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N8n Agents Official

n8nOfficialPublished by n8n

  • Skill
  • Tool integration
  • Automation and integrations
  • Apache-2.0
  • No issues found

From the author

Use when the user wants an agent, assistant, or bot in n8n, or is building, editing, testing, publishing, or debugging a first-class n8n Agent. Covers choosing an Agent vs a workflow vs the AI Agent node, the Agent MCP tools (create_agent, mutate_agent, validate_agent, call_agent, publish_agent, update_agent_integration), Agent config JSON, tools and approvals, skills, sub-agents, model routing, knowledge files, vector stores, memory and sessions, Slack / Telegram / Discord / Linear / n8n Chat channels, scheduled tasks, the Message an Agent node, and reading Agent sessions and traces. Triggers on "build me an agent", "assistant", "chatbot", "Slack bot", "Telegram bot", "agent skills", "sub-agent", "knowledge base", "agent memory", "session", "Message an Agent", "publish the agent", or any Agent tool call.
— the description by n8n

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides the framework for building, testing, and deploying first-class n8n Agents. Unlike standard AI Agent nodes within workflows, these Agents are independent resources with their own memory, tools, skills, and channel integrations. The skill covers the full lifecycle from initial configuration via MCP tools to publishing and managing active sessions.

What it does

  • Guides the creation and configuration of first-class n8n Agents using MCP tools.
  • Manages agent memory, sub-agents, knowledge bases, and tool integrations.
  • Handles agent publishing and channel connectivity for platforms like Slack and Telegram.
  • Provides debugging support through session traces and validation tools.
  • Defines the distinction between first-class Agents and AI Agent nodes in workflows.
  • Ensures safe deployment through explicit user approval and validation steps.

When to use it

  • When you need an interactive assistant or chatbot that maintains state and memory.
  • When an agent requires autonomous capabilities like scheduled tasks or multi-channel support.
  • When you need to delegate complex tasks to sub-agents with model routing.
  • When building a solution that requires persistent sessions across multiple interactions.

Advantages

  • Centralized management of agent instructions, tools, and memory.
  • Native support for popular communication channels like Slack and Discord.
  • Robust validation and testing workflow to prevent production errors.
  • Clear separation of concerns between agent logic and workflow automation.

Usage examples

“Build me an agent that can answer questions from our internal knowledge base.”
The agent will be initialized with a vector store or knowledge file, configured with memory, and tested via the preview tool before deployment.
“How do I connect my n8n agent to Slack?”
The agent will use the update_agent_integration tool to configure the Slack channel, ensuring the correct credentials and workspace settings are applied.
“My agent is forgetting the conversation history, how do I fix it?”
The agent configuration will be updated to include the required memory block: { "enabled": true, "storage": "n8n" }.
“How can I add a sub-agent to my main agent?”
The main agent will be mutated to include the sub-agent as a tool, ensuring both the parent and child agents are published correctly.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/n8n-io/n8n-agents-official/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip n8n-agents-official.zip -d .agents/skills

Documentation by n8n

n8n Agents

A first-class n8n Agent is a saved resource with its own instructions, model, tools, skills, sub-agents, memory, channels, scheduled tasks, versions, and spend limits. It is configured as JSON through the Agent MCP tools, never with Workflow SDK code. The AI Agent node inside a workflow is a different thing, covered by n8n-ai-nodes-official.

<!-- TEMPORARY: revisit when the Preview tag is removed from Agents in the UI, and when Agents are declared ready for Enterprise. -->

Agents are generally available. The UI shows a Preview tag because they change often and some features you might expect aren't there yet. They aren't ready for Enterprise yet: an Enterprise instance may have them enabled, but don't assume it. If create_agent isn't in your tool list, Agents aren't enabled on this instance. Say so and offer a workflow instead of guessing.

Non-negotiables

  1. Always send a memory block when creating over MCP: "memory": { "enabled": true, "storage": "n8n" }. The UI adds it on every save. create_agent saves exactly what you send, and without it the Agent forgets the previous turn even inside one session (verified live).

  2. Never publish without the user's explicit OK. A built Agent is a draft. Configuring a channel on an already published Agent connects it immediately, so confirm that too.

  3. Publish what the Agent calls. Workflow tools need a When Executed by Another Workflow trigger, the same project, and a published workflow (validate_agent reports it). Saved sub-agents must be published too, and nothing checks that: validation and publishing pass, then production delegation fails and the parent quietly answers on its own.

  4. Approval on anything that sends, changes, deletes, or spends, and know what the approver will see. See references/APPROVALS.md.

  5. One mutate_agent per change, always with the latest configHash. A stale hash returns stale_config: call get_agent and retry.

Agent, workflow, or AI Agent node?

Decide before calling any build tool. The first question is the "say it out loud" test.

