This article provides a complete, practical walkthrough of the Splitout Http Send Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

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 Support Chatbot with n8n and Your Existing Help Center Search
   
   Meta Description:  
   Learn how to create a powerful AI-powered chatbot using n8n and OpenAI that connects directly to your existing support portal—no vector database needed. Ideal for SaaS companies and tech teams looking for a pragmatic RAG solution.
   
   Keywords:  
   n8n chatbot, support chatbot, OpenAI GPT-4o-mini, RAG without vector store, AcuityScheduling Help Center, Algolia API, AI customer support, knowledgebase tool, automate support, LangChain, HTTP request tool, no-code AI chatbot, SaaS AI support, help center integration
   
   Third-Party APIs Used:
   
   1. OpenAI API – for powering the conversational AI model (GPT-4o-mini)
   2. Algolia Search API – used to query AcuityScheduling's help center content
   
   —
   
   Article:
   
   How to Build a Support Chatbot Using n8n Without a Vector Store
   
   Creating a helpful AI chatbot to handle customer questions is often seen as a complex endeavor requiring advanced machine learning, vector databases, and constant data syncing. But what if you could skip all of that and still build a powerful support assistant using your existing support documentation?
   
   Thanks to n8n and OpenAI, that’s exactly what this workflow does. Using Retrieval-Augmented Generation (RAG) principles—but without the baggage of vectors—you can effortlessly connect to your knowledgebase (like AcuityScheduling.com’s Help Center) and provide instant, helpful answers to your users.
   
   Let’s dive into how this is achieved using a visual no-code automation builder: n8n.
   
   The Architecture at a Glance
   
   This workflow showcases how to build a chatbot using n8n’s built-in AI Agent node and extend it with a custom tool that fetches real-time help content from AcuityScheduling’s support portal. Here's what it accomplishes:
   
   - Receives chat messages from a user
   - Uses GPT-4o-mini (OpenAI) to understand and respond
   - Queries a support knowledgebase via Algolia Search API
   - Formats and presents accurate help articles
   - Delivers clean and token-optimized answers
   
   No vector databases. No sync jobs. Just intelligent support where and when your users need it.
   
   Step-by-Step Breakdown
   
   1. Chat Interaction Starts  
   The workflow begins with the “When Chat Message Received” node. This is your trigger node that activates whenever a user sends a message. It feeds input directly into the AI agent, setting the conversation in motion.
   
   2. GPT-Powered AI Agent  
   The "AcuityScheduling Support Chatbot" is configured using n8n’s LangChain-powered AI Agent node. Behind the scenes, it uses OpenAI’s GPT-4o-mini model, integrated via the OpenAI Chat Model node. The agent is constrained to only assist with topics related to AcuityScheduling.com and gives responses that always include a link back to relevant documentation.
   
   3. Long-Term Memory (Optional)  
   Adding a “Simple Memory” node allows the AI to maintain conversational context, refining its understanding across multi-message interactions. This is especially helpful in extended support chats.
   
   4. Tool-Based Knowledge Search  
   Rather than hardcoding answers or relying on old training data, the chatbot leverages a “Knowledgebase Tool.” This is a custom subworkflow functioning like a plugin or external tool.
   
   This tool sends the user’s query to AcuityScheduling.com’s support search API (powered by Algolia) and returns the top 5 results.
   
   Key benefits:
   - Uses real-time data from your live support portal.
   - No need to sync or update a vector store.
   - Uses HTTP request with standard headers to mimic web-based search.
   
   5. Response Filtering & Token Optimization  
   Search results are typically web-centric and can be verbose. To make them suitable for chat, the workflow:
   - Extracts relevant fields like title, body, and article URL.
   - Filters results for cleanliness.
   - Aggregates responses to create a concise payload.
   This prevents token-bloat and reduces usage costs on OpenAI, keeping your support bot responsive and cost-effective.
   
   6. Clean Failover for Empty Queries  
   If no search hits are found, the chatbot gracefully returns an empty result set, ensuring users aren’t presented with irrelevant or confusing content.
   
   7. Ready to Customize  
   The workflow includes sticky notes with tooltips and documentation links encouraging further customization. For example:
   - Swapping in your own help portal or API.
   - Adding authentication headers for protected endpoints.
   - Integrating other LLM providers if OpenAI isn’t your choice.
   
   Why This is a Smart RAG Alternative
   
   Retrieval-Augmented Generation is great—but managing a vector database just for RAG can be overkill. This workflow shows you can build a highly accurate, AI-powered support agent by simply leveraging your existing help portal’s search capabilities.
   
   It’s intuitive, scalable, and easily extensible using n8n’s node ecosystem. Teams can skip the data engineering effort and simply point to what they already have.
   
   Getting Started
   
   Try example queries like:
   - “How do I download past invoices for my Acuity account?”
   - “How do I connect my iCloud to AcuityScheduling?”
   
   If your Help Center is powered by something like Zendesk, Intercom, or HelpScout, simply swap out the Algolia API credentials and endpoint configs. Want to connect multiple sources? Add additional subworkflows as tools!
   
   Requirements:
   
   - OpenAI credentials
   - An existing help center with a search API (in this case, Algolia for Acuity)
   - An n8n instance (cloud or self-hosted)
   
   Conclusion
   
   Smart AI chatbots don’t have to be complicated. With n8n, OpenAI, and your existing support documentation, you can deploy useful, cost-effective automation in minutes. Whether you're serving thousands of users or running a lean support team, this workflow is a robust example of what you can achieve using no-code automation and large language models.
   
   Join the n8n community to see this in action and get support with customizing your version!
   
   Helpful Links:
   - n8n Docs on AI Agents
   - HTTP Request Node Docs
   - RAG Primer by Databricks
   - Join the Discord
   - Ask in the Community Forum
   
   Happy automating!

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.