This article provides a complete, practical walkthrough of the Wait Splitout Send Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Expert setup in 2-4 hours. One‑time purchase: €149.

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 a Smart WhatsApp Chatbot with n8n and Google Gemini: A Step-by-Step Guide
   
   Meta Description:  
   Learn how to build an AI-powered WhatsApp chatbot using n8n, Google Gemini, and LangChain. This guide walks you through processing text, audio, video, and image messages with intelligent responses sent directly over WhatsApp.
   
   Keywords:  
   n8n WhatsApp chatbot, WhatsApp AI automation, Google Gemini API, LangChain AI agent, multimedia message handling, AI assistant, n8n tutorial, WhatsApp automation, image analysis chatbot, voice note transcription, video chatbot AI, generative AI workflow
   
   Third-Party APIs Used:
   
   1. WhatsApp Business API – for message receiving and sending
   2. Google Gemini API (via PaLM) – for processing and describing audio, video, and image content
   3. LangChain – provides AI agent handling and integration of tools like Wikipedia
   
   Article:
   
   ---
   
   # Build a Smart WhatsApp Chatbot with n8n and Google Gemini: A Step-by-Step Guide
   
   AI-powered messaging apps have become essential tools for support, marketing, and personal productivity. With tools like n8n—a powerful automation platform—it’s now easier than ever to create intelligent workflows that respond to multimedia WhatsApp messages using advanced AI models like Google Gemini. In this article, we explore a complete n8n workflow that transforms your WhatsApp account into a full-fledged smart assistant capable of handling images, audio, video, and text in real time.
   
   ## What You’ll Build
   
   This n8n-powered chatbot listens for incoming WhatsApp messages—whether they are text, voice notes, videos, or image attachments—and intelligently processes them using various AI services to produce a human-like, accurate reply, which is then sent back over WhatsApp. It uses:
   
   - WhatsApp Business Trigger and Send nodes to listen and reply
   - LangChain’s AI Agent to simulate human conversation
   - Google Gemini for analyzing multimedia content
   - Wikipedia for external factual knowledge
   
   Let’s explore how each part works.
   
   ---
   
   ## 1. Listening for WhatsApp Messages
   
   The workflow starts with the WhatsApp Trigger node, which waits for messages from users. n8n parses the message payload and detects whether the content is a simple text or a rich message (like audio, video, or images). A Split Out Message Parts node is used to handle multiple message types in a single payload effectively.
   
   ---
   
   ## 2. Smart Message Type Handling
   
   The core logic lies in a Switch node, where the message is categorized as either:
   
   - Text Message
   - Audio Message
   - Video Message
   - Image Message
   
   Each category is sent down a separate branch of the workflow optimized to handle that type of content.
   
   ---
   
   ## 3. Understanding Audio Messages with AI
   
   When an audio message or voice note is detected, the media is fetched using WhatsApp’s media endpoint, downloaded, and sent to Google Gemini (PaLM API) via HTTP request. Here, the AI model transcribes the audio to text for downstream processing. This allows your chatbot to understand voice commands and spoken questions.
   
   ---
   
   ## 4. Describing and Transcribing Videos
   
   Videos are similarly handled. After downloading the media, the content is sent to Google Gemini with a request to describe the video. By doing so, the AI effectively summarizes or identifies important elements within the video, making your chatbot capable of understanding video inputs—a powerful feature for use cases like multimedia customer support or content validation.
   
   ---
   
   ## 5. Image Analysis with LangChain and Gemini
   
   For images, the workflow passes the media to LangChain’s Image Explainer node, again powered by Gemini, which describes what’s in the image and even transcribes visible text. This makes the bot suitable for document verification, meme understanding, or customer-submitted photos.
   
   ---
   
   ## 6. Simple Text Summarization
   
   Text messages are directly sent through a summarization chain. This step isn’t always required, but summarizing helps sanitize or compress the user’s intent before passing it to the AI agent for better context understanding.
   
   ---
   
   ## 7. Memory and AI Agent Response
   
   n8n’s Window Buffer Memory enables semantic memory so the AI agent understands context across multiple interactions. The AI Agent node—powered by LangChain—receives the interpreted message, whether transcribed, summarized, or described, and crafts a contextual response. The agent also uses a Wikipedia tool to enhance factual accuracy in its replies.
   
   ---
   
   ## 8. Sending the Reply Back via WhatsApp
   
   Finally, the response is delivered back to the user using the WhatsApp node. This closes the automated conversation loop, making the entire experience seamless and interactive.
   
   n8n supports sending not just text but also images, location, documents, and more—making this chatbot extensible based on your future needs.
   
   ---
   
   ## Applications and Use Cases
   
   This chatbot isn't just a toy—it’s battle ready. Some potential implementations include:
   
   - Automated customer support with FAQs
   - Appointment booking through voice notes
   - Multimedia submission and analysis (e.g., receipts, screenshots, ID scans)
   - Language agnostic natural conversation agents
   
   ---
   
   ## Final Thoughts: Automation with a Human Touch
   
   This workflow showcases the power of combining automation with artificial intelligence. By enabling your WhatsApp inbox to intelligently process and respond to rich inputs, you can offer superior user experience with zero manual intervention. With n8n's flexibility and integrations with state-of-the-art models like Google Gemini and LangChain, WhatsApp becomes not just a messaging platform—but an intelligent assistant.
   
   Want to try it out? Make sure your WhatsApp Business API is set up correctly and your n8n workflow is activated. For help or questions, check out the [n8n community forum](https://community.n8n.io/) or [Discord](https://discord.com/invite/XPKeKXeB7d).
   
   ---
   
   By leveraging no-code and low-code platforms like n8n alongside cutting-edge AI models, we step closer to a world of smarter, more responsive automation. Try it today—and let AI do the talking.
   
   --- 
   
   Let me know if you'd like a downloadable PDF or cloning instructions for this workflow!

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.