This article provides a complete, practical walkthrough of the Manual Stickynote Automate Triggered 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:  
   Monitoring Twitch Stream Status Using n8n and GraphQL
   
   Meta Description:  
   Learn how to build an automated workflow in n8n to check if a Twitch streamer is live using Twitch's GraphQL API. A simple and practical example for content creators and developers.
   
   Keywords:  
   Twitch API, n8n automation, stream status monitoring, GraphQL query, automation for streamers, Twitch GraphQL, API integration, no-code automation, n8n tutorial, Twitch workflow
   
   Third-party APIs Used:  
   - Twitch GraphQL API (https://gql.twitch.tv/gql)
   
   Article:
   
   Automating Twitch Stream Monitoring with n8n and GraphQL
   
   In the growing ecosystem of streaming platforms, Twitch is a giant. Whether you're a content creator looking to build your own tools, or a developer automating Twitch integrations, n8n offers a powerful, no-code way to interact with Twitch’s APIs. In this workflow, we demonstrate how to check whether a Twitch user is currently live using the Twitch GraphQL API and n8n.
   
   This example showcases the power of simple, modular automation flows, useful for alerting fans, updating Discord bots, or triggering emails when a stream goes online.
   
   📌 What the Workflow Does
   
   This n8n workflow queries the Twitch GraphQL endpoint to check if a given user is currently streaming live. By examining the response structure of Twitch’s GraphQL API, we can identify online status reliably and efficiently using just a boolean check against the stream object.
   
   Let’s break down each step of the workflow:
   
   1. Manual Trigger  
   The workflow starts with a Manual Trigger node titled “When clicking ‘Test workflow’.” This allows you to manually execute the flow from within the n8n editor for testing and development purposes.
   
   2. Document Node (Username Configuration)  
   In this node (labeled "Document"), you manually define the Twitch username you'd like to monitor. It's currently set to a placeholder value: “YOUR-TWITCH-USERNAME”. For production use, this could be modified to dynamically pull usernames from an external source, like a Google Sheet or database.
   
   3. GraphQL Query to Twitch  
   Next, a “Twitch GraphQL” node uses a direct GraphQL query to request stream information for the specified username. The endpoint used is https://gql.twitch.tv/gql—Twitch’s internal GraphQL endpoint, commonly used for frontend requests.
   
   The query structure is:
   ```graphql
   {
     user(login: "YOUR-TWITCH-USERNAME") {
       stream {
         id
         viewersCount
         title
         type
         game {
           id
         }
       }
     }
   }
   ```
   
   A static client-id header is used: kimne78kx3ncx6brgo4mv6wki5h1ko. This particular client ID is a known value used for anonymous calls in Twitch's frontend JavaScript and is functional for public, read-only data queries.
   
   4. Online Check  
   Now comes the logic check. Using an IF node, the workflow inspects the value returned at user.stream. If this object is not null, it indicates that the Twitch user is currently live. If the stream object is null, it means the user is offline. The logic reads:
   
   - IF user.stream ≠ null ➜ User is live
   - ELSE ➜ User is offline
   
   This simple boolean logic ensures that you don’t need to dig through multiple fields or interpret vague values—presence or absence of stream data is enough.
   
   🗒️ Sticky Notes and Developer Hints
   
   The workflow includes sticky notes with helpful documentation inside the n8n canvas:
   
   - A reminder that the username is manually set within the Document node for testing purposes.
   - Context on the client-id being a fixed, public value used by Twitch.
   - A tip that checking if the stream object is null suffices to determine stream status.
   
   💬 Why Use GraphQL Instead of REST?
   
   Twitch also provides REST APIs, but the GraphQL endpoint allows fetching nested data in a single query—like the game the streamer is playing and viewer count—without needing multiple REST calls. This reduces round trips and simplifies the workflow.
   
   🛠️ Potential Use Cases
   
   This workflow is a flexible base you can expand on. Here are a few ideas:
   
   - Send a notification (email, Slack, Telegram) when your favorite streamer goes live.
   - Update a Discord bot with stream status or metadata.
   - Log stream metrics like title and viewer count to a Google Sheet or database for trend analysis.
   - Trigger OBS or lighting scenes via smart home APIs when you go live.
   
   🔐 Consider Authentication
   
   While the client-id used here allows anonymous queries, Twitch’s GraphQL API could evolve to require OAuth authentication. For more advanced or user-specific queries (followers, chat, etc.), you’ll need a proper OAuth token and authorized client ID.
   
   ✅ Final Thoughts
   
   With its drag-and-drop interface and support for GraphQL, n8n provides an intuitive way to automate workflows around Twitch streaming status. Whether you're an individual streamer or building tools for the streaming community, this workflow is a fantastic starting point.
   
   You can easily extend it with new nodes to perform actions such as sending alerts, storing logs, or triggering integrations—all without writing a single line of code.
   
   Happy streaming (and automating)! 🎮✨
   
   —  
   By using modern no-code platforms like n8n, complex integrations become approachable and reproducible, empowering creators and developers alike to automate smarter.

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.