Discover / Automation

Dify

Name: Dify
Author: langgenius

by langgeniusTypeScript

Open source platform for building and orchestrating LLM powered workflows and agents

Repositorystable

Maturity: stable because 3y old, 1.16.1 released 6d ago. Derived from release and commit history, not a rating.

Stars: 151k
Forks: 24k
Downloads / mo: —
Last commit: 2026-08-03
License: NOASSERTION
Open issues: 951

Market and trust evidence

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In practice

Written by AI from this repository’s README · medium confidence

Prototypes built from scattered scripts have to be rebuilt when they need workflows, retrieval and monitoring in production.

Use it when

Use it when a team needs a shared workspace to build and operate agentic workflows without wiring the stack together.

Not the right pick when

Not a lightweight library, since it is a full server platform with minimum CPU and RAM requirements to self host.

Capabilities

AI workflow and RAG pipeline builder
agent capabilities and model management
observability integrations including Opik, Langfuse and Arize Phoenix
cloud, VPC or self-hosted deployment
Docker Compose as the easiest self-host path

Requirements

CPU >= 2 Core
RAM >= 4 GiB
Docker Compose

Cost: Open source with a paid cloud option

Compare Repository ↗Homepage ↗

Video walkthroughs

Dify GitHub: How LangGenius Dify Builds Multi-Agent AI Workflows

Alex Hitt · 540 views · ≈Jun 2026

Dify Quickstart Guide: Build Your First AI Workflow

Dify · 22K views · ≈Oct 2025

How to Build AI Chatbots & Chatflow Automation with Dify.ai

Eric Tech · 24K views · ≈Aug 2025

Third-party YouTube uploads matched to this tool by title, channel and repository name on 2026-08-03. Not made, reviewed or endorsed by SkillPilot. View counts and publish months are as of the match date and the month is approximate. Nothing loads from YouTube until you press play.

What the repository ships

Ships CLAUDE.mdHas docsSecurity policyCI configured

Detected from the actual files in the repository root.

Latest release 1.16.1

Published 2026-07-28

What's Changed

New Features

Tool Multi-Select Input

Workflow tool nodes now support multi-select dropdown inputs, allowing users to choose multiple values from a predefined list when configuring tool parameters. (#39346)

Workflow Node Locator

You can now click a node_id in workflow run logs or error messages to locate and highlight the corresponding node in the workflow editor canvas. (#38187)

Workflow Block Selector Improvements

The block selector panel now features improved keyboard navigation, category previews, and a more intuitive search experience when adding nodes to workflows. (#39212)

Export Agent DSL from Sidebar

Agent apps can now be exported as DSL YAML directly from the app sidebar menu, making it easy to back up or version-control agent configurations. (#39299)

Knowledge Tracing

Added more observability traces to knowledge/RAG document processing operations, improving visibility into indexing pipelines. (#38959)

Bug Fixes

Workflow & Collaboration

Fixed hidden-tab collaboration leader saving stale drafts when the tab is inactive (#38997)
Fixed unsaved edits being dropped when collaboration never connects (#39579)
Preserved latest collaboration session after reconnection (#39646)
Fixed false draft save error during Strict Mode initialization (#39353)
Show persistent error notification when workflow draft save fails (#39293)
Wait for workflow worker threads to finish before Celery tasks complete, preventing orphaned executions (#39614)
Lazy-load Loro collaboration runtime to reduce initial bundle size (#39631)
Don't open node settings panel when clicking a node in comment mode (#39500)
Don't close active comment panel when a different comment is resolved (#39491)

RAG & Knowledge

Fixed document cleaner stripping valid characters ï (U+00EF), ¿ (U+00BF), ¾ (U+00BE) (#39215)
Preserved spaces during recursive text splitting (#39498)
Used flush() instead of commit() in dataset service to preserve caller transaction boundaries (#39223)
Sent valid body in external knowledge validation probe (#39410)

Agent

Fixed agent preview: start new Preview conversations without inheriting an old ID (#39463)
Fixed agent chat input being replaced when conversation history updates (#39289)
Preserved complete model usage pricing in agent logs (#39201)
Show workflow node runs in agent execution logs (#39471)
Include workflow runs in agent monitoring stats (#39354)
Block unpublished agents from being used in workflows (#39532)
Prevent agent preview actions from being clipped (#39510)
Prevent build reset from clearing preview chat history (#39490)
Fixed agent build draft UI flashing during apply (#39642)

Web App & UI

Fixed webapp JWT expiration time being incorrectly set (#39537)
Fixed TTS playback in Safari and Firefox (#39444)
Fixed clipboard writes failing in embedded apps (#39511)
Fixed text generation web app showing infinite loading after timeout (#39268)
Fixed chat not passing document if model doesn't support vision (#39461)
Stopped chunk length/overlap inputs collapsing to unusable width in narrow containers (#39600)
Fixed search hotkey hydration mismatch (#39574)
Improved DSL import/export dialog states (#39432, #39409)
Fixed basePath doubling in auth refresh redirects (#39273)
Show unavailable state for disabled Web Apps (#39392)
Fixed permission selector semantics (#39269)
Fixed customization page horizontal overflow (#39557)

API & Backend

Fixed MCP tool output_schema being treated as required when it's optional (#39453)
Honored array-element limit and byte budget for list[File] in VariableTruncator (#39220)
Prevented identity logging deadlock (#39449)
Rejected host:port-shaped PLUGIN_REMOTE_INSTALL_PORT values with an actionable hint (#39329)
Fixed PaginatedResult.pages returning 1 instead of 0

