Open-source workflow automation that lets you connect anything to everything with AI-powered nodes.
n8n is a fair-code workflow automation platform that bridges the gap between no-code simplicity and full programming flexibility. With over 177,000 GitHub stars and backing from a $2.3 billion valuation, it has become one of the most popular automation tools for technical teams who need more control than Zapier provides but less overhead than building custom integrations from scratch. The platform offers 400+ pre-built integrations spanning databases, APIs, SaaS tools, and AI services. What distinguishes n8n from competitors is its hybrid approach: you can build workflows visually using the drag-and-drop canvas, then drop into JavaScript or Python code nodes when you need custom logic. This makes it equally accessible to operations teams building simple notification flows and developers orchestrating complex multi-step data pipelines. n8n's AI capabilities have expanded significantly with dedicated nodes for OpenAI, Anthropic, Google Gemini, and local models via Ollama. The AI Agent node lets you build autonomous workflows where an LLM decides which tools to call, retrieves context from vector stores, and chains multiple reasoning steps together. Combined with the ability to self-host on your own infrastructure, this makes n8n particularly attractive for enterprises handling sensitive data who cannot send information to third-party automation platforms. The self-hosted community edition is genuinely free with no artificial limits on workflows or executions. The cloud offering starts at $24 per month for 2,500 executions and scales to enterprise plans with SSO, audit logs, and dedicated infrastructure. However, the learning curve is steeper than Zapier or Make — building complex workflows requires understanding concepts like webhook triggers, expression syntax, and error handling branches. The documentation is comprehensive but can feel overwhelming for newcomers. Production deployments also require careful consideration of queue workers, database scaling, and execution timeouts that simpler platforms handle transparently.