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](https://ai.google.dev/) 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 `{chat_history}` 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](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.code/))*
