Dify vs n8n

Dify Cloud · Self-hosting · Documentation · Dify edition overview

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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. Install Docker with a running daemon, Docker Compose v2.35.0 or later, and OpenSSL.

For a new deployment, run the following commands from the repository root. For an existing deployment, preserve your .env and follow the upgrade guidance.

cd docker
cp .env.example .env
openssl rand -hex 32

Open .env and set DIFY_AGENT_LOCAL_SANDBOX_AUTH_TOKEN to the generated value. Keep the default COMPOSE_PROFILES value for the bundled services. Service defaults load automatically; no additional configuration files are required.

From the same docker directory, start the services:

docker compose up -d
docker compose ps --all

Once the services are running, open http://localhost/install to complete the initial setup. The one-time init_permissions service is expected to exit with code 0.

For optional settings and service overrides, see the Docker deployment guide.

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: Autonomous agents with a sandbox of their own: they run commands, install software, and handle files to get open-ended tasks done. Describe the agent you want and it builds itself. Give it skills, connect tools from Dify Marketplace, MCP servers, or your own APIs, and put it to work as a chat app or as a step in any workflow.

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 Dify Cloud so you can try Dify without managing a server. See the pricing page for current plans and usage allowances. If you run into issues, 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 Enterprise
  
  For organizations requiring self-hosting, SSO, RBAC Security and Enterprise Support SLAs, please fill out this form to speak to a solution representative.
  

Staying ahead

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

Advanced Setup

For custom configuration, observability, and deployment options, see Advanced Setup.

Contributing

Dify welcomes contributions of all kinds:

• Code: Read the Contribution Guide, then browse good first issues.
• Ideas and feedback: Start or join a GitHub Discussion.
• Translations: Follow the internationalization guide to add or update a locale.
• Community: Share the apps you build, help other users, and spread the word about Dify.

Contributors

Community & contact

Choose the channel that best fits your question:

• GitHub Discussions: Get help, share feedback, and propose ideas.
• GitHub Issues: Report reproducible bugs and track engineering work. Read the Contribution Guide before opening one.
• Discord: Chat in real time, share your apps, and connect with other Dify users.
• X: Follow Dify for release news and project updates.

Star History

Security disclosure

Report vulnerabilities privately through GitHub Security Advisories, following our security policy. Do not disclose them in public issues, discussions, or pull requests.

License

This repository is licensed under the Dify Open Source License, based on Apache 2.0 with additional conditions.

n8n – The Platform for AI Agents and Workflow Automation

Fair-code platform to build and deploy AI agents and workflows. Combine a visual canvas with custom code, run it self-hosted or in the cloud, and connect to 1500+ integrations. AI automation you can trust with real work, from prototype to production.

Key Capabilities

• AI-Native Automation Platform: Build and operationalize AI workflows and multi-step agents using your own data, models, and tools
• Model Flexibility, No Lock-In: Connect to OpenAI, Anthropic, Google, or open-source models and switch providers without changing your architecture
• From Prototype to Production: Design multi-step AI workflows with logic, tool use, human approvals, and full observability
• Code When You Need It: Combine visual building with JavaScript, Python, and npm packages for advanced AI workflows
• Enterprise-Ready AI: Self-host or deploy securely with role-based access, audit trails, and support for sensitive data
• Leverage What Already Exists: 1500+ integrations and 9,000+ workflow templates to connect AI with your existing systems

Quick Start

Try n8n instantly with our install script (requires Docker):

curl -fsSL https://get.n8n.io | sh

Or deploy manually with Docker:

docker volume create n8n_data
docker run -it --rm --name n8n -p 5678:5678 -v n8n_data:/home/node/.n8n docker.n8n.io/n8nio/n8n

Access the editor at http://localhost:5678

Resources

• 📚 Documentation
• 🔧 1500+ Integrations
• 💡 Example Workflows
• 🤖 AI & LangChain Guide
• 👥 Community Forum
• 📖 Community Tutorials

Support

Need help? Our community forum is the place to get support and connect with other users:
community.n8n.io

License

n8n is fair-code distributed under the Sustainable Use License and n8n Enterprise License.

• Source Available: Always visible source code
• Self-Hostable: Deploy anywhere
• Extensible: Add your own nodes and functionality

Enterprise Licenses available for additional features and support.

Additional information about the license model can be found in the docs.

Contributing

Found a bug 🐛 or have a feature idea ✨? Check our Contributing Guide for a setup guide & best practices.

Join the Team

Want to shape the future of automation? Check out our job posts and join our team!

What does n8n mean?

Short answer: It means "nodemation" and is pronounced as n-eight-n.

Long answer: "I get that question quite often (more often than I expected) so I decided it is probably best to answer it here. While looking for a good name for the project with a free domain I realized very quickly that all the good ones I could think of were already taken. So, in the end, I chose nodemation. 'node-' in the sense that it uses a Node-View and that it uses Node.js and '-mation' for 'automation' which is what the project is supposed to help with. However, I did not like how long the name was and I could not imagine writing something that long every time in the CLI. That is when I then ended up on 'n8n'." - Jan Oberhauser, Founder and CEO, n8n.io