pi-continual-harness

Online self-improvement layer for the pi coding agent.

This package owns only the online optimizer layer: a unified, in-trajectory harness-state store (prompt notes, memory, skill descriptions, sub-agent specs) plus a manual /refine that proposes evidence-backed structured CRUD deltas.

It deliberately does not reinvent storage or offline refinement. It composes with:

• pi-reflect — offline transcript → behavioral-file refinement (the "deep" path).
• pi-mem / pi-memory — durable memory storage.

The durable markdown layers are the composition seam — the shared ~/.pi/agent/harness-state.md plus per-project files under ~/.pi/agent/harness-state/<slug>.md — and they are two-way: /refine --commit and /harness export write them; /harness import parses them back and merges into the live store (offline edits win on conflict), so refinements pi-reflect makes flow back online. /harness push-mem pushes active items into pi-mem's semantic store (see Composing with pi-mem).

Why

Grounded in two lines of research:

• Continual Harness (arXiv 2605.09998) — reset-free online CRUD over prompt / sub-agents / skills / memory drawn from the trajectory. Distinct from prompt-optimization methods that need episode resets.
• ACE — Agentic Context Engineering (arXiv 2510.04618) — context as an evolving playbook. Key design lesson applied here: the optimizer emits structured, itemized deltas, never prose prompt rewrites, to prevent context collapse and brevity bias.

Prime Intellect's Prime Agent ships a Continual Harness built on pi; this package is a minimal, package-shaped take on the same idea — online only, leaving offline and storage to the packages that already do them well.

Install

pi install npm:pi-continual-harness

Or drop src/index.ts into ~/.pi/agent/extensions/.

Usage

/refine              # review last 25 turns, propose deltas
/refine 50           # review last 50 turns
/refine 25 --commit  # also export durable state (global + project layers)
/refine --proposer dedupe  # run the rule-based dedupe proposer instead of steering
/refine --proposer dedupe --threshold 0.75  # dedupe with a one-shot threshold

Durable I/O (round-trip with pi-reflect):

/harness status                  # counts + durable layer presence/mtime
/harness export [path]           # layered export (or full snapshot to a path)
/harness import [--prune] [path] # layered import (or single file from a path)
/harness prune [--decay <days>]  # drop items below the importance floor
/harness keep <id>               # nudge importance up (+0.1)
/harness drop <id>               # nudge importance down (−0.1)
/harness move <id> global|project  # move an item between durable layers
/harness split                   # steer the agent to classify every item's scope
/harness push-mem [--all|--kind <kind>|--model <provider/id|active>]  # persist active items to pi-mem (save_memory)

Subcommands, flags, --kind/--model values, move scopes, and keep/ drop/move item ids all autocomplete in the TUI as you type after /harness .

Durable layers. Every item carries its own durable scope: global (the shared ~/.pi/agent/harness-state.md) or project (~/.pi/agent/harness-state/<slug>.md, slug derived from the session cwd). /harness export without a path partitions items by their scope into the layer files; /harness import without a path merges the global file always, plus the current project's file (the project layer wins id collisions; --prune drops only items absent from every layer). An explicit path keeps the classic single-file semantics. Move items between layers with /harness move, or classify the whole store at once with /harness split (the agent proposes scope-only harness_mutate updates — visible in the transcript, /tree-rollback-able).

import reconciles the file(s) into the live store: items whose id matches an existing entry are updated (offline edits win on content/evidence/importance), new entries are created. By default nothing is deleted — --prune also drops active items whose id is no longer in the file(s) (inactive items are always preserved). Point pi-reflect at either layer to refine it offline:

/reflect ~/.pi/agent/harness-state.md
/reflect ~/.pi/agent/harness-state/<project-slug>.md

The model-facing tools:

• harness_list({ kind?, model? }) — read current state. model defaults to the active model's items (what gets injected this turn); "*" returns every model.
• harness_mutate { deltas: [...] } — apply a batch of create / update / delete deltas. Every create requires evidence. New items are stamped automatically with the active model (see Model binding). create/update deltas also accept scope: "global" | "project" (the project slug is stamped server-side from the session cwd — used by /harness split; normally managed via /harness move).

Active items are injected into the system prompt each turn as a structured block, appended to (never replacing) the base prompt.

