/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.

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:

MODE=$(python3 -c "import json; print(json.load(open('.evolver.json')).get('mode', 'balanced'))")

If not --no-interactive, confirm or switch:

{
  "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

$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

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):

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"]):

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)

Proposer count: MODES[MODE]["proposers"] (light=2, balanced=3, heavy=5). Cap lenses at this number. Waves: MODES[MODE]["waves"] (light=1 single wave, balanced/heavy=2 two-wave).

Build IDENTICAL shared prefix (objective + files_to_read + context) for KV-cache sharing. Only the <lens> block differs — place it LAST. Include evolution_archive/ in <files_to_read> so proposers can grep prior candidates.

IMPORTANT: After each proposer worktree is created, copy untracked files and set trace nesting. Always use absolute paths:

SRC="$(dirname "$(git rev-parse --git-common-dir)")"
[ -n "$PROJECT_DIR" ] && SRC="$SRC/$PROJECT_DIR"
# If langsmith-tracing companion is installed, proposer traces nest under iteration:
[ -n "$ITER_DOTTED_ORDER" ] && export CC_LANGSMITH_PARENT_DOTTED_ORDER="$ITER_DOTTED_ORDER"
# For each worktree (after Agent creates it, before agent reads files):
cp "$SRC/.evolver.json" "$WT_PROJECT/.evolver.json"
[ -f "$SRC/.env" ] && cp "$SRC/.env" "$WT_PROJECT/.env"
[ -d "$SRC/evolution_archive" ] && cp -r "$SRC/evolution_archive" "$WT_PROJECT/evolution_archive"

Do NOT suppress stderr with 2>/dev/null — if the copy fails, you need to see the error.

Wave 1 — critical + high severity lenses, run independently in parallel:

Agent(
  subagent_type: "harness-proposer",
  isolation: "worktree",
  run_in_background: true,
  prompt: "{SHARED_PREFIX}\n\n<lens>\n{lens.question}\nSource: {lens.source}\n</lens>"
)

Wait for wave 1 to complete. Report each completion as it happens.

Wave 2 — medium + open lenses, see wave 1 results before starting:

Add to the shared context for wave 2 proposers:

<prior_proposals>
Wave 1 proposers completed:
- Proposer {id} ({lens}): {approach from proposal.md} — {committed/abstained}
...
</prior_proposals>

Wave 2 proposers see what wave 1 tried and can build on it, avoid duplication, or take complementary approaches. Research shows +14% quality when agents observe prior outputs.

If only 1-2 lenses total, run as single wave.

4. Evaluate Candidates

Run evaluations with mode parameters. run_eval.py auto-copies config files to worktrees:

CONCURRENCY=$(python3 -c "m={'light':5,'balanced':3,'heavy':3}; print(m.get('$MODE',3))")
TIMEOUT=$(python3 -c "m={'light':60,'balanced':120,'heavy':300}; print(m.get('$MODE',120))")
SAMPLE=$(python3 -c "m={'light':'10','balanced':'','heavy':''}; s=m.get('$MODE',''); print(f'--sample {s} --sample-split train' if s else '')")

for WT in {worktree_paths_with_commits}; do
    WT_PROJECT="$WT"
    [ -n "$PROJECT_DIR" ] && WT_PROJECT="$WT/$PROJECT_DIR"
    $EVOLVER_PY $TOOLS/run_eval.py --config "$SRC/.evolver.json" --worktree-path "$WT_PROJECT" --experiment-prefix v{NNN}-{id} --concurrency $CONCURRENCY --timeout $TIMEOUT $SAMPLE &
done
wait  # CRITICAL: wait for ALL evals before judge

Note: $SRC is set via git rev-parse --git-common-dir — resolves to the main repo root even when CWD is inside a worktree (--show-toplevel returns the worktree root, which is wrong).

Auto-spawn LLM-as-judge — check if LLM evaluators are configured and automatically spawn the evaluator agent. Do NOT leave this as a manual step for the user:

LLM_EVALS=$(python3 -c "import json; c=json.load(open('.evolver.json')); llm=[k for k in c['evaluators'] if k in ('correctness','conciseness')]; print(','.join(llm) if llm else '')")

If LLM_EVALS is non-empty, spawn the evaluator agent immediately after evals complete:

Agent(
  subagent_type: "harness-evaluator",
  prompt: "Experiments: {names}. Evaluators: {LLM_EVALS}. Dataset: {dataset_name}. Use rubrics from example metadata when available."
)

Wait for evaluator to complete before comparing. This is NOT optional — the combined score is meaningless without LLM-judge scores.

5. Compare + Constraint Gate + Merge

$EVOLVER_PY $TOOLS/read_results.py --experiments "{names}" --config .evolver.json --split held_out --output comparison.json

Winner = highest score on held-out data. Report Pareto front and diversity grid if multiple non-dominated candidates.

