This article provides a complete, practical walkthrough of the Redis Schedule Import 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:  
   Preventing Duplicate Workflow Executions in n8n Using Redis Flags
   
   Meta Description:  
   Learn how to prevent concurrent executions of your workflows in n8n by using Redis to set atomic status flags. This guide explains a smart interval-running pattern that ensures safe, non-overlapping workflow automation.
   
   Keywords:  
   n8n, Redis, workflow automation, node.js automation, prevent duplicate execution, n8n Redis flag, asynchronous task management, scheduled workflows, n8n best practices, automation workflow design
   
   Third-Party APIs & Services Used:  
   - Redis (via the n8n Redis node module)
   
   —
   
   Article:
   
   Automating workflows with tools like n8n can dramatically boost productivity, but managing execution concurrency becomes critical as systems scale. One common issue is "duplicate runs"—when a scheduled workflow is triggered again while a previous instance is still processing. This redundancy can overconsume system resources or lead to data inconsistencies.
   
   This article walks you through a robust pattern built in n8n that uses Redis to avoid concurrent executions by implementing a simple, effective state-flag mechanism.
   
   Why Avoid Concurrent Execution?
   
   While repeating workflows are powerful for things like monitoring queues, syncing data, or processing scheduled actions, problems arise if a slow-running instance overlaps with the next trigger cycle. This overlapping can crash your system or cause invalid results—especially problematic in payment systems, APIs with rate limits, or database operations.
   
   The Solution: Workflow Run State Management Using Redis
   
   With this pattern, your workflow:
   
   - Checks a Redis key to determine if a previous run is still executing.
   - Only proceeds if the run state is marked as "idle".
   - Sets the state to "running" before executing.
   - Resets the state back to "idle" once execution completes.
   
   Let's break down how this is achieved within n8n.
   
   Step-by-Step Execution Breakdown
   
   1. Schedule Trigger Node (every 5 seconds)  
      The workflow starts with a Schedule Trigger node that runs every five seconds. This can be changed depending on your needs. The idea is to run the main logic frequently—but with intelligent management in place.
   
   2. Get Workflow Status from Redis  
      The next node fetches a Redis key associated with this workflow: workflowStatus_{{ $workflow.id }}. This key stores the current status: either "idle" or "running".
   
   3. Check for Redis Key (Branch 1)  
      The “Redis Key exists” IF node checks whether this status key exists. If not, the logic considers this as a first-time run and moves to set the status.
   
   4. Conditional Execution Based on Status ‘idle’ (Branch 2)  
      If the key exists, another IF condition ("Continue if Idle") checks whether the value is "idle". Only then can the main logic be triggered.
   
   5. Set Status to “running”  
      Before executing the target logic or workflow, the status key is updated to "running" to prevent further parallel triggers until the current run is complete.
   
   6. Trigger Execution of Main Workflow  
      The “Execute Workflow” node launches the main task or business logic via another workflow (defined by workflowId: "4cnnwIeC9Sr5ngGZ").
   
   7. Reset to "idle" on Completion  
      After the execution concludes, the flag is reset back to "idle" so the scheduler can execute this logic again on the next interval cycle.
   
   8. No-Op for Blocked Executions  
      If the flag indicates the workflow is already running (i.e., the Redis key is "running"), the scheduler exits doing nothing. This avoids wasteful or corrupt operations.
   
   Manual Reset Functionality for Recoveries
   
   Sometimes, performance hiccups or a system crash may leave the status flag stuck in "running". A fallback mechanism is included with a Manual Trigger and a Reset node. Operators can activate this node to forcefully reset the status flag to "idle". This ensures the scheduler resumes normal operation safely.
   
   Sticky Note Tips: Built-in Documentation
   
   The workflow uses sticky note nodes throughout the logic path offering in-editor documentation. They explain:
   
   - Interval settings
   - Flag purpose and usage
   - How to manually reset execution
   - Safe practices for reliability
   
   This makes collaboration and maintenance much easier across teams.
   
   Scenarios Where This Pattern Shines
   
   - APIs with non-idempotent calls
   - Payment processing pipelines
   - Scheduled data extraction or ETL tasks
   - Long-running operations triggered frequently
   - Ensuring a “non-reentrance” policy for your logic
   
   Advantages of the Redis Method
   
   - Lightweight and fast
   - Scalable flag storage
   - Supports distributed systems
   - Easy manual override
   - Prevents nested or duplicate executions
   - Stateless workflow logic—all state managed externally
   
   Wrap-Up
   
   If you’re running automated processes on a schedule and need to enforce a strict single-instance execution model, this Redis-based flag approach is a secure and scalable solution.
   
   Using n8n’s integrated Redis node, you can build this logic right into your visual automation without needing scripts, external daemons, or complex locking algorithms.
   
   This pattern isn't just limited to Redis. You can adapt the same logic using databases, in-memory caches, or even file locks, but Redis offers the best blend of speed, reliability, and integration simplicity for n8n users.
   
   Give your workflow the synchronization it deserves.
   
   —
   
   Stay automated and safe!

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.