Composing with pi-mem

/harness push-mem copies active harness items into pi-mem's semantic memory store, so they become searchable across sessions. It works by steering the agent to call pi-mem's save_memory tool — there is no dependency on pi-mem: it is a soft-fail composition. If pi-mem (or any memory tool) is not installed, the agent tells you so rather than fabricating one.

pi install npm:pi-mem        # optional companion

By default only memory-kind items are pushed (the clean 1:1 mapping); use --all for every active item, --kind prompt|skill|subagent for a specific kind, or --model <provider/id|active> to scope the push to one model's items (--model active = the model driving the command). The harness store itself is unchanged by a push — pi-mem gets a separate copy.

Configuration

Optional config at ~/.pi/agent/harness.json (missing or malformed → defaults):

{
  "autoImport": false,
  "proposer": "steering",
  "dedupe": { "threshold": 0.6, "merge": true },
  "injection": { "enabled": true, "maxTokens": 1500, "maxPerKind": 10, "charsPerToken": 4 },
  "remindRefine": { "enabled": false, "everyTurns": 50 },
  "autoRefine": { "enabled": false, "everyTurns": 100, "commit": false },
  "outcomeImportance": { "enabled": false, "bump": 0.03 }
}

• autoImport — opt-in durable sync (off by default). true bundles both directions: on session_start the durable layers are imported automatically (global file always + the current project's file, with the same loss-free merge as /harness import — an import that changes nothing stays silent), and on turn_end the layers are re-exported whenever the live store changed since the last export. This is what makes /refine output survive into new sessions without manual export/import ceremony. Both halves are visible (one notify line) and use the same persisted harness-state entries as every other mutation (/tree rollback covers them).
• injection — the selection policy for WHAT gets surfaced in the system prompt each turn (see Injection selection). ON by default: items are importance-ordered, capped at maxPerKind (default
  10. per kind and maxTokens (default 1500) total. Set enabled: false to restore the legacy "inject all items, in store order" behaviour.
• proposer — which delta proposer /refine and auto-refine use. Defaults to steering (the agent reasons via a steering message). dedupe applies a rule-based merge directly. See Proposers.
• dedupe — the rule-based dedupe proposer's policy. threshold (default 0.6, valid (0,1]) is the token-overlap level at which two items sharing the key fields (kind, owner model, durable layer) count as duplicates; merge (default true) merges each duplicate into its keeper — the keeper keeps its content verbatim, its evidence becomes the line-wise union of both, applied as one audited update + delete pair — while false restores the pre-0.10 delete-only behavior. /refine --proposer dedupe --threshold 0.75 overrides the threshold for one run. A fourth key, similarity, is API-only (functions cannot come from harness.json): companion packages inject a richer comparator whose return may abstain per pair ({ score, abstain } — see SimilarityResult); an abstaining pair is always kept, never merged or deleted.
• remindRefine — opt-in turn_end nudge. { "enabled": true, "everyTurns": 50 } notifies you to run /refine on a cadence. It is informational only — it never mutates state.
• autoRefine — opt-in autonomous self-improvement (off by default). When enabled, the agent runs /refine itself every everyTurns turns (default 100). It is one of the package's opt-in autonomous paths: it reuses the exact /refine routine (audited REFINE_ENTRY tagged source: "auto", branch-local, /tree rollback) and notifies before firing. commit: true also flushes durable state on each run.
• outcomeImportance — opt-in autonomous promotion loop (off by default). When enabled, a turn_end hook bumps (+bump, default 0.03) the importance of any active item the agent cited by its [h_xxxx] tag in the turn's output. Referenced items gain importance and get their updatedAt touched (so they survive time-based decay); ignored items keep decaying. This is the package's second opt-in autonomous path — promotion only, never deletes, persisted/branchable like harness_mutate. Autonomous demotion from outcomes is intentionally NOT done (high false-positive); use /harness drop, prune --decay, or the dedupe proposer for that.

Deprecated: durableScope (0.9.0). Items now carry their own scope (/harness move), and durable I/O is always layered — the key no longer switches anything but is still parsed so existing configs keep loading. Migration from a durableScope: "project" setup: run /harness import once in the project (its file's items adopt project scope), then move any strays with /harness move (or classify the whole store with /harness split).

