From Data to Delight: Using n8n and LLMs to Automate Insightful Weather Updates
Throughout my exploration of automation’s vast potential, I’ve documented various experiences—from grappling with conditional JSON challenges in Make.com to uncovering more streamlined solutions with n8n’s Code Node during a Luma API integration. Each project provided valuable insights, but my latest venture stands apart. Building Kairos, an AI-powered weather scout, has vividly demonstrated how integrating robust automation tools with the advanced reasoning capabilities of Large Language Models (LLMs) can effectively address complex, real-world problems with remarkable efficiency.
The initial idea was simple: find the best weather in Europe and tweet about it. But “best” is subjective, and sifting through forecasts for dozens of cities, each with multiple data points (temperature, conditions, precipitation probability over several days), to make a human-like judgment and then craft an engaging tweet? That’s not a trivial coding task. Traditionally, this would mean complex conditional logic, weighting systems, and perhaps even sentiment analysis libraries to get the tone right for a tweet.
This is where the synergy of n8n and LLMs truly shines.
The Challenge: Drowning in Data, Dreaming of Simplicity
My n8n workflow diligently fetches detailed 5-day forecasts for 30 European cities from OpenWeatherMap. Using n8n’s Split Out and HTTP Request nodes, this part is fairly standard. Then, a Code Node steps in to transform and clean this data, stripping unnecessary fields, formatting values, and crucially, adding city names to each forecast. The result is a single, clean JSON object — an array where each element represents a city’s detailed weather report, all under a top-level fetch_date.
Here’s a conceptual glimpse of what that data structure looks like before it heads to the AI:
Now, imagine writing the code to:
Parse this for 30 cities.
Define what “best weather” means. Is it just warmest? What about “sunny but not too hot”? Or “surprisingly good for this time of year”?
Compare all 30 cities based on these nuanced criteria.
Select a single winner.
Compose a tweet that sounds natural, engaging, and includes relevant details like temperature ranges and emojis.
The logic would be extensive and brittle.
The LLM Breakthrough: Outsourcing Complex Logic and Creativity
Instead of coding that mountain of logic, I turned to an LLM (Gemini 2.5 Pro, accessed via n8n’s OpenAI node configured for OpenRouter). I fed it the entire JSON object of weather reports and, through careful prompt engineering, asked it to do the heavy lifting.
My prompt instructed the LLM to:
Act as Kairos, whose mission is to find appealing weather for spontaneous trips.
Analyze all provided city forecasts.
Identify the city with the overall “best weather in Europe,” considering factors like pleasant temperatures (e.g., 18–28°C), sunshine, and low precipitation.
Crucially, give preference to cities experiencing “unusually pleasant” weather for their location/season (this was a key refinement for more interesting picks).
Return its findings in a structured JSON format, including the chosen city, reasoning, and, importantly, a suggested_tweet.
The result? The LLM not only intelligently analyzed the data — weighing multiple factors across numerous cities in a way that mimics human intuition — but it also drafted a ready-to-post tweet, complete with relevant emojis and a call to action.
This ability to hand off complex data analysis and creative content generation to an LLM, simply by providing structured data and clear instructions, felt like a superpower. The hundreds, if not thousands, of lines of conventional code I might have written were replaced by a well-crafted prompt.
n8n: The Indispensable Orchestrator
The LLM, powerful as it is, needs a robust platform to manage the overall workflow. Here’s how n8n played that crucial role:
Data Ingestion & Iteration: Efficiently fetching data from OpenWeatherMap for each city using HTTP Request and Split Out nodes.
Data Preparation: The Code Node ensured the LLM received clean, well-structured JSON tailored for analysis.
AI Integration: The OpenAI node seamlessly handled the LLM call, abstracting the API complexities.
Action & Communication: The X (Twitter) node took the LLM’s suggested_tweet and posted it, while the Gmail node sends me a confirmation.
Reliability & Scheduling: n8n also handles scheduling the workflow to run twice a week and incorporates a separate error-handling workflow that emails me if anything goes amiss, ensuring robust operation.
The platform’s visual interface allowed me to design the overall flow, while specific nodes like the Code Node and OpenAI node provided the power needed for the more intricate parts.
Key Learnings for an AI & Automation Toolkit:
LLMs as Data Analysts: LLMs can ingest large, structured datasets and extract nuanced insights based on natural language instructions, saving vast amounts of custom coding.
LLMs as Content Generators: Beyond analysis, they can generate contextually relevant and stylistically appropriate content (like tweets) based on that analysis.
Prompt Engineering is Key: The quality of the LLM’s output is directly proportional to the clarity, specificity, and iterative refinement of the prompt. This is a skill in itself.
n8n + LLMs = A Power Duo: n8n provides the robust framework for data flow, service integration, scheduling, and error handling, while the LLM provides the “brain” for complex decision-making and generation.
The Future is Orchestration: My role increasingly feels like an orchestrator of these powerful tools, designing the interaction points and guiding the AI, rather than coding every single logical step from scratch.
This Kairos project has been incredibly rewarding, not just for the functional automation it produced, but for the deeper understanding it gave me of how AI can be practically integrated into workflows to solve real-world problems in surprisingly elegant ways.
You can see the results of this automation live on the @kairos_sun X (Twitter) account, which is now autonomously tweeting its weather picks. It’s a small demonstration, but for me, it represents a big step forward in leveraging AI within automation.