This article provides a complete, practical walkthrough of the Manual Twitter Create 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:
   
   Automating Twitter Monitoring with n8n and Airtable: Capturing New Tweets in Real Time
   
   Meta Description:
   
   Learn how to build an automated workflow using n8n to track new tweets about Max Verstappen and store them in Airtable. An efficient solution for social media monitoring and data collection.
   
   Keywords:
   
   n8n workflow, Twitter automation, Airtable integration, social media tracking, Max Verstappen tweets, tweet monitoring, tweet to Airtable, n8n Airtable Twitter, no-code automation, Twitter API, Airtable API
   
   Third-Party APIs Used:
   
   - Twitter API
   - Airtable API
   
   Article:
   
   Streamlining Twitter Monitoring with n8n and Airtable
   
   Social media platforms like Twitter produce vast quantities of real-time content, making it a powerful channel for brand monitoring, trend analysis, and data aggregation. But with so much content being generated by the second, it becomes nearly impossible to manually scan tweets for relevant information.
   
   Enter n8n — a powerful, open-source automation tool that allows users to create workflows connecting multiple applications and services without writing code. In this article, we’ll walk through a practical use-case: monitoring Twitter for new tweets about "Verstappen" and storing only the unseen ones into an Airtable base.
   
   Overview of the Workflow
   
   This workflow is designed to search for tweets containing the word "Verstappen", compare them against previously stored tweets in Airtable, and then store only the new ones. Here’s how it works, step-by-step:
   
   Step 1: Manual Trigger to Start Execution
   
   The workflow begins with a manual trigger node ℹ️ labeled “On clicking 'execute’.” This gives the workflow user full control over when to initiate the search and comparison process — ideal for spot checks or periodic manual runs.
   
   Step 2: Search Tweets Using the Twitter API
   
   Next, the Twitter node queries the Twitter API for up to 100 tweets that contain the keyword “Verstappen.” The search type is set to "mixed", which includes a combination of popular and recent tweets. The Twitter node outputs essential metadata such as the tweet content, author, time of creation, tweet ID, and number of likes.
   
   Step 3: Format Fetched Tweets
   
   The "set twitter data" node structures the retrieved tweet data into a consistent format, which includes:
   
   - Tweet content
   - Tweet ID
   - Tweet URL
   - Author
   - Time of posting
   - Like count
   
   This ensures the data can easily be processed in later steps and inserted into Airtable correctly.
   
   Step 4: Fetch Existing Tweets from Airtable
   
   Simultaneously, the workflow connects to Airtable via the "get airtable list" node. It pulls existing tweet records from a specific base and table. These records also include Tweet IDs, which are critical for determining whether a fetched tweet is new or already stored.
   
   Step 5: Prepare Airtable Data for Comparison
   
   Using the "Set_AT_list" node, the retrieved Airtable entries are restructured to match the format of the Twitter data. Although unnecessary fields are stripped, key details like tweet ID, URL, content, author, time, and like count are kept for direct comparison.
   
   Step 6: Filter Out Previously Seen Tweets
   
   Now comes the intelligence of the workflow. A Merge node named “Leave only new tweets” performs a comparison between the Twitter dataset and the Airtable dataset using the unique "Tweet_id" field. The merge mode “removeKeyMatches” ensures that any tweet with an existing match is discarded, leaving only new (previously unseen) tweets in the output.
   
   Step 7: Append New Tweets to Airtable
   
   The final step is handled by another Airtable node labeled “Append new tweets to Airtable.” It inserts newly discovered tweets into the Airtable base automatically. All the fields prepared earlier (Tweet, Tweet_id, Author, etc.) are added as-is.
   
   Why This Workflow Matters
   
   This small but powerful automation solves a real problem — deduplicating and organizing relevant Twitter content for later analysis. Whether you’re a social media analyst, motorsport journalist, or even a Verstappen fan club moderator, this workflow makes your task easier.
   
   Benefits include:
   
   - Time-saving: Automates what would otherwise be a manual search-and-filter process.
   - Data cleanliness: Prevents duplicate tweets by comparing IDs before inserting.
   - Easy scalability: n8n’s visual interface and built-in integrations make it simple to modify the search keyword or change the data destination.
   - Real-time insight: The system can be enhanced with scheduled triggers or Webhook-based architecture for near real-time performance.
   
   Potential Enhancements
   
   - Add Email/Slack notifications when new tweets are found.
   - Integrate sentiment analysis to detect positivity or negativity in tweets.
   - Include hashtags or more refined filters in the Twitter search.
   - Use CRON-node to automate periodic tweet checks without manual triggering.
   
   Conclusion
   
   This n8n workflow demonstrates how powerful and accessible automation can be for social media monitoring. By seamlessly bridging Twitter and Airtable, it provides an elegant solution for archiving real-time content efficiently and accurately. Whether you're tracking sports tweets, mentions of a brand, or global trends, this workflow can serve as your starting point toward advanced social data monitoring.
   
   Start automating smarter today, and let workflows like this take care of the repetitive tasks so you can focus on insights and strategy.
   
   Ready to try it? Download the workflow JSON and import it into your n8n instance to get started!
   
   — End of Article —

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.