How it works

1. /refine gathers recent trajectory evidence from the current session branch.
2. A proposer decides what to do. The default (steering) sends a steering user message asking the agent to propose evidence-backed CRUD deltas via harness_mutate; rule-based proposers (e.g. dedupe) return deltas the harness applies directly. See Proposers.
3. The agent calls the tools (steering path); each accepted delta updates the in-memory state, which is snapshotted to the session via appendEntry("harness-state", ...). Direct-apply proposers snapshot the same way. New items are stamped with the active model (see Model binding).
4. Because pi's session tree branches at any entry, /tree navigation gives rollback to any pre-refinement point for free — no bespoke snapshot system.

This reuses the existing agent loop (no nested/hidden model calls), is model-agnostic, and keeps every delta visible and reviewable in the transcript.

Model binding (per-model isolation)

Every item is bound to exactly one model as ownerModel ("provider/id"). An item is only injected for the model it belongs to — so switching to a brand-new model id starts from a blank harness, and one model's notes never leak into another's context. Binding is at the exact model id (not family or vendor): a new version id is a clean slate, by design.

How the binding is set and respected:

• Created items are stamped automatically with the model driving the turn. The model-facing tools cannot read the active model, so before_agent_start (which always fires first in a turn, with the model) caches it; harness_mutate stamps creates from that cache, and direct-apply proposers stamp from their ctx.model. You never name the model yourself.
• Injection filters by owner. Only items whose ownerModel matches the active model are appended to the system prompt. An unknown model injects nothing.
• harness_list defaults to the active model (pass model: "*" for every model, or an explicit "provider/id").
• Orphan adoption. Items with no owner — from a legacy session snapshot, an old durable file, or created while the model was unknown — are adopted by the active model on first contact (the next before_agent_start). This is the migration path: existing harnesses transition cleanly with no manual steps, and it's persisted as a normal harness-state entry (so /tree rollback covers it).
• Durable round-trip preserves owner. /harness export tags each item with model: provider/id; /harness import restores it. An item whose tag pi-reflect stripped becomes an orphan and is adopted by the active model.

Manual commands (export, import, move, split, keep, drop, prune, push-mem, status) operate on the whole store by design — they are explicit human actions with full control. Isolation is enforced only where pollution would leak automatically: injection, listing, create-stamping, and the outcome loop. In particular, /harness push-mem pushes every model's active items into pi-mem by default (which can yield near-duplicate memories across models); pass --model <provider/id|active> to scope it to one model.

Injection selection (on by default)

The harness ACCUMULATES notes (create / refine / auto-refine / outcome-promotion), but the system prompt is finite. So since 0.8 the block appended each turn is the result of a selection policy, not the whole store — and it is on by default. The store itself is never changed by selection (nothing is lost); only what is surfaced changes.

The policy (pure, deterministic; src/select.ts):

1. Filter — only active items bound to the active model (strict per-model isolation, unchanged).
2. Order — importance desc; ties keep store/insertion order (stable). The highest-fitness notes lead each section.
3. Cap — maxPerKind (default 10: balanced sections, no single kind drowns the block), then maxTokens (default 1500: total budget). The budget is filled round-robin across kinds by importance rank, so one kind cannot starve the others; within that order an item that doesn't fit is skipped (not a hard stop), so a large item never blocks smaller higher-priority ones.

The defaults are deliberately generous — a no-op for small stores (nothing trimmed) and protective as the harness grows. When the policy drops items, the block ends with a one-line transparency note:

_(3 item(s) not shown — below the injection budget. Raise `injection.maxTokens`/`maxPerKind` in harness.json or run `/harness prune`.)_

Tune or disable it in harness.json:

{ "injection": { "maxTokens": 3000, "maxPerKind": 20 } }   // raise the ceiling
{ "injection": { "maxPerKind": 5 } }                        // trim harder, per kind
{ "injection": { "enabled": false } }                       // opt out: legacy "all, in order"

Selection is factored into a pure function (selectForInjection) and re-exported from the package entry, so a companion package can layer richer policies (e.g. relevance to the current turn, via the shared tokenize / tokenOverlap helpers) without touching inject.ts.

Proposers

/refine is split into two stages: propose (given evidence + state, decide what deltas to pursue) and apply (send a steering message, or apply returned deltas directly). The propose stage is pluggable via a registry (src/proposer.ts).

Name	What it does
steering (default)	Delegates reasoning to the agent via a steering message — reuses the agent loop, model-agnostic, fully visible.
dedupe	Rule-based: merges near-duplicate active items (token-overlap ≥ the configured threshold, same kind / owner model / durable layer) into the higher-importance keeper — one evidence-union update per keeper, then deletes the duplicates. "dedupe": { "merge": false } restores delete-only. No model call.

