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Smart RSS feed monitoring with AI filtering, Baserow storage, and Slack alerts

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Important notice

This workflow is provided as-is. Please review and test before using in production.

1. Workflow Overview

This workflow automates the process of monitoring multiple RSS feeds, intelligently identifying new articles, maintaining a record of processed co...

Best for

  • Market Research automation workflows
  • AI Summarization automation workflows
  • advanced n8n builders looking for reusable templates

Tools used

n8n-nodes-base.slack, @n8n/n8n-nodes-langchain.agent, @n8n/n8n-nodes-langchain.lmchatopenai, @n8n/n8n-nodes-langchain.memorybufferwindow, @n8n/n8n-nodes-langchain.outputparserstructured, n8n-nodes-base.splitout, n8n-nodes-base.baserow, n8n-nodes-base.code

Source and attribution

This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Daniel Shashko.

Original n8n.io source

1.1 Workflow description

Title
Smart RSS feed monitoring with AI filtering, Baserow storage, and Slack alerts
Workflow name
Smart RSS feed monitoring with AI filtering, Baserow storage, and Slack alerts

This workflow automates the process of monitoring multiple RSS feeds, intelligently identifying new articles, maintaining a record of processed content, and delivering timely notifications to a designated Slack channel. It leverages AI to ensure only truly new and relevant articles are dispatched, preventing duplicate alerts and information overload. ๐Ÿš€

Main Use Cases

  • Automated News Aggregation: Continuously monitor industry news, competitor updates, or specific topics from various RSS feeds. ๐Ÿ“ˆ
  • Content Curation: Filter and deliver only new, unprocessed articles to a team or personal Slack channel. ๐ŸŽฏ
  • Duplicate Prevention: Maintain a persistent record of seen articles to avoid redundant notifications. ๐Ÿ›ก๏ธ
  • Enhanced Information Delivery: Provide a streamlined and intelligent way to stay updated without manual checking. ๐Ÿ“ง

How it works

The workflow operates in distinct, interconnected phases to ensure efficient and intelligent article delivery:

1. RSS Feed Data Acquisition ๐Ÿ“ฅ

  • Initiation: The workflow is manually triggered to begin the process. ๐Ÿ–ฑ๏ธ
  • RSS Link Retrieval: It connects to a Baserow database to fetch a list of configured RSS feed URLs. ๐Ÿ”—
  • Individual Feed Processing: Each RSS feed URL is then processed independently. ๐Ÿ”„
  • Content Fetching & Parsing: An HTTP Request node downloads the raw XML content of each RSS feed, which is then parsed into a structured JSON format for easy manipulation. ๐Ÿ“„โžก๏ธ๐ŸŒณ

2. Historical Data Management ๐Ÿ“š

  • Seen Articles Retrieval: Concurrently, the workflow queries another Baserow table to retrieve a comprehensive list of article GUIDs or links that have been previously processed and notified. This forms the basis for duplicate detection. ๐Ÿ”

3. Intelligent Article Filtering with AI ๐Ÿง 

  • Data Structuring for AI: A Code node prepares the newly fetched articles and the list of already-seen articles into a specific JSON structure required by the AI Agent. ๐Ÿ—๏ธ
  • AI-Powered Filtering: An AI Agent, powered by an OpenAI Chat Model and supported by a Simple Memory component, receives this structured data. It is precisely prompted to compare the new articles against the historical "seen" list and return only those articles that are genuinely new and unprocessed. ๐Ÿค–
  • Output Validation: A Structured Output Parser ensures that the AI Agent's response adheres to a predefined JSON schema, guaranteeing data integrity for subsequent steps. โœ…
  • JSON Cleaning: A final Code node takes the AI's raw JSON string output, parses it, and formats it into individual n8n items, ready for notification and storage. ๐Ÿงน

4. Notification & Record Keeping ๐Ÿ””

  • Persistent Record: For each newly identified article, its link is saved to the Baserow "seen products" table, marking it as processed and preventing future duplicate notifications. ๐Ÿ’พ
  • Slack Notification: The details of the new article (title, content, link) are then formatted and sent as a rich message to a specified Slack channel, providing real-time updates. ๐Ÿ’ฌ

Summary Flow:

Manual Trigger โ†’ RSS Link Retrieval (Baserow) โ†’ HTTP Request โ†’ XML Parsing | Seen Articles Retrieval (Baserow) โ†’ Data Structuring (Code) โ†’ AI-Powered Filtering (AI Agent, OpenAI, Memory, Parser) โ†’ JSON Cleaning (Code) โ†’ Save Seen Articles (Baserow) โ†’ Slack Notification ๐ŸŽ‰

