n8n AI Agents
Designs n8n AI agent nodes with tools, memory, and structured output.
Install
npx skills add czlonkowski/n8n-mcp --skill n8n-agents -g -a claude-code -y- Repository
- czlonkowski/n8n-mcp23.2k
- Category
- AI and agents
- Updated
- 8d ago
- License
- MIT
- Context cost
- Mediumabout 3,000 tokens
- Runs scripts
- No, instructions only
What it does
Design n8n AI agents the right way. Use when building or editing any @n8n/n8n-nodes-langchain.* AI node, an AI Agent, LLM chain, Text Classifier, or Information Extractor, and whenever the user mentions AI agents, LLM with tools, tool calling, $fromAI, system prompts, agent memory, sessionId, structured/JSON output, output parser, RAG, vector store, a chat assistant/bot, or human-in-the-loop review. Covers Agent-vs-chain-vs-classifier choice, the model/memory/tools/outputParser slots, tool names/descriptions as prompt, structured output with autoFix, memory, RAG, human review, and chat topologies.
n8n Agents
The n8n AI Agent node (@n8n/n8n-nodes-langchain.agent) is a multi-turn LLM driver with sub-nodes for the model, memory, tools, and an optional output parser. This skill is the deep guide to designing agents and the LangChain family around them. For the high-level "where an agent fits in a workflow" picture, see n8n-workflow-patterns ai_agent_workflow.md, this skill goes one level down into how to build it well.
For node-type formats: in workflow JSON the LangChain nodes use the long @n8n/n8n-nodes-langchain.* form (.agent, .lmChatOpenAi, .memoryBufferWindow, .outputParserStructured, .toolWorkflow, .toolHttpRequest, .toolCode). When you call get_node / validate_node, use the short form (nodes-langchain.agent). See n8n-mcp-tools-expert for the format rules.
Pick the right node first
Reaching for an Agent when the task is one-shot classification or extraction is the most common over-build. Decide before you wire anything:
