Dify — AI Agent Review & Live Stats

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: Apr 2, 2026 • Sentiment updated: Mar 17, 2026

GitHub Statistics

Community Sentiment

Community Buzz: The project seems to be actively maintained with regular commits and issues being resolved. However, there is limited community engagement and discussion.

Why Dify Stands Out

Dify stands out from other AI development platforms due to its production-ready agentic workflow development platform, which combines AI workflow, RAG pipeline, agent capabilities, model management, and observability features. This approach enables developers to quickly go from prototype to production, reducing the time and effort required to build complex AI applications. Additionally, Dify's integration with tools like Opik, Langfuse, and Arize Phoenix provides a comprehensive solution for data-driven content creation and AI-powered virtual assistants.

Built With

Build a research agent that reads 50 papers and writes a literature review — DeerFlow chains search, extraction, and synthesis agents automatically, Build a workflow automation tool for data scientists — Dify's GUI interface and RAG pipeline simplify task composition and orchestration, Build a conversational AI chatbot that understands and responds to user queries — Dify's agent capabilities and model management features enable seamless dialogue flow, Build a content generation platform that produces high-quality articles on-demand — Dify's observability features and integration with tools like Opik, Langfuse, and Arize Phoenix ensure data-driven content creation, Build a custom AI-powered virtual assistant for businesses — Dify's agentic workflow development platform and model management capabilities make it easy to create tailored assistants

Getting Started

  1. Install Dify using the following command: `cd dify && cd docker && cp .env.example .env && docker compose up -d`
  2. Access the Dify dashboard in your browser at `http://localhost/install`
  3. Configure the Dify server by editing the `.env` file and setting the required environment variables
  4. Deploy your AI workflow by creating a new project in the Dify dashboard and configuring the RAG pipeline
  5. Try accessing the Dify dashboard to verify that it's installed and running correctly

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

The project appears to be a low-code, agentic AI framework for automating tasks, which is a growing trend in the AI and automation space.