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Dify

langgenius · Workflow automation

GitHub stars

157,705
—Collecting 30d data

Stats updated

157,330 stars on Sep 27 to 157,705 stars on Oct 2, up 375.6 measured star snapshots

What is Dify?

Dify brings prompt logic, model connections, retrieval, tools, and deployment into one workspace. Teams can assemble an AI application visually, test its execution path, and publish the same reviewed logic as a web app, API, or MCP-compatible tool without rebuilding the workflow for each surface.

Dify is a platform for building AI applications with visual workflows, RAG pipelines, and agents. Teams can self-host it or deploy through Dify Cloud, then publish applications as web experiences or APIs.

Core capabilities

  • Build agentic workflows and chatflows on a visual canvas
  • Prepare retrieval pipelines and reusable knowledge bases
  • Publish reviewed applications as web apps, APIs, or MCP tools

Visible agentic workflows

The workflow studio makes execution logic inspectable rather than hiding it behind one prompt. Builders can connect agent, retrieval, tool, decision, and human-input steps, run them with test data, and review how information moves before an application reaches users.

  • workflows
  • agents
  • testing
  • human input

From knowledge to deployment

Knowledge pipelines prepare source material for retrieval, while the marketplace supplies model providers, tools, data sources, and MCP integrations. A finished application can then be exposed through a hosted interface, an API, an embed, or an MCP-compatible tool with operational logs retained in the workspace.

  • RAG
  • plugins
  • API
  • MCP

Where it fits

Use cases

  1. 01

    Prototype and ship an AI workflow

    Connect prompts, models, tools, branching, and human input in a visible execution path, test it with real inputs, and publish the resulting application without translating the flow into a separate service first.

  2. 02

    Ground agents in managed knowledge

    Create repeatable ingestion and indexing pipelines for documents and connected data sources, test retrieval quality, and reuse the resulting knowledge base across agents and chat applications.

  3. 03

    Operate a shared AI application workspace

    Give a team one place to manage applications, approved model providers, plugins, logs, feedback, and deployment while choosing Dify Cloud, a private VPC, or a self-hosted Community Edition.