From 2fdd87aad6b22ad8a57b844fe3bffab7dc802264 Mon Sep 17 00:00:00 2001 From: Ahmad Ragab Date: Sun, 5 Jul 2026 06:45:36 -0400 Subject: [PATCH 1/2] feat: upgrade gepa and add observability benchmark --- EXAMPLES.md | 6 +- README.md | 26 +- WALKTHROUGH.md | 10 +- commands/optimize.md | 7 +- evaluators/evaluator_generation_training.sh | 143 + evaluators/evaluator_generation_validation.sh | 109 + examples/optimization-observability/README.md | 88 + .../evaluator-generation-benchmark.json | 26 + .../.openspec.yaml | 2 + .../design.md | 113 + .../proposal.md | 28 + .../specs/optimization-observability/spec.md | 72 + .../tasks.md | 37 + .../.openspec.yaml | 2 + .../design.md | 90 + .../proposal.md | 32 + .../specs/optimization-runtime/spec.md | 81 + .../tasks.md | 33 + openspec/config.yaml | 20 + .../specs/optimization-observability/spec.md | 72 + openspec/specs/optimization-runtime/spec.md | 81 + pyproject.toml | 7 +- scripts/optimization_observer.py | 477 +++ src/optimize_anything/cli.py | 13 +- src/optimize_anything/cli_optimize.py | 41 +- tests/test_cli.py | 85 + tests/test_dependency_safety.py | 44 + tests/test_optimization_observer.py | 254 ++ tests/test_spec_loader.py | 104 + uv.lock | 3510 ++++++++--------- 30 files changed, 3837 insertions(+), 1776 deletions(-) create mode 100755 evaluators/evaluator_generation_training.sh create mode 100755 evaluators/evaluator_generation_validation.sh create mode 100644 examples/optimization-observability/README.md create mode 100644 examples/optimization-observability/evaluator-generation-benchmark.json create mode 100644 openspec/changes/archive/2026-07-04-observe-realistic-optimization-loop/.openspec.yaml create mode 100644 openspec/changes/archive/2026-07-04-observe-realistic-optimization-loop/design.md create mode 100644 openspec/changes/archive/2026-07-04-observe-realistic-optimization-loop/proposal.md create mode 100644 openspec/changes/archive/2026-07-04-observe-realistic-optimization-loop/specs/optimization-observability/spec.md create mode 100644 openspec/changes/archive/2026-07-04-observe-realistic-optimization-loop/tasks.md create mode 100644 openspec/changes/archive/2026-07-04-upgrade-gepa-011-parallel-default/.openspec.yaml create mode 100644 openspec/changes/archive/2026-07-04-upgrade-gepa-011-parallel-default/design.md create mode 100644 openspec/changes/archive/2026-07-04-upgrade-gepa-011-parallel-default/proposal.md create mode 100644 openspec/changes/archive/2026-07-04-upgrade-gepa-011-parallel-default/specs/optimization-runtime/spec.md create mode 100644 openspec/changes/archive/2026-07-04-upgrade-gepa-011-parallel-default/tasks.md create mode 100644 openspec/config.yaml create mode 100644 openspec/specs/optimization-observability/spec.md create mode 100644 openspec/specs/optimization-runtime/spec.md create mode 100755 scripts/optimization_observer.py create mode 100644 tests/test_dependency_safety.py create mode 100644 tests/test_optimization_observer.py diff --git a/EXAMPLES.md b/EXAMPLES.md index 3f5bb82..67696ef 100644 --- a/EXAMPLES.md +++ b/EXAMPLES.md @@ -89,10 +89,14 @@ optimize-anything optimize prompt.txt \ --dataset data/train.jsonl \ --model openai/gpt-4o-mini \ --budget 120 \ - --parallel --workers 6 \ + --workers 6 \ --cache --run-dir runs ``` +Optimization runs evaluator calls in parallel by default. Use `--workers` to cap +concurrency, or `--no-parallel` when an evaluator writes shared temp files, +depends on process-global state, or must stay below strict provider rate limits. + With validation set: ```bash diff --git a/README.md b/README.md index 161dfd5..e62ec34 100644 --- a/README.md +++ b/README.md @@ -22,7 +22,7 @@ optimize-anything optimize seed.txt \ --objective "Improve clarity and specificity" \ --model openai/gpt-4o-mini \ --budget 20 \ - --parallel --workers 4 \ + --workers 4 \ --cache \ --run-dir runs \ --output result.txt @@ -60,7 +60,7 @@ optimize-anything optimize prompt.txt \ --dataset data/train.jsonl \ --valset data/val.jsonl \ --model openai/gpt-4o-mini \ - --budget 120 --parallel --workers 6 --cache --run-dir runs + --budget 120 --workers 6 --cache --run-dir runs ``` ### Multi-provider validation @@ -91,6 +91,9 @@ optimize-anything optimize --no-seed \ - Early stop is auto-enabled when `--budget > 30` (or force with `--early-stop`) - Reuse prior evaluator cache with `--cache-from` (requires `--cache` + `--run-dir`) +- Evaluator calls run in parallel by default; pass `--no-parallel` for evaluators + that write shared temp files, depend on process-global state, or need strict + provider rate-limit control. ```bash optimize-anything optimize seed.txt \ @@ -115,6 +118,22 @@ optimize-anything optimize seed.txt \ --score-range any ``` +### Optimization observability benchmark + +Use the maintainer benchmark when you want evidence that an optimization loop +improved a useful artifact and was not just a seed-only or scorer-gaming run. + +```bash +uv run python scripts/optimization_observer.py setup +uv run python scripts/optimization_observer.py report \ + --run-dir integration_runs/optimization-observability/evaluator-generation-guidance/runs/run-YYYYMMDD-HHMMSS \ + --benchmark examples/optimization-observability/evaluator-generation-benchmark.json \ + --strict +``` + +See `examples/optimization-observability/README.md` for the full benchmark +commands, expected report fields, acceptance criteria, and troubleshooting. + ## CLI Subcommands - `optimize` @@ -288,7 +307,8 @@ Exactly one evaluator source is required: `--evaluator-command` OR `--evaluator- | `--api-base ` | Override LiteLLM API base | -- | | `--diff` | Print unified diff (seed vs best) to stderr | `false` | | `--run-dir ` | Save run artifacts in timestamped run dir | -- | -| `--parallel` | Enable parallel evaluator calls | `false` | +| `--parallel` | Explicitly enable parallel evaluator calls | `true` | +| `--no-parallel` | Run evaluator calls serially | -- | | `--workers ` | Max workers for parallel evaluation | -- | | `--cache` | Enable evaluator cache | `false` | | `--cache-from ` | Copy prior `fitness_cache` into new run | -- | diff --git a/WALKTHROUGH.md b/WALKTHROUGH.md index b64f22b..34feb5d 100644 --- a/WALKTHROUGH.md +++ b/WALKTHROUGH.md @@ -105,9 +105,10 @@ uv run optimize-anything optimize seed.txt \ --budget 120 --cache --run-dir runs ``` -## Step 9.5: Speed up with parallel workers +## Step 9.5: Tune parallel workers -For expensive evaluators, add parallel execution: +Optimization runs evaluator calls in parallel by default. For expensive +evaluators, cap concurrency with workers: ```bash uv run optimize-anything optimize seed.txt \ @@ -115,9 +116,12 @@ uv run optimize-anything optimize seed.txt \ --objective "Improve quality" \ --model openai/gpt-4o-mini \ --budget 100 \ - --parallel --workers 8 + --workers 8 ``` +Use `--no-parallel` if the evaluator writes shared temp files, depends on +process-global state, or needs strict provider rate-limit control. + ## Step 10: Accept or rerun If improved, copy best artifact back into source. Otherwise adjust objective/intake and rerun. diff --git a/commands/optimize.md b/commands/optimize.md index cda1b21..be3ee6b 100644 --- a/commands/optimize.md +++ b/commands/optimize.md @@ -51,7 +51,12 @@ Mode guidance: If optimization fails: check error message. Common issues: missing API key, model quota exceeded, evaluator script error. -For larger budgets (especially >50), suggest `--parallel` (and optionally `--workers`) when evaluator setup can support concurrency. +Optimization runs evaluator calls in parallel by default. For larger budgets +(especially >50), suggest `--workers` when evaluator setup can support +concurrency. Suggest `--no-parallel` for evaluators that write shared temp +files, depend on process-global state, or need strict provider rate-limit +control. `--parallel` remains valid when users want to enable parallel mode +explicitly. ## Step 5: Present results clearly After completion, provide: diff --git a/evaluators/evaluator_generation_training.sh b/evaluators/evaluator_generation_training.sh new file mode 100755 index 0000000..58769b4 --- /dev/null +++ b/evaluators/evaluator_generation_training.sh @@ -0,0 +1,143 @@ +#!/usr/bin/env bash +# Deterministic training scorer for the evaluator-generation benchmark. +# Input: {"candidate": ""} +# Output: {"score": <0..1>, dimension scores, "feedback": [...]} + +set -euo pipefail + +input_file=$(mktemp) +trap 'rm -f "$input_file"' EXIT +cat > "$input_file" + +python3 - "$input_file" <<'PY' +from __future__ import annotations + +import json +import math +import re +import sys + + +def clamp(value: float) -> float: + return max(0.0, min(1.0, value)) + + +def contains_any(text: str, options: list[str]) -> bool: + return any(option in text for option in options) + + +def ratio(hits: list[bool]) -> float: + return sum(1 for hit in hits if hit) / max(len(hits), 1) + + +def length_score(text: str) -> float: + length = len(text) + if length < 700: + return clamp(length / 700.0) + if length <= 5200: + return 1.0 + if length <= 7500: + return clamp(1.0 - ((length - 5200) / 3500.0)) + return 0.25 + + +try: + with open(sys.argv[1], "r", encoding="utf-8") as fh: + payload = json.load(fh) +except json.JSONDecodeError: + print(json.dumps({"score": 0.0, "error": "Input must be valid JSON"})) + raise SystemExit(0) + +if "candidate" not in payload or not isinstance(payload["candidate"], str): + print(json.dumps({"score": 0.0, "error": "Payload must include string field 'candidate'"})) + raise SystemExit(0) + +candidate = payload["candidate"] +if not candidate.strip(): + print(json.dumps({"score": 0.0, "error": "Candidate must not be empty"})) + raise SystemExit(0) + +lower = candidate.lower() +feedback: list[str] = [] +code_blocks = re.findall(r"```[a-zA-Z0-9_-]*\n(.