@bitmonk8/pi-theta

pi-theta

pi-theta is a Pi Coding Agent extension that adds Theta, a scripting language for Pi agents. Write the predictable parts of an agent task as code and leave only the genuinely fuzzy parts to the model — no custom extension required.

A .theta file mixes ordinary code — variables, loops, conditionals, functions — with the text you send to the model. Running a theta adds turns to a conversation: either the caller's current one (prompt mode) or a fresh, isolated one (subagent mode). When it succeeds it can also return a final value — the theta's last expression, or the value you return — which callers can use and pass back across the subagent boundary. Thetas can't write files, use the network, or spawn processes on their own; those effects happen only through the Pi tools a theta is allowed to call (its callable set). .thetalib files are library modules that share Theta's grammar and types and are imported by .theta files; they are never run directly.

The problem

Pi's built-in prompt and subagent features are just Markdown with some fill-in-the-blanks — static text with YAML frontmatter. They can't branch, loop, read a model's response, carry a conversation across several turns, or hand a typed value back to the caller. Theta does all of that: your code decides what text goes to the model, the model's replies come back as values, and every run ends in one of three ways — success, failure, or cancellation — defined in Errors and Results.

Example: an agent loop

The pattern people call an agent loop (or a Ralph loop) is: run the model, check the result, then stop or go again. The usual version is a shell loop that re-runs the model and hopes it eventually declares itself done. In Theta the loop is real code, so your code owns the stop condition and the model just does the work inside each round.

The worker docs/examples/ralph-step.theta does one round of work on a fresh context and hands back a typed result. State lives on disk — the files it edits, the commits it makes — not in the conversation:

---
description: Do the next task toward the objective on a fresh context, then report status
mode: subagent
params:
  objective: string
tools:
  - read
  - bash
---
schema Progress {
  done: boolean,
  summary: string
}

let status: Progress = @`Objective: ${objective}

Inspect the current state of the project, do the single most important unfinished
task toward the objective, run the test suite with bash, commit the result, and
report whether the objective is now fully met.`?
status

The loop docs/examples/ralph.theta takes an objective, passes it to the worker, and re-runs the worker on a fresh context until it reports done — or hits the round ceiling:

---
description: Re-run the worker on a fresh context until the objective is met (a Ralph loop)
mode: subagent
params:
  objective: string
tools:
  - ./ralph-step.theta
---
let mut round = 0
while round < 20 {
  round += 1
  let status = ralph_step(objective)?
  if status.done {
    return status.summary
  }
}
"stopped at the 20-round ceiling"

Put both files on the discovery path with --theta, and ralph.theta is available as the /ralph slash command — the argument becomes its objective:

pi --theta docs/examples -p "/ralph get the integration tests passing"

Or, from inside a running pi session (once the directory is on your theta discovery path), just type the slash command:

/ralph get the integration tests passing

The while bound, the done check, and the round ceiling are ordinary code — not a magic "done" string grepped out of the model's prose. See How to write an agent loop for a second, self-contained example you can run without any external tools.

Status

Theta is at its initial version (0.1.x). The whole documented language works and is tested end-to-end, but this is an early release and may still contain bugs.

Report issues against the behaviour the Reference defines.

Documentation

• Guide — how Theta works: mixing code with the text you send to the model, prompt vs. subagent mode, .theta vs. .thetalib, and the final value.
• Tutorial — build your first theta, from an empty file to a working one.
• How-to guides — short recipes for specific tasks, including writing an agent loop.
• Reference — the full details: grammar, type system, frontmatter fields, errors and results, limits, diagnostics, and the CLI.