This article provides a complete, practical walkthrough of the Splitout Code Update 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:  
   Intelligent Fact-Checking with n8n: Automating AI-Powered Content Verification Using Ollama and LangChain
   
   Meta Description:  
   Discover how to automate AI-driven fact-checking using n8n, Ollama LLMs, and LangChain. Learn how this workflow processes written content, analyzes factual claims, and summarizes accuracy in real-time.
   
   Keywords:  
   n8n workflow, fact-checking automation, Ollama models, LangChain, AI fact-checking, content validation, sentence segmentation, machine learning, truth detection, prompt engineering, NLP pipeline
   
   Third-Party APIs Used:
   
   1. Ollama API  
      - Used for accessing LLM models such as bespoke-minicheck and qwen2.5:1.5b to analyze and evaluate text content for factual correctness.
   2. LangChain (n8n LangChain Nodes)  
      - Integrates LLM chains to provide prompt-based interactions with large language models, enabling sequential logic and complex instructions during fact-checking.
   
   Article:
   
   —
   
   In an era where misinformation can go viral in seconds, the need for reliable fact-checking has never been greater. Enter n8n, the low-code workflow automation platform that, when combined with LangChain and Ollama’s powerful language models, enables a new frontier of hands-free, AI-driven content verification. This article explores a comprehensive, automated fact-checking workflow built in n8n that splits input text into sentences, validates each for factual accuracy, and summarizes inaccuracies—with minimal human involvement.
   
   Let’s dive into the anatomy of a workflow purpose-built for truth.
   
   🧠 Overview: The Mission & Design
   
   The primary function of this workflow is simple: assess the factual correctness of an article or body of text. It begins with either manual entry or external input and follows a multi-step process involving NLP sentence parsing, individual claim checking against trusted content, and a final summary report indicating factual issues within the content.
   
   While this may sound complex to implement, n8n’s graphical logic builder makes it intuitive. The workflow orchestrates various LangChain nodes, Ollama small language models, and built-in logic nodes to segment, validate, and summarize.
   
   🧩 Step-by-Step Breakdown
   
   1. 🎬 Entry Point
   
      - Two triggers are present: a Manual Trigger (for testing within n8n) and Execute Workflow Trigger (enabling API calls or integration within larger automation workflows).
      - These feed into a Set node to define inputs, including a block of text (e.g., a news article) and a reference document with “facts” for cross-validation.
   
   2. ✂️ Text Segmentation
   
      - A custom Code node takes the main article and runs JavaScript to smartly split it into sentences.  
      - The logic preserves context such as dates and list formatting, using regex carefully tuned for real-world syntax (like German month names or list indicators).
   
   3. 🧬 Merging & Sentence Preparation
   
      - The Split Out node then creates individual items from the array of sentences.
      - The Merge node combines each sentence (“claim”) with the reference document (“facts”), preparing them for evaluation side-by-side.
   
   4. ⚖️ Factual Evaluation via LangChain & Ollama
   
      - The Basic LLM Chain node passes the merged data to a LangChain-powered prompt.
      - This is fueled by Ollama’s bespoke-minicheck model—a lightweight, accurate language model explicitly trained for granular fact-checking tasks.
      - Each sentence is returned with a “yes” (factually correct) or “no” (factually incorrect) flag from the model.
   
   5. 🚫 Filtering Errors
   
      - A Filter node grabs only the claims marked "no", signifying detected factual inaccuracies.
      - This helps isolate issues without human inspection of every line.
   
   6. 📊 Summary Generation
   
      - An Aggregate node collects the results into a structured format.
      - Another LLM Chain, armed with a detailed system prompt, instructs the AI to:
        - Count factual errors,
        - List only “factually incorrect” statements (ignoring idle commentary), and
        - Deliver a concise accuracy assessment.
   
      - It classifies outcomes into tiers: low error (0–1), moderate (2–3), or high (4+), helping users prioritize revisions.
   
   7. 🤖 AI-Powered Summarization
   
      - The final analysis is passed through another Ollama model (qwen2.5:1.5b), trained for summarization, to produce a human-readable summary of factual issues, ideal for writing teams or editors.
   
   🧠 Why Use This Workflow?
   
   This automated system is an enormous timesaver for content creators, journalists, academic writers, or anyone publishing large volumes of data-sensitive content. It ensures:
   
   - Sentence-level precision: Claims are isolated, making it easy to pinpoint issues.
   - Contextual verification: Claims are verified against a truth source.
   - Structural understanding: Chit-chat or fluff sentences are ignored.
   - AI transparency: The result includes a structured error report that is easy to act upon.
   
   🔧 Tech Stack Highlights
   
   - n8n: Workflow automation backbone.
   - Ollama: Brings local and cloud-powered AI language models.
     - bespoke-minicheck: Fact-specific validation model.
     - qwen2.5:1.5b: Compact model for summarization and classification.
   - LangChain: Provides intelligent LLM chaining for modular prompt instructions.
   - JavaScript: Customized regex-based sentence segmentation.
    
   💡 How to Use It
   
   To use this workflow effectively:
   
   1. Install Ollama locally and pull the models:
      - `ollama pull bespoke-minicheck`
      - `ollama pull qwen2.5:1.5b`
   2. Load the n8n workflow into your editor.
   3. Trigger it manually or call via API.
   4. Feed in your article and known factual reference.
   5. Review the final summary for issues to correct.
   
   📈 The Bottom Line
   
   This workflow isn’t just another AI tool; it’s a blueprint for scalable truth verification. As information flows faster and more freely, automated integrity checks like this are no longer just helpful—they’re essential.
   
   From rigorous documentation to responsible media, this n8n automation is an invaluable ally in the fight against misinformation.
   
   —
   
   Want to run this in production? Clone and adapt it to your own workflows or embed it into publishing pipelines for a smarter, safer publishing future.

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.