*?)```", candidate, re.DOTALL) + +contract_completeness = ratio([ + "candidate" in lower, + "json" in lower and contains_any(lower, ["stdin", "post body", "http post"]), + '"score"' in candidate or "`score`" in candidate or "score output" in lower, + contains_any(lower, ["float", "numeric", "finite", "[0,1]", "[0, 1]"]), + contains_any(lower, ["feedback", "diagnostic", "dimension", "reasoning"]), + contains_any(lower, ["dataset", "example"]), +]) +if contract_completeness < 0.85: + feedback.append("Clarify candidate input, numeric score output, score range, and diagnostic side fields.") + +runnable_examples = ratio([ + bool(code_blocks), + "optimize-anything generate-evaluator" in lower, + "echo" in lower and "candidate" in lower, + contains_any(lower, ["python3", "bash", "--evaluator-command"]), + "--objective" in lower, +]) +if runnable_examples < 0.8: + feedback.append("Add copy-pasteable commands that generate an evaluator and test it with a JSON payload.") + +feedback_quality = ratio([ + contains_any(lower, ["feedback", "diagnostic", "reasoning"]), + contains_any(lower, ["dimension", "quality_dimensions", "subscore"]), + contains_any(lower, ["reflection", "weak", "improve"]), + contains_any(lower, ["hard constraint", "constraint failure", "safety gate"]), + contains_any(lower, ["customize", "scoring logic", "rubric"]), +]) +if feedback_quality < 0.8: + feedback.append("Explain how evaluators return actionable diagnostics for reflection, not just a scalar score.") + +calibration_guidance = ratio([ + contains_any(lower, ["0.3-0.7", "0.3 to 0.7", "0.85"]), + contains_any(lower, ["discrimination", "non-discriminating", "too easy"]), + contains_any(lower, ["baseline", "threshold", "score range"]), + contains_any(lower, ["test", "validate"]), + contains_any(lower, ["edge case", "malformed", "empty"]), +]) +if calibration_guidance < 0.65: + feedback.append("Add calibration guidance for weak or non-discriminating evaluators.") + +structure = ratio([ + candidate.lstrip().startswith("---"), + lower.count("\n## ") >= 4, + lower.count("\n### ") >= 2, + candidate.count("- ") >= 6, + len(code_blocks) >= 2, +]) +if structure < 0.8: + feedback.append("Use frontmatter, section headings, lists, and fenced examples for scanability.") + +conciseness = length_score(candidate) +if conciseness < 0.8: + feedback.append("Keep the guidance concise enough to remain usable as a skill file.") + +score = ( + contract_completeness * 0.28 + + runnable_examples * 0.22 + + feedback_quality * 0.20 + + calibration_guidance * 0.16 + + structure * 0.08 + + conciseness * 0.06 +) +score = clamp(score) +if not math.isfinite(score): + score = 0.0 +if not feedback: + feedback.append("Strong baseline; look for sharper calibration, held-out validation, or scorer-debugging guidance.") + +print(json.dumps({ + "score": round(score, 4), + "contract_completeness": round(contract_completeness, 4), + "runnable_examples": round(runnable_examples, 4), + "feedback_quality": round(feedback_quality, 4), + "calibration_guidance": round(calibration_guidance, 4), + "structure": round(structure, 4), + "conciseness": round(conciseness, 4), + "feedback": feedback[:5], +})) +PY diff --git a/evaluators/evaluator_generation_validation.sh b/evaluators/evaluator_generation_validation.sh new file mode 100755 index 0000000..912fe46 --- /dev/null +++ b/evaluators/evaluator_generation_validation.sh @@ -0,0 +1,109 @@ +#!/usr/bin/env bash +# Deterministic held-out scorer for the evaluator-generation benchmark. +# Uses a different rubric from the training scorer to discourage scorer gaming. + +set -euo pipefail + +input_file=$(mktemp) +trap 'rm -f "$input_file"' EXIT +cat > "$input_file" + +python3 - "$input_file" <<'PY' +from __future__ import annotations + +import json +import re +import sys + + +def clamp(value: float) -> float: + return max(0.0, min(1.0, value)) + + +def any_of(text: str, phrases: list[str]) -> bool: + return any(phrase in text for phrase in phrases) + + +def score_hits(hits: list[bool]) -> float: + return sum(1 for hit in hits if hit) / max(len(hits), 1) + + +try: + with open(sys.argv[1], "r", encoding="utf-8") as fh: + payload = json.load(fh) +except json.JSONDecodeError: + print(json.dumps({"score": 0.0, "error": "Input must be valid JSON"})) + raise SystemExit(0) + +if "candidate" not in payload or not isinstance(payload["candidate"], str): + print(json.dumps({"score": 0.0, "error": "Payload must include string field 'candidate'"})) + raise SystemExit(0) + +candidate = payload["candidate"] +lower = candidate.lower() +blocks = re.findall(r"```[a-zA-Z0-9_-]*\n(.*?)```", candidate, re.DOTALL) +feedback: list[str] = [] + +contract_safety = score_hits([ + any_of(lower, ["invalid json", "malformed", "empty"]), + any_of(lower, ["score range", "[0,1]", "[0, 1]", "finite"]), + any_of(lower, ["stderr", "exit", "return code", "timeout"]), + any_of(lower, ["candidate", "example"]), +]) +if contract_safety < 0.5: + feedback.append("Held-out check: include failure handling for malformed payloads and invalid scores.") + +pattern_coverage = score_hits([ + "judge" in lower, + "command" in lower, + "http" in lower, + "composite" in lower, + "dataset" in lower or "valset" in lower, +]) +if pattern_coverage < 0.8: + feedback.append("Held-out check: cover judge, command, HTTP, composite, and dataset-aware evaluators.") + +reviewability = score_hits([ + any_of(lower, ["baseline", "acceptance", "threshold"]), + any_of(lower, ["discrimination", "too easy", "0.85"]), + any_of(lower, ["validate", "cross-check", "held-out"]), + len(blocks) >= 2, +]) +if reviewability < 0.5: + feedback.append("Held-out check: explain how maintainers decide whether an evaluator is good enough.") + +workflow_fit = score_hits([ + candidate.lstrip().startswith("---"), + lower.count("\n## ") >= 4, + "--objective" in lower, + "optimize-anything" in lower, +]) +if workflow_fit < 0.75: + feedback.append("Held-out check: preserve skill structure and optimize-anything command context.") + +brevity = 1.0 +if len(candidate) > 7000: + brevity = 0.5 + feedback.append("Held-out check: guidance is long; tighten before accepting.") +elif len(candidate) < 700: + brevity = 0.4 + feedback.append("Held-out check: guidance is too short to be operational.") + +score = clamp( + contract_safety * 0.25 + + pattern_coverage * 0.25 + + reviewability * 0.20 + + workflow_fit * 0.20 + + brevity * 0.10 +) + +print(json.dumps({ + "score": round(score, 4), + "contract_safety": round(contract_safety, 4), + "pattern_coverage": round(pattern_coverage, 4), + "reviewability": round(reviewability, 4), + "workflow_fit": round(workflow_fit, 4), + "brevity": round(brevity, 4), + "feedback": feedback[:5], +})) +PY diff --git a/examples/optimization-observability/README.md b/examples/optimization-observability/README.md new file mode 100644 index 0000000..9fe2c22 --- /dev/null +++ b/examples/optimization-observability/README.md @@ -0,0 +1,88 @@ +# Optimization Observability Benchmark + +This benchmark verifies that an optimization loop improved a useful project +artifact, not just that the CLI ran. The first benchmark targets +`skills/generate-evaluator/SKILL.md`, because better evaluator-generation +guidance should improve future optimize-anything workflows. + +The benchmark never overwrites the tracked source artifact. `setup` copies the +seed into a work directory, and optimization writes candidate artifacts into a +run directory. Accepting a result is a separate review step. + +## Run The Benchmark + +Create a copied seed and print the exact run metadata: + +```bash +uv run python scripts/optimization_observer.py setup +``` + +The JSON output includes: + +- `seed_file`: copied seed to optimize +- `objective`: benchmark objective +- `constraints`: acceptance constraints +- `run_base_dir`: parent directory for optimizer run artifacts +- `training_evaluator_command`: deterministic training scorer +- `heldout_evaluator_command`: deterministic held-out scorer +- `optimize_command`: starter command for the optimization run + +Run optimization with a small budget. Set `OPTIMIZE_ANYTHING_MODEL`, or add +`--model ` before `--evaluator-command`. + +```bash +uv run optimize-anything optimize \ + integration_runs/optimization-observability/evaluator-generation-guidance/seed.md \ + --objective "Improve the evaluator-generation guidance so it helps maintainers create reliable evaluators with clear JSON contracts, runnable examples, diagnostic feedback, calibration guidance, and concise acceptance criteria." \ + --budget 6 \ + --run-dir integration_runs/optimization-observability/evaluator-generation-guidance/runs \ + --evaluator-command bash evaluators/evaluator_generation_training.sh +``` + +Generate the observation report for the created `run-*` directory: + +```bash +uv run python scripts/optimization_observer.py report \ + --run-dir integration_runs/optimization-observability/evaluator-generation-guidance/runs/run-YYYYMMDD-HHMMSS \ + --benchmark examples/optimization-observability/evaluator-generation-benchmark.json \ + --strict +``` + +When `--benchmark` is provided, the report uses the benchmark's held-out +validation scorer automatically. + +## Expected Report + +The report JSON includes: + +- `training.initial_score`, `training.best_score`, and `training.score_delta` +- `training.candidate_count` and `training.metric_calls` +- `training.diagnostics` from the optimizer summary +- `diff_summary.added`, `diff_summary.removed`, and a short diff preview +- `validation.seed_score`, `validation.best_score`, `validation.score_delta` +- `warnings` with actionable recommendations for weak evidence +- `accepted`: `true` only when training improvement