πŸ”§ Build custom AI agent with LangChain & Gemini (self-hosted)

⚑ 5,873 views Β· πŸ”§ Miscellaneous

Description

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).
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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 β€œChat” 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)

πŸ”— Nodes Used

LangChain Code, Simple Memory, Chat Trigger, Google Gemini Chat Model

πŸ“₯ Import

Download workflow.json and import into n8n: Workflow menu β†’ Import from File

πŸ“– Importing guide Β· πŸ”‘ Credential setup