The dedupe planner's similarity seam (DedupeOptions.similarity, re-exported from the package entry) is how a companion package upgrades the comparison — e.g. embedding cosine similarity, or a local decision engine such as pi-jev (a local-only project for now). A comparator may return { score, abstain } instead of a plain number: an abstaining pair is treated as not duplicates — both items are kept — so an engine with conformal uncertainty guarantees can safely decline to merge. Plain numeric returns keep working (the default remains token Jaccard).

Select a proposer per run with /refine --proposer <name>, or set the default for auto-refine via proposer in the config. Both paths are audited: the harness-refinement entry records which proposer ran and how many deltas it applied directly.

A dedicated-model proposer — one that makes its own (hidden) LLM call to produce deltas directly — is the obvious next alternate. This package still does not ship one (hidden model spend is a tradeoff kept as a separate decision; see docs/ROADMAP.md), but it now enables one: when a model is available, /refine injects a one-shot complete(prompt, opts?) into ProposeInput and records any modelCall telemetry a proposer returns (model, tokens, latency, ok/error) in the harness-refinement audit entry — so a companion package can ship a dedicated-model proposer whose spend stays audited, not hidden. One ships as a companion: pi-harness-model-proposer (pi install npm:pi-harness-model-proposer). Both complete and modelCall are optional; steering and dedupe ignore them, so the default behavior is unchanged.

Register your own proposer from another extension (the registry is re-exported from the package entry):

import { registerProposer } from "pi-continual-harness";

registerProposer({
  name: "my-proposer",
  async propose({ evidence, state, complete }) {
    // `complete` is injected when a model is available (undefined otherwise);
    // a dedicated-model proposer calls it and returns deltas + modelCall telemetry.
    /* inspect evidence + state, return deltas (and/or a steering message) */
    return { deltas: [/* { delta, rationale } */] };
  },
});

Then "my-proposer" is selectable via /refine --proposer my-proposer or "proposer": "my-proposer" in the config.

Scope and non-goals

• In scope: unified state store, online /refine, structured deltas, prompt injection, branching rollback, durable markdown export.
• Out of scope (compose instead): durable storage engines (pi-mem), offline deep refinement of behavioral files (pi-reflect), live sub-agent orchestration (pi-boss / pi-room). Sub-agent specs are stored as data only.

Status

0.9.x. Implemented:

• Unified harness-state store with branch-local snapshots (/tree rollback).
• Online /refine + harness_mutate / harness_list tools.
• Two-way durable round-trip with pi-reflect (/harness import|export|status), layered on per-item scope: every item is global or project (/harness move, /harness split), and /harness export|import operate on both layers.
• Importance hygiene: /harness prune [--decay <days>] and /harness keep|drop <id>.
• pi-mem composition: /harness push-mem [--all|--kind|--model] steers the agent to persist active items into pi-mem (soft-fail; no dependency).
• Opt-in durable sync ("autoImport": true): session_start layered auto-import + turn_end layered auto-export when the store changed — /refine output survives into new sessions with zero ceremony. Off by default; every action visible and /tree-rollback-able.
• Optional config (~/.pi/agent/harness.json): the durable-sync opt-in, an opt-in turn_end reminder, opt-in turn_end auto-refine, and an opt-in turn_end outcome-importance loop — the package's opt-in autonomous paths (all off by default).
• Pluggable delta proposers with a registry: steering (default) and dedupe (rule-based) shipped; registerProposer() for custom ones.
• Per-model isolation: every item is bound to a provider/id and injected only for that model; new items are stamped automatically and orphans adopted on first contact. A new model id starts from a blank harness, and the durable round-trip preserves the owner tag.
• Bounded injection (on by default): the supplemental block is importance-ordered and capped per kind + by a total token budget, so a growing harness never balloons the system prompt. Tunable / opt-out via the injection config key; the store is never changed by selection.

Open extension points (see docs/ROADMAP.md):

• A dedicated-model proposer (the enabler landed: complete injection + modelCall telemetry; the proposer logic itself is left to a companion package — the hidden-model-spend tradeoff is resolved by making the spend audited rather than shipping it invisible).
• Correction-side outcome signals (promotion is shipped; autonomous demotion is high-false-positive, so it is served by /harness drop, prune --decay, and the dedupe proposer — a fuzzy corrections proposer is the natural future extension).

License

MIT