and held-out validation pass +- `artifacts`: paths to `seed.txt`, `best_artifact.txt`, `summary.json`, and GEPA artifacts + +## Acceptance Criteria + +Accept an optimized artifact only when: + +- training score improves by at least `meaningful_delta` from the benchmark config +- held-out validation improves by at least `validation_delta` +- candidate count is greater than one +- diagnostics are present and specific enough to explain the improvement +- the diff improves evaluator guidance without bloating or weakening the skill +- the tracked source file is updated manually after review + +Do not accept a run solely because the training scorer improved. Treat the +observer report as evidence for review, not as an automatic source change. + +## Troubleshooting + +- `seed_only`: increase budget, confirm model credentials, and inspect GEPA artifacts. +- `missing_diagnostics`: return dimension scores or feedback fields from the scorer. +- `flat_scores`: sharpen the objective or make the scorer more discriminating. +- `insufficient_improvement`: rerun with a larger budget or improve scorer feedback. +- `heldout_regression`: inspect the diff for scorer gaming before accepting. +- `validation_not_run`: pass `--benchmark` or `--validation-command` to the report. diff --git a/examples/optimization-observability/evaluator-generation-benchmark.json b/examples/optimization-observability/evaluator-generation-benchmark.json new file mode 100644 index 0000000..2ab09a9 --- /dev/null +++ b/examples/optimization-observability/evaluator-generation-benchmark.json @@ -0,0 +1,26 @@ +{ + "id": "evaluator-generation-guidance", + "name": "Evaluator-generation guidance benchmark", + "seed_artifact": "skills/generate-evaluator/SKILL.md", + "why": "The generate-evaluator skill teaches users how to build scoring functions for optimize-anything. Improving it should make future optimization workflows easier to run, debug, and validate.", + "objective": "Improve the evaluator-generation guidance so it helps maintainers create reliable evaluators with clear JSON contracts, runnable examples, diagnostic feedback, calibration guidance, and concise acceptance criteria.", + "constraints": [ + "Preserve the SKILL.md frontmatter and trigger intent.", + "Keep the artifact useful as an instruction file, not a marketing page.", + "Explain the evaluator JSON contract, including candidate input and numeric score output.", + "Include runnable command examples for generating and testing evaluators.", + "Tell maintainers how to diagnose weak or non-discriminating evaluators.", + "Do not overwrite the tracked source artifact during benchmark runs." + ], + "training_evaluator_command": [ + "bash", + "evaluators/evaluator_generation_training.sh" + ], + "heldout_evaluator_command": [ + "bash", + "evaluators/evaluator_generation_validation.sh" + ], + "meaningful_delta": 0.03, + "validation_delta": 0.01, + "recommended_budget": 6 +} diff --git a/openspec/changes/archive/2026-07-04-observe-realistic-optimization-loop/.openspec.yaml b/openspec/changes/archive/2026-07-04-observe-realistic-optimization-loop/.openspec.yaml new file mode 100644 index 0000000..d86f152 --- /dev/null +++ b/openspec/changes/archive/2026-07-04-observe-realistic-optimization-loop/.openspec.yaml @@ -0,0 +1,2 @@ +schema: spec-driven +created: 2026-07-04 diff --git a/openspec/changes/archive/2026-07-04-observe-realistic-optimization-loop/design.md b/openspec/changes/archive/2026-07-04-observe-realistic-optimization-loop/design.md new file mode 100644 index 0000000..302a548 --- /dev/null +++ b/openspec/changes/archive/2026-07-04-observe-realistic-optimization-loop/design.md @@ -0,0 +1,113 @@ +## Context + +`optimize-anything` has several verification surfaces today: + +- `scripts/smoke_harness.py` proves the CLI can run and produce contract-shaped output, but its evaluator is a toy length proxy. +- `scripts/score_check.py` protects accepted artifacts from score regression, including `skills/generate-evaluator/SKILL.md`. +- `scripts/live_integration.py` already models GREEN optimization and RED scoring phases, but it is more of an experiment helper than a clear observability workflow. +- `optimize-anything optimize --run-dir` persists `seed.txt`, `best_artifact.txt`, `summary.json`, and GEPA runtime artifacts. + +The missing piece is a repeatable, realistic workflow that answers: "Did the optimizer explore useful candidates and improve something we actually care about?" + +## Goals / Non-Goals + +**Goals:** + +- Provide a realistic benchmark centered on a project-relevant text artifact, initially evaluator-generation guidance. +- Preserve source artifacts during optimization; acceptance remains a separate review step. +- Use deterministic training scoring for repeatability. +- Add held-out validation so improvements are not accepted solely because they satisfy the training scorer. +- Produce a compact observation report with enough loop telemetry to debug ineffective optimization. +- Document commands and acceptance criteria for maintainers. + +**Non-Goals:** + +- Do not replace GEPA's internal telemetry or candidate tree artifacts. +- Do not require live LLM/provider credentials for the default deterministic benchmark path. +- Do not automatically commit or overwrite optimized artifacts. +- Do not redesign the evaluator protocol. +- Do not make broad changes to `score_check.py` unless the benchmark needs a small reusable helper. + +## Decisions + +### Benchmark Artifact + +Use evaluator-generation guidance as the first benchmark target. It is already useful to this project, already appears in `scores.json`, and has a clear product purpose: better evaluator guidance should improve optimization outcomes for future users. + +Implementation should avoid optimizing the tracked file in place. A setup step can copy `skills/generate-evaluator/SKILL.md` into a run input directory, or the benchmark can pass the original as a seed while writing optimized output to a run directory. + +Alternative considered: use the smoke harness launch blurb. That is cheaper, but it does not prove real product usefulness and encourages superficial improvements. + +### Training Scorer + +Use a deterministic scorer for the primary benchmark so it can run in CI and local development without model credentials. The scorer should emit: + +- `score` +- dimension scores, such as contract completeness, runnable examples, feedback quality, calibration guidance, and conciseness +- concise feedback strings that GEPA can use for reflection + +The existing `evaluators/skill_clarity.sh` may be reused if it is sufficient, but implementation should be willing to add a more benchmark-specific scorer if the existing rubric is too easy to game. + +Alternative considered: use only an LLM judge. That is semantically attractive, but it introduces cost, provider variance, and credential requirements into the default effectiveness check. + +### Held-Out Validation + +Acceptance should require a second view of quality. The default held-out check can be deterministic if it measures different dimensions than the training scorer. An optional LLM or multi-provider judge path can provide stronger semantic validation when credentials are available. + +The observation report should distinguish: + +- training improvement: optimizer beat the deterministic scorer +- validation acceptance: held-out check agrees the artifact is better enough to consider + +This prevents a run from being called successful when it only learned to satisfy shallow scorer features. + +### Observation Report + +Prefer deriving the report from existing outputs: + +- CLI summary JSON for initial/best scores, candidate count, metric calls, diagnostics, and plateau guidance +- persisted run directory for seed, best artifact, and GEPA artifacts +- text diff between seed and best artifact for reviewability +- optional held-out validation JSON for acceptance + +The implementation can live in one of two shapes: + +1. Extend `scripts/live_integration.py` with a named realistic benchmark/report mode. +2. Add a small dedicated script such as `scripts/optimization_observer.py` if separating benchmark/report logic keeps `live_integration.py` simpler. + +A new public CLI command should be avoided unless the script proves broadly useful. This change is primarily a maintainer-facing verification workflow. + +### Ineffective Run Detection + +The report should explicitly flag weak evidence: + +- no candidate beyond the seed +- missing score summary or diagnostics +- best score equal to or below initial score +- improvement below a meaningful threshold +- held-out validation regression + +These flags should not necessarily make every run exit non-zero. For CI-style gates, implementation can add a strict mode or threshold argument that returns non-zero when effectiveness evidence is insufficient. + +## Risks / Trade-offs + +- Deterministic scorers can be gamed -> Use held-out validation and inspect diffs before accepting artifacts. +- Benchmark-specific scripts can become stale -> Keep the benchmark close to existing scorer assets and document ownership. +- Live LLM validation can be noisy or unavailable -> Make it optional; keep deterministic validation as the default path. +- Too much telemetry can obscure the decision -> Produce a compact report first, with paths to deeper GEPA artifacts for inspection. +- Source artifact optimization can accidentally overwrite reviewed docs -> Always write to run directories or copies until maintainers explicitly accept a result. + +## Migration Plan + +1. Add benchmark assets and report logic without changing existing source artifact behavior. +2. Add tests around deterministic scorer output, observation report