
active
许可证
NOASSERTION
未解决 Issue
1.1k
Dify is an open-source LLM app development platform with an intuitive interface that combines AI workflow, RAG pipeline, agent capabilities, model management, and observability features, enabling rapid prototyping to production.
Key Features
- Visual workflow builder for AI applications - Integrated RAG pipeline and agent capabilities - Model management and observability features (Opik, Langfuse, Arize Phoenix)
Automation
Workflow automation, integration glue, and orchestration
LLM Tool
Libraries and tools for LLM apps, RAG, prompts, and evals
AI Agent
Agent frameworks, autonomous workflows, and tool-use systems
MCP Server
Model Context Protocol servers, clients, and integrations
docker compose up -d100
健康评分
活跃
提交活跃度
Apr 12, 2023
创建于
Jul 18, 2026
最近提交
+3.3k
今日增长
+3.4k
7天增长
+3.4k
30天增长
复刻
未解决
关注者
langgenius
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😎 Awesome lists about all kinds of interesting topics
freeCodeCamp/freeCodeCamp
freeCodeCamp.org's open-source codebase and curriculum. Learn math, programming, and computer science for free.
public-apis/public-apis
A collective list of free APIs
EbookFoundation/free-programming-books
:books: Freely available programming books
✓
License
✓
Forked
✓ Active
Maintained
Problem Solved
Dify solves the complexity of integrating multiple AI components (LLMs, vector stores, tools) into a cohesive agent workflow. Unlike building from scratch, it offers a visual drag-and-drop interface and pre-built templates, reducing development time from weeks to hours while maintaining production scalability.
Capabilities
Developers can build customer support chatbots, automated research assistants, document analysis pipelines, and multi-step reasoning agents. The platform supports custom tool integration, RAG with various vector databases, and deployment to cloud or self-hosted environments, enabling enterprise-grade AI applications with monitoring and logging.
Bottom Line
Dify is ideal for teams wanting to quickly build and deploy AI agents without extensive coding, especially for customer service and internal tools. Developers needing fine-grained control or handling massive scale may find it limiting. The key trade-off is ease-of-use versus deep customization.