Pairwise comparison (mode-dependent: light=never, balanced=if top 2 within 5%, heavy=always):

$EVOLVER_PY $TOOLS/read_results.py --pairwise "{winner},{runner_up}" --config .evolver.json --split held_out

If pairwise disagrees with independent scoring, flag for user review.

Resolve project_dir for constraint worktree path. Baseline stays . because CWD is already the project directory:

WINNER_PROJECT="{winner_wt}"
[ -n "$PROJECT_DIR" ] && WINNER_PROJECT="{winner_wt}/$PROJECT_DIR"
$EVOLVER_PY $TOOLS/constraint_check.py --config .evolver.json --worktree-path "$WINNER_PROJECT" --baseline-path "."

If constraints fail, try next-best. If none pass, skip merge.

Efficiency gate (before merge): Check if winner's tokens or latency regressed significantly:

• If tokens increased >2x AND score improved <2%: reject this candidate, try next-best
• If latency increased >50% AND score improved <5%: reject this candidate, try next-best
• In interactive mode: ask user to override if desired. In background mode: auto-reject.

If winner beats current best AND passes efficiency gate:

# 1. Backup config (merge will overwrite with worktree's stale copy)
$EVOLVER_PY $TOOLS/update_config.py --config .evolver.json --action backup

# 2. Merge
git merge {winner_branch} --no-edit -m "evolve: merge v{NNN} (score: {score})"

# 3. Restore config (merge brought stale copy)
$EVOLVER_PY $TOOLS/update_config.py --config .evolver.json --action restore

# 4. Update config with enriched history (one command, no inline Python)
$EVOLVER_PY $TOOLS/update_config.py --config .evolver.json --action update \
    --winner-experiment "{winner}" --winner-score {score} \
    --approach "{approach}" --lens "{lens}" \
    --tokens {tokens} --latency-ms {latency} --error-count {errors} \
    --passing {passing} --total {total} \
    --per-evaluator '{json_dict}' --code-loc {loc}

5. Git-tag for rollback:

git tag "evo-iter-v{NNN}" -m "harness: v{NNN} score={score}"

Note: uses evo-iter- prefix to avoid conflicts with /harness:deploy tags.

6. Post-Iteration

Archive candidates (light=winner only, balanced/heavy=all) for future proposer reference:

for CANDIDATE in {all_worktree_paths}; do
    $EVOLVER_PY $TOOLS/archive.py --config .evolver.json --version v{NNN}-{id} --experiment "{exp}" --worktree-path "$CANDIDATE" --score {score} --approach "{approach}" --lens "{lens}" $([ "{exp}" = "{winner}" ] && echo "--won")
done

Regression tracking (if not first iteration):

$EVOLVER_PY $TOOLS/regression_tracker.py --config .evolver.json --previous-experiment "$PREV" --current-experiment "$WINNER" --add-guards --auto-guard-failures --max-guards 5

Report: Iteration {i}/{N}: v{NNN} scored {score} (best: {best_score})

End iteration trace:

ITER_DURATION=$(( $(date +%s) - ITER_START ))
$EVOLVER_PY $TOOLS/log_iteration.py --config .evolver.json --action end \
    --run-id "$ITER_RUN_ID" --score {winner_score} --merged {true|false} \
    --approach "{approach}" --lens "{lens}" --candidates {num_evaluated} \
    --duration $ITER_DURATION 2>/dev/null

Consolidate (background):

Agent(subagent_type: "harness-consolidator", run_in_background: true, prompt: "Update evolution_memory.md...")

Proactive evaluator evolution: After reading all proposal.md files, check for ## Suggested Evaluators sections. If any proposer suggested new evaluators or rubrics, surface them:

Proposer v{NNN}-{id} suggested new evaluator: "{name}" — {description}

If multiple proposers suggest the same evaluator, prioritize it. Do NOT add evaluators that have no implementation — add_evaluator.py only supports code evaluators with templates (see CODE_EVALUATOR_TEMPLATES in the tool) and LLM evaluators (correctness, conciseness). If a suggestion doesn't match a known template, log it for the architect/critic to implement manually rather than silently adding a no-op entry.

Auto-trigger critic if score jumped >0.3 or hit target in <3 iterations.

Auto-trigger architect (opus model) if 3 consecutive iterations within 1% or score dropped.

Cleanup worktrees (free disk space after eval):

$EVOLVER_PY $TOOLS/cleanup_worktrees.py --dir "$SRC"

7. Gate Check

• Score plateau: 3 scores within 2% → consider architect or stop
• Target reached: best_score >= target_score → stop
• Diminishing returns: avg improvement <0.5% over 5 iterations → stop

(Cost/latency regressions are now checked pre-merge in step 5, not post-merge.)

Final Report

$EVOLVER_PY $TOOLS/evolution_chart.py --config .evolver.json

Plus: LangSmith URL, git log --oneline summary, suggest /harness:deploy.