Build custom AI agent with LangChain & Gemini (self-hosted)
Overview This workflow leverages the LangChain code node to implement a fully customizable conversational agent. Ideal for users who need granular control over their agent's prompts while reducing unnecessary token consumption from reserved tool-calling functionality (compared to n8n's built-in Conversation Agent). Setup Instructions 1. Configure Gemini Credentials: Set up your Google Gemini API key (Get API key here if needed). Alternatively, you may use other AI provider nodes. 2. Interact
At a glance
Build custom AI agent with LangChain & Gemini (self-hosted) is a ready-made n8n workflow you import as a workflow JSON file — no build required. It connects Gemini. It's free to download. Follow the 5-step import below to go live in minutes.
- Platform
- n8n
- Connects
- Gemini
- Modules
- 4
- Price
- Free
- Version
- v1.0

About this workflow
Overview This workflow leverages the LangChain code node to implement a fully customizable conversational agent. Ideal for users who need granular control over their agent's prompts while reducing unnecessary token consumption from reserved tool-calling functionality (compared to n8n's built-in Conversation Agent). Setup Instructions 1. Configure Gemini Credentials: Set up your Google Gemini API key (Get API key here if needed). Alternatively, you may use other AI provider nodes. 2. 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 Received node Customization Options 1. Interface Settings: Configure chat UI elements (e.g., title) in the When Chat Message Received node 2. Prompt Engineering: - Define agent personality and conversation structure in the Construct & Execute LLM Prompt node's template variable - ⚠️ Template must preserve {chathistory} and {input} placeholders for proper LangChain operation 3. Model Selection: Swap language models through the language model input field in Construct & Execute LLM Prompt 4. Memory Control: Adjust conversation history length in the Store Conversation History node Requirements: ⚠️ This workflow uses the LangChain Code node, which only works on self-hosted n8n. (Refer to LangChain Code node docs)
How to import this n8n workflow
- 1
Download the workflow JSON file after purchase.
- 2
Open n8n → click the menu → Import from File.
- 3
Select the downloaded JSON and import.
- 4
Set up credentials for each node that requires them.
- 5
Click Execute Workflow to test, then activate.
Setup guide
Setup guide included
Purchase to unlock the full step-by-step guide
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- JSON blueprint — instant download
- Setup guide PDF included
- 5 downloads · valid 30 days
- Works with n8n
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