This article provides a complete, practical walkthrough of the Redis Code Create Scheduled 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:**
   Automated Latency and Packet Loss Monitoring with Cisco Meraki Using n8n
   
   **Meta Description:**
   Discover how to automate network performance monitoring by integrating Cisco Meraki and n8n to detect and alert on high latency and packet loss across multiple sites.
   
   **Keywords:**
   n8n, Cisco Meraki API, network monitoring, automation, packet loss, latency alerting, Microsoft Teams integration, Redis database, API workflow, IT operations automation, uplink monitoring, site performance tracking
   
   **Third-Party APIs Used:**
   
   1. Cisco Meraki Dashboard API
      - Endpoints used:
        - https://api.meraki.com/api/v1/organizations
        - https://api.meraki.com/api/v1/organizations/{{OrgID}}/networks
        - https://api.meraki.com/api/v1/organizations/{{OrgID}}/devices/uplinksLossAndLatency
   
   2. Microsoft Teams API (via n8n’s Microsoft Teams node)
   
   3. Redis (as a key-value store for caching alert state)
   
   ---
   
   **Article:**
   
   # Automated Latency and Packet Loss Monitoring with Cisco Meraki Using n8n
   
   In today’s fast-paced digital environments, reliable network performance is non-negotiable. Detecting issues such as high latency or excessive packet loss early can significantly reduce downtime and maintain optimal user experience. Fortunately, with tools like Cisco Meraki and n8n, you can build automated network monitoring workflows that trigger alerts and improve operational efficiency.
   
   This article explores a powerful n8n workflow that leverages the Cisco Meraki API to monitor all connected organizations and networks for latency and packet loss, and sends real-time alerts via Microsoft Teams. It also uses Redis to ensure you aren’t overwhelmed with repeated notifications. Let’s dive into how this workflow is structured and how it can benefit IT teams.
   
   ---
   
   ## Overview of the Workflow
   
   The workflow pulls Meraki organization and network data, monitors uplink performance, calculates latency and packet loss averages, filters out problematic networks, and generates actionable alerts. Here’s how the n8n workflow breaks it down into three main phases:
   
   ### 1. Pulling in the Data
   
   The automation is kicked off with a trigger that runs every five minutes during business hours (Mon–Fri, 8 AM–5 PM), or it can be manually triggered for testing.
   
   Using HTTP Request nodes, the workflow does the following:
   - Authenticates with the Cisco Meraki Dashboard API using API keys.
   - Retrieves all Meraki organizations accessible under the account.
   - Pulls the networks linked to each organization.
   - Retrieves their uplink latency and packet loss statistics over the past 5 minutes.
   
   To do this effectively, the script dynamically injects Org IDs into API URLs and organizes the returned data with `Set` nodes for clarity, assigning values like Network ID, Network Name, and respective URLs.
   
   ### 2. Analyzing and Filtering Problematic Networks
   
   After gathering raw metrics over a time series of 5 snapshots (spanning 5 minutes), a JavaScript function (`Average Latency & Loss over 5m`) calculates the average latency and packet loss values.
   
   Another code node named `Filters Problematic Sites` reviews this data and allows only the items where:
   - Latency > 300ms, or
   - Packet loss > 2%
   
   This ensures only genuinely problematic networks are flagged for alerting.
   
   ### 3. Alerting and Notification Handling
   
   Before sending an alert, the workflow checks if the same alert has already been triggered with the help of Redis.
   
   The node `Check if Alert Exists` queries Redis using the network’s name. If an alert is found, it means the issue has already been flagged and should not be alerted again immediately.
   
   To address how n8n handles null values, the workflow uses `Create Response` to set an `alertExists` flag, simplifying filtering for the next merge node. The `Merge` node passes only the responses that do not already exist in the Redis key store.
   
   Alerts for new problem sites are sent to a Microsoft Teams chat room. These messages contain:
   - A bolded alert subject
   - Organization and network identifiers
   - Average latency and packet loss
   - A direct hyperlink to the Meraki dashboard for the affected network
   
   Finally, Redis logs the alert with a TTL (Time-To-Live) of 3 hours, preventing repeat alerts within that timeframe.
   
   ---
   
   ## Benefits of This Automation
   
   - ✅ **Real-Time Monitoring:** By running every 5 minutes, it ensures you’re immediately aware of performance drops.
   - ✅ **Noise Reduction via Redis:** Keeps distractions at bay by alerting only once per issue per network within 3 hours.
   - ✅ **Custom Thresholds:** Tune the JavaScript filter to your SLAs or internal standards.
   - ✅ **Team Collaboration:** Microsoft Teams integration makes alerts actionable and visible to IT teams instantly.
   - ✅ **Scalable:** Whether you're managing 2 sites or 200, this setup dynamically adapts to the networks under your Meraki org.
   
   ---
   
   ## Extensibility and Use Cases
   
   In addition to Teams alerts, this system could be extended to:
   - Open tickets in a PSA tool like ConnectWise Manage.
   - Send SMS alerts via Twilio.
   - Notify through Slack instead of Teams.
   - Export data to a reporting dashboard.
   
   With minimal adjustments, this framework can monitor a variety of metrics beyond just latency and packet loss—like uptime statuses, device firmware mismatches, or even security events from Meraki.
   
   ---
   
   ## Conclusion
   
   Through the power of n8n and the Cisco Meraki API, it's entirely possible to create a dynamic and production-level network monitoring system—without writing thousands of lines of backend code. By using this workflow, IT teams can rapidly identify performance issues and react before users ever notice something is broken.
   
   For organizations invested in Cisco Meraki, this kind of observability and automation isn't just helpful—it's essential.
   
   --- 
   
   Ready to get started with your own network automation? Make sure you have your Meraki API key and Redis service ready, then clone and customize this n8n workflow to suit your infrastructure. Better alerts mean faster resolutions, and faster resolutions mean happier users.

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.