fields, and ineffective-run detection. +3. Document the maintainer workflow. +4. Optionally wire the benchmark into `scripts/check.py` only after it is stable and cheap enough. + +Rollback is straightforward: remove the benchmark assets and report script/docs. Existing optimization and scoring commands continue to work. + +## Open Questions + +- Should the first benchmark optimize `skills/generate-evaluator/SKILL.md` directly from the tracked file, or should it use a dedicated copied seed under `examples/`? +- Should strict effectiveness thresholds be hardcoded for the benchmark or configurable by command-line flags? +- Should optional semantic validation use `optimize-anything validate`, `score --judge-model`, or the existing `live_integration.py --phase red` path? diff --git a/openspec/changes/archive/2026-07-04-observe-realistic-optimization-loop/proposal.md b/openspec/changes/archive/2026-07-04-observe-realistic-optimization-loop/proposal.md new file mode 100644 index 0000000..b30a322 --- /dev/null +++ b/openspec/changes/archive/2026-07-04-observe-realistic-optimization-loop/proposal.md @@ -0,0 +1,28 @@ +## Why + +The project can prove that optimization commands run, but it does not yet give a realistic, repeatable way to observe whether an optimization loop made a useful artifact better. We need a workflow that shows score movement, candidate exploration, evaluator feedback quality, and held-out validation on an artifact this project actually wants to improve. + +## What Changes + +- Add a realistic optimization effectiveness benchmark built around a useful text artifact, starting with evaluator-generation guidance. +- Add or extend scorer assets so the benchmark includes both deterministic training feedback and a held-out quality check that discourages scorer gaming. +- Add an observation workflow that records and summarizes the optimization loop: initial score, best score, score delta, candidate count, metric calls, top diagnostics, diff summary, and run-dir locations. +- Add warnings or failure states for ineffective runs such as seed-only optimization, flat scores, missing diagnostics, or no meaningful improvement. +- Document how maintainers run the benchmark, inspect the run, and decide whether an optimized artifact should be accepted. + +## Capabilities + +### New Capabilities + +- `optimization-observability`: Defines realistic optimization benchmark inputs, scorer expectations, loop telemetry, effectiveness checks, and acceptance evidence for optimized text artifacts. + +### Modified Capabilities + +- None. + +## Impact + +- Affected scripts: likely `scripts/live_integration.py`, `scripts/score_check.py`, or a new focused observation/report script. +- Affected CLI/runtime surfaces: `optimize-anything optimize --run-dir` output contract and any command used to summarize a saved run. +- Affected assets: benchmark seed artifact, scorer/rubric files, expected output examples, and documentation. +- Affected tests: unit tests for scoring/report logic and integration-style tests using deterministic mock evaluators. diff --git a/openspec/changes/archive/2026-07-04-observe-realistic-optimization-loop/specs/optimization-observability/spec.md b/openspec/changes/archive/2026-07-04-observe-realistic-optimization-loop/specs/optimization-observability/spec.md new file mode 100644 index 0000000..92beabc --- /dev/null +++ b/openspec/changes/archive/2026-07-04-observe-realistic-optimization-loop/specs/optimization-observability/spec.md @@ -0,0 +1,72 @@ +## ADDED Requirements + +### Requirement: Realistic optimization benchmark +The system SHALL provide a repeatable benchmark for optimizing a useful text artifact that maintainers would plausibly accept into the project. + +#### Scenario: Benchmark uses a project-relevant artifact +- **WHEN** a maintainer runs the benchmark setup +- **THEN** the seed artifact is project-relevant documentation or guidance, not a toy length or marketing example +- **AND** the benchmark states the optimization objective and acceptance constraints + +#### Scenario: Benchmark can run without changing the source artifact +- **WHEN** a maintainer runs the benchmark +- **THEN** the optimization uses a copied seed or explicit output path +- **AND** the tracked source artifact is not overwritten unless the maintainer separately accepts the result + +### Requirement: Training scorer emits actionable diagnostics +The system SHALL include a deterministic training scorer for the benchmark that returns a numeric score plus diagnostic fields useful to the optimizer. + +#### Scenario: Scorer returns optimizer-compatible JSON +- **WHEN** the scorer receives an evaluator payload containing `candidate` +- **THEN** it returns a JSON object with a finite numeric `score` +- **AND** the score is valid for the configured score range + +#### Scenario: Scorer reports improvement dimensions +- **WHEN** the scorer evaluates a candidate +- **THEN** the JSON response includes dimension scores or feedback fields that identify why the candidate scored as it did +- **AND** those diagnostics are specific enough to guide a subsequent reflection step + +### Requirement: Observation report summarizes loop behavior +The system SHALL produce an observation report for a benchmark run that summarizes whether the optimization loop explored candidates and improved the artifact. + +#### Scenario: Report includes core loop telemetry +- **WHEN** a benchmark run completes +- **THEN** the report includes the initial score, best score, score delta, candidate count, total metric calls, top diagnostics, diff summary, and run directory + +#### Scenario: Report references persisted artifacts +- **WHEN** a benchmark run writes a run directory +- **THEN** the report identifies where to find the seed, best artifact, summary JSON, and relevant optimizer runtime artifacts + +### Requirement: Ineffective optimization signals +The system SHALL flag runs that do not provide credible evidence of optimization effectiveness. + +#### Scenario: Seed-only run is flagged +- **WHEN** the optimization evaluates only the seed candidate +- **THEN** the report marks the run as ineffective +- **AND** it recommends checking model configuration, budget, evaluator behavior, or API credentials + +#### Scenario: Flat or trivial improvement is flagged +- **WHEN** the best score does not exceed the initial score by the configured meaningful improvement threshold +- **THEN** the report marks the improvement as insufficient +- **AND** it surfaces scorer diagnostics or next-step guidance for improving the evaluator or objective + +### Requirement: Held-out validation evidence +The system SHALL support held-out validation of the optimized artifact so benchmark acceptance is not based only on the training scorer. + +#### Scenario: Held-out validation runs after optimization +- **WHEN** a benchmark run produces a best artifact +- **THEN** maintainers can score that artifact with a held-out rubric, scorer, or multi-provider judge +- **AND** the validation output records whether the optimized artifact passes the acceptance threshold + +#### Scenario: Validation detects scorer gaming +- **WHEN** the training scorer improves but held-out validation fails or regresses +- **THEN** the report marks the result as not accepted +- **AND** it preserves enough evidence to inspect the discrepancy + +### Requirement: Documented acceptance workflow +The system SHALL document how to run the realistic benchmark, inspect the observation report, validate the result, and accept or reject an optimized artifact. + +#### Scenario: Maintainer follows benchmark documentation +- **WHEN** a maintainer reads the documentation for optimization effectiveness verification +- **THEN** it provides concrete commands, expected outputs, acceptance criteria, and troubleshooting guidance +- **AND** it distinguishes benchmark evidence from source changes that should be reviewed separately diff --git a/openspec/changes/archive/2026-07-04-observe-realistic-optimization-loop/tasks.md b/openspec/changes/archive/2026-07-04-observe-realistic-optimization-loop/tasks.md new file mode 100644 index 0000000..dd1cb3d --- /dev/null +++ b/openspec/changes/archive/2026-07-04-observe-realistic-optimization-loop/tasks.md @@ -0,0 +1,37 @@ +## 1. Benchmark Assets + +- [x] 1.1 Choose the first realistic seed artifact and document why it is useful to optimize. +- [x] 1.2 Add benchmark setup that uses a copied seed or explicit output path so tracked source artifacts are not overwritten. +- [x] 1.3 Define the benchmark objective and acceptance constraints in a reusable file or documented command. + +## 2. Training Scorer + +- [x] 2.1 Review `evaluators/skill_clarity.sh` against the benchmark objective and identify any missing dimensions. +- [x] 2.2 Add or update a deterministic benchmark scorer that returns `score` plus actionable dimension diagnostics. +- [x] 2.3 Add tests that verify the scorer accepts valid evaluator payloads and rejects malformed or non-discriminating output paths appropriately. + +## 3. Observation Report + +- [x] 3.1 Add report logic that reads optimization summary/run-dir outputs and extracts initial score, best score, score delta, candidate count, metric calls, diagnostics, diff summary, and artifact paths. +- [x] 3.2 Add ineffective-run detection for seed-only runs, missing diagnostics, flat scores, and improvements below the meaningful threshold. +- [x] 3.3 Add tests for successful, insufficient-improvement, and seed-only observation reports. + +## 4. Held-Out Validation + +- [x] 4.1 Add a held-out validation path using a different deterministic rubric or an optional judge/multi-provider scoring command. +- [x] 4.2 Ensure validation output records