GareArc
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The release script tests could only observe behaviour through a process exit code, because die() called process.exit from inside otherwise-pure functions. That forced every test to spawn a subprocess, which forced its input to come from cli/package.json, which is why editing that file kept breaking tests — most recently #39658, and next the difyctl version bump that cli-release.yml requires on every release. Move the decisions into lib/release-rules.mjs and lib/edge-manifest.mjs, which take every input as an argument and return values instead of exiting. release-naming.mjs and release-r2-edge.mjs keep argv parsing, manifest reading, stdout and exit codes, and nothing else. Tests now split by what they ask. Logic is unit-tested against literal inputs, the shells are tested for plumbing only, and release-config.test.ts is the single place that reads the real manifest — asserting it is internally consistent, never what it currently contains. Editing cli/package.json no longer breaks a logic test; a malformed config still fails. Verified by mutating each field in both directions. Three intentional behaviour changes: - compat-check compares the numeric A.B.C core only, ignoring prerelease and build suffixes, so Dify 1.16.0-rc1 now satisfies a 1.16.0 window. This matches the shipped runtime check in src/version/compat.ts, which already stripped suffixes; the release gate was the one disagreeing. Removes the hand-rolled prerelease ordering entirely. - validate now rejects a missing or inverted compat window. It previously passed such a config while github-env emitted minDify=undefined. - release-r2-edge reports a bad channel version under its own name rather than release-naming's, since it no longer borrows that script's die(). release-config.test.ts also pins the install scripts to the naming config. They hardcode artifact names by necessity — they run standalone on a user machine with no repo and no node — so changing tagPrefix or checksumsSuffix now fails until all four installers are updated to match, instead of silently publishing names no installer looks for.
Dify Cloud · Self-hosting · Documentation · Dify edition overview
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.
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.
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.
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.
- Helm Chart by @LeoQuote
- Helm Chart by @BorisPolonsky
- Helm Chart by @magicsong
- YAML file by @Winson-030
- YAML file by @wyy-holding
- 🚀 NEW! YAML files (Supports Dify v1.6.0) by @Zhoneym
Using Terraform for Deployment
Deploy Dify to Cloud Platform with a single click using terraform
Azure Global
Google Cloud
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-userschannel 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
Star History
Security disclosure
To protect your privacy, please avoid posting security issues on GitHub. Instead, report issues to security@dify.ai, and our team will respond with detailed answer.
License
This repository is licensed under the Dify Open Source License, based on Apache 2.0 with additional conditions.