README

cover-v5-optimized

<a href="https://cloud.dify.ai">Dify Cloud</a> ·

<a href="https://docs.dify.ai/getting-started/install-self-hosted">Self-hosting</a> ·

<a href="https://docs.dify.ai">Documentation</a> ·

<a href="https://dify.ai/pricing">Dify edition overview</a>

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Dify is an open-source LLM app development platform. Its intuitive interface combines AI workflow, RAG pipeline, agent capabilities, model management, observability features (including Opik, Langfuse, and Arize Phoenix) and more, letting you quickly go from prototype to production. Here's a list of the core features:

Quick start

Before installing Dify, make sure your machine meets the following minimum system requirements:

- CPU >= 2 Core

- RAM >= 4 GiB

The easiest way to start the Dify server is through Docker Compose. Before running Dify with the following commands, make sure that Docker and Docker Compose v2.24.0 or later are installed on your machine:


cd dify
cd docker
cp .env.example .env
docker compose up -d

After running, you can access the Dify dashboard in your browser at http://localhost/install and start the initialization process.

Seeking help

Please refer to our FAQ if you encounter problems setting up Dify. Reach out to the community and us if you are still having issues.

If you'd like to contribute to Dify or do additional development, refer to our guide to deploying from source code

Key features

1. Workflow:

Build and test powerful AI workflows on a visual canvas, leveraging all the following features and beyond.

2. Comprehensive model support:

Seamless integration with hundreds of proprietary / open-source LLMs from dozens of inference providers and self-hosted solutions, covering GPT, Mistral, Llama3, and any OpenAI API-compatible models. A full list of supported model providers can be found here.

providers-v5

3. Prompt IDE:

Intuitive interface for crafting prompts, comparing model performance, and adding additional features such as text-to-speech to a chat-based app.

4. RAG Pipeline:

Extensive RAG capabilities that cover everything from document ingestion to retrieval, with out-of-box support for text extraction from PDFs, PPTs, and other common document formats.

5. Agent capabilities:

You can define agents based on LLM Function Calling or ReAct, and add pre-built or custom tools for the agent. Dify provides 50+ built-in tools for AI agents, such as Google Search, DALL·E, Stable Diffusion and WolframAlpha.

6. LLMOps:

Monitor and analyze application logs and performance over time. You could continuously improve prompts, datasets, and models based on production data and annotations.

7. Backend-as-a-Service:

All of Dify's offerings come with corresponding APIs, so you could effortlessly integrate Dify into your own business logic.

Using Dify

Cloud

We host a Dify Cloud service for anyone to try with zero setup. It provides all the capabilities of the self-deployed version, and includes 200 free GPT-4 calls in the sandbox plan. If you run into issues with Dify Cloud, contact our Cloud support team.

Self-hosting Dify Community Edition

Quickly get Dify running in your environment with this starter guide.

Use our documentation for further references and more in-depth instructions.

Dify for enterprise / organizations

We provide additional enterprise-centric features. Send us an email to discuss your enterprise needs.

Staying ahead

Star Dify on GitHub and be instantly notified of new releases.

star-us

Advanced Setup

Custom configurations

If you need to customize the configuration, edit docker/.env. The essential startup defaults live in docker/.env.example, and optional advanced variables are split under docker/envs/ by theme. After making any changes, re-run docker compose up -d from the docker directory. You can find the full list of available environment variables here.

Metrics Monitoring with Grafana

Import the dashboard to Grafana, using Dify's PostgreSQL database as data source, to monitor metrics in granularity of apps, tenants, messages, and more.

Grafana Dashboard by @bowenliang123

Deployment with Kubernetes

If you'd like to configure a highly available setup, there are community-contributed Helm Charts and YAML files which allow Dify to be deployed on Kubernetes.

Using Terraform for Deployment

Deploy Dify to Cloud Platform with a single click using terraform

Azure Global

Azure Terraform by @nikawang

Google Cloud

Google Cloud Terraform by @sotazum

Using AWS CDK for Deployment

Deploy Dify to AWS with CDK

AWS

Using Alibaba Cloud Computing Nest

Quickly deploy Dify to Alibaba cloud with Alibaba Cloud Computing Nest

Using Alibaba Cloud Data Management

One-Click deploy Dify to Alibaba Cloud with Alibaba Cloud Data Management

Deploy to AKS with Azure Devops Pipeline

One-Click deploy Dify to AKS with Azure Devops Pipeline Helm Chart by @LeoZhang

Contributing

For those who'd like to contribute code, see our Contribution Guide.

At the same time, please consider supporting Dify by sharing it on social media and at events and conferences.

We are looking for contributors to help translate Dify into languages other than Mandarin or English. If you are interested in helping, please see the i18n README for more information, and leave us a comment in the global-users channel of our Discord Community Server.

Community & contact

GitHub Discussion. Best for: sharing feedback and asking questions.
GitHub Issues. Best for: bugs you encounter using Dify.AI, and feature proposals. See our Contribution Guide.
Discord. Best for: sharing your applications and hanging out with the community.
X(Twitter). Best for: sharing your applications and hanging out with the community.

Contributors

</a>

Star History

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Truncated. Read the full README on GitHub ↗