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langgenius/dify

🔥 Hot★ 140,342TypeScriptAudience · developerComplexity · 4/5ActiveSetup · hard

TLDR

Open-source platform for building and deploying LLM-powered applications with visual workflows, document retrieval, agent logic, and observability, no coding required.

Mindmap

mindmap
  root((Dify))
    What it does
      Visual workflow builder
      RAG pipeline
      Agent capabilities
      Model management
    Key features
      Prompt IDE
      Multi-model support
      Document extraction
      Text-to-speech
    Use cases
      Build AI apps
      Deploy agents
      Test workflows
      Manage prompts
    Tech stack
      TypeScript
      Docker
      LLM APIs
    Deployment
      Docker Compose
      Self-hosted
      Dify Cloud
    Integrations
      Opik
      Langfuse
      Arize Phoenix

Things people build with this

USE CASE 1

Build a customer support chatbot that retrieves answers from your company's internal documents without retraining the model.

USE CASE 2

Create an AI agent that can browse the web, call APIs, and execute tasks autonomously using function calling.

USE CASE 3

Deploy a multi-model prompt testing environment where you compare GPT, Mistral, and Llama outputs side-by-side.

USE CASE 4

Set up an end-to-end LLM application with observability dashboards to monitor performance and debug issues in production.

Tech stack

TypeScriptDockerOpenAI APIPostgreSQLRedis

Getting it running

Difficulty · hard Time to first run · 1h+

Requires Docker, PostgreSQL, Redis, and OpenAI API key; multiple services must be orchestrated.

License could not be detected automatically. Check the repository's LICENSE file before use.

In plain English

Dify is an open-source platform for building applications powered by large language models. In plain terms, large language models are the AI brains behind chatbots and assistants, and Dify gives you an interface to wire them up into real apps without writing everything from scratch. The README describes it as a production-ready platform for agentic workflow development, where an agent is software that can decide which steps to take to answer a request, and a workflow is a defined sequence of steps that agent follows. How it works, according to the README, is through a visual canvas where you can build and test AI workflows, a prompt editor it calls Prompt IDE for crafting and comparing prompts across models, a retrieval-augmented generation pipeline that ingests documents like PDFs and PowerPoints so the AI can answer questions about your own content, and an agent builder that lets you attach pre-built or custom tools to an LLM using function calling or the ReAct pattern. It supports hundreds of models from many providers, covering GPT, Mistral, Llama3, and anything compatible with the OpenAI API. It also integrates observability tools named in the README, including Opik, Langfuse, and Arize Phoenix. You can use Dify Cloud or self-host. The README walks through self-hosting via Docker Compose, with a minimum of two CPU cores and four gigabytes of RAM. You would use Dify when you want to ship an LLM-powered app, chatbot, or agent without stitching together the infrastructure yourself. The repository is tagged TypeScript. The full README is longer than what was provided.

Copy-paste prompts

Prompt 1
I want to build an LLM app that answers questions about my PDF documents. How do I set up a RAG pipeline in Dify?
Prompt 2
Show me how to create a visual workflow in Dify that chains multiple LLM calls together with conditional logic.
Prompt 3
How do I deploy a Dify instance on my own server using Docker Compose, and what are the minimum hardware requirements?
Prompt 4
I need to test different prompts across GPT-4, Mistral, and Llama3 simultaneously. How does Dify's Prompt IDE help me compare them?
Prompt 5
Can I build an autonomous agent in Dify that uses function calling to interact with external APIs and tools?
Open on GitHub → Explain another repo

Generated 2026-05-21 · Model: sonnet-4-6 · Verify against the repo before relying on details.