n8n (HTTP Request)
Your canvas stays the scheduler and the brain. Glasswarp is one hop: when the flow hits a Windows app with no connector, start a session on that PC, observe or act, end the session, continue in n8n. There is no official Glasswarp community node. Use HTTP Request against the Platform API. Import the smoke workflow (text+targets observe, no click) from Use with n8n.Chat-driven (MCP)
Your assistant drives the PC step by step through MCP tools: it captures the screen, decides, clicks or types, and captures again. This is great for quick tasks and supervision — “what’s on my screen?”, “open Notepad and type hello”, approving a dialog while you watch. The tradeoff: each step costs a model turn. Predictable sequences — menus, dialogs, typing into a field you just clicked — can be batched into one call (send_actions), so you are not paying a turn per keystroke. MCP defaults to
text+targets observes (set image=true / observe_image=true only when you need
vision). Adaptive loops where every move depends on new information — clearing a
board, anything that repeats — still add up, and that is what the SDK path is for.
Setup: Connect via MCP.
Scaffolded SDK agents
For multi-step work, a small Python script loopsobserve → decide → act at
full speed. The decision logic lives inside the script — heuristics, computer
vision, or an LLM call — so there’s no chat round-trip per step. This is the
right tool for games, workflows, and anything that repeats.
If you’re connected through a code-capable assistant, it will typically offer to
write this script for you; you can also start from the
SDK quickstart or the agent loop guide.
Pre-built demos
Some pre-built demo agents ship with the SDK — install and run, no code to write:
MCP clients can browse the same catalog with the
list_demos / get_demo
tools, which return the install command and the exact command to run.