n8n AI Engineering Product
Contributed by Sabrina Ramonov 🍄. From the community directory at agents.sabrina.dev, republished with permission and full credit.
Overview
This workflow leverages the LangChain code node to implement a fully customizable conversational agent. It is 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 InstructionsConfigure 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 Received node.
Customization OptionsInterface Settings: Configure chat UI elements (e.g., title) in the When Chat Message Received node.
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.
Model Selection: Swap language models through the language model input field in Construct & Execute LLM Prompt.
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)
Tools used: LangChain, Google Gemini
Download the workflow template (JSON)
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