This article provides a complete, practical walkthrough of the Stickynote Create Triggered n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Simple setup in 5-15 minutes. One‑time purchase: €9.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   **Title:**  
   Build Your Own AI Girlfriend Chatbot Using n8n, LangChain & Google Gemini
   
   **Meta Description:**  
   Learn how to create a customized AI chatbot with a defined personality using n8n, LangChain, and Google Gemini. This guide covers workflow configuration, memory handling, prompt engineering, and more.
   
   **Keywords:**  
   AI chatbot, n8n workflow, LangChain, Google Gemini, LLM, conversational AI, prompt engineering, memory buffer, agent persona, chatbot development, Google Gemini API
   
   **Third-Party APIs Used:**  
   1. Google Gemini (PaLM) API via Google AI (models/gemini-2.0-flash-exp)  
   2. LangChain JavaScript SDK (PromptTemplate, ConversationChain, MemoryBuffer)  
   
   ---
   
   **Article:**
   
   ### Build a Persona-Driven Conversational AI with n8n, LangChain & Google Gemini
   
   As conversational AI continues to evolve, the ability to create authentic, personality-rich chatbots is becoming increasingly accessible. In this article, we walk you through how to use a custom n8n workflow to build a self-hosted conversational AI agent powered by LangChain and Google's Gemini language model. Whether you’re prototyping a companion chatbot or experimenting with character-based interfaces, this setup offers both flexibility and control.
   
   Let’s break down how this workflow functions—and how you can use it to craft an intelligent, context-aware virtual agent.
   
   ---
   
   ### Overview: What Does This Workflow Do?
   
   At its core, this n8n workflow listens for incoming chat messages, processes them using a Gemini 2.0 language model via LangChain, and replies with character-accurate responses. The agent roleplays as a fictional girlfriend named “Bunny”—a witty, toned, ambitious woman with a reserved-yet-playful personality. Most notably, she always speaks in Chinese, avoids asking questions, and keeps her replies succinct and emotionally flavorful.
   
   The workflow is modular and customizable, enabling developers to swap models, tweak memory settings, and edit prompts to suit entirely different use cases or personas.
   
   ---
   
   ### Key Components of the Workflow
   
   Here’s how each node works together to bring your AI to life:
   
   #### 1. **When Chat Message Received (Trigger)**
   This node acts as the webhook and UI for the conversational interface. Once activated, it listens for incoming messages, which can be sent via a browser or directly within the n8n editor using the “Chat” button. It also supports session memory using LangChain’s memory system.
   
   - Public access is enabled for ease of testing.
   - Chat response behavior is set to use the final node of the pipeline (for more accurate responses).
   - The chat interface can be further customized via settings in this node.
   
   #### 2. **Google Gemini Chat Model (Language Model Processor)**
   Powered by Gemini 2.0 Flash from Google’s AI suite, this node serves as the intelligence behind the replies. Tuned with a temperature of 0.7, the model balances creativity and context reliability.
   
   - Safety settings are configured to allow flexibility, specifically instructing it not to block sexually explicit content, which may be relevant depending on the agent's tone and personality.
   
   #### 3. **Store Conversation History (Memory Buffer)**
   To maintain continuity, this node stores your conversation history in a buffer window using LangChain’s memory management module. This enables the AI to reference past messages for context and character consistency.
   
   - The memory is passed to the next node to inform better replies.
   - The memory window can be tuned to maintain longer or shorter conversational histories, depending on use case.
   
   #### 4. **Construct & Execute LLM Prompt (Core Logic)**
   This is where the magic happens. Inside this node resides a chunk of JavaScript code leveraging LangChain’s PromptTemplate and ConversationChain modules. The detailed prompt defines the chatbot’s persona as well as communication constraints.
   
   Here’s what makes this step powerful:
   - The character prompt includes behavioral instructions and labels for both the AI ("Bunny") and user ("Darling").
   - It locks the language strictly to Chinese and disables the AI's ability to ask questions—forcing a direct and continuous tone.
   - Template variables `{input}` and `{chat_history}` are inserted dynamically for accurate, contextual responses.
   
   A ConversationChain instance is created from these elements and executed on each message received from the user.
   
   ---
   
   ### Why This Workflow Stands Out
   
   This workflow isn’t just a chat assistant. It’s a full-fledged personality simulation. With deliberate constraint crafting, memory control, and access to state-of-the-art LLMs, it opens the door to:
   
   - Companionship bots with emotional nuance
   - Immersive character-based game NPCs
   - Language learning tutors practicing roleplay
   - Interactive fiction experiences
   
   Moreover, its modular design via n8n means developers can:
   - Switch out Gemini for any other compatible LLM (like OpenAI’s GPT)
   - Expand memory logic to include external file or vector store support
   - Design different personas by modifying just one node
   
   ---
   
   ### Setup Instructions
   
   Before you can bring your AI agent online, follow these steps:
   
   1. **API Configuration**  
      Set up your Google Gemini API key via Google’s AI developer portal: [https://ai.google.dev](https://ai.google.dev). Paste this into your n8n credentials for the Gemini node.
   
   2. **Interaction Options**  
      - Use the “Chat” button in the workflow editor to test replies interactively.
      - Activate the workflow and use the unique URL generated by the `When Chat Message Received` node to chat with your agent in a browser window.
   
   3. **Customize Your Agent**  
      All the hard prompt work is done in just one node. Change the language. Modify the backstory. Adjust tone and output guidelines to create your own characters.
   
   ---
   
   ### Final Thoughts
   
   By combining the visual logic of n8n with the intelligent chaining capabilities of LangChain and the linguistic insight of Google Gemini, you can bring highly personalized AI agents to life, even without machine learning expertise. Whether you're a developer, storyteller, or just tech-curious, this is an exciting gateway into the future of interactive AI.
   
   So, what kind of agent will you build next?
   
   ---
   
   Looking for the exported n8n workflow JSON? It’s already preconfigured—all you need is your API key. Happy building!

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.