This article provides a complete, practical walkthrough of the Wait Splitout Automation 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:  
   Automating API Documentation Discovery and Extraction Using n8n and Google Gemini
   
   Meta Description:  
   Discover how this powerful n8n workflow automates API schema discovery, intelligent web scraping, and endpoint extraction using Apify, LangChain, and Google Gemini—all managed through a multi-stage process with seamless integration to Google Sheets and Drive.
   
   Keywords:  
   n8n, API discovery automation, API schema extraction, Google Gemini API, Apify web scraper, LangChain, Qdrant, vector search, Google Sheets automation, REST API operations, AI API documentation parser, Gemini embeddings, automated web search API
   
   Third-Party APIs and Services Used:
   1. Google Sheets – for data source and tracking workflow stages.
   2. Google Drive – for storing the final JSON API schemas.
   3. Apify Web Scraper – for crawling and extracting rich content from URLs.
   4. Apify Google Search Scraper (serping) – for conducting site-specific Google searches for API documentation.
   5. Google Gemini (LangChain integration):
      - Gemini Pro and Flash – used for conversational language modeling.
      - Gemini Text Embeddings – for vectorizing content to feed into Qdrant.
   6. Qdrant – vector store used for semantic document search and indexing.
   7. LangChain – powering LLM-driven classification and extraction on unstructured web content.
   8. JMESPath – for JSON filtering and transformation within n8n nodes.
   
   Article:
   
   Automating The API Schema Discovery Workflow with n8n, LangChain & Google Gemini
   
   In a world increasingly driven by integrations and developer tools, keeping track of public REST APIs is mission-critical—especially for technical marketers, developer relations teams, and integration engineers. Manually locating, parsing, and structuring API schema documentation can be a tedious and error-prone process.
   
   With the help of n8n—a powerful open-source workflow automation tool—and advanced AI models like Google’s Gemini, we can now fully automate this end-to-end process, taking a list of services and generating neatly packaged API schemas with minimal human input.
   
   Let’s explore a groundbreaking n8n workflow that does just that, in three key stages.
   
   Stage 1: Discovering API Documentation
   
   It starts by querying a Google Sheet where a list of services is maintained. Each row contains the service name and its website. Using Apify's Google Search Scraper (serping~fast-google-search-results-scraper), the workflow performs an intelligent full-text search for API developer documentation related to the service. The search query is dynamically constructed using expressions like:
   
   site:domain.com "service name" api developer (intext:reference OR intext:resource) (inurl:api OR intitle:api)
   
   The top results are automatically deduplicated and passed to Apify’s Web Scraper, which crawls each result to extract page content. Scripts, images, and media are stripped, leaving clean HTML text for further analysis.
   
   Next, LangChain’s classification capabilities are applied. By leveraging Google Gemini’s LLM model, the content is classified for the presence of REST API schema information. Only relevant pages are retained and stored in Qdrant, a vector database optimized for semantic search.
   
   Stage 2: Extracting API Operations
   
   Once documentation has been captured, it’s time to extract actionable data.
   
   The process begins with a second semantic search against Qdrant to determine the service’s primary products or solutions. These are identified using templated prompts handled by the Chat Gemini model. With this product context, another round of targeted questions retrieves related API sections from the documentation stored in Qdrant.
   
   At this point, Gemini’s extraction model is invoked with a strict schema. It parses texts to identify:
   - Endpoint URLs
   - HTTP methods (GET, POST, etc.)
   - Resource groupings
   - Operation names
   - Descriptions
   - Documentation links
   
   LangChain’s informationExtractor node facilitates this zero-shot schema parsing task and enforces output consistency.
   
   The endpoint objects are then de-duplicated and stored back into Google Sheets, creating a structured dataset per service.
   
   Stage 3: Generating the Custom Schema
   
   The final stage is packaging all discovered API operations into a JSON schema file.
   
   A JavaScript "Code" node collates endpoints by resource group, formats them with operation labels, and applies naming conventions. For example:
   
   Resource: Indicators  
   Operations:  
   - List Indicators: GET /v1/api/indicators/list  
   - Create Indicator: POST /v1/api/indicators/create  
   
   The schema combines this structure with metadata—including documentation URLs—and gets saved as a JSON file to a specific folder in Google Drive.
   
   The workflow’s status is continuously updated through Google Sheets, logging the result of each phase (research, extraction, generation).
   
   Why This Workflow Matters
   
   This architecture demonstrates the tremendous potential of combining:
   - n8n’s flexible orchestration
   - Google Gemini’s language understanding
   - LangChain’s schema-compatible extraction tools
   - Qdrant's vector-based document storage
   - Apify’s web scraping automation
   
   It allows teams to quickly analyze hundreds of SaaS tools or developer platforms to identify potential integration points, build AI agents that reason over documentation, or create searchable knowledge bases of API infrastructure.
   
   Conclusion
   
   If you’re looking to industrialize how you map out REST APIs across the web, this workflow lays the foundation. It not only finds the right content but intelligently extracts and structures it into developer-ready artifacts—automatically.
   
   Whether for product research, competitive analysis, or integration opportunities, this is automation that scales intelligence.

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.