Practonaut MCP server

Practonaut

A local practice gym for AI agents on real apps. Demonstrate a task once, let any MCP agent practise it, and get an independent, reproducible score — one npx command, no VM, no Docker.

Practonaut turns a real application — today Excalidraw — into a practice environment. A task is a start drawing plus a goal. You, or an agent over MCP, change the diagram one action at a time; each action is applied to a real, pinned Excalidraw engine and read back before it counts. When the attempt ends, a separate checker decides whether the goal is met. People and agents work on the same session at the same time, so you can watch an agent in your browser: the history marks each change as yours or as the agent's (Agent · claude-code).

Quick start in 60 seconds

Requires Node.js 22.18 or newer (macOS, Linux or Windows).

npx practonaut setup           # once: Chromium for the real Excalidraw engine (about 550 MB)
npx practonaut                 # opens the example task in your browser

Without setup everything still works, model-only: actions are not confirmed in the real app, and Practonaut says so when it starts.

Let an agent practise (Claude Code shown; any MCP client works):

claude mcp add practonaut -- npx -y practonaut mcp --task excalidraw/connect

On native Windows (not WSL) npx is a .cmd file that an MCP client cannot start directly; add cmd /c in front: claude mcp add practonaut -- cmd /c npx -y practonaut mcp --task excalidraw/connect.

Score it on a task, with a fresh session per run and an independent check:

npx practonaut bench --agent claude-code --tasks excalidraw/connect

More in getting started.

Create your own task

Draw the start and the goal in Excalidraw, then:

npx practonaut tasks from-scenes start.excalidraw goal.excalidraw --out my-task
npx practonaut play my-task/world

The difference becomes the goal (steps, labels, shapes, colours, frames, groups, arrows). Practonaut only writes the task if the goal passes its own check, the start fails, a generated reference solution passes and generated near misses fail. The same works in the Player under Create, where you can also record a demonstration in the real editor and have the rule confirmed by a second recording. See the authoring guide.

Benchmark your agent

npx practonaut bench --agent oracle --tasks all --seeds 2            # reference solutions: the ceiling
npx practonaut bench --agent random --tasks all --max-actions 30       # random actions: the floor
npx practonaut bench --agent claude-code --tasks excalidraw/insert-between --repeats 3
npx practonaut bench --agent "my-agent --mcp {mcp_config} --prompt {prompt}"   # any agent
npx practonaut report ~/.practonaut/bench/<id>/report.json --html report.html
npx practonaut report ~/.practonaut/bench/<id>/report.json --verify     # re-judge from the run files

Shortened. The full README is on GitHub.