This article provides a complete, practical walkthrough of the Splitout Webhook Automate 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:
   Building an Intelligent Bitrix24 Open Channel Chatbot with RAG and Webhook Integration Using n8n
   
   Meta Description:
   Explore how to configure a dynamic chatbot for Bitrix24 Open Channels using n8n’s powerful automation engine. Learn how it integrates Webhooks, LangChain RAG processing, and Qdrant vector storage to deliver intelligent, document-aware responses.
   
   Keywords:
   Bitrix24 chatbot, n8n workflow, Open Channel chatbot, RAG chatbot, LangChain, Qdrant vector database, webhook integration, document-based chatbot, AI chatbot automation, Ollama embeddings, Gemini chat API
   
   —
   
   Article:
   
   Building an Intelligent Bitrix24 Open Channel Chatbot with RAG and Webhook Integration Using n8n
   
   As businesses increasingly rely on automation and AI to streamline communication, smart chatbot solutions have become critical for real-time customer engagement. Bitrix24, a popular CRM and collaboration platform, provides Open Channels to integrate chatbots into customer interaction workflows. In this article, we break down a practical n8n workflow designed to power a Retrieval-Augmented Generation (RAG) chatbot for Bitrix24 using webhook integration, LangChain components, and document vector storage in Qdrant.
   
   Overview of the Chatbot Workflow
   
   This n8n workflow facilitates a fully functional chatbot for Bitrix24 Open Channels, offering document-aware responses and dynamic interaction capabilities. The key features include:
   
   - Automatic bot registration within Bitrix24,
   - Intelligent message processing from users,
   - Integration with Qdrant for vector-based document storage and retrieval,
   - Machine learning-powered natural language responses,
   - Real-time data fetching and webhook-triggered flows.
   
   How It Works
   
   1. Webhook Trigger for Incoming Events
   
   The workflow begins with a webhook node (“Bitrix24 Handler”) configured to receive POST requests from Bitrix24 when users interact with the chatbot within an Open Channel. These trigger events include:
   
   - ONIMBOTMESSAGEADD: When a message is received,
   - ONIMBOTJOINCHAT: When the bot joins a chat,
   - ONAPPINSTALL: When the app is installed,
   - ONIMBOTDELETE: When the bot is removed.
   
   2. Token and Credential Validation
   
   The workflow validates incoming credentials and application tokens for security. Unauthorized requests return a 401 error response to avoid misuse.
   
   3. Event Routing and Contextual Processing
   
   The “Route Event” node uses a switch function to route events such as chat messages or bot joins to corresponding handlers:
   
   - Process Message: Extracts user messages and metadata for processing.
   - Process Join: Sends an initial welcome menu when the bot joins.
   - Process Install: Registers the bot in Bitrix24 Open Channels using REST API.
   - Process Delete: Handles cleanup tasks if the bot is deleted.
   
   4. Intelligent Message Response via LangChain RAG
   
   At its core, this chatbot workflow leverages Retrieval-Augmented Generation (RAG) to provide accurate and contextually relevant responses:
   
   - Vector Store Retrieval: User questions are matched against relevant documents stored in Qdrant using vector embeddings.
   - LangChain ChainRetrievalQa: A question-answering chain powered by context from the vector store and logic defined in system prompts.
   - Google Gemini Chat Integration: The AI-generated responses are crafted using the Gemini 2.0 Flash model API.
   
   Examples of interactions include:
   - If a user types “what’s hot,” the bot replies with an introduction.
   - If a user asks “find out more about me,” the system returns details about its RAG-based architecture.
     
   5. Document Handling and Vector Storage
   
   The workflow supports document storage and semantic search by:
   
   - Connecting to Bitrix24’s shared drive API,
   - Listing and filtering folder contents,
   - Downloading PDFs or other files,
   - Splitting content for chunked indexing,
   - Embedding the content using Ollama’s embedding API with langchain,
   - Storing the data in a Qdrant vector database for retrieval.
   
   Once processed, the files are moved to a designated “vector stored” folder for archival or future reference.
   
   6. Scalable Subworkflow Execution
   
   The system architecture supports modularization through a subworkflow strategy. After bot registration, parameters are passed to a “Register Bot” subworkflow that handles file loading and knowledge injection, ensuring scalability and clean separation of responsibilities.
   
   Use Cases and Benefits
   
   - Automated Customer Support: Answer queries using uploaded manuals, policies, or FAQs.
   - Sales Enablement: Provide instant product information stored in structured documents.
   - HR Bots: Help employees retrieve company resources stored in shared drives.
   
   Key Third-Party APIs Used
   
   1. Bitrix24 REST API
      - For webhook event reception, bot registration, sending messages, and accessing shared drive files and folders.
   
   2. LangChain (via n8n LangChain Nodes)
      - Used for chaining AI-based steps such as retrieval and QA generation.
   
   3. Qdrant Vector Database
      - For semantic search and document vector storage enabling fast retrievals.
   
   4. Ollama Embeddings (nomic-embed-text:latest)
      - Generates embeddings from document chunks for insertion into Qdrant.
   
   5. Google Gemini Chat API (Gemini 2.0 Flash)
      - Used to generate intelligent, natural-sounding responses.
   
   Conclusion
   
   This n8n workflow represents a powerful example of how various automation tools and AI services can come together to deliver an immersive chatbot experience. By combining Bitrix24 Open Channels, LangChain RAG models, and vector-based document retrieval, businesses can unlock contextual, intelligent chatbots that boost productivity while enhancing customer and employee interactions.
   
   Whether you're a developer, AI engineer, or operations manager, this workflow serves as a blueprint for implementing smart, document-aware bots without compromising on scalability or complexity.
   
   — 
   
   Let AI do the talking — but make sure it has the right documents to speak from.
   
   —

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.