pass/fail status, threshold, score, and discrepancy details when training improvement and validation disagree. +- [x] 4.3 Add tests showing scorer-gaming or held-out regression prevents acceptance. + +## 5. Documentation + +- [x] 5.1 Document the realistic optimization benchmark commands and expected output. +- [x] 5.2 Document acceptance criteria for reviewing and accepting an optimized artifact. +- [x] 5.3 Document troubleshooting guidance for ineffective optimization runs. + +## 6. Verification + +- [x] 6.1 Run focused tests for the benchmark scorer, report logic, and held-out validation. +- [x] 6.2 Run `uv run pytest`. +- [x] 6.3 Run `uv run python scripts/check.py --skip-smoke`. +- [x] 6.4 Run the realistic benchmark with a small budget and record the observation report outcome. Budget 1 report was accepted=false with missing_diagnostics, seed_only, flat_scores, insufficient_improvement, seed_and_best_identical, and heldout_validation_failed warnings. +- [x] 6.5 Validate this OpenSpec change with `openspec validate observe-realistic-optimization-loop --json`. diff --git a/openspec/changes/archive/2026-07-04-upgrade-gepa-011-parallel-default/.openspec.yaml b/openspec/changes/archive/2026-07-04-upgrade-gepa-011-parallel-default/.openspec.yaml new file mode 100644 index 0000000..d86f152 --- /dev/null +++ b/openspec/changes/archive/2026-07-04-upgrade-gepa-011-parallel-default/.openspec.yaml @@ -0,0 +1,2 @@ +schema: spec-driven +created: 2026-07-04 diff --git a/openspec/changes/archive/2026-07-04-upgrade-gepa-011-parallel-default/design.md b/openspec/changes/archive/2026-07-04-upgrade-gepa-011-parallel-default/design.md new file mode 100644 index 0000000..1ffb910 --- /dev/null +++ b/openspec/changes/archive/2026-07-04-upgrade-gepa-011-parallel-default/design.md @@ -0,0 +1,90 @@ +## Context + +The project currently depends on unconstrained `gepa` and `litellm` in `pyproject.toml`, while `uv.lock` pins `gepa 0.1.0` and `litellm 1.81.14`. GEPA `0.1.1` changes `EngineConfig` defaults to parallel evaluation with default worker sizing, adds candidate tree visualization methods, writes richer run artifacts, and improves state persistence. LiteLLM `1.82.7` and `1.82.8` were compromised in a March 2026 supply-chain incident, so any dependency movement must explicitly resolve beyond those versions. + +The CLI currently overrides GEPA's new default by always passing `parallel=args.parallel or (args.workers is not None)`. That means upgrading GEPA alone would not adopt parallel-by-default behavior. The implementation should make the behavior change explicit in this wrapper. + +## Goals / Non-Goals + +**Goals:** + +- Upgrade the optimizer runtime to GEPA `0.1.1`. +- Ensure LiteLLM resolution cannot land on compromised versions. +- Make `optimize-anything optimize` parallel by default. +- Provide clear serial opt-outs for one-off CLI calls and repeatable spec files. +- Preserve existing `--parallel` invocations so scripts do not break. +- Keep dependency updates scoped and reviewable through package-targeted `uv` commands. + +**Non-Goals:** + +- Do not redesign the evaluator contract. +- Do not make command evaluators concurrency-safe automatically. +- Do not expose GEPA candidate tree visualization as a new CLI feature in this change. +- Do not change scoring semantics, early-stop thresholds, or result summary schema except where needed for run artifact documentation. + +## Decisions + +### Dependency Resolution + +Use package-scoped `uv` upgrades rather than a global upgrade. The implementation should dry-run first, then update only GEPA and LiteLLM: + +```bash +uv lock --dry-run -P gepa==0.1.1 -P litellm==1.83.0 +uv lock -P gepa==0.1.1 -P litellm==1.83.0 +``` + +The project metadata should express minimum safe constraints, including `gepa>=0.1.1,<0.2.0` and `litellm>=1.83.0,!=1.82.7,!=1.82.8`. The lockfile pins the exact resolved artifacts; the metadata prevents downstream or future lock refreshes from selecting affected LiteLLM versions. + +Alternative considered: upgrade LiteLLM to latest during implementation. That gets newer fixes but broadens the change surface. The conservative default is `1.83.0`, the first post-incident safe release identified during exploration, unless implementation-time verification justifies a newer version. + +### Parallel-by-Default CLI Model + +Use a tri-state concurrency flag model: + +- No CLI concurrency flag: parallel evaluation is enabled. +- `--parallel`: parallel evaluation is enabled, retained for compatibility and explicitness. +- `--no-parallel`: parallel evaluation is disabled. +- `--workers N`: parallel evaluation is enabled with `max_workers=N`. + +This requires changing argparse so `args.parallel` can represent unspecified, true, or false instead of defaulting to `False`. The runtime builder can then compute the effective engine settings intentionally rather than relying on GEPA defaults by accident. + +Alternative considered: remove the explicit `parallel=` argument and rely on GEPA defaults. That is simpler but loses a clean local policy layer and makes `--no-parallel` harder to express consistently. + +### Spec File Concurrency + +Spec files already parse `optimization.parallel` as a boolean. With parallel-by-default, `parallel = false` must become meaningful. The implementation should update spec application logic so optional boolean spec values can apply both true and false when the CLI did not explicitly set the corresponding option. + +CLI flags continue to take precedence over spec file values. A CLI `--parallel` or `--no-parallel` should override `optimization.parallel`. + +### Invalid Combinations + +Reject `--no-parallel --workers N` because worker count has no effect in serial mode. A spec-file combination of `parallel = false` and `workers = N` should likewise fail unless a CLI flag overrides the spec value to parallel mode. + +### Documentation and Tests + +Documentation should state that optimization is now parallel by default and that command, HTTP, and LLM evaluators must tolerate concurrent calls. It should also show `--no-parallel` for evaluators that write shared temp files, depend on process-global state, or hit provider rate limits. + +Tests should cover default parallel behavior, explicit serial opt-out, worker interactions, spec-file false handling, security constraints, and the targeted `uv` upgrade expectation. + +## Risks / Trade-offs + +- Non-concurrency-safe evaluators may fail or produce flaky scores -> Provide `--no-parallel`, document evaluator expectations, and add tests for the serial opt-out. +- Provider rate limits may be hit faster in LLM judge mode -> Document `--workers` for controlling concurrency and `--no-parallel` for strict serial calls. +- Existing scripts may assume serial execution without saying so -> Treat this as a deliberate behavior change and document it prominently. +- GEPA `0.1.1` creates additional run-dir artifacts -> Update docs/tests to avoid assuming the wrapper-created files are the only run artifacts. +- Lockfile update could accidentally move unrelated packages -> Use `uv lock -P` package-scoped commands and inspect the diff before accepting. + +## Migration Plan + +1. Update dependency constraints and regenerate `uv.lock` with package-scoped `uv` commands. +2. Update CLI parsing and spec-file application to support tri-state parallel settings. +3. Update runtime config construction and validation for default parallel behavior and conflicts. +4. Update tests and documentation. +5. Run the full project gate, including dependency safety checks and CLI smoke tests. + +Rollback is straightforward: restore the previous lockfile and revert the CLI default to serial mode. Existing `--no-parallel` support can remain if already released because it is additive. + +## Open Questions + +- Should the lockfile use LiteLLM `1.83.0` for minimal change or the current latest safe version at implementation time? The conservative default is `1.83.0`. +- Should generated TOML examples start including `parallel = false` for known non-concurrency-safe evaluator examples? diff --git a/openspec/changes/archive/2026-07-04-upgrade-gepa-011-parallel-default/proposal.md b/openspec/changes/archive/2026-07-04-upgrade-gepa-011-parallel-default/proposal.md new file mode 100644 index 0000000..d3ba3c6 --- /dev/null +++ b/openspec/changes/archive/2026-07-04-upgrade-gepa-011-parallel-default/proposal.md @@ -0,0 +1,32 @@ +## Why + +GEPA `0.1.1` adds useful runtime improvements, including parallel evaluation by default, richer run artifacts, candidate tree visualization, and safer state persistence. This project should adopt the upgrade intentionally while avoiding the LiteLLM versions compromised in the March 2026 supply-chain incident. + +## What Changes + +- Upgrade the project to GEPA `0.1.1`. +- Constrain LiteLLM resolution so dependency updates land on versions after the compromised `1.82.7` and `1.82.8` releases. +- Change `optimize-anything optimize` to run evaluator calls in parallel by default. +- Add a `--no-parallel` opt-out for evaluators that are not concurrency-safe. +- Preserve `--parallel` as an accepted explicit enable flag for compatibility with existing scripts. +- Reject conflicting concurrency options such as `--no-parallel --workers`. +- Update tests and documentation to describe the new default and concurrency expectations. + +## Capabilities + +### New Capabilities + +- `optimization-runtime`: Defines optimizer dependency safety, default evaluation parallelism, and user-facing controls for evaluator concurrency. + +### Modified Capabilities + +- None. + +## Impact + +- Dependencies: `pyproject.toml`, `uv.lock`. +- CLI behavior: `optimize-anything optimize` defaults to