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AI & researchAgentIfOpenAI Chat ModelMemory Buffer WindowNo Op

Check for Bot Responses

Configured with Agent, OpenAI Chat Model, Memory Buffer Window, Respond To Webhook, and additional nodes. Review the connections and setup before adapting this template.

Workflow preview
Web / APIVerify WebhookRespond To WebhookAI & modelsOpenAI Chat ModelOpenAI Chat ModelRoutingNo Operation, donothingNo OpData storeWindow Buffer MemoryMemory Buffer WindowRoutingCheck if BotIfMessagingSend Initial MessageSlackMessagingDelete Initial MessageSlackMessagingSend MessageSlackTriggerReceive DMsWebhookSubworkflowCall ConfluenceWorkflow ToolTool WorkflowAI & modelsAI AgentAgent

Node categories in this workflow

  • Web / API
  • AI & models
  • Routing
  • Data store
  • Messaging
  • Trigger
  • Subworkflow

Icons describe general node roles, inferred from node types. Inspect the parameters for the configured action.

Drag to pan; use + and − to zoom. Reconstructed from the exported node positions; nothing runs here. Dashed lines represent non-main connections. Full node names and notes appear below.

How it works

The exported workflow contains the following configured nodes. Their connections are shown above; inspect the JSON for the complete logic, expressions, and external actions.

Workflow node names, types, and operations
NodeCategoryTypeOperation
Verify WebhookWeb / APIRespond To WebhookSee node parameters
OpenAI Chat ModelAI & modelsOpenAI Chat ModelSee node parameters
No Operation, do nothingRoutingNo OpSee node parameters
Window Buffer MemoryData storeMemory Buffer WindowSee node parameters
Check if BotRoutingIfSee node parameters
Send Initial MessageMessagingSlackSee node parameters
Delete Initial MessageMessagingSlackdelete
Send MessageMessagingSlackSee node parameters
Receive DMsTriggerWebhookSee node parameters
Call Confluence Workflow ToolSubworkflowTool WorkflowSee node parameters
AI AgentAI & modelsAgentSee node parameters

Setup

  1. Download the JSON and import it into an isolated n8n instance. Leave the workflow inactive.
  2. Connect your own credentials through n8n’s credential store. Replace REPLACE_WITH_* values with your resource identifiers.
  3. Inspect API destinations, Code nodes, database queries, file paths, and outbound actions.
  4. Run with synthetic data and harmless destinations. Enable triggers only after checking the actual outputs.

Requirements

An n8n instance with the listed node types and compatible versions. Service-connected workflows may require accounts, credentials, or paid APIs.

Credential types declared in the source

  • openAiApi
  • slackApi

Workflow notes

Webhook Trigger

Webhook Trigger

The first node receives all messages from Slack API via Subscription Events API. You can find more information about setting up the subscription events API by clicking here. The second node responds to the periodic security challenges that Slack sends to ensure the N8n webhook is still active.

Check for Bot Responses

Check for Bot Responses

If the message received is from a Bot instead of a real user, it will ignore the message.

Delete Receipt and Send Response

Delete Receipt and Send Response

Once the AI response is generated in response to the slack message, n8n delete's it's original Message Received message to avoid cluttering up the user's DMs, and then sends the final Slack message back to the user.

Parse Response with AI Model

Parse Response with AI Model

This workflow currently uses OpenAI to power it's responses, but you can open the AI Agent node below and set your own AI LLM using the n8n options offered.

Response Received

Response Received

Once N8n sees that the messaged received is from a user, it will respond right away to acknowledge a message was received. You can edit the message by opening the node below.

Build n8n workflow to query Knowledge Base

Build n8n workflow to query Knowledge Base

Building your own tools for an AI Agent to use is simple and straightforward, but requires that you build a second workflow and then connect it to this one by inputting the workflow ID from the workflow URL in the Custom n8n KB Tool sub node.

This gives you the freedom to work with any tool, whether n8n has support for it or not. In this sample build, we have connected the AI agent to Confluence, which does not have a native built in n8n node. For this we use the HTTP request node and pointed it to Confluence's search api. It then returns a response that the AI agent uses to generate a final slack message response to the user.

Remembers the last 5 messages that a user sent

Remembers the last 5 messages that a user sent

Because we are passing the channel ID of the user to the memory module, n8n is storing the last 5 slack messages sent to it per slack channel. This means that it will remember all your users conversations separately from one another and not get confused by different requests from different users. You can increase the memory storage by using a different storage medium and increase the number of prompts and responses it should remember.

Change the AI Agents LLM

Change the AI Agents LLM

To change the model used, simply delete the ChatGPT model and replace with a different supported model by hitting the plus sign under model in the AI Agent.

Streamline IT Inquiries with n8n & AI!

Streamline IT Inquiries with n8n & AI!

Introducing the IT Ops AI SlackBot Workflow---a sophisticated solution designed to automate and optimize the management of IT-related inquiries via Slack.

When an employee messages the IT department slack app, the workflow kicks off with the "Receive DMs" node, which captures incoming messages and ensures a secure and active communication line by responding to Slack's webhook challenges.

How It Works:

  • Verify Webhook: Responds to slacks challenge and respond requests to ensure is still active.
  • Check if bot: Checks whether the message sender is a bot to prevent unnecessary processing.
  • Send Initial Message: Sends a quick confirmation, like "On it!", to let the user know their query is being handled.
  • AI-Driven Responses: Employs the "AI Agent" node with OpenAI to craft relevant replies based on the conversation history maintained by the "Window Buffer Memory" node.
  • Knowledge Integration tool: Uses a custom Knowledge Base tool to fetch pertinent information from confluence, enhancing the quality of responses.
  • Cleanup and Reply: Deletes the initial acknowledgment to tidy up before sending the final detailed response back to the user.

Get Started:

  • Ensure your Slack and OpenAI integrations are properly set up.
  • Customize the workflow to align with your IT department's protocols.

Need Help?

  • Join the discussion on our Forum or check out resources on Discord!

Deploy this workflow to improve response times and enhance the efficiency of your IT support services.

Check for Bot Responses

Check for Bot Responses

Purpose and structure

This folder contains the Check for Bot Responses workflow. Its executable nodes use agent, if, lmChatOpenAi, memoryBufferWindow, noOp, respondToWebhook, slack, toolWorkflow, webhook. The table and connection map below are derived from the JSON, not an inferred guarantee of behavior.

Setup

Import inactive. Configure your own credentials and replace REPLACE_WITH_* values. Review external destinations and Code nodes before running.

Entry points

Receive DMs

Verification

Sanitized source candidate; not runtime-tested. This overview is generated from node metadata. Read the folder README for the connection map, credential types, and license.

Attribution

https://github.com/Zie619/n8n-workflows/blob/94007c1445d9258a7da116646b79473e7c7c3282/workflows/Webhook/1502_Webhook_Slack_Automate_Webhook.json MIT; preserve source notices.

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