Build with the precision of code or the speed of drag-n-drop. Host with on-prem control or in-the-cloud convenience.
Connect to a vast array of data sources, tools, LLMs, vector stores, MCP servers, and even other agents. Access any REST API with the HTTP request node. Want to call n8n workflows from external AI systems? Use the MCP Server Trigger. It’s the maximum flexibility in AI architecture you’ve been looking for.
Start building multi-agent systems with a declarative UI. Add complexity with Python or Javascript when it makes sense. No more “do-it-our-way” arbitrary abstractions. In n8n, you can branch, loop, and dynamically adapt to AI outputs. AKA, the nuanced development you know and love.
Test changes without resending API calls or waiting for external services with data replay. Understand every step with inline logs. Spot issues across your agentic system with visual workflows of agent interactions and data flow. Because workflows are more fun to build when they’re fast to fix.
With our built-in AI nodes, it can, allowing seamless interaction with your data. Use multi-step agents to summarize content or answer complex queries.
Get straightforward deployment to staging and production environments. Choose from various models tailor-fit to your needs, or run the LLM locally for complete control.
On top of LangChain that integrate smoothly into your existing workflows. It doesn’t only enhance your processes, it allows you to embed AI into your own products.
Keep your agents in line by adding human-in-the-loop interventions for approval steps, safety checks, or manual overrides before AI actions take effect. And ensure only authorized users can modify workflows with role-based action control*. Because your users – and Compliance – have expectations. n8n makes sure you meet them.
Stop AI from acting up by setting behavioral boundaries before responses reach critical systems or end users. Define decision-making rules. Set up scriptable filters to validate data before sending it to an LLM. And apply output parsers to enforce structure. Because you’ll sleep better knowing your AI is behaving like it should.
Monitor AI decisions and data transformations with custom log streaming to any aggregator*. Test your workflows safely in isolated environments before going live. Integrate with your Git-based deployment tools to track every update and roll back when you need. Because building hard-to-audit workflows is hard to enjoy.
Implement event-driven triggers to make sure your AI only runs when it should. Use error triggers and fallback logic to reroute requests or pause executions so that you can catch failures — before your budget catches fire. Because if your workflow is full of surprises, it’s unscalable too.
When you build complex workflows, explicit architecture with guardrails works best. Self-hosted or in the cloud, wavenet allows you to mix AI, code, and human steps in a reliable way.
Ensure human brains hold the reins
Keep your agents in line by adding human-in-the-loop interventions for approval steps, safety checks, or manual overrides before AI actions take effect. And ensure only authorized users can modify workflows with role-based action control*. Because your users – and Compliance – have expectations. n8n makes sure you meet them.
Stop AI from acting up by setting behavioral boundaries before responses reach critical systems or end users. Define decision-making rules. Set up scriptable filters to validate data before sending it to an LLM. And apply output parsers to enforce structure. Because you’ll sleep better knowing your AI is behaving like it should.
Monitor AI decisions and data transformations with custom log streaming to any aggregator*. Test your workflows safely in isolated environments before going live. Integrate with your Git-based deployment tools to track every update and roll back when you need. Because building hard-to-audit workflows is hard to enjoy.
Implement event-driven triggers to make sure your AI only runs when it should. Use error triggers and fallback logic to reroute requests or pause executions so that you can catch failures — before your budget catches fire. Because if your workflow is full of surprises, it’s unscalable too.
