easy to set up, easy to use, runs very stable, lots of functionality (plugins)

What I like most about Kestra is how it separates the data infrastructure from the AI reasoning layer. First, the visual UI gives excellent observability. When a real-time event fails, I do not have to search through messy terminal logs. The topology and Gantt views show the exact execution flow step by step. I can click into a failed task, inspect the exact JSON payload or API error, and fix it immediately. Second, the AI agent plugin allows you to expose infrastructure tasks directly to the model as tools using only YAML. For example, I exposed a PostgreSQL database query and a background web scraper subflow as tools to Gemini. The model can autonomously choose to run a database search, or trigger the scraping subflow if the database returns zero results. Kestra manages the state transitions, connections, and retries behind the scenes without requiring custom Python wrappers. Finally, it is highly resource efficient. I develop on an older laptop with less than 6 GB of available RAM. Kestra runs smoothly as a standalone server with a small memory footprint, allowing me to build high-throughput, event-driven automation without overloading my system hardware.

I liked how easy it was to get started with Kestra using Docker. The YAML-based approach made workflows easy to understand, and the execution view helped me inspect each task independently during development. I also appreciate how Kestra helped me turn a manual process into a repeatable workflow. I used it to build a Developer Advocate Release Assistant, which involved retrieving release information from GitHub and generating markdown reports. The ease of setting it up locally with Docker, defining workflows using YAML, and troubleshooting issues during development through the execution view is what I like the most about Kestra.