Conversation history is stored in memory using the body token as the chatsession id
Configured with Agent, LM Chat Google Gemini, Memory Buffer Window, Slack, and additional nodes. Review the connections and setup before adapting this template.
Node categories in this workflow
- AI & models
- Data store
- Messaging
- Trigger
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.
| Node | Category | Type | Operation |
|---|---|---|---|
| Google Gemini Chat Model | AI & models | LM Chat Google Gemini | See node parameters |
| Window Buffer Memory | Data store | Memory Buffer Window | See node parameters |
| Send response back to slack channel | Messaging | Slack | See node parameters |
| Webhook to receive message | Trigger | Webhook | See node parameters |
| Agent | AI & models | Agent | See node parameters |
Setup
- Download the JSON and import it into an isolated n8n instance. Leave the workflow inactive.
- Connect your own credentials through n8n’s credential store. Replace
REPLACE_WITH_*values with your resource identifiers. - Inspect API destinations, Code nodes, database queries, file paths, and outbound actions.
- 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
None declared. HTTP requests and Code nodes may still need configuration.
Workflow notes
This is a POST Webhook endpoint
This is a POST Webhook endpoint
Make 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
Once 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.
To 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.
The 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.
We can advance this using the tools or data sources for it to be more custom tailored for your company.
Conversation history is stored in memory using the body token as the chatsession id
Conversation history is stored in memory using the body token as the chatsession id
Purpose and structure
This folder contains the Conversation history is stored in memory using the body token as the chatsession id workflow. Its executable nodes use agent, lmChatGoogleGemini, memoryBufferWindow, slack, 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
Webhook to receive message
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/Slack/0552_Slack_Stickynote_Send_Webhook.json MIT; preserve source notices.
Canvas section labels
These name areas of the original canvas; they do not include explanatory instructions.
- Conversation history is stored in memory using the body token as the chatsession id
- The chat LLM to process the prompt. Use any AI model here
- Send the response from AI back to slack channel
- Receive a POST webhook, process data and return response