You can...Build
Say every step before the request arrives, none needs judgmentWorkflow
Say every step, one needs judgment (label, extract, summarize, write)Workflow with an AI step: Text Classifier, Information Extractor, chain, or AI Agent node (n8n-ai-nodes-official)
Not say the steps until you see the request, and people talk to it or it acts on its ownFirst-class n8n Agent
Not say the steps, but one of the limits below appliesAI Agent node in a workflow
Not say the steps, and a fixed pipeline also needs the same brainFirst-class Agent, called from the workflow with Message an Agent. The agent decides, the workflow acts

"Build me an agent" or "assistant" means a first-class Agent unless a limit applies. A Chat Trigger plus an AI Agent node is not a substitute for a requested Agent. If a request is really a fixed trigger plus fixed steps, explain the mismatch and ask before building a workflow instead.

Current limits that point to the AI Agent node

<!-- TEMPORARY: revisit when n8n adds custom approval messages for Agent tools. -->

<!-- TEMPORARY: revisit when channel approval cards (Slack, Telegram, Discord) show the tool's parameters. -->

<!-- TEMPORARY: revisit when Agent tools can read run context (trigger or upstream node data) in expressions. -->

<!-- TEMPORARY: revisit if n8n lets a workflow key into a native channel session (or the reverse). -->

NeedWhy an Agent can't do it todayUse instead
Custom approval message, or the approver must see the parameters in SlackApproval is on/off with a fixed message. Channel cards show the tool name onlyA workflow tool that runs its own send-and-wait approval with the values (can post in the same Slack thread)
Trigger or node data mapped exactly into a tool parameterTool expressions only see the model's own arguments (verified live for node and workflow tools)AI Agent node, or Message an Agent with structured output and downstream nodes doing the mapping
One conversation shared by a native channel and workflowsChannel threads and Message an Agent session keys live in separate namespacesA shell workflow on the platform's trigger, calling the Agent with Message an Agent
Approval inside a Message an Agent callFails with "Suspend/resume is not supported in workflow execution context"Gate the action in the calling workflow

MCP build sequence

  1. get_agent_builder_reference once. Its configSchema is the source of truth for field names; trust it over this skill when they differ.

  2. search_projects, then discover_agent_assets (models, integrations, workflows, subagents, mcpServers), list_credentials, and search_nodes with usage: "agentTool" + get_node_types for node tools.

  3. create_agent with model, credential, instructions, memory block, and ordinary tools. Skills, tasks, and custom tools come after, through mutate_agent, because they need server-generated ids.

  4. mutate_agent, one operation per call. skill.upsert doesn't change configHash; config patches do.

  5. validate_agent until valid: true and missing is empty.

  6. One representative call_agent. It runs the draft through Preview with real tools, so side effects happen: if any tool without approval sends, writes, or pays, ask the user before the test. Every returned approval goes to the human; resume each with its continuation.

  7. Report the draft with the url from validate_agent, plus the session link (see references/SESSIONS_AND_DEBUGGING.md), and ask whether to publish.

  8. publish_agent only on an explicit yes. Channels: update_agent_integration (references/CHANNELS.md).

Details on every operation, error code, and version tool: references/MCP_BUILD.md.

Where depth lives

FileRead when
references/MCP_BUILD.mdAny mutate_agent operation, error codes, versions, revert, delete, availableInMCP
references/CONFIG.mdWriting or reviewing Agent JSON: every field, limits, defaults, what is versioned
references/TOOLS.mdNode, workflow, custom, MCP server, and built-in tools, input modes, what expressions can see
references/APPROVALS.mdAdding approval, what each surface shows, where approval can't happen, custom approval through a workflow tool
references/SKILLS.mdAdding Agent skills: how loading works, writing descriptions, references
references/SUB_AGENTS.mdDelegation, saved vs inline sub-agents, model routing by difficulty
references/KNOWLEDGE.mdUploaded knowledge files vs external vector stores
references/MEMORY.mdMemory kinds, session scopes, session keys
references/CHANNELS.mdSlack, Telegram, Discord, Linear, n8n Chat, or "native channel or shell workflow?"
references/TASKS.mdScheduled tasks
references/MESSAGE_AN_AGENT.mdCalling an Agent from a workflow
references/SESSIONS_AND_DEBUGGING.mdAnything went wrong, or the user asks where to see what the Agent did

For a chat bot built as a workflow shell (platforms without a native channel, slash commands, custom routing), the shell patterns live in n8n-ai-nodes-official references/CHAT_SURFACE_PATTERNS.md, section "Core as Message an Agent". Its one rule that can't wait: on surfaces that deliver the bot's own messages (Slack, Discord), a shell that posts replies must filter out the bot's user ID right after the trigger, or it triggers itself forever.

Writing instructions

Instructions are the system prompt. Same split as the node: persona, rules, output format, and inter-tool flow go in instructions; how to call a specific tool goes in that tool's description. Long procedures the Agent needs only sometimes belong in a skill, which costs tokens only when loaded. Say where to deliver results ("post the summary in #ops"), because a scheduled run has no one to reply to.

