This article provides a complete, practical walkthrough of the Wait Http 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 AI Image Generation and Google Drive Uploads with n8n and Fal.ai
   
   Meta Description:  
   Learn how to build an automated image generation workflow using Fal.ai’s Flux API and n8n. This step-by-step walkthrough covers generating high-quality AI images, checking job statuses, downloading the result, and uploading it to Google Drive—all without writing code.
   
   Keywords:  
   n8n workflow, Fal.ai, Flux AI image generation, Google Drive integration, automation, AI arts, workflow automation, HTTP request, image generation API, low-code automation tool
   
   Third-Party APIs Used:
   
   1. Fal.ai Flux Developer Endpoint (https://queue.fal.run/fal-ai/flux/dev) – For AI image generation
   2. Fal.ai Request Status Check API – For monitoring image generation status
   3. Google Drive API – For uploading the generated image to a specified Google Drive folder
   
   —
   
   Article:
   
   Creating an Automated AI Image Generation Workflow with n8n and Fal.ai
   
   With the rise of AI-generated media, integrating automated processes into your creative workflow is more accessible than ever. One powerful tool that enables such automation is n8n, a flexible, low-code workflow automation platform. In this article, we explore a complete end-to-end n8n workflow that harnesses the power of Fal.ai’s Flux image generation API to create AI-generated images, monitors their progress, downloads them, and uploads the result to a specified Google Drive folder — all in just a few clicks.
   
   ➡️ What Is n8n?
   
   n8n (short for “nodemation”) is a powerful automation tool built for developers, marketers, and creators alike. It allows users to create custom workflows by linking together nodes of logic, API calls, and data processing — combining the simplicity of no-code interfaces with developer-level control when needed.
   
   ➡️ Overview of the Workflow
   
   This specific n8n workflow is named “Flux Dev Image Generation Fal.ai” and performs the following steps:
   
   1. Manually triggers the workflow
   2. Sets custom generation parameters for the AI image
   3. Sends the prompts and settings to Fal.ai’s Flux model for image generation
   4. Periodically checks the generation status
   5. Retrieves the image once the process is completed
   6. Downloads the file from the returned image URL
   7. Uploads the image directly to a specified folder in Google Drive
   
   Let’s break it down node by node.
   
   🟢 Step 1: Manual Trigger & Parameter Setup
   
   The workflow begins with a “Manual Trigger” node (“When clicking ‘Test workflow’”), allowing users to invoke it manually during testing. Immediately afterward, a "Set" node named "Edit Fields" injects the generation parameters such as:
   
   - Prompt: Description of the desired image (e.g., "Thai young woman net idol 25 yrs old, walking on the street")
   - Image dimensions: Width (1024 px) and Height (768 px)
   - Number of AI inference steps (30)
   - Guidance scale (3.5) to control how closely the result aligns with the prompt
   
   🧠 Step 2: Sending Data to Fal.ai Flux API
   
   With parameters set, the workflow makes an HTTP POST request to Fal.ai’s Flux API endpoint:  
   https://queue.fal.run/fal-ai/flux/dev
   
   This sends the prompt and settings to start the generation process through the “Fal Flux” HTTP Request node. Authorization is handled via a generic HTTP header containing your Fal.ai Authorization Key.
   
   ⏱️ Step 3: Polling for Completion
   
   Since AI image generation can take time, the workflow adds a 3-second pause (“Wait 3 Sec”) before each status check. It then queries the request ID status through the “Check Status” node, targeting this endpoint:  
   https://queue.fal.run/fal-ai/flux/requests/{{ request_id }}/status
   
   A conditional check follows via the “Completed?” node. If the image generation status equals COMPLETED, the workflow continues to retrieval. If not, it loops back to “Wait 3 Sec” and checks again—implementing a simple polling mechanism.
   
   📥 Step 4: Retrieving the Final Image
   
   Once the job is done, a node named “Get Image Result URL” retrieves the final image’s URL using another Fal.ai request:  
   https://queue.fal.run/fal-ai/flux/requests/{{ request_id }}
   
   The image is then downloaded using the direct URL via an HTTP GET request in the “Download Image” node. The image is attached to the workflow’s binary data.
   
   ☁️ Step 5: Uploading to Google Drive
   
   Finally, the image is uploaded to a designated Google Drive folder (ID: 1R3PSyHXWHlY9DRFdOUEAPEop2fZy-_-K) via the “Google Drive” node. The binary file is saved with the filename from the response and stored in a folder labeled “Flux Image” in the user's Drive.
   
   🔑 Authentication Details
   
   Both the Fal.ai and Google Drive connections are protected with credentials configured in n8n:
   
   - Fal.ai uses HTTP Header Authentication, where the Authorization header key is set using a custom API Key (e.g., Key 6f2960ba*******).
   - Google Drive uses OAuth2 credentials for secure access.
   
   📌 Workflow Highlights
   
   - Fully automated loop: The loop waits for completion intelligently by checking the job status every few seconds.
   - Customization: You can modify the prompt, image size, quality levels, and more through the parameter-setting node.
   - File management: Immediate upload to Google Drive removes the need for local downloads or manual uploads.
   - Modular: The design can be extended to include Slack notifications, email alerts, or support for multiple image outputs.
   
   —
   
   Final Thoughts
   
   If you’re seeking a way to streamline creative workflows using AI-generated images, this n8n automation is a game-changer. Not only does it reduce manual clicks, it also ensures consistent results and eliminates file handling headaches. Whether you're a content creator, marketer, or developer, combining n8n with Fal.ai can supercharge your productivity.
   
   And the best part? No traditional coding is required—just a clear understanding of what you want, where you want it, and when. Happy automating!
   
   —  
   🔗 Interested in trying it yourself? Clone this n8n workflow, grab your Fal.ai and Google Drive credentials, and start generating your first AI image in minutes.

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.