parallel GEPA engine execution. +- CLI options: add `--no-parallel` while keeping `--parallel`. +- Tests: engine configuration wiring, flag validation, dependency safety checks, and documentation contract tests. +- Documentation: README, command docs, examples, and any run-dir/concurrency guidance. +- Runtime risk: command/HTTP/LLM evaluators may need concurrency-safe file access, rate-limit handling, or explicit `--no-parallel`. diff --git a/openspec/changes/archive/2026-07-04-upgrade-gepa-011-parallel-default/specs/optimization-runtime/spec.md b/openspec/changes/archive/2026-07-04-upgrade-gepa-011-parallel-default/specs/optimization-runtime/spec.md new file mode 100644 index 0000000..2a234ba --- /dev/null +++ b/openspec/changes/archive/2026-07-04-upgrade-gepa-011-parallel-default/specs/optimization-runtime/spec.md @@ -0,0 +1,81 @@ +## ADDED Requirements + +### Requirement: Optimizer dependencies are security constrained +The project SHALL require GEPA `0.1.1` or later within the compatible `0.1` release line and SHALL require a LiteLLM version newer than the compromised `1.82.7` and `1.82.8` releases. + +#### Scenario: Dependency metadata excludes compromised LiteLLM versions +- **WHEN** project dependency metadata is inspected +- **THEN** GEPA allows `0.1.1` +- **AND** LiteLLM requires at least `1.83.0` +- **AND** LiteLLM excludes `1.82.7` and `1.82.8` + +#### Scenario: Lockfile resolves to safe versions +- **WHEN** the project lockfile is inspected after the upgrade +- **THEN** the locked GEPA version is `0.1.1` +- **AND** the locked LiteLLM version is not `1.82.7` or `1.82.8` +- **AND** the locked LiteLLM version is at least `1.83.0` + +### Requirement: Optimize runs in parallel by default +The `optimize-anything optimize` command SHALL configure GEPA evaluation to run in parallel when the user does not provide an explicit concurrency override. + +#### Scenario: Default optimize run is parallel +- **WHEN** a user runs `optimize-anything optimize` without `--parallel`, `--no-parallel`, or `--workers` +- **THEN** the GEPA engine configuration uses `parallel = true` +- **AND** GEPA may use its default worker count + +### Requirement: Optimize accepts explicit parallel mode +The `optimize-anything optimize` command SHALL continue accepting `--parallel` as an explicit request for parallel evaluation. + +#### Scenario: Existing parallel flag still works +- **WHEN** a user runs `optimize-anything optimize --parallel` +- **THEN** the GEPA engine configuration uses `parallel = true` + +### Requirement: Optimize supports serial opt-out +The `optimize-anything optimize` command SHALL provide `--no-parallel` to disable parallel evaluator calls. + +#### Scenario: User disables parallel execution +- **WHEN** a user runs `optimize-anything optimize --no-parallel` +- **THEN** the GEPA engine configuration uses `parallel = false` +- **AND** no explicit worker count is passed to GEPA + +### Requirement: Worker count implies parallel execution +The `optimize-anything optimize` command SHALL treat `--workers N` as a request for parallel evaluation with a bounded worker count. + +#### Scenario: User sets worker count +- **WHEN** a user runs `optimize-anything optimize --workers 4` +- **THEN** the GEPA engine configuration uses `parallel = true` +- **AND** the GEPA engine configuration uses `max_workers = 4` + +### Requirement: Conflicting concurrency options are rejected +The `optimize-anything optimize` command SHALL reject configurations that disable parallelism while also specifying a worker count. + +#### Scenario: CLI flags conflict +- **WHEN** a user runs `optimize-anything optimize --no-parallel --workers 4` +- **THEN** the command exits with an error +- **AND** the error explains that `--workers` requires parallel execution + +#### Scenario: Spec file values conflict +- **WHEN** a spec file contains `parallel = false` and `workers = 4` +- **THEN** the command exits with an error +- **AND** the error explains that workers require parallel execution + +### Requirement: Spec files can configure concurrency +Optimization spec files SHALL be able to enable or disable parallel execution when no CLI concurrency flag overrides the spec value. + +#### Scenario: Spec file disables parallel execution +- **WHEN** a spec file contains `parallel = false` +- **AND** the user does not pass `--parallel`, `--no-parallel`, or `--workers` +- **THEN** the GEPA engine configuration uses `parallel = false` + +#### Scenario: CLI concurrency flag overrides spec file +- **WHEN** a spec file contains `parallel = false` +- **AND** the user runs `optimize-anything optimize --parallel --spec-file ` +- **THEN** the GEPA engine configuration uses `parallel = true` + +### Requirement: Documentation describes concurrency expectations +User-facing documentation SHALL explain that optimization runs evaluator calls in parallel by default and SHALL document when to use `--no-parallel`. + +#### Scenario: User needs serial evaluator execution +- **WHEN** a user reads optimize command documentation +- **THEN** the documentation describes `--no-parallel` +- **AND** the documentation warns that evaluators with shared files, process-global state, or strict provider rate limits may need serial execution diff --git a/openspec/changes/archive/2026-07-04-upgrade-gepa-011-parallel-default/tasks.md b/openspec/changes/archive/2026-07-04-upgrade-gepa-011-parallel-default/tasks.md new file mode 100644 index 0000000..703e4a5 --- /dev/null +++ b/openspec/changes/archive/2026-07-04-upgrade-gepa-011-parallel-default/tasks.md @@ -0,0 +1,33 @@ +## 1. Dependency Upgrade Safety + +- [x] 1.1 Update `pyproject.toml` dependency constraints for `gepa>=0.1.1,<0.2.0` and `litellm>=1.83.0,!=1.82.7,!=1.82.8`. +- [x] 1.2 Run a package-scoped dry run with `uv lock --dry-run -P gepa==0.1.1 -P litellm==1.83.0` and inspect the planned package changes. +- [x] 1.3 Regenerate `uv.lock` with package-scoped `uv lock -P gepa==0.1.1 -P litellm==1.83.0`. +- [x] 1.4 Add or update a dependency safety test/check that fails if `uv.lock` resolves LiteLLM to `1.82.7` or `1.82.8`. + +## 2. CLI Concurrency Model + +- [x] 2.1 Change optimize argparse wiring so `--parallel` and `--no-parallel` write a tri-state concurrency value instead of defaulting to `False`. +- [x] 2.2 Update `_build_optimize_runtime` to configure `EngineConfig.parallel=True` by default, `False` for explicit serial mode, and `True` when `--workers` is present. +- [x] 2.3 Ensure `EngineConfig.max_workers` is passed only when a worker count is explicitly provided. +- [x] 2.4 Add validation errors for `--no-parallel --workers` and equivalent spec-file conflicts. + +## 3. Spec File Concurrency + +- [x] 3.1 Update spec application logic so `optimization.parallel = false` applies when no CLI concurrency flag overrides it. +- [x] 3.2 Preserve CLI precedence so explicit `--parallel`, `--no-parallel`, or `--workers` overrides `optimization.parallel`. +- [x] 3.3 Add tests for `parallel = false`, `parallel = true`, and CLI-overrides-spec behavior. + +## 4. Tests and Documentation + +- [x] 4.1 Update engine config wiring tests for default parallel mode, explicit `--parallel`, `--no-parallel`, and `--workers`. +- [x] 4.2 Update run-dir/documentation tests to allow GEPA `0.1.1` additional artifacts such as logs, candidate JSON, or candidate tree HTML. +- [x] 4.3 Update README, command docs, and examples to state that optimization is parallel by default. +- [x] 4.4 Document when users should pass `--no-parallel`, including shared temp files, process-global state, and provider rate limits. + +## 5. Verification + +- [x] 5.1 Run `uv run pytest`. +- [x] 5.2 Run `uv run python scripts/check.py --skip-smoke`. +- [x] 5.3 Run `uv run optimize-anything optimize --help` and confirm both `--parallel` and `--no-parallel` are documented. +- [x] 5.4 Inspect `git diff -- pyproject.toml uv.lock` and confirm no unrelated dependency churn. diff --git a/openspec/config.yaml b/openspec/config.yaml new file mode 100644 index 0000000..392946c --- /dev/null +++ b/openspec/config.yaml @@ -0,0 +1,20 @@ +schema: spec-driven + +# Project context (optional) +# This is shown to AI when creating artifacts. +# Add your tech stack, conventions, style guides, domain knowledge, etc. +# Example: +# context: | +# Tech stack: TypeScript, React, Node.js +# We use conventional commits +# Domain: e-commerce platform + +# Per-artifact rules (optional) +# Add custom rules for specific artifacts. +# Example: +# rules: +# proposal: +# - Keep proposals under 500 words +# - Always include a "Non-goals" section +# tasks: +# - Break tasks into chunks of max 2 hours diff --git a/openspec/specs/optimization-observability/spec.md b/openspec/specs/optimization-observability/spec.md new file mode 100644 index 0000000..e4bc7ff --- /dev/null +++ b/openspec/specs/optimization-observability/spec.md @@ -0,0 +1,72 @@ +## Requirements + +### Requirement: Realistic optimization benchmark +The system SHALL provide a repeatable benchmark for optimizing a useful text artifact that maintainers would plausibly accept into the project. + +#### Scenario: Benchmark uses a project-relevant artifact +- **WHEN** a maintainer runs the benchmark setup +- **THEN** the seed artifact is project-relevant documentation or guidance, not a toy length or marketing example +- **AND** the benchmark states the optimization objective and acceptance constraints + +#### Scenario: Benchmark can run without changing the source artifact +- **WHEN** a maintainer runs the benchmark +- **THEN** the optimization uses a copied seed or explicit output path +- **AND** the tracked source artifact is not overwritten unless the maintainer separately accepts the result + +### Requirement: Training