Anti-patterns

Anti-patternWhat goes wrongFix
create_agent without memoryAgent forgets the previous turn even in one sessionSend { "enabled": true, "storage": "n8n" }
Chat Trigger + AI Agent node when the user asked for an agentLoses channels, sessions, approvals, versions, tasks, and budgetsFirst-class Agent, unless a limit applies
Publishing because validation passedThe user didn't approve a live Agent, and channels go live with itReport the draft and ask
Saved sub-agent left unpublishedProduction delegation fails silently; the parent answers without itPublish children before the parent goes live
Expression in a tool input that reads $('Webhook') or $json.<trigger field>Only the model's arguments are visible, so the value is empty or wrongPass it through the message, or keep the mapping in a workflow
Approval on a tool the Agent calls under Message an Agent or a taskRun fails, or parks with no one to answerGate in the calling workflow; give tasks only safe tools
Same Message an Agent session key for two Agents in a project (e.g. both keyed on a bare Telegram id)Keys are project-wide, not per Agent: the second Agent fails with "Failed to create the agent execution record"Prefix keys per Agent: support-<telegramId>
Skill body says "read references/x.md" vaguelyThe model often skips it"Load references/x.md with load_skill (filePath) before answering"
Integrations set through config.patchRejectedupdate_agent_integration
Slack connected with a credential built for a Slack TriggerPasses every check, receives nothingManaged setup, or an app whose request URL points at this Agent

Files

references/APPROVALS.md

Approvals

Before adding or skipping approval, ask the user: who signs off and how much they trust the model is their call. Recommend it for anything that sends, changes, deletes, or spends.

How to turn it on

  • Node, workflow, and custom tools: "requireApproval": true.

  • MCP servers: "approval": { "mode": "global" } or { "mode": "selected", "tools": [...] }.

  • Channel actions: discover_agent_assets kind=integrations lists each channel's approvableActions, marked sensitive (e.g. Slack send_dm, send_channel_message).

What the approver sees

<!-- TEMPORARY: revisit when n8n adds custom approval messages for Agent tools. -->

<!-- TEMPORARY: revisit when channel approval cards (Slack, Telegram, Discord) show the tool's parameters. -->

Approval is on or off. There's no custom message, title, or button label.

SurfaceShowsButtons
Preview and n8n Chat"Approval required", "The agent wants to run the {tool} tool.", and the tool's arguments as raw JSONAllow once, allow for the rest of the session, deny
Slack, Telegram, Discord"The agent wants to run this tool: {tool}" and a Tool field. No argumentsApprove, Allow for this session, Deny
call_agentapprovals[] with toolName, displayName, args, and a continuationResume with { "type": "approval", "approved": true or false, "continuation": {...} }
Sessions trace"Approval request for {tool}" with the arguments under Request details, then "Approval response for {tool}" marked Approved or Declined

Consequences:

  • A Slack approver signs off blind. If the values matter (amount, recipient), approval in a channel isn't enough; use a custom approval workflow tool (below) or the AI Agent node's human review tools.

  • The label is the display name. For a workflow tool, that's the workflow's name, not the tool name the model sees. Name approval-gated workflows so the label reads well ("Refund customer", not "WF 12 v2").

  • Anyone who can see a channel card can click it. Approval in a shared channel is approval by whoever clicks first.

  • A denial goes back to the model as a refusal; it continues the conversation without the tool.

Where approval can't happen

ContextResult
Message an Agent (workflow call)The node fails: "Agent execution suspended waiting for tool approval. Suspend/resume is not supported in workflow execution context." (verified live)
Scheduled taskNo one is in the conversation, so the run parks waiting for an answer that never comes

Give Agents that run from workflows or tasks only tools that are safe without approval, and gate the action in the calling workflow instead (agent returns a structured decision, workflow sends a send-and-wait approval, then acts).

Custom approval through a workflow tool

<!-- TEMPORARY: workaround for fixed approval messages and parameter-less channel cards. Update when custom approval messages are added. -->

When the approver must see real values, or the message must be custom, build the approval into a workflow tool: send-and-wait (Gmail, Slack, and others) with the values in the message, then do the action only on approve. Verified end to end with Gmail send-and-wait.

  • While the sub-workflow waits, the Agent suspends. The chat shows "Waiting on {workflow}" with Check for the result and Stop waiting; call_agent returns approval_required with suspensions[]. When the execution finishes, the Agent resumes with its output.

  • Return an explicit result from both branches (approved or denied, plus what happened), so the Agent can report it. Check the field mapping on the approval response: a wrong mapping drops into the not-approved or timeout branch and the Agent reports that instead.

  • Don't also set requireApproval on this tool, or the user approves twice.

  • Approve in the same Slack thread. For an Agent on Slack, give the tool ai inputs for the channel and thread ts, and have the send-and-wait node post into that thread. The approval then appears in the conversation, with the values. The model supplies these ids, so tell it to take them from get_current_message_context, not the message text. When approvals must go to one place (a finance channel), make the channel a fixed input so the model can't redirect it.

  • Expect it to fail under Message an Agent like any other suspension.

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an MCP server, Needs Node.js

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by
n8n
Publisher
n8n (Official)
Source
n8n-io/skills · skills/n8n-agents-official · commit 27b6b9f
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

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