n8n Agents Skill

The deep guide to designing n8n AI agents and the LangChain family around them (@n8n/n8n-nodes-langchain.*) — the AI Agent node, its model/memory/tools/outputParser slots, and the chains and classifiers you'd reach for instead.

This is the biggest skill in the pack. The parent n8n-workflow-patterns ai_agent_workflow.md gives the high-level "agent in a workflow" shell; this skill goes one level down into how to build it well.

Pick the right node first

The most common over-build is reaching for an Agent when the job is one-shot:

What This Skill Teaches

Core Concepts

1. The four sub-node slots — model / memory / tools / outputParser, each on its own ai_* connection
2. Tool names and descriptions ARE the prompt — the model picks tools by reading them; generic names degrade routing silently
3. $fromAI() anatomy — how agent-filled tool params are declared (name / description / type); JSON only, never binary
4. Structured output must parse AND autoFix — outputParserStructured + a coding-capable fixer model
5. Memory + sessionId continuity — memoryBufferWindow, keyed on a stable session key
6. The binary boundary — the model can see images; tools can't receive them

Common Mistakes This Skill Prevents

1. Agent + Switch to route on natural language — Text Classifier is one node with N outputs
2. A tool the agent ignores — generic name (tool1) or empty description
3. Malformed JSON crashing the workflow — outputParserStructured without autoFix
4. A chat bot in an infinite loop — no bot-user-ID filter
5. Wrapping image/audio/video generation in an Agent — binary doesn't flow through
6. Crossed conversations — hardcoded sessionId or sessionId behind $fromAI
7. "Max iterations reached" — the low default cap left untouched on a multi-tool agent

Skill Activation

Activates when you:

• Build or edit any @n8n/n8n-nodes-langchain.* AI node
• Mention AI agents, LLM with tools, tool calling, $fromAI, system prompts
• Mention agent memory, sessionId, structured/JSON output, an output parser
• Mention RAG, a vector store, a chat assistant/bot, or human-in-the-loop review
• Choose between an Agent, an LLM chain, a Text Classifier, or an Information Extractor

Example queries:

• "My AI agent ignores a tool I named tool1. Why?"
• "Should I use an Agent + Switch to route messages into three branches?"
• "My agent sometimes returns malformed JSON and the workflow crashes despite an output parser."
• "How do I keep a Slack bot from triggering itself?"
• "Where should per-tool instructions go — the system prompt or the tool description?"

File Structure

SKILL.md

Main skill content — loaded when the skill activates.

• Pick the right node (Agent vs chain vs classifier vs extractor vs native media)
• The four sub-node slots and their ai_* connection types
• Two non-negotiables: tool names/descriptions as prompt; structured output parse + autoFix
• The four tool types and $fromAI() anatomy
• System prompt vs tool description split
• Memory mental model; the binary boundary; human review; chat topologies; RAG
• Anti-patterns, "what's NOT available via the MCP", integration, checklist

Reference files

Quick Reference

Wiring a sub-node (connection lives on the sub-node)

"Main LLM": { "ai_languageModel": [[{ "node": "AI Agent", "type": "ai_languageModel", "index": 0 }]] }

Agent-filled tool parameter

={{ $fromAI('recipient', 'Email address of the recipient', 'string') }}

Plumbed (hidden-from-agent) tool parameter

={{ $('Chat Trigger').first().json.user.id }}

Memory keyed on a stable session

{ "sessionIdType": "customKey", "sessionKey": "={{ $json.threadId }}", "contextWindowLength": 50 }

Human-review approval message — literal params, never $fromAI

=Refund {{ $tool.parameters.amount }} to {{ $tool.parameters.customerId }}?

Node-type formats

• Workflow JSON: long form — @n8n/n8n-nodes-langchain.agent
• get_node / validate_node: short form — nodes-langchain.agent

Integration with Other Skills

n8n-workflow-patterns (ai_agent_workflow.md): the high-level agent shape. Start there for architecture; this skill is the deep dive.

n8n-mcp-tools-expert: node-type formats and tool selection — consult before any MCP call.

n8n-node-configuration: displayOptions-driven fields on the agent and sub-nodes; Slack/Block Kit message shapes.

n8n-expression-syntax: {{ }}, $json.output, $now, $fromAI, $tool.parameters.

n8n-code-tool: the Custom Code Tool's string-in/string-out contract (a different runtime from this skill's tools).

n8n-subworkflows: the sub-workflow primitive .toolWorkflow builds on.

n8n-binary-and-data: owns the agent-tool binary boundary mechanics.

n8n-validation-expert: interpreting validate_workflow, including AI-connection issues (a tool on main instead of ai_tool flags as disconnected).

n8n-error-handling: onError: 'continueErrorOutput' on tool sub-workflows and the agent-core call; error UX on chat shells.

When to Use Which Tool Type

Rule of thumb: if you want $fromAI() inside Code, you want .toolWorkflow instead.

Success Metrics

After using this skill, you should be able to:

• Pick Agent vs Basic LLM Chain vs Text Classifier vs Information Extractor correctly
• Wire model/memory/tools/outputParser via the right ai_* connection types
• Write tool names and descriptions the model actually selects against
• Declare $fromAI() params well, and plumb identity/limits/sessionId deterministically
• Configure outputParserStructured with a manual schema + autoFix + a coding-capable fixer
• Key memory on a stable session and avoid crossed conversations
• Gate destructive tools behind human review using literal $tool.parameters
• Build a chat bot that doesn't loop on its own messages

Version

Version: 1.0.0 Compatibility: n8n with @n8n/n8n-nodes-langchain.agent and the LangChain sub-node family. Node versions and model availability shift between releases — verify on the target instance with search_nodes / get_node.

Remember: the model can't see your wiring — it sees a system prompt and a list of named, described tools. Design those like an API and most "the agent won't behave" problems disappear.