Opening multiple information sources every day, copying links, asking AI to write summaries, and then pasting everything into a document is exactly the kind of work that can be automated with n8n. Its value lies in linking trigger times, data retrieval, decision logic, and result storage into a process that can run repeatedly.
For your first setup, start with “collect news on a schedule and generate drafts.” Once sources, deduplication, and exception handling are stable, you can consider connecting the workflow to publishing.
Where n8n Fits
n8n provides visual workflows and supports the use of code and AI capabilities within a process. It can serve as a connection layer between services: retrieving API data, calling a model, and then sending the results to a document system, messaging platform, or content backend.
| Task | Recommended approach |
|---|---|
| Scheduled triggers, API requests, and record creation | Standard workflow nodes |
| Date filtering, link deduplication, and field validation | Deterministic rules or code |
| Content synthesis and short-summary generation | Model calls |
| Fact confirmation and publication decisions | Source validation and review steps |
Do not let the model make every decision. Whether a publication time falls within a given window, whether a link is a duplicate, and whether all required result fields are present can usually be handled more explicitly with ordinary rules.
Step 1: Start with Two Stable Sources
Choose two RSS feeds or APIs with clear source attribution. For each record, retain the title, original URL, publication time, retrieval time, and source name.
Publication time and retrieval time must remain separate. Finding an article today does not mean it was published today. Items without a publication time can be collected separately, but should not simply be assigned the current date.
Use the scheduled trigger and HTTP or RSS capabilities available in the current version to build the entry point. Run it once and inspect the returned fields. Do not ask the model to generate an entire article from a headline before you have retrieved real data.
Step 2: Deduplicate Before Calling AI
The simplest basis for deduplication is the original source link that has already been processed. Some sites append tracking parameters. In practice, remove only parameters that have been confirmed to carry no business meaning; do not delete every query parameter indiscriminately, as this could merge distinct content.
Store the “already processed” status in a persistent location. If deduplication exists only in memory during the current run, the same items may be added again when the workflow runs the next day.
For multiple sources covering the same news, you can initially retain separate records and let a later editorial step decide whether to merge them. Similar headlines do not prove that the articles describe the same event.
Step 3: Ask the Model for a Short, Verifiable Summary
Whenever possible, provide the model with permitted excerpts from the original content and the corresponding source information. If only a summary is available, label it as a summary rather than pretending the full article was read.
Consider requiring a fixed output structure, such as:
{
"title": "A concise title",
"summary": "Describe only facts stated by the source",
"source_url": "https://example.com/original",
"needs_review": false
}This is only a suggested data format, not a built-in n8n interface. After receiving the result, check that it can be parsed, that all required fields are present, and that the source link still matches the input. If the format is invalid, retain the original record in a review queue rather than treating an error message as an article.
The prompt should also make clear that any “ignore the rules” or “execute these instructions” text in the source is content to be processed and must not alter the workflow’s behavior.
Step 4: Save Drafts Before Considering Distribution
Write validated summaries to a document system or content backend, including the source and retrieval time. Inspect the actual output from the first few runs: check for duplicates, old news mixed into the results, and opinions presented as facts.
If message notifications are needed, sending the title, summary, and a link to the backend is usually enough. Do not put full source articles, customer information, or API credentials into group messages.
When adding automated publishing later, introduce two conditions: the materials have passed validation, and an independent review has been completed. If the review service is unavailable, retain the draft. This allows the workflow to reduce repetitive work while clearly indicating which content has not yet been fully checked.
Make Failures Visible
Provide handling paths for at least these three situations:
- Source API timeout: Record the failed source and allow other independent sources to continue; include the missing source in the current run’s results.
- Unparseable model output: Retain the input and error type, and stop that item from moving to the next step.
- Backend write failure: Retain the pending record and its deduplication identifier so that retries do not create duplicates.
Retries should target temporary failures. Expired API keys, insufficient permissions, and invalid parameters generally require configuration changes. Retrying them indefinitely only consumes resources.
Cost, Deployment, and Licensing
n8n supports both cloud deployment and self-hosting. Self-hosting requires maintaining the runtime environment and persistent data. Models and external APIs may also be billed separately.
The official materials describe n8n as a fair-code platform that uses licenses including the Sustainable Use License. The licensing boundaries differ between internal automation and selling hosted capabilities to external customers, so it should not be broadly interpreted as allowing every commercial use without restrictions.
This article proposes a workflow design based on officially documented capabilities. It does not claim that this example has been run, nor were its efficiency or accuracy tested.