Dify — AI Agent Framework: Live Stats & TrendScore

Dify is a fast-rising LLM app platform. We track its GitHub momentum, compare it to Langflow, and surface what the community thinks of it for building production AI workflows.

GitHub data synced: Jun 28, 2026 • Sentiment updated: Jun 28, 2026

GitHub Statistics

Community Sentiment

Community Buzz: As seen on GitHub, 'AI 智能体的工程化与安全' is a trending topic, with one user stating 'NVIDIA 推出的 `SkillSpector` 和 Addy Osmani 的 `safety scan` 工具霸榜'

Pros & Cons

What People Love

Innovative AI features, Community support on GitHub and Dev.to

Common Complaints

Buggy deployments, Limited documentation

Biggest Positive: Innovative AI solutions

Biggest Negative: Buggy deployments

Why Dify Stands Out

Dify stands out from alternatives due to its unique combination of AI workflow, RAG pipeline, and agent capabilities, making it an ideal platform for building custom LLM apps. Its intuitive interface and observability features, including integrations with Opik, Langfuse, and Arize Phoenix, provide a comprehensive platform for developers. By leveraging Dify, developers can quickly go from prototype to production, solving the problem of lengthy development cycles and high costs associated with building custom LLM apps.

Built With

Build a conversational AI assistant — Dify's agent capabilities and model management enable rapid development of custom conversational interfaces, Build a low-code workflow automation platform — Dify's intuitive interface and RAG pipeline allow for easy automation of complex workflows, Build a custom LLM app — Dify's open-source platform and observability features provide a solid foundation for building and deploying LLM apps, Build a multi-agent system — Dify's agent capabilities and workflow management enable the creation of complex multi-agent systems, Build a natural language processing pipeline — Dify's RAG pipeline and model management enable the creation of custom NLP pipelines

Getting Started

  1. Install Dify using the command `docker pull langgenius/dify-web`
  2. Configure the environment variables using the command `export DFY_CONFIG_FILE=/path/to/config.json`
  3. Initialize the database using the command `docker run -it langgenius/dify-web dfy init`
  4. Start the Dify server using the command `docker run -p 8080:8080 langgenius/dify-web`
  5. Try accessing the Dify interface at `http://localhost:8080` to verify it works

About

Production-ready platform for agentic workflow development.

Official site: https://dify.ai

Category & Tags

Category: multi-agent

Tags: agent, agentic-ai, agentic-framework, agentic-workflow, ai, automation, gemini, genai, gpt, gpt-4, llm, low-code, mcp, nextjs, no-code, openai, orchestration, python, rag, workflow

Market Context

Competing with other AI platforms