Benefits:

  • Fully Automated: Eliminates manual checking of RSS feeds and Slack notifications. โฑ๏ธ
  • Intelligent Filtering: Leverages AI to accurately identify and deliver only new content, avoiding duplicates. ๐Ÿ’ก
  • Centralized Data Management: Utilizes Baserow for robust storage of RSS feed configurations and processed article history. ๐Ÿ—„๏ธ
  • Real-time Alerts: Delivers timely updates directly to your team or personal Slack channel. โšก
  • Scalable & Customizable: Easily adaptable to monitor various RSS feeds and integrate with different Baserow tables and Slack channels. โš™๏ธ

Setup Requirements:

  • Baserow API Key: Required for accessing and updating your Baserow databases. ๐Ÿ”‘
  • OpenAI API Key: Necessary for the AI Agent to function. ๐Ÿค–
  • Slack Credentials: Either a Slack OAuth token (recommended for full features) or a Webhook URL for sending messages. ๐Ÿ—ฃ๏ธ
  • Baserow Table Configuration:
    • A table with an rssLink column to store your RSS feed URLs.
    • A table with a Nom column to store the links of processed articles.

For any questions or further assistance, feel free to connect with me on LinkedIn: https://www.linkedin.com/in/daniel-shashko/

1.2 Logical Blocks

This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.

2. Block-by-Block Analysis

Block 1 - Slack

Type / Role
n8n-nodes-base.slack - slack
Config choices
Version 2.3

Block 2 - AI Agent

Type / Role
@n8n/n8n-nodes-langchain.agent - agent
Config choices
Version 2

Block 3 - OpenAI Chat Model

Type / Role
@n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
Config choices
Version 1.2

Block 4 - Simple Memory

Type / Role
@n8n/n8n-nodes-langchain.memoryBufferWindow - memoryBufferWindow
Config choices
Version 1.3

Block 5 - Structured Output Parser

Type / Role
@n8n/n8n-nodes-langchain.outputParserStructured - outputParserStructured
Config choices
Version 1.2

Block 6 - Split Out

Type / Role
n8n-nodes-base.splitOut - splitOut
Config choices
Version 1

Block 7 - Read Rss Link

Type / Role
n8n-nodes-base.baserow - baserow
Config choices
Version 1

Block 8 - Save seen products

Type / Role
n8n-nodes-base.baserow - baserow
Config choices
Version 1

Block 9 - Clean JSON

Type / Role
n8n-nodes-base.code - code
Config choices
Version 2

Block 10 - Edit data structure

Type / Role
n8n-nodes-base.code - code
Config choices
Version 2

Block 11 - Sticky Note

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 12 - Sticky Note1

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 13 - Sticky Note2

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 14 - Fetch HTML

Type / Role
n8n-nodes-base.httpRequest - httpRequest
Config choices
Version 4.2

Block 15 - Sticky Note3

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 16 - XML Converter

Type / Role
n8n-nodes-base.xml - xml
Config choices
Version 1

Block 17 - Sticky Note4

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 18 - Sticky Note5

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 19 - Get Seen Products

Type / Role
n8n-nodes-base.baserow - baserow
Config choices
Version 1

Block 20 - Sticky Note6

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 21 - Sticky Note7

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 22 - Sticky Note8

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 23 - Sticky Note9

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 24 - Sticky Note10

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Showing the first 24 of 28 workflow blocks. Download the JSON for the full node graph.

3. Summary Table

Workflow Smart RSS feed monitoring with AI filtering, Baserow storage, and Slack alerts
Complexity advanced
Nodes 28
Categories Market Research, AI Summarization
Author Daniel Shashko
Published 24 Jul 2025

4. Reproducing the Workflow from Scratch

  1. 1. Download the workflow JSON

    Use the JSON export at /data/workflows/6389/6389.json as the source template for this automation.

  2. 2. Import the template into n8n

    Open n8n, import the downloaded JSON, and review each node before activating the workflow.

  3. 3. Configure credentials and variables

    Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.

  4. 4. Test with sample data

    Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.

  5. 5. Activate and monitor

    Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.

5. General Notes & Resources

Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.

Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.

Frequently asked questions

What does Smart RSS feed monitoring with AI filtering, Baserow storage, and Slack alerts do?

This workflow automates the process of monitoring multiple RSS feeds, intelligently identifying new articles, maintaining a record of processed co...

What do I need before importing this workflow?

Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.

Can I customize this workflow?

Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Market Research, AI Summarization use case.