Each node in the graph is a plain English instruction. An AI agent executes them in order inside a Docker container with a full browser and desktop. Because each node is independent, the agent stays on task, it doesn't drift or hallucinate its way through a free-form prompt.
No programming required to build workflows. If you can describe a step, you can add a node.
I personally use it for:
- Applying to jobs automatically using my saved credentials, exactly the way I would do it manually
- Scraping websites and running data analysis on the results in the same workflow
- Checking my university LMS on a schedule for pending assignments
Each node in the graph is a plain English instruction. An AI agent executes them in order inside a Docker container with a full browser and desktop. Because each node is independent, the agent stays on task, it doesn't drift or hallucinate its way through a free-form prompt.
No programming required to build workflows. If you can describe a step, you can add a node.
I personally use it for:
- Applying to jobs automatically using my saved credentials, exactly the way I would do it manually
- Scraping websites and running data analysis on the results in the same workflow
- Checking my university LMS on a schedule for pending assignments
A few technical details:
- Nodes: Navigate, Do, Read, Fill, Check, Code, ForEach, Bootstrap
- Any LLM via LiteLLM: Gemini, GPT-4o, Claude, Ollama, OpenRouter
- Watch it work over noVNC, pause and take control, hand back anytime
- Chrome sessions persist across restarts via a named Docker volume
- Webhook + cron triggers, secrets vault, human-in-the-loop confirmation per step
GitHub: github.com/aadya940/orbit-ui
Docs: orbit-cua.com