How do you set up AI agents in n8n?

Quick Answer: AI agents are built in n8n using the AI Agent node (based on LangChain), which combines an LLM, tools, and memory. Connect an LLM node (OpenAI, Anthropic, or Ollama), add tool nodes for capabilities (HTTP requests, database queries, calendar), attach memory (buffer or vector store), and trigger via chat, webhook, or schedule. The AI Agent reasons over user input and calls tools as needed.

Setting Up AI Agents in n8n

n8n added native LangChain integration in 2024, with the AI Agent node enabling function-calling agents that use tools to accomplish multi-step tasks. As of April 2026, n8n is one of the most widely used self-hosted AI agent platforms.

Core Nodes

  • AI Agent: Orchestrator that plans and calls tools
  • Chat Model (sub-node): OpenAI Chat Model, Anthropic, Ollama, Google Gemini
  • Memory (sub-node): Window Buffer Memory, Vector Store Memory, Motorhead
  • Tools (sub-nodes): HTTP Request, Calculator, Wikipedia, custom workflow tools

Step-by-Step Setup

1. Trigger

Choose a trigger for the agent:

  • Chat Trigger: Built-in chat UI for testing
  • Webhook: HTTP endpoint for integration
  • Schedule Trigger: Run on interval
  • Telegram/Slack Trigger: Accept messages from chat platforms

2. Add AI Agent Node

The AI Agent node expects:

  • Input: The user message or task
  • System Message: Instructions defining the agent's role
  • Chat Model: Connected LLM node
  • Memory: Optional conversation memory
  • Tools: Array of connected tool nodes

3. Connect a Chat Model

As a sub-node, add:

  • OpenAI Chat Model (gpt-4o, gpt-4o-mini, o1)
  • Anthropic Chat Model (claude-3-7-sonnet, claude-4-sonnet)
  • Ollama Chat Model (for local models like Llama, Mistral)
  • Google Vertex Chat Model

4. Add Tools

Tools extend the agent's capabilities:

  • HTTP Request Tool: Call any API
  • Workflow Tool: Execute another n8n workflow as a tool
  • Calculator: Math operations
  • Code Tool: Execute JavaScript
  • Wikipedia/SerpAPI: Web search
  • Custom: Build tools from any n8n node

5. Configure Memory

  • Window Buffer Memory: Keep last N messages
  • Vector Store Memory: Semantic memory using embeddings (e.g., Pinecone, Qdrant)
  • Motorhead: Summarization-based memory

Example: Customer Support Agent

Chat Trigger
  → AI Agent
     - System: "You are a customer support agent..."
     - Chat Model: OpenAI gpt-4o-mini
     - Memory: Window Buffer (k=10)
     - Tools:
        - HTTP Request: Lookup order by ID
        - Workflow Tool: Create support ticket
        - SerpAPI: Search docs

Testing

Use the Chat Trigger for interactive testing:

  • Open the workflow in editor
  • Click the Chat Trigger node
  • Open the chat window
  • Send test messages and observe agent reasoning

Self-Hosted Considerations

  • Local LLMs: Use Ollama for privacy-sensitive deployments
  • Vector stores: Self-host Qdrant or use managed Pinecone
  • Cost control: Log token usage to Airtable for monitoring

Production Tips

  • Set max iterations (usually 5-10) to prevent infinite loops
  • Use structured output for deterministic downstream workflows
  • Monitor tool call failures and implement retries
  • Version prompts in a dedicated node with environment variables

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Last updated: | By Rafal Fila

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