This article provides a complete, practical walkthrough of the Stopanderror Wait 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:**
   Prevent Concurrent Workflow Runs in n8n Using Redis: A Robust Pattern for Workflow Locking
   
   **Meta Description:**
   Learn how to use Redis in n8n to prevent concurrent runs of the same workflow. This detailed guide explains how to implement a reliable locking mechanism for workflow executions using Redis.
   
   **Keywords:**
   n8n workflow concurrency, prevent overlapping n8n runs, Redis workflow lock, n8n Redis integration, n8n automation best practices, distributed workflow locking, n8n Redis TTL, queue management in n8n, automation with Redis and n8n, n8n Redis limiter
   
   **Article:**
   
   ---
   
   In automation platforms like n8n, concurrency issues can arise when the same workflow is triggered simultaneously—for example, two users upload files at the same time or two scheduled workflows overlap. If your process involves accessing shared resources, duplicate records, or long-running operations, this can lead to errors, race conditions, or even data corruption.
   
   To solve this, we present a robust solution: using Redis to prevent concurrent workflow executions in n8n. This article explores how you can use Redis as a locking mechanism within your workflows to ensure only one instance of a specific process is running at a time.
   
   ### Why Use Redis for Workflow Locking?
   
   Redis is an in-memory data store known for its speed and its ability to handle transient data through features like Time-to-Live (TTL). Thanks to n8n’s native Redis node and its composability via workflow execution nodes, you can build a reusable pattern that:
   
   - Sets a “lock” in Redis when a workflow starts.
   - Prevents new runs if a workflow with the same key is already running.
   - Automatically clears the lock on completion or timeout.
   - Allows for progress tracking through key states (optional).
   
   ### Overview of the Solution
   
   This n8n workflow uses the following Redis operations:
   
   - **Set:** To store a unique key with a short TTL (timeout), marking that an instance is in progress.
   - **Get:** To check if a key exists before a new run starts.
   - **Delete (Unset):** To remove the key when the workflow completes or errors out.
   
   Let’s break down the approach used in this workflow.
   
   ### Key Components
   
   1. **Key-Based Locking:**
      The locking mechanism is based on a unique key provided at runtime. This could be a static name ("my-task") or a dynamic concatenation like "process_{{userId}}" depending on the scenario.
   
   2. **Set with Expiry:**
      The Redis “Set” operation stores the process status under this key with a TTL (default 600 seconds). This prevents stale locks from lingering.
   
   3. **Redis Action Handler:**
      There's a sub-workflow capable of interpreting `get`, `set`, and `unset` actions for keys in Redis. This sub-workflow is reusable and invoked via "Execute Workflow" nodes.
   
   4. **Pre-Run Check:**
      Before starting the actual logic, a check is performed using the “get” action on Redis. If the key is found, the workflow terminates early with an error, ensuring no concurrent execution.
   
   5. **Optional Progress Tracking:**
      You can go beyond the "working" status and update the key's value to indicate different stages like "started", "loading", "finishing", etc., allowing holistic monitoring of workflow execution state.
   
   6. **Workflow Completion Cleanup:**
      At the end of the workflow, a call to “unset” the Redis key removes the lock, allowing future executions.
   
   ### How the Workflow Works
   
   Here's a simplified flow of the solution:
   
   1. On trigger:
      - Get the current value of `process_status_<key>` via Redis.
      - If empty, continue; if not, stop execution and raise an error ("Already Executing").
   
   2. If free:
      - Set `process_status_<key>` = "working" with TTL (e.g., 10 minutes).
      - Run your long-running business logic (e.g., wait, transform data, or API calls).
   
   3. On completion:
      - Unset `process_status_<key>` in Redis.
   
   You can enhance this further for debugging by checking current status using a separate workflow call.
   
   ### Example Use Cases
   
   - Preventing concurrent data imports.
   - Locking access while updating shared resources.
   - Managing scheduled workflows to prevent duplicates.
   - Providing visibility into workflow states (e.g., start → processing → done).
   
   ### Benefits
   
   - Stateless and ultra-fast thanks to Redis in-memory processing.
   - Prevents race conditions.
   - Easily extensible and modular.
   - TTL ensures that locks won’t last forever even in case of a crash.
   
   ---
   
   ### Third-Party Integrations Used
   
   Here are the third-party APIs/services utilized in the workflow:
   
   1. **Redis** - Used as the in-memory data store for setting, getting, and unsetting workflow execution states (via n8n's native Redis node).
   
   ---
   
   This workflow template provides a highly modular and reusable locking mechanism that can be easily integrated with any automation requiring concurrency prevention. Whether you're building mission-critical systems or robust data pipelines, using Redis with n8n ensures your workflows run purposefully—only once at a time.

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.