Introduction: The challenge of information overload
As a Developer Relations professional, I engage with multiple communities, attend meetups, speak at conferences, and contribute to open-source projects. Each of these communities often has its own Slack workspace, and over time, I have joined more than 90 Slack instances with my main account. Some of these are highly active, with discussions happening around the clock.
Keeping up with all these conversations is impossible. The sheer amount of messages, threads, and shared links creates an overwhelming amount of information, making it difficult to extract the key discussions and stay informed.
While discussing this challenge with a colleague, an idea struck me: I needed a tool to generate a daily or weekly summary of each Slack workspace. Instead of scrolling through endless messages, this tool would highlight the main conversation topics and provide direct links to relevant messages. In just two minutes, I could get a quick overview of what happened yesterday (or last week) in any given community.
Using n8n and leveraging Clever Cloud’s Clever AI, I built exactly that—in just a few minutes. This blog post will show you how I automated Slack summaries and, more importantly, how LLMs (Large Language Models) are transforming automation workflows for developers.
Choosing the Right Tools for the Job
Why n8n?
n8n is a powerful low-code automation platform that simplifies the process of connecting heterogeneous systems, such as Slack and LLMs, by handling all the underlying “plumbing” almost painlessly. Instead of writing extensive custom scripts, developers can visually build workflows that integrate multiple APIs and services with ease. Whether deployed in a cloud-hosted or self-hosted environment, n8n provides full control and flexibility, allowing users to automate complex workflows efficiently. Being open-source and extensible, it supports a broad range of integrations and can be deployed on Clever Cloud, where it benefits from automatic scaling and hassle-free management.
Why Clever Cloud for deployment?
Deploying and managing automation workflows can be a challenge, but Clever Cloud simplifies the process. With Clever Cloud, hosting n8n is completely hassle-free—there’s no need to maintain infrastructure, set up databases, or manage scaling manually. Everything is automated, allowing users to focus on building their workflows instead of handling server maintenance.
For those who want a quick start, Clever Cloud provides a pre-configured n8n-example repository, making it even easier to deploy n8n on Clever Cloud with best practices already set up.
Why Clever AI?
Clever AI brings powerful LLM-driven automation into the workflow, allowing us to process and summarize Slack messages intelligently. Unlike traditional NLP approaches that require rule-based processing, Clever AI can extract key insights from unstructured messages with remarkable accuracy. Its built-in context awareness enables it to recognize trends, group related discussions, and even reformat the output to match Slack’s Markdown style. With a single API call, it can transform scattered messages into structured, readable, and actionable summaries, making it an invaluable tool for workflow automation.
Additionally, as Clever AI is currently in a pre-release version, this is the perfect time to experiment with different use cases and see how it fits into various workflows. Using a self-hosted open-source LLM, our application remains GDPR compliant, ensuring that we keep full control over our data, as it should be. This means sensitive personal information never leaves our infrastructure, providing an extra layer of privacy and security compared to third-party AI services.
Building the n8n workflow step by step
📌 Step 1: fetch messages from Slack
First, we use the Slack “Channel History” node to retrieve messages from a specific Slack channel.
- Filter messages from the last 24 hours using a timestamp expression.
- Extract message text, user IDs, timestamps, and links.
📌 Step 2: group messages by threads
Once we have the raw messages, the next step is to organize them into their respective threads. Slack messages can be independent or part of a conversation thread, and grouping them helps provide better context for summarization.
- Identify messages that are part of a thread using their thread_ts field.
- Group all replies under their respective parent messages.
- Ensure standalone messages remain ungrouped so they are still included in the summary.
📌 Step 3: summarize messages with LLMs
- Pass the cleaned messages to Clever AI (or OpenAI) for summarization.
- Use Slack’s Markdown formatting for clarity:
📝 *Daily Slack Summary*:
*1️⃣ Project Alpha Release*
– *Alice:* “We’re 80% done, final testing starts tomorrow.”
– *Bob:* “Bug #123 still open, should we prioritize?”
– 🔗 <https://example.com/results|Test Results>
📌 Step 4: post the summary back to Slack
- The Slack “Post Message” node sends the summary to a dedicated channel.
- Ensures better visibility of important discussions.
- Formats messages using bold, bullet points, and links.
🖼 A screenshot of the n8n workflow
Why LLMs make this so much easier
How would we do this without AI?
Before the rise of LLMs, automating Slack summaries required a mix of complex and often fragile techniques. One approach involved regex-based and rule-based NLP, which required carefully crafted expressions to detect key topics—an approach that was both difficult to maintain and prone to errors when faced with variations in message structure. Another method was manual tagging, where users had to label important messages, a process that was not only time-consuming but also inconsistent across different users. More advanced solutions relied on custom ML models, which demanded large amounts of training data, continuous fine-tuning, and ongoing maintenance to remain effective. Each of these methods had significant drawbacks, making automation far from seamless.
What LLMs do instead
LLMs transform the way we approach automation by eliminating the need for rigid rules and manual intervention. Instead of relying on predefined patterns, they understand context and extract key points naturally, adapting to different message structures. Unlike traditional systems, LLMs can dynamically format summaries in Slack Markdown, making them structured and easy to read. Moreover, they can process a wide variety of message types—announcements, discussions, and even complex question threads—ensuring that all relevant insights are captured without requiring manual curation.
Deploying n8n on Clever Cloud
Deploying n8n on Clever Cloud is effortless:
- Clone the n8n on Clever Cloud example repository.
- Follow the steps in the README.
🚀 And your n8n workflow engine is online. And all in Clever Cloud way, this means no DevOps overhead, no database headaches, and automatic scaling when needed.
And of course, all is GDPR-friendly
Slack contains a wealth of personal information, including usernames, avatars, and names, all of which are subject to GDPR and other data privacy regulations. This makes it crucial to handle data responsibly when automating processes involving Slack messages.
In this workflow, no personal data is stored. The system simply extracts messages, generates a summary, and sends it back to Slack for immediate consumption. At no point is sensitive user information retained, ensuring compliance with privacy best practices.
Additionally, the LLM used for summarization is a self-hosted instance of Clever AI, running on Clever Cloud. This means that no data is sent to third-party providers, stored externally, or monetized. Everything remains within a controlled environment, ensuring full compliance with data sovereignty and GDPR requirements.
Conclusion: the future of developer productivity with LLMs
The integration of n8n, Clever Cloud, and LLMs showcases how automation can move beyond simple workflows into intelligent decision-making. Instead of dealing with fragmented APIs, custom scripts, or complex NLP pipelines, we now have an elegant, scalable, and maintainable solution for extracting and summarizing information.
This project highlights a key transformation: LLMs aren’t just for chatbots; they can be seamlessly embedded into real-world automation processes. Whether it’s summarizing conversations, extracting insights, or enhancing workflows, AI-driven automation unlocks new levels of productivity.
By combining n8n’s powerful workflow automation, Clever Cloud’s robust hosting and scaling, and Clever AI’s intelligent processing, developers gain a flexible, low-maintenance, and high-impact automation stack. This approach not only simplifies the implementation of AI-driven automation but also ensures full control over data, security, and compliance.
🚀 What else could we automate? Try deploying n8n on Clever Cloud today, experiment with LLMs in your own workflows, and see how effortless intelligent automation can be!