Creating a AI Slack Bot with Google Gemini
Build an intelligent Slack bot using Google Gemini to automate responses and enhance team communication directly within your n8nautomation.cloud environment. This workflow connects a Slack webhook to an AI agent powered by Google Gemini, allowing your team to interact with a sophisticated AI model directly from any Slack channel. When a message is received via the Webhook to receive message node, it's passed to the Agent node, which leverages the Google Gemini Chat Model and Window Buffer Memory to generate context-aware and helpful responses. The AI's reply is then sent back to the original Slack channel using the Send response back to slack channel node, creating a seamless conversational experience. This is ideal for support teams needing instant answers to common questions, developers looking for quick code snippets, or any team seeking to offload repetitive inquiries to an AI assistant, saving significant time and improving response efficiency by providing instant, AI-driven support and information retrieval.
Workflow JSON
{"meta": {"instanceId": "84ba6d895254e080ac2b4916d987aa66b000f88d4d919a6b9c76848f9b8a7616", "templateId": "2370"}, "nodes": [{"id": "2ce91ec6-0a8c-438a-8a18-216001c9ee07", "name": "Sticky Note", "type": "n8n-nodes-base.stickyNote", "position": [380, 240], "parameters": {"width": 407.6388140161723, "height": 490.24769122000794, "content": "## This is a POST Webhook endpoint\n\nMake sure to configure this webhook using a https:// wraper and dont use the default http://localhost:5678 as that will not be recognized by your slack webhook\n\n\nOnce the data has been sent to your webhook, the next step will be passing it via an AI Agent to process data based on the queries we pass to our agent.\n\nTo have some sort of a memory, be sure to set the slack token to the memory node. This way you can refer to other chats from the history.\n\nThe final message is relayed back to slack as a new message. Since we can not wait longer than 3000 ms for slack response, we will create anew message with reference to the input we passed.\n\nWe can advance this using the tools or data sources for it to be more custom tailored for your company.\n"}, "typeVersion": 1}, {"id": "7a0c84a8-90ef-4de8-b120-700c94c35a51", "name": "Sticky Note2", "type": "n8n-nodes-base.stickyNote", "position": [1180, 560], "parameters": {"color": 4, "width": 221.73584905660368, "height": 233, "content": "### Conversation history is stored in memory using the body token as the chatsession id"}, "typeVersion": 1}, {"id": "9b843e0e-42a6-4125-8c59-a7d5620a15f7", "name": "Sticky Note3", "type": "n8n-nodes-base.stickyNote", "position": [942.5229110512129, 560], "parameters": {"color": 4, "width": 217.47708894878716, "height": 233, "content": "### The chat LLM to process the prompt. Use any AI model here"}, "typeVersion": 1}, {"id": "4efa968f-ebf5-42ec-80d3-907ef2622c61", "name": "Google Gemini Chat Model", "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini", "position": [1020, 640], "parameters": {"options": {}, "modelName": "models/gemini-1.5-flash-latest"}, "typeVersion": 1}, {"id": "fd1efd7c-7cd0-4edf-960e-19bd4567293e", "name": "Window Buffer Memory", "type": "@n8n/n8n-nodes-langchain.memoryBufferWindow", "position": [1260, 660], "parameters": {"sessionKey": "={{ $('Webhook to receive message').item.json.body.token }}", "sessionIdType": "customKey", "contextWindowLength": 10}, "typeVersion": 1.2}, {"id": "60d1eb77-492d-4a18-8cec-fa3f6ef8d707", "name": "Sticky Note4", "type": "n8n-nodes-base.stickyNote", "position": [1467.5148247978436, 260], "parameters": {"color": 4, "width": 223.7196765498655, "height": 236.66152029520293, "content": "### Send the response from AI back to slack channel\n"}, "typeVersion": 1}, {"id": "186069c0-5c79-4738-9924-de33998658bc", "name": "Sticky Note1", "type": "n8n-nodes-base.stickyNote", "position": [840, 180], "parameters": {"color": 4, "width": 561.423180592992, "height": 340.09703504043114, "content": "## Receive a POST webhook, process data and return response"}, "typeVersion": 1}, {"id": "2bfce117-a769-46e1-a028-ed0c7ba62653", "name": "Send response back to slack channel", "type": "n8n-nodes-base.slack", "position": [1540, 320], "parameters": {"text": "={{ $('Webhook to receive message').item.json.body.user_name }}: {{ $('Webhook to receive message').item.json.body.text }}\n\nEffibotics Bot: {{ $json.output.removeMarkdown() }} ", "select": "channel", "channelId": {"__rl": true, "mode": "id", "value": "={{ $('Webhook to receive message').item.json.body.channel_id }}"}, "otherOptions": {"mrkdwn": true, "sendAsUser": "Effibotics Bot", "includeLinkToWorkflow": false}}, "typeVersion": 2.1}, {"id": "cfcf2bbc-8ed5-4a9f-8f35-cf2715686ebe", "name": "Webhook to receive message", "type": "n8n-nodes-base.webhook", "position": [880, 320], "webhookId": "28b84545-96aa-42f5-990b-aa8783a320ca", "parameters": {"path": "slack-bot", "options": {"responseData": ""}, "httpMethod": "POST"}, "typeVersion": 1}, {"id": "dc93e588-fc0b-4561-88a5-e1cccd48323f", "name": "Agent", "type": "@n8n/n8n-nodes-langchain.agent", "position": [1100, 320], "parameters": {"text": "={{ $json.body.text }}", "options": {"systemMessage": "You are Effibotics AI personal assistant. Your task will be to provide helpful assistance and advice related to automation and such tasks. "}}, "typeVersion": 1}], "pinData": {}, "connections": {"Agent": {"main": [[{"node": "Send response back to slack channel", "type": "main", "index": 0}]]}, "Window Buffer Memory": {"ai_memory": [[{"node": "Agent", "type": "ai_memory", "index": 0}]]}, "Google Gemini Chat Model": {"ai_languageModel": [[{"node": "Agent", "type": "ai_languageModel", "index": 0}]]}, "Webhook to receive message": {"main": [[{"node": "Agent", "type": "main", "index": 0}]]}}}How to Import This Workflow
- 1Copy the workflow JSON above using the Copy Workflow JSON button.
- 2Open your n8n instance and go to Workflows.
- 3Click Import from JSON and paste the copied workflow.
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