pi-continual

A minimal self-improving harness for pi, inspired by Prime Agent's RLM + Continual Harness abstractions — implemented entirely as a pi extension, no changes to pi (or pi-pod) required.

What you get

repl tool — persistent Python kernel (RLM-style programmatic tool calling). One kernel per session; variables and imports survive across calls. Pre-loaded globals:

• rlm(task, name=None, agent=None, model=None) — spawn a persistent sub-agent: a full pi -p session with its own session file under .pi/harness/subagents/<name>/. Returns a handle immediately (non-blocking) so the model can fan out parallel work. Calling rlm() again with the same name sends a follow-up turn into the same sub-agent session — it keeps its context. handle.wait(), handle.result(), handle.running(), subagents().
• harness — CRUD over the harness's own state, stored as markdown files: | kind | where | effect | | --- | --- | --- | | memory | .pi/harness/memory/ | injected into the system prompt every turn | | prompt | .pi/harness/prompts/ | injected into the system prompt every turn | | agent | .pi/harness/agents/ | sub-agent spec, used via rlm(..., agent=name) | | skill | .pi/skills/ | native pi skill (SKILL.md with frontmatter) |
• history(n=None) — this session's own JSONL entries, including context that was compacted away: the model has programmatic access to its full past.

/refine command + refine tool — evidence-backed self-improvement. Runs a background pi -p agent over the current session's trajectory that applies the smallest useful edit to the harness state (create/update one memory, prompt note, agent spec, or skill) and appends a record to .pi/harness/refine-log.jsonl. Non-blocking; the agent itself can call refine mid-task when it notices a repeated failure or reusable tactic.

/goal, /gate, goal_complete tool, --goal flag — bounded autonomous mode. /goal <text> sets a persistent objective; after every settled turn the harness re-prompts the agent to continue (capped at 12 turns). The agent ends the run by calling goal_complete; if /gate <cmd> is set, the gate command must exit 0 first — a failing gate returns its output to the agent for another attempt.

Install

pi install npm:pi-continual            # user-wide (recommended)
pi install -l npm:pi-continual         # project (shared via .pi/settings.json)
pi -e npm:pi-continual                 # try once without installing

# or from git:
pi install git:github.com/pi-pod/pi-continual

Requires python3 on PATH.

Self-improvement that travels through git

All harness state lives in the repository (.pi/harness/, .pi/skills/). Commit it and refinements become reviewable diffs that follow the repo — including into fresh pi-pod pods, where each session starts from a clean clone. Nothing here needs pod-side support.

Layout

extensions/continual.ts   # the extension: repl tool, prompt injection, /refine, goals
extensions/kernel.py      # persistent Python kernel: rlm(), harness, history()

Notes / limits (deliberately minimal)

• The kernel restarts (state lost) on repl timeout or abort; sub-agent sessions survive on disk.
• Goal state is session-scoped and not persisted across restarts.
• Sub-agent messaging is parent→child only (rlm() follow-up turns); there is no cross-session daemon. Inside a pi-pod pod that boundary is intentional.