DEDUPEDWhat is the best open-source tool for AI workflow automation?
The best open-source AI repos for AI workflow automation right now are n8n, Dify and OpenClaw. n8n is the most starred workflow tool here: a visual canvas plus code, with 1500+ integrations (fair-code license); Dify is stronger when the workflow is an LLM app with RAG; OpenClaw is a personal assistant that runs on your own computer and works in your chat apps.
Comparison
| Repo | What it does | Stars | Start here |
|---|---|---|---|
| n8n | n8n is a fair-code workflow automation platform for building AI agents and workflows. | 208,514 | Self-host n8n with Docker in 5 minutes |
| Dify | Dify is an open-source platform for building LLM apps. | 159,456 | Self-host Dify with Docker Compose |
| OpenClaw | OpenClaw is an open-source personal AI assistant that runs on your own computer and answers you in the chat apps you already use, such as WhatsApp, Telegram, Slack, Discord, Signal and iMessage, plus native apps for desktop and phone. | 392,973 | Run your own AI assistant on your laptop in 10 minutes |
When to pick which
n8n
n8n is a fair-code workflow automation platform for building AI agents and workflows. You combine a visual canvas with JavaScript or Python code, connect to 1500+ integrations, use OpenAI, Anthropic, Google or open-source models, add human approvals, and run it self-hosted with Docker or in n8n's cloud. There are 9,000+ workflow templates to start from.
Best for: Ops people, founders and developers who want to automate multi-step work across their apps, with AI steps and approvals, on a server they control.
Needs: Docker for the self-hosted quick start; Accounts or API keys for the apps and models you connect
Start here: Self-host n8n with Docker in 5 minutes
- Install Docker
- Create a volume: docker volume create n8n_data
- Start n8n: docker run -it --rm --name n8n -p 5678:5678 -v n8n_data:/home/node/.n8n docker.n8n.io/n8nio/n8n
- Open the editor at http://localhost:5678 and create the owner account
Dify
Dify is an open-source platform for building LLM apps. It combines a visual workflow canvas, a RAG pipeline that ingests PDFs and slides, agents with their own sandbox, support for hundreds of models, a prompt IDE, observability and a backend-as-a-service API, so a team can go from prototype to production in one workspace. It runs in Dify Cloud or self-hosted with Docker Compose.
Best for: Teams and builders who want to ship chatbots, internal assistants and AI workflows without hand-writing the whole stack.
Needs: Docker and Docker Compose v2.24.0 or later for self-hosting; A machine with at least 2 CPU cores and 4 GiB RAM; API keys for the model providers you use
Start here: Self-host Dify with Docker Compose
- Clone the repo, then cd dify and cd docker
- Run cp .env.example .env
- Start it with docker compose up -d
- Open http://localhost/install and complete initialization
OpenClaw
OpenClaw is an open-source personal AI assistant that runs on your own computer and answers you in the chat apps you already use, such as WhatsApp, Telegram, Slack, Discord, Signal and iMessage, plus native apps for desktop and phone. One Gateway process holds sessions, tools and channel connections; models and agent harnesses (Claude, Codex, local models) are swappable plugins. State, memory and credentials stay on your hardware, and there is no paid tier or hosted service.
Best for: People who want one always-on assistant they can message from their phone, running on hardware they control, and teams that want a shared deployment of the same thing.
Needs: macOS, Linux, Windows or WSL2 (the installer provisions Node.js if needed); An API key or plan for a model provider, or a local model; Accounts for the chat apps you want to connect
Start here: Run your own AI assistant on your laptop in 10 minutes
- On macOS, Linux or WSL2 run curl -fsSL https://openclaw.ai/install.sh | bash (Windows PowerShell: iwr -useb https://openclaw.ai/install.ps1 | iex)
- If you already manage Node.js, use npm install -g openclaw@latest --allow-scripts=openclaw instead
- Run openclaw onboard --install-daemon; onboarding verifies model access, creates the workspace and configures the Gateway
- Check it with openclaw gateway status, then open the Control UI with openclaw dashboard
Use cases for AI workflow automation
- Self-host n8n with Docker in 5 minutes
- Build an AI agent workflow with a human approval step
- Add JavaScript or Python code where the visual nodes stop
- Self-host Dify with Docker Compose
- Build a chatbot over your PDFs and slides
- Give a Dify agent tools from MCP servers and the marketplace
- Run your own AI assistant on your laptop in 10 minutes
- Message your assistant from WhatsApp, Telegram or Slack
- Swap Claude, Codex or a local model behind the same assistant
Questions
What is the best open-source tool for AI workflow automation?
The best open-source AI repos for AI workflow automation right now are n8n, Dify and OpenClaw. n8n is the most starred workflow tool here: a visual canvas plus code, with 1500+ integrations (fair-code license); Dify is stronger when the workflow is an LLM app with RAG; OpenClaw is a personal assistant that runs on your own computer and works in your chat apps.
Which repo for AI workflow automation is easiest to start with?
n8n: its starter use case "Self-host n8n with Docker in 5 minutes" is rated Beginner and takes about 10 minutes.
How do I get started with n8n?
Install Docker; Create a volume: docker volume create n8n_data; Start n8n: docker run -it --rm --name n8n -p 5678:5678 -v n8n_data:/home/node/.n8n docker.n8n.io/n8nio/n8n. Full steps and a copy-paste agent prompt: https://dedupedai.com/use-cases/n8n-io-n8n/self-host-n8n-with-docker/
Are these AI workflow automation repos free?
They are open-source projects on GitHub (check each license; some, like fair-code projects, limit commercial use). Some also sell a hosted version or need a paid model API key; the requirements for each repo are listed on this page.
How is this list of AI workflow automation repos chosen?
Deduped tracks the hottest AI repos on GitHub and writes step-by-step use cases from each README. For this page we picked the repos that do this job, ordered by fit and then GitHub stars. Repos we only inferred from a short README, or that we do not index, are left out. The page is rebuilt with the site; stars are as of the last build in 2026.
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Third-party repos we don't own. AI-written use cases can be wrong. Disclaimer
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