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AI agents

Pargar was designed with AI agents as users from the start, not as a plugin added later. The idea is simple: if every operation is already a typed command, an agent needs no special access. It calls the same commands a person triggers from the toolbar.

The kernel exposes one entry point that takes a JSON command and returns a JSON result. A command carries the actor that issued it, human or agent, and the resulting events land in the same hash-chained log. Agent edits undo like any other edit, and the log shows who did what.

packages/mcp runs a Model Context Protocol server. Its tool-definitions.ts is the canonical list of 90+ tools, grouped by category:

Category Examples
draw draw_line, draw_circle, draw_polyline, draw_hatch, draw_dimension
edit move, copy, rotate, trim, offset, fillet, array
query / measure find entities, read properties, measure distances and areas
layer, style, block layers, text and dimension styles, blocks and groups
constraint horizontal, parallel, coincident, distance and more
view, UCS named views and coordinate systems

Each tool has a name, a description, typed parameters and a mapping to a kernel command. Any MCP-capable client can use them.

The app has an agent panel that runs the tool-calling loop in the browser, next to the kernel, so tool calls don’t round-trip through a server. It builds context from the current drawing, sends the request with the tool schemas, executes the returned tool calls against the kernel and shows each step. In development it can talk to a model running on your own machine.

AI sits on top of the 2D product, not in place of it. The roadmap ships solid 2D drafting first; because the tool layer already exists, the AI phase after launch is mostly UX: a chat bar, multi-step flows, and an action log you can replay.

Pargar is open source · Code Apache-2.0 · Docs CC BY 4.0 · GitHub