A runtime for Code Mode agents
Build AI agents that are bounded in what they can do, easy to change, observable in operation, and designed to improve from evidence.
Today, agents usually choose between two extremes. They either call tools one at a time, paying a model round trip and adding context for every step, or use a full coding environment and an external sandbox for more complex work. PtcRunner provides the missing middle: it lets a model write a small, bounded program that calls several approved tools, processes their results, and returns only what matters.
You normally do not write that program. You provide the task, model, approved tools, data, limits, and agent components. The model writes the mission program; PtcRunner executes it and records what happened.
Inside those two environments, the agent loop itself is ordinary PTC-Lisp (a small, bounded subset of Clojure — see the language specification). It is a library you select, not behaviour baked into the runtime, so you can replace it with a domain-specific loop of your own — and doing so grants no new authority.
On macOS it is a one-command install, or a container image if you would rather have one. Installing directly needs neither a container nor any separate runtime — the executable carries its own. Once it is on your machine:
ptc init hello-ptc
ptc run hello-ptc/ptc-project.json
{"greeting":"hello world"}
That runs without contacting a model, and writes a structured trace you can
open in the viewer. The same ptc run drives agentic projects.
0.x, under active development. Breaking changes are expected, and the install routes above land with the next release. The availability table tracks exactly where each one stands today.
ptc_runner