harness:evolve

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# /harness:evolve

Run the propose-evaluate-iterate loop. LangSmith is the evaluation backend, git worktrees provide isolation.

## Setup

`.evolver.json` must exist. If not, tell user to run `harness:setup`.

```bash
TOOLS="${EVOLVER_TOOLS:-$([ -d ".evolver/tools" ] && echo ".evolver/tools" || echo "$HOME/.evolver/tools")}"
EVOLVER_PY="${EVOLVER_PY:-$([ -f "$HOME/.evolver/venv/bin/python" ] && echo "$HOME/.evolver/venv/bin/python" || echo "python3")}"
```

**Never pass `LANGSMITH_API_KEY` inline.** Tools resolve it automatically via `_common.ensure_langsmith_api_key()`.

## Arguments

- `--iterations N` (default: ask or 5)
- `--mode light|balanced|heavy` — override mode from config
- `--no-interactive` — skip prompts, use defaults (for cron/background runs)

If interactive, ask iterations (3/5/10), target score (0.8/0.9/0.95/none), and execution mode (interactive/background).

## Mode Parameters

```
MODES = {
  "light":    {"proposers": 2, "waves": 1, "concurrency": 5, "timeout": 60, "sample": 10, "analysis": "summary", "pairwise": False, "archive": "winner"},
  "balanced": {"proposers": 3, "waves": 2, "concurrency": 3, "timeout": 120, "sample": None, "analysis": "summary", "pairwise": "if_close", "archive": "all"},
  "heavy":    {"proposers": 5, "waves": 2, "concurrency": 3, "timeout": 300, "sample": None, "analysis": "full", "pairwise": True, "archive": "all"},
}
```

Read mode from config, allow `--mode` override:
```bash
MODE=$(python3 -c "import json; print(json.load(open('.evolver.json')).get('mode', 'balanced'))")
```

If not `--no-interactive`, confirm or switch:
```json
{
  "question": "Mode: {MODE}. Continue?",
  "header": "Mode",
  "options": [
    {"label": "Yes, continue with {MODE}"},
    {"label": "Switch to light (~2 min/iter)"},
    {"label": "Switch to balanced (~8 min/iter)"},
    {"label": "Switch to heavy (~25 min/iter)"}
  ]
}
```

If changed, update config and re-read MODE.

## Pre-Loop

### Preflight

```bash
$EVOLVER_PY $TOOLS/preflight.py --config .evolver.json
```

Validates API key, config schema, LangSmith state, dataset health, and canary in one pass. If it fails, ask user: fix and retry, continue anyway, or abort. If health issues are auto-correctable, run `/harness:health` first.

### Baseline LLM-Judge

If LLM evaluators (correctness, conciseness) are configured but baseline only has code-based scores, spawn the evaluator agent on the baseline experiment. Re-read and update `best_score` in `.evolver.json` after scoring.

### Resolve Project Directory

Read `project_dir` from config. If non-empty, all worktree paths include it: `{worktree}/{project_dir}/`.

## The Loop (per iteration)

### 0. Read State + Start Iteration Trace

```bash
BEST=$(python3 -c "import json; b=json.load(open('.evolver.json')).get('best_experiment'); print(b if b else '')")
PROJECT_DIR=$(python3 -c "import json; print(json.load(open('.evolver.json')).get('project_dir', ''))")
ITER_START=$(date +%s)
```

**Start iteration trace** (logs to LangSmith for observability):
```bash
ITER_TRACE=$($EVOLVER_PY $TOOLS/log_iteration.py --config .evolver.json --action start --version v{NNN} 2>/dev/null)
ITER_RUN_ID=$(echo "$ITER_TRACE" | python3 -c "import sys,json; print(json.load(sys.stdin).get('run_id',''))" 2>/dev/null)
ITER_DOTTED_ORDER=$(echo "$ITER_TRACE" | python3 -c "import sys,json; print(json.load(sys.stdin).get('dotted_order',''))" 2>/dev/null)
```

If log_iteration.py fails (no LangSmith, no key), the loop continues — tracing is optional.

If `$BEST` is empty (no baseline ran), skip data gathering — proposers work from code analysis only.

### 1. Gather Data (parallel)

Analysis format depends on mode (`MODES[MODE]["analysis"]`):

```bash
if [ -n "$BEST" ]; then
    ANALYSIS_FMT=$(python3 -c "m={'light':'summary','balanced':'summary','heavy':'full'}; print(m.get('$MODE','summary'))")
    $EVOLVER_PY $TOOLS/trace_insights.py --from-experiment "$BEST" --format $ANALYSIS_FMT --output trace_insights.json &
    $EVOLVER_PY $TOOLS/read_results.py --experiment "$BEST" --config .evolver.json --split train --format $ANALYSIS_FMT --output best_results.json &
    wait
fi
```

### 2. Generate Strategy + Lenses

From trace_insights.json, best_results.json, evolution_memory.md, production_seed.json:

**strategy.md** — Current iteration data ONLY. No stale info. Contents: target files, failure clusters (latest experiment), top 3 promoted memory insights (rec >= 2), approaches to avoid, top 3 failing examples with judge feedback. **Cap at 1500 tokens.**

**lenses.json** — Investigation questions for proposers:
- One per failure cluster (max 3), one architecture, one production, one evolution_memory, one open
- If `evolution_archive/` has 3+ iterations, one `archive_branch` lens that suggests revisiting a losing candidate's approach
- Sort by severity, cap at 5 lenses

### 3. Spawn Proposers (mode-dependent)

From raphaelchristi/harness-evolver, MIT.

More from raphaelchristi/harness-evolver