How do you set up AI agents in n8n?

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