scorer emits actionable diagnostics +The system SHALL include a deterministic training scorer for the benchmark that returns a numeric score plus diagnostic fields useful to the optimizer. + +#### Scenario: Scorer returns optimizer-compatible JSON +- **WHEN** the scorer receives an evaluator payload containing `candidate` +- **THEN** it returns a JSON object with a finite numeric `score` +- **AND** the score is valid for the configured score range + +#### Scenario: Scorer reports improvement dimensions +- **WHEN** the scorer evaluates a candidate +- **THEN** the JSON response includes dimension scores or feedback fields that identify why the candidate scored as it did +- **AND** those diagnostics are specific enough to guide a subsequent reflection step + +### Requirement: Observation report summarizes loop behavior +The system SHALL produce an observation report for a benchmark run that summarizes whether the optimization loop explored candidates and improved the artifact. + +#### Scenario: Report includes core loop telemetry +- **WHEN** a benchmark run completes +- **THEN** the report includes the initial score, best score, score delta, candidate count, total metric calls, top diagnostics, diff summary, and run directory + +#### Scenario: Report references persisted artifacts +- **WHEN** a benchmark run writes a run directory +- **THEN** the report identifies where to find the seed, best artifact, summary JSON, and relevant optimizer runtime artifacts + +### Requirement: Ineffective optimization signals +The system SHALL flag runs that do not provide credible evidence of optimization effectiveness. + +#### Scenario: Seed-only run is flagged +- **WHEN** the optimization evaluates only the seed candidate +- **THEN** the report marks the run as ineffective +- **AND** it recommends checking model configuration, budget, evaluator behavior, or API credentials + +#### Scenario: Flat or trivial improvement is flagged +- **WHEN** the best score does not exceed the initial score by the configured meaningful improvement threshold +- **THEN** the report marks the improvement as insufficient +- **AND** it surfaces scorer diagnostics or next-step guidance for improving the evaluator or objective + +### Requirement: Held-out validation evidence +The system SHALL support held-out validation of the optimized artifact so benchmark acceptance is not based only on the training scorer. + +#### Scenario: Held-out validation runs after optimization +- **WHEN** a benchmark run produces a best artifact +- **THEN** maintainers can score that artifact with a held-out rubric, scorer, or multi-provider judge +- **AND** the validation output records whether the optimized artifact passes the acceptance threshold + +#### Scenario: Validation detects scorer gaming +- **WHEN** the training scorer improves but held-out validation fails or regresses +- **THEN** the report marks the result as not accepted +- **AND** it preserves enough evidence to inspect the discrepancy + +### Requirement: Documented acceptance workflow +The system SHALL document how to run the realistic benchmark, inspect the observation report, validate the result, and accept or reject an optimized artifact. + +#### Scenario: Maintainer follows benchmark documentation +- **WHEN** a maintainer reads the documentation for optimization effectiveness verification +- **THEN** it provides concrete commands, expected outputs, acceptance criteria, and troubleshooting guidance +- **AND** it distinguishes benchmark evidence from source changes that should be reviewed separately diff --git a/openspec/specs/optimization-runtime/spec.md b/openspec/specs/optimization-runtime/spec.md new file mode 100644 index 0000000..6fa1625 --- /dev/null +++ b/openspec/specs/optimization-runtime/spec.md @@ -0,0 +1,81 @@ +## Requirements + +### Requirement: Optimizer dependencies are security constrained +The project SHALL require GEPA `0.1.1` or later within the compatible `0.1` release line and SHALL require a LiteLLM version newer than the compromised `1.82.7` and `1.82.8` releases. + +#### Scenario: Dependency metadata excludes compromised LiteLLM versions +- **WHEN** project dependency metadata is inspected +- **THEN** GEPA allows `0.1.1` +- **AND** LiteLLM requires at least `1.83.0` +- **AND** LiteLLM excludes `1.82.7` and `1.82.8` + +#### Scenario: Lockfile resolves to safe versions +- **WHEN** the project lockfile is inspected after the upgrade +- **THEN** the locked GEPA version is `0.1.1` +- **AND** the locked LiteLLM version is not `1.82.7` or `1.82.8` +- **AND** the locked LiteLLM version is at least `1.83.0` + +### Requirement: Optimize runs in parallel by default +The `optimize-anything optimize` command SHALL configure GEPA evaluation to run in parallel when the user does not provide an explicit concurrency override. + +#### Scenario: Default optimize run is parallel +- **WHEN** a user runs `optimize-anything optimize` without `--parallel`, `--no-parallel`, or `--workers` +- **THEN** the GEPA engine configuration uses `parallel = true` +- **AND** GEPA may use its default worker count + +### Requirement: Optimize accepts explicit parallel mode +The `optimize-anything optimize` command SHALL continue accepting `--parallel` as an explicit request for parallel evaluation. + +#### Scenario: Existing parallel flag still works +- **WHEN** a user runs `optimize-anything optimize --parallel` +- **THEN** the GEPA engine configuration uses `parallel = true` + +### Requirement: Optimize supports serial opt-out +The `optimize-anything optimize` command SHALL provide `--no-parallel` to disable parallel evaluator calls. + +#### Scenario: User disables parallel execution +- **WHEN** a user runs `optimize-anything optimize --no-parallel` +- **THEN** the GEPA engine configuration uses `parallel = false` +- **AND** no explicit worker count is passed to GEPA + +### Requirement: Worker count implies parallel execution +The `optimize-anything optimize` command SHALL treat `--workers N` as a request for parallel evaluation with a bounded worker count. + +#### Scenario: User sets worker count +- **WHEN** a user runs `optimize-anything optimize --workers 4` +- **THEN** the GEPA engine configuration uses `parallel = true` +- **AND** the GEPA engine configuration uses `max_workers = 4` + +### Requirement: Conflicting concurrency options are rejected +The `optimize-anything optimize` command SHALL reject configurations that disable parallelism while also specifying a worker count. + +#### Scenario: CLI flags conflict +- **WHEN** a user runs `optimize-anything optimize --no-parallel --workers 4` +- **THEN** the command exits with an error +- **AND** the error explains that `--workers` requires parallel execution + +#### Scenario: Spec file values conflict +- **WHEN** a spec file contains `parallel = false` and `workers = 4` +- **THEN** the command exits with an error +- **AND** the error explains that workers require parallel execution + +### Requirement: Spec files can configure concurrency +Optimization spec files SHALL be able to enable or disable parallel execution when no CLI concurrency flag overrides the spec value. + +#### Scenario: Spec file disables parallel execution +- **WHEN** a spec file contains `parallel = false` +- **AND** the user does not pass `--parallel`, `--no-parallel`, or `--workers` +- **THEN** the GEPA engine configuration uses `parallel = false` + +#### Scenario: CLI concurrency flag overrides spec file +- **WHEN** a spec file contains `parallel = false` +- **AND** the user runs `optimize-anything optimize --parallel --spec-file ` +- **THEN** the GEPA engine configuration uses `parallel = true` + +### Requirement: Documentation describes concurrency expectations +User-facing documentation SHALL explain that optimization runs evaluator calls in parallel by default and SHALL document when to use `--no-parallel`. + +#### Scenario: User needs serial evaluator execution +- **WHEN** a user reads optimize command documentation +- **THEN** the documentation describes `--no-parallel` +- **AND** the documentation warns that evaluators with shared files, process-global state, or strict provider rate limits may need serial execution diff --git a/pyproject.toml b/pyproject.toml index 7569d31..a33d67e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -11,7 +11,12 @@ classifiers = [ "Programming Language :: Python :: 3", "Topic :: Software Development :: Quality Assurance", ] -dependencies = ["gepa", "httpx", "litellm", "cloudpickle"] +dependencies = [ + "gepa>=0.1.1,<0.2.0", + "httpx", + "litellm>=1.83.0,!=1.82.7,!=1.82.8", + "cloudpickle", +] [project.urls] Homepage = "https://github.com/ASRagab/optimize-anything" diff --git a/scripts/optimization_observer.py b/scripts/optimization_observer.py new file mode 100755 index 0000000..3240b5f --- /dev/null +++ b/scripts/optimization_observer.py @@ -0,0 +1,477 @@ +#!