This article provides a complete, practical walkthrough of the Github Cron 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:  
   Automating Upstream Release Tracking with n8n and GitHub/GitLab Integration
   
   Meta Description:  
   Learn how to automate upstream release monitoring and issue creation using an n8n workflow with GitHub and GitLab integration. Save engineering time and keep your repositories up to date effortlessly.
   
   Keywords:  
   n8n automation, GitHub GitLab integration, open source DevOps, create GitLab issue from GitHub release, GitHub release checker, GitLab issue automation, monitor upstream projects, workflow orchestration, n8n tutorial, DevOps automation
   
   Third-party APIs Used:
   
   1. GitHub API – to fetch the latest release information
   2. GitLab API – to list issues and create new ones in a specified repository
   
   Article:
   
   Keeping Your Projects in Sync: Automate Upstream Release Tracking with n8n
   
   In the modern DevOps ecosystem, keeping track of upstream dependencies is crucial. Whether your project wraps an open-source library, relies on a container base image, or follows another tool’s versioning cycle, staying in sync with upstream changes is a necessity — but one that often proves to be repetitive and error-prone.
   
   This is where automation tools like n8n come in. In this article, we explore a real-world example of how you can set up an automated workflow in n8n to monitor upstream GitHub releases and automatically create a GitLab issue if a corresponding issue does not yet exist. This ensures that updates to upstream dependencies aren’t missed — and your infrastructure reviewers or release managers are always in the loop.
   
   🧰 Tools of the Trade
   
   This workflow uses:
   
   - GitHub API to fetch the latest release from a public upstream repository
   - GitLab API to query existing issues and create new ones
   - Cron scheduling from n8n to automate checks weekly
   
   Let’s walk through how each component works.
   
   ⏲ Step 1: Schedule a Weekly Trigger
   
   The process starts with an n8n "Cron" node. This node is configured to trigger once every week, checking for the latest release of an upstream project on GitHub and evaluating whether it's been accounted for in your own issue tracker.
   
   By scheduling periodic checks, the workflow becomes fully autonomous — no need to remember to manually check releases or rely on developers to track tags.
   
   📦 Step 2: Fetch the Latest GitHub Release
   
   Next, a “Get latest release” node queries the GitHub Releases API. It’s configured to:
   
   - Access a specific GitHub repository
   - Retrieve only the latest release (using the “limit: 1” parameter)
   
   This gives the workflow a snapshot of the most recent upstream release, including metadata like the tag name, version, and release notes.
   
   📋 Step 3: List Existing GitLab Issues
   
   Simultaneously, another node — “List issues” — calls the GitLab API to retrieve all open issues in a corresponding internal repository. This repo is where you maintain your tracking issues for upstream changes.
   
   These issues may describe upgrade tasks, compatibility checks, or CI/CD validation efforts, depending on your team’s workflow.
   
   🔄 Step 4: Merge Release and Issue Data
   
   The workflow then merges the data streams from the two sources (GitHub and GitLab) using n8n's “Merge” node. This node is key for comparing the fetched release against issue titles, to determine whether action is needed.
   
   💡 Step 5: Filter for Missing Issues
   
   A Function node labeled “No issue for release?” contains custom logic to evaluate all merged inputs. It uses lodash (a JavaScript utility library) to:
   
   - Identify the object containing release data
   - Compare the release tag with existing GitLab issue titles
   - Return the release only if there’s no matching issue found
   
   This conditional branching is what prevents duplicate issues from being created.
   
   🐙 Step 6: Auto-Create a GitLab Issue
   
   If a matching issue doesn’t already exist, the workflow proceeds to the final step. The “Create issue” node uses the GitLab API to open a new issue that:
   
   - Is titled using the release tag (e.g., Upstream release: v1.2.3)
   - Includes a link to the release and its description
   - Assigns the issue to a predefined GitLab repository
   
   The result? A hands-off, fully automated way to track upstream releases and keep collaborators aware of what’s new. No more release tags slipping through the cracks or outdated dependencies lingering unnoticed.
   
   🔁 Repeat and Relax
   
   Because this workflow runs weekly on a schedule, engineers can focus on higher-value tasks, while n8n handles the mechanical work of monitoring and reporting new versions upstream. This is particularly useful for teams managing infrastructure, containers, libraries, or CI pipelines.
   
   The modular nature of n8n also means this framework can be extended easily — for example, to notify Slack, send emails, or open merge requests in response to new releases.
   
   🧠 Conclusion
   
   This simple, elegant n8n workflow demonstrates the power of low-code automation in DevOps. By integrating GitHub and GitLab through scheduled workflows, it's possible to create robust systems that reduce manual effort, improve release cycle visibility, and bolster your team’s responsiveness to upstream changes.
   
   With hundreds of built-in nodes and a growing third-party ecosystem, n8n makes automation accessible to development teams of all sizes. Whether you're checking for upstream updates, syncing tasks across systems, or reacting to alerts, n8n provides the building blocks to streamline and scale your operations.
   
   Ready to level up your project management and stay ahead of upstream changes? Let n8n do the watching for you.

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.