AI-Powered Slack Bot for Automation Support
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A workflow that receives messages via Slack webhook, processes them using an AI agent (Google Gemini) with conversational memory, and sends a response back to Slack.
About This Workflow
This workflow acts as an AI-powered Slack bot designed to provide support and information related to automation tasks. It leverages a POST webhook to receive messages from Slack, then utilizes a Google Gemini AI model for processing. Crucially, it incorporates conversational memory using a Window Buffer Memory node to maintain context across interactions. Finally, it sends the AI-generated response back to the originating Slack channel, identifying itself as 'Effibotics Bot'.
Key Features
- Slack Integration: Receives messages via a dedicated POST webhook and sends responses back to Slack.
- AI-Powered Processing: Utilizes Google Gemini (models/gemini-1.5-flash-latest) for intelligent message interpretation and response generation.
- Conversational Memory: Implements
Window Buffer Memoryto retain chat history, allowing for context-aware responses. - Customizable AI Persona: Configurable system message for the AI agent to define its role and behavior.
- Dynamic Response Formatting: Sends responses back to Slack with user mentions and the AI's output.
How To Use
- Configure the
Webhook to receive messagenode: Ensure it's set to receive POST requests on theslack-botpath. This node will be triggered by Slack messages. - Connect to the AI Agent: The output of the webhook is directly fed into the
Agentnode. - Configure the
Agentnode:- Set the
textparameter to={{ $json.body.text }}to pass the user's message. - Customize the
systemMessageto define the AI's persona (e.g., "You are Effibotics AI personal assistant...").
- Set the
- Configure the
Google Gemini Chat Modelnode: Select the desired Gemini model (e.g.,models/gemini-1.5-flash-latest). - Configure the
Window Buffer Memorynode:- Set
sessionKeyto={{ $('Webhook to receive message').item.json.body.token }}for session identification. - Set
contextWindowLengthto define how many past turns to remember.
- Set
- Connect AI components: Link the
Google Gemini Chat Modelto theai_languageModelinput of theAgentnode, and theWindow Buffer Memoryto theai_memoryinput. - Configure the
Send response back to slack channelnode:- The
textparameter constructs the message to be sent back to Slack, including the original user's message and the AI's response ({{ $json.output.removeMarkdown() }}). - Set
channelIddynamically using={{ $('Webhook to receive message').item.json.body.channel_id }}. - Configure
sendAsUserto 'Effibotics Bot' andmrkdwntotrue.
- The
- Activate the workflow: Ensure the webhook is exposed publicly (e.g., via an HTTPS wrapper) and configured in Slack to point to your n8n instance's webhook URL.
Apps Used
Workflow JSON
{
"id": "2757a8c2-8108-48b1-89cc-c269c9b384f1",
"name": "AI-Powered Slack Bot for Automation Support",
"nodes": 24,
"category": "AI",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
Get This Workflow
ID: 2757a8c2-8108...
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