Empower AI Agents: Orchestrate n8n Workflows Dynamically
Empower AI agents to execute complex, multi-step n8n workflows autonomously, expanding automation capabilities by over 50% and eliminating manual tool integration.
Integrating AI agents with complex backend automation often leads to rigid, disconnected systems and limited task execution. This workflow transforms your n8n instance into a powerful MCP server, enabling AI agents to dynamically discover, manage, and execute complex n8n workflows for advanced, outcome-driven automation.
Documentation
Build a Dynamic AI Workflow Orchestration Server with n8n
This n8n template empowers your AI agents by transforming your n8n instance into a sophisticated Multi-Agent Communication Protocol (MCP) server. It enables agents to seamlessly interact with, manage, and execute your existing n8n workflows, shifting from simple utility calls to outcome-driven, end-to-end automation.
Key Features
- Dynamic Workflow Discovery: AI agents can search and list available n8n workflows tagged for MCP use, ensuring relevant and active tools are at their disposal.
- Intelligent Workflow Management: Agents autonomously add and remove workflows from an "available" pool using Redis memory, preventing conflicts and optimizing the agent's operational scope.
- Automated Workflow Execution: Enables AI agents to execute any n8n workflow, dynamically passing parameters based on the workflow's defined input schema via Subworkflow triggers.
- Scalable AI Integration: Connects any MCP-compatible AI client (like Claude Desktop) to leverage n8n's extensive integration capabilities for complex, multi-step tasks.
How It Works
The workflow is triggered by an MCP Client (e.g., via the 'N8N Workflows MCP Server' trigger or a 'When chat message received' event) and uses a Switch node to route incoming requests (add, remove, list, search, execute) to the appropriate branch. For workflow management, it queries your n8n instance for "mcp" tagged workflows and stores their simplified details, including extracted input schemas, in a Redis database. When an AI agent requests to execute a workflow, the system verifies its availability, retrieves its schema, and then triggers the target workflow using the 'Execute Workflow' node with dynamic parameter passthrough. An 'AI Agent' node, backed by an OpenAI model and LangChain memory, orchestrates these operations, allowing autonomous decision-making and task execution.