Build Custom AI Agent with LangChain & Gemini (Self-Hosted)
Configured with Chat Trigger, LM Chat Google Gemini, Memory Buffer Window. Review the connections and setup before adapting this template.
Node categories in this workflow
- Trigger
- AI & models
- Data store
- Code
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 |
|---|---|---|---|
| When chat message received | Trigger | Chat Trigger | See node parameters |
| Google Gemini Chat Model | AI & models | LM Chat Google Gemini | See node parameters |
| Store conversation history | Data store | Memory Buffer Window | See node parameters |
| Construct & Execute LLM Prompt | Code | Code | 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
googlePalmApi
Workflow notes
Sticky Note
👇 Prompt Engineering
- Define agent personality and conversation structure in the
Construct & Execute LLM Promptnode's template variable - ⚠️ Template must preserve
{chat_history}and{input}placeholders for proper LangChain operation
Setup Instructions
Setup Instructions
- Configure Gemini Credentials: Set up your Google Gemini API key (Get API key here if needed). Alternatively, you may use other AI provider nodes.
- Interaction Methods:
- Test directly in the workflow editor using the "Chat" button
- Activate the workflow and access the chat interface via the URL provided by the
When Chat Message Receivednode
Sticky Note2
👆 Interface Settings
Configure chat UI elements (e.g., title) in the When Chat Message Received node
Sticky Note3
👆 Model Selection
Swap language models through the language model input field in Construct & Execute LLM Prompt
Sticky Note4
👆 Memory Control
Adjust conversation history length in the Store Conversation History node
Build Custom AI Agent with LangChain & Gemini (Self-Hosted)
Build Custom AI Agent with LangChain & Gemini (Self-Hosted)
Purpose and structure
This folder contains the Build Custom AI Agent with LangChain & Gemini (Self-Hosted) workflow. Its executable nodes use chatTrigger, code, lmChatGoogleGemini, memoryBufferWindow. 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
When chat message received
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/Stickynote/2023_Stickynote_Create_Triggered.json MIT; preserve source notices.