This article provides a complete, practical walkthrough of the Splitout Code Monitor 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:
   Automated Fact-Checking with n8n and LangChain: An AI-Powered Workflow for Verifying Factual Accuracy
   
   Meta Description:
   Learn how an advanced n8n workflow leverages sentence parsing, LangChain LLM chains, and AI models like Ollama’s bespoke-minicheck to perform automated fact-checking for written content. Perfect for content creators, researchers, and editors.
   
   Keywords:
   n8n automation, LangChain, fact-checking AI, Ollama, NLP workflow, sentence segmentation, content verification, AI assistants, bespoke-minicheck, text analysis, article validation, LLM workflow, data pipeline, content integrity
   
   Third-Party APIs Used:
   1. Ollama API (for AI language models: bespoke-minicheck and qwen2.5)
   2. LangChain LLM Chains (integrated via n8n with Ollama)
   
   Article:
   
   —
   
   💡 Automated Fact-Checking with n8n and LangChain: An AI-Powered Workflow for Verifying Factual Accuracy
   
   The growing presence of AI-generated content and massive digital information exchange has created an urgent demand for precise, scalable fact-checking processes. Enter n8n, the powerful open-source workflow automation tool, armed with integrations like LangChain and Ollama's purpose-built large language models (LLMs). This article explores a creative, modular n8n workflow designed to automatically fact-check articles and summarize potential inaccuracies using modern AI techniques.
   
   🧠 Why Automate Fact-Checking?
   
   Whether you're a journalist, educator, or content marketer, maintaining factual accuracy is essential. Manual verification is time-consuming, inconsistent, and often lacks scalability.
   
   This workflow, built in n8n, solves that by:
   - Breaking input text into individual factual claims
   - Comparing those claims against a known body of factual "evidence"
   - Using dedicated language models to assess the validity of each claim
   - Aggregating and summarizing findings to streamline editorial decisions
   
   📦 The Workflow Breakdown
   
   Let’s unpack the key stages and components of this fact-checking pipeline.
   
   1. Manual or Triggered Input
   
   The workflow can be started manually via the Manual Trigger node or triggered from another workflow using the Execute Workflow Trigger node. Input data includes:
   - A source text (e.g., a news article or blog post)
   - A factual background or reference document (termed as “facts”)
   
   In the provided example, the article outlines the AI-driven ecological research of Professor Sara Beery at MIT, while the “facts” provide background on her work and the subject.
   
   2. Input Parsing and Preparation
   
   The “Edit Fields” node statically injects both the article and facts for testing. In practical use, this data could be dynamically pulled from a CMS, email, or user input form.
   
   3. Sentence Segmentation
   
   Next, a “Code” node processes the article text and splits it into individual sentences (claims), using a JavaScript function. This function is designed to:
   - Accurately preserve punctuation
   - Avoid breaking sentences at dates or bullet points
   - Return an array of trimmed, clean claims
   
   This step is crucial as it allows each sentence to be independently analyzed.
   
   4. Splitting Content for Evaluation
   
   A “Split Out” node extracts each sentence (claim) into its own iteration, preparing it for targeted validation. Each sentence is merged alongside the contextual "facts" using a “Merge” node, ensuring every claim is evaluated in the correct factual context.
   
   5. Claim Validation Using Bespoke LLM
   
   Now comes the AI part. An “Ollama Chat Model” loaded with the lightweight and specialized model bespoke-minicheck evaluates each claim's truthfulness in context. Bespoke-minicheck was designed specifically for micro-scale fact verification, boasting efficient performance.
   
   Each sentence, presented alongside the source context, is evaluated for factual accuracy by LangChain’s “Basic LLM Chain” node, which feeds input into bespoke-minicheck and receives a “yes” (correct) or “no” (incorrect) response.
   
   6. Filtering for Incorrect Claims
   
   A “Filter” node isolates sentences tagged with a “No” response, effectively creating a shortlist of suspected factual errors. Non-factual sentences and correct statements are excluded from further attention.
   
   7. Aggregation & Summary
   
   The flagged statements are aggregated using the “Aggregate” node and passed to a final “LLM Chain” node connected to another Ollama LLM (in this case, qwen2.5). This final chain is designed to:
   - Count the number of incorrect factual statements
   - List the specific errors
   - Deliver a final assessment on the article’s factual soundness
   
   The output follows a clean markdown structure with sections like "Problem Summary", "List of Incorrect Statements", and "Final Assessment", making it ideal for editorial action or reporting.
   
   🏆 Benefits of the Workflow
   
   - Fully automated pipeline with a user-friendly, no-code interface (thanks to n8n)
   - Modular structure allows integration into broader audit or publishing workflows
   - Uses lightweight, local-compatible LLMs — no cloud dependency required
   - Reduces editorial pressure by pinpointing factual inaccuracies quickly
   
   🔧 Extending the Workflow
   
   Possible enhancements include:
   - Integrating a CMS (like WordPress or Ghost) to automatically scan articles before publishing
   - Adding email, Slack, or Discord notifications to alert editors about errors
   - Storing flagged content in Airtable or Notion for further review
   - Including multilingual support for international content validation
   
   🛠 Third-Party Tools and Models Involved
   
   1. Ollama API — Provides two AI models used in this workflow:
      - bespoke-minicheck: A purpose-built model tailored for fact validation
      - qwen2.5: A larger model used for generating summaries and human-like conclusions
   
   2. LangChain LLM Chains — n8n integrates LangChain nodes to structure the interaction between the language models and the user-defined prompts.
   
   🎯 Final Thoughts
   
   This workflow demonstrates how we can blend logic, data science, and natural language processing into a seamless system that enhances content credibility. Automated fact-checking isn't just for newsrooms — anyone looking to improve trust in their communication will benefit from this accessible and extensible solution.
   
   By harnessing the modular power of n8n and pairing it with LLMs via Ollama and LangChain, content producers gain a trustworthy AI co-pilot for truth.
   
   —
   
   Want to try it yourself? Make sure to install the bespoke-minicheck model using:
   ollama pull bespoke-minicheck
   
   Then, load the workflow, add your own article and reference facts as inputs, and let your AI fact-checker do the rest.

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.