This article provides a complete, practical walkthrough of the Manual Openai Automation 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 Reddit Monitoring and Content Categorization with n8n and OpenAI
   
   Meta Description:
   Discover how to automate Reddit searches and use OpenAI to classify and summarize user-generated content about n8n. Learn how this workflow filters, analyzes, and organizes Reddit posts with high relevance and engagement.
   
   Keywords:
   n8n workflow, Reddit API automation, Reddit analysis, OpenAI classification, OpenAI summarization, workflow automation, low-code workflow, content monitoring, social listening, user-generated content, Reddit scraper, OpenAI prompt engineering
   
   Third-Party APIs Used:
   
   - Reddit API (via n8n’s Reddit node for searching posts)
   - OpenAI API (for content classification and summarization)
   
   Article:
   
   In today’s digital landscape, engaging with online communities is essential for understanding how users are discussing your product. With platforms like Reddit hosting candid and often insightful user contributions, automating the discovery and analysis of relevant content can unlock significant value.
   
   In this article, we’ll explore a powerful yet flexible workflow built with n8n—a low-code, open-source automation tool—that monitors Reddit for content discussing n8n itself, filters for quality and recency, determines whether posts actually mention the product, and leverages OpenAI to summarize and categorize the findings for further analysis.
   
   ⏯ The Workflow at a Glance
   
   At a high level, this workflow automates the following:
   
   1. Queries Reddit for posts mentioning "n8n".
   2. Filters posts using quality heuristics (minimum upvotes, recent posts, and presence of self-text).
   3. Uses OpenAI to classify whether a post is genuinely about n8n.
   4. Summarizes relevant posts using AI.
   5. Structures the output for storage or further analysis.
   
   Let’s break down what makes this workflow effective.
   
   🔍 Step 1: Searching Reddit Posts
   
   The workflow begins with a Manual Trigger node to allow on-demand execution, followed by a Reddit node configured to search all of Reddit for the keyword “n8n”. By sorting the results with the "new" option, the workflow ensures that it's analyzing the most recent content.
   
   ✅ Step 2: Filtering for Relevance
   
   Posts are run through an n8n IF node that only allows through those that meet the following criteria:
   
   - At least 5 upvotes
   - Non-empty self-text (the body of the post)
   - Created within the last 7 days
   
   This ensures that the content is both timely and likely to be of sufficient interest to the community.
   
   🧹 Step 3: Data Cleaning
   
   A Set node extracts only the relevant metadata—like subreddit, upvotes, link, and a trimmed version of the post body (first 500 characters)—to streamline AI operations and reduce unnecessary API calls.
   
   🤖 Step 4: Is It Really About n8n?
   
   One core innovation of this workflow is the use of OpenAI's completion model via n8n’s OpenAI node, where it’s asked:
   
   “Decide whether a Reddit post is about n8n.io, a workflow automation low code tool that can be self-hosted, or not.”
   
   Posts that receive a “Yes” are passed to the next stage. Posts containing “No” in the AI’s response are filtered out.
   
   📝 Step 5: Summarization with OpenAI
   
   If a post is confirmed to be about n8n, it’s passed to OpenAI again to generate a brief meta-style summary of what the post is about. This helps in quickly understanding topics being discussed by the community, such as deployment challenges, new features, or best practices.
   
   Interestingly, two summary nodes are implemented: a primary "OpenAI Summary" node (currently disabled but configured with careful prompt engineering) and a backup summarizer. This redundancy helps test and compare output quality, especially given how sensitive OpenAI can be to prompt phrasing and token limits.
   
   📄 Final Output
   
   The final step uses a SetFinal node to neatly format the output: including the post metadata (e.g., subreddit name, upvotes, date), together with the AI-generated summary. This structured data could be used for visuals, dashboards, reports, or even triggering social media replies.
   
   🧠 Lessons Learned
   
   While building and testing this workflow, the creator included a few thoughtful sticky notes to document insights:
   
   - Prompt engineering hugely impacts response accuracy, especially when using OpenAI for summarization or binary classification.
   - Reducing max_tokens can improve classification responses (e.g., 32 tokens for yes/no questions).
   - OpenAI doesn’t always obey constraints like “one sentence” unless clearly instructed.
   - The default OpenAI node options in n8n could be enhanced to follow best practices from OpenAI documentation more explicitly.
   
   🚧 Known Limitations
   
   As noted, this workflow only considers the first 500 characters of a Reddit post. Any mention of n8n after that cutoff won’t be analyzed. Although this saves API costs and performance time, it may exclude relevant mentions.
   
   🚀 Next Steps & Expansion
   
   The current version is focused solely on Reddit and the term “n8n.” But the architecture is flexible for broader application. Future improvements might include:
   
   - Expanding to Slack, Discord, Twitter, and other community platforms
   - Enhancing the AI summarization prompt to ensure higher-quality, well-structured output
   - Classifying users into personas, e.g., potential power users, devs, or sales prospects
   - Creating subworkflows for multi-channel content aggregation and analysis
   
   🎯 Final Thoughts
   
   This workflow illustrates a pragmatic blend of automation, AI, and human understanding. Using n8n’s visual interface and integration capabilities, combined with the natural language power of OpenAI, you can build intelligent content monitors that do the heavy lifting of community listening for you.
   
   Whether you're part of a dev relations team, a community manager, or a curious technologist, this setup provides a robust template for staying on top of how your technology is being discussed—and perceived—across public platforms.
   
   Now, imagine what’s possible when you plug more community data sources into it.
   
   Stay tuned.

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.