/usr/bin/env python3 +"""Set up and summarize realistic optimization benchmark runs.""" + +from __future__ import annotations + +import argparse +import difflib +import json +import math +import shutil +import subprocess +import sys +from pathlib import Path +from typing import Any + +REPO_ROOT = Path(__file__).resolve().parents[1] +DEFAULT_BENCHMARK_PATH = ( + REPO_ROOT / "examples/optimization-observability/evaluator-generation-benchmark.json" +) + + +def setup_benchmark(benchmark_path: Path, work_dir: Path) -> dict[str, Any]: + """Copy the benchmark seed into a work directory and return run metadata.""" + config = _load_benchmark(benchmark_path) + benchmark_id = str(config["id"]) + seed_source = str(config["seed_artifact"]) + source_path = REPO_ROOT / seed_source + if not source_path.exists(): + raise FileNotFoundError(f"Benchmark seed not found: {source_path}") + + target_dir = work_dir / benchmark_id + target_dir.mkdir(parents=True, exist_ok=True) + seed_file = target_dir / "seed.md" + shutil.copyfile(source_path, seed_file) + + objective_file = target_dir / "objective.txt" + objective = str(config["objective"]) + objective_file.write_text(objective + "\n", encoding="utf-8") + + run_base_dir = target_dir / "runs" + run_base_dir.mkdir(exist_ok=True) + + training_command = list(config["training_evaluator_command"]) + heldout_command = list(config["heldout_evaluator_command"]) + optimize_command = [ + "uv", + "run", + "optimize-anything", + "optimize", + str(seed_file), + "--objective", + objective, + "--budget", + str(config.get("recommended_budget", 6)), + "--run-dir", + str(run_base_dir), + "--evaluator-command", + *training_command, + ] + + return { + "benchmark": benchmark_id, + "why": config["why"], + "seed_source": seed_source, + "seed_file": str(seed_file), + "objective_file": str(objective_file), + "objective": objective, + "constraints": list(config.get("constraints", [])), + "run_base_dir": str(run_base_dir), + "training_evaluator_command": training_command, + "heldout_evaluator_command": heldout_command, + "meaningful_delta": float(config.get("meaningful_delta", 0.03)), + "validation_delta": float(config.get("validation_delta", 0.01)), + "optimize_command": optimize_command, + } + + +def build_observation_report( + run_dir: Path, + *, + benchmark_path: Path | None = None, + validation_command: list[str] | None = None, + meaningful_delta: float | None = None, + validation_delta: float | None = None, +) -> dict[str, Any]: + """Summarize optimization telemetry and optional held-out validation.""" + config = _load_benchmark(benchmark_path) if benchmark_path else {} + threshold = _coalesce_float( + meaningful_delta, + config.get("meaningful_delta"), + 0.03, + ) + heldout_threshold = _coalesce_float( + validation_delta, + config.get("validation_delta"), + 0.01, + ) + validation_command = validation_command or config.get("heldout_evaluator_command") + + seed_path = run_dir / "seed.txt" + best_path = run_dir / "best_artifact.txt" + summary_path = run_dir / "summary.json" + _require_file(seed_path) + _require_file(best_path) + _require_file(summary_path) + + seed_text = seed_path.read_text(encoding="utf-8") + best_text = best_path.read_text(encoding="utf-8") + summary = json.loads(summary_path.read_text(encoding="utf-8")) + + score_summary = summary.get("score_summary") + warnings: list[dict[str, str]] = [] + if not isinstance(score_summary, dict): + score_summary = {} + _warn( + warnings, + "missing_score_summary", + "summary.json does not include a score_summary object.", + "Inspect optimizer output and result-contract generation.", + ) + + initial_score = _optional_float(score_summary.get("initial")) + best_score = _optional_float(score_summary.get("best")) + score_delta = _score_delta(score_summary, initial_score, best_score) + candidate_count = _optional_int(score_summary.get("num_candidates")) + metric_calls = _optional_int(summary.get("total_metric_calls")) + if metric_calls is None: + budget = summary.get("budget_utilization") + if isinstance(budget, dict): + metric_calls = _optional_int(budget.get("evaluator_calls")) + + diagnostics = summary.get("top_diagnostics") + if not isinstance(diagnostics, list): + diagnostics = [] + has_actionable_diagnostics = any( + isinstance(item, dict) and item.get("name") != "overall_score" + for item in diagnostics + ) + if not has_actionable_diagnostics: + _warn( + warnings, + "missing_diagnostics", + "No actionable top diagnostics were recorded for the best candidate.", + "Return dimension scores from the evaluator so reflection has actionable feedback.", + ) + + if candidate_count is None or candidate_count <= 1: + _warn( + warnings, + "seed_only", + "The run did not show candidate exploration beyond the seed.", + "Increase budget, confirm proposer model credentials, and inspect GEPA artifacts.", + ) + + if score_delta is None: + _warn( + warnings, + "missing_score_delta", + "The report could not compute best-vs-initial score movement.", + "Check score_summary.initial and score_summary.best in summary.json.", + ) + elif abs(score_delta) < 1e-12: + _warn( + warnings, + "flat_scores", + "Best score did not move from the initial score.", + "Improve scorer diagnostics, objective specificity, or budget.", + ) + + if score_delta is None or score_delta < threshold: + _warn( + warnings, + "insufficient_improvement", + f"Score delta is below the meaningful threshold ({threshold:.4f}).", + "Treat this as weak evidence until a larger gain appears.", + ) + + diff_summary = _diff_summary(seed_text, best_text) + if not diff_summary["changed"]: + _warn( + warnings, + "seed_and_best_identical", + "Best artifact is identical to the seed artifact.", + "Inspect whether optimization produced or accepted any candidate changes.", + ) + + validation = None + if validation_command: + validation = _validate_heldout( + seed_text, + best_text, + list(validation_command), + heldout_threshold, + ) + if not validation["accepted"]: + kind = ( + "heldout_regression" + if validation["score_delta"] is not None + and validation["score_delta"] < 0 + else "heldout_validation_failed" + ) + _warn( + warnings, + kind, + "Held-out validation does not agree that the optimized artifact improved.", + "Review the diff and scorer rubrics before accepting the artifact.", + ) + else: + _warn( + warnings, + "validation_not_run", + "No held-out validation command was provided.", + "Run with --validation-command or use the benchmark default validation scorer.", + ) + + blocking_kinds = { + "missing_diagnostics", + "missing_score_summary", + "seed_only", + "missing_score_delta", + "flat_scores", + "insufficient_improvement", + "seed_and_best_identical", + "heldout_regression", + "heldout_validation_failed", + "validation_not_run", + } + accepted = not any(warning["kind"] in blocking_kinds for warning in warnings) + + return { + "benchmark": config.get("id"), + "run_dir": str(run_dir), + "training": { + "initial_score": initial_score, + "best_score": best_score, + "score_delta": score_delta, + "candidate_count": candidate_count, + "metric_calls": metric_calls, + "diagnostics": diagnostics, + }, + "diff_summary": diff_summary, + "validation": validation, + "warnings": warnings, + "accepted": accepted, + "artifacts": { + "seed": str(seed_path), + "best_artifact": str(best_path), + "summary": str(summary_path), + "optimizer_artifacts": _optimizer_artifacts(run_dir), + }, + } + + +def main(argv: list[str] | None = None) -> int: + parser = argparse.ArgumentParser( + prog="optimization-observer", + description="Set up and summarize realistic optimization benchmark runs.", + ) + subparsers = parser.add_subparsers(dest="command", required=True) + + setup_parser = subparsers.add_parser("setup", help="Copy benchmark seed into a work dir") + setup_parser.add_argument( + "--benchmark", + default=str(DEFAULT_BENCHMARK_PATH), + help="Benchmark config JSON path.", + ) + setup_parser.add_argument( + "--work-dir", + default="integration_runs/optimization-observability", + help="Directory for copied seed, objective, and run outputs.", + ) + + report_parser = subparsers.add_parser("report", help="Summarize a saved run directory") + report_parser.add_argument("--run-dir", required=True, help="Run dir containing summary.json") + report_parser.add_argument( + "--benchmark", + default=None, + help="Benchmark config JSON path. Supplies thresholds and held-out scorer.", + ) + report_parser.add_argument( + "--meaningful-delta", + type=float, + default=None, + help="Minimum training score delta considered meaningful.", + ) + report_parser.add_argument( + "--validation-delta", + type=float, + default=None, + help="Minimum held-out score delta required for acceptance.", + ) + report_parser.add_argument( + "--strict", + action="store_true", + default=False, + help="Exit non-zero when the report is not accepted.", + ) + report_parser.add_argument( + "--validation-command", + nargs="+", + help="Held-out evaluator command. Must be last when provided.", + ) + + args = parser.parse_args(argv) + if args.command == "setup": + result = setup_benchmark(Path(args.benchmark), Path(args.work_dir)) + print(json.dumps(result, indent=2)) + return 0 + + if args.command == "report": + report = build_observation_report( + Path(args.run_dir), + benchmark_path=Path(args.benchmark) if args.benchmark else None, + validation_command=args.validation_command, + meaningful_delta=args.meaningful_delta, + validation_delta=args.validation_delta, + ) + print(json.dumps(report, indent=2)) + return 1 if args.strict and not report["accepted"] else 0 + + return 1 + + +def _load_benchmark(path: Path | None) -> dict[str, Any]: + if path is None: + return {} + resolved = path if path.is_absolute() else REPO_ROOT / path + return json.loads(resolved.read_text(encoding="utf-8")) + + +def _require_file(path: Path) -> None: + if not path.exists(): + raise FileNotFoundError(f"Required run artifact missing: {path}") + + +def _coalesce_float(*values: Any) -> float: + for value in values: + parsed = _optional_float(value) + if parsed is not None: + return parsed + raise ValueError("No numeric value provided") + + +def _optional_float(value: Any) -> float | None: + if value is None: + return None + try: + parsed = float(value) + except (TypeError, ValueError): + return None + return parsed if math.isfinite(parsed) else None + + +def _optional_int(value: Any) -> int | None: + if isinstance(value, bool): + return None + try: + return int(value) + except (TypeError, ValueError): + return None + + +def _score_delta( + score_summary: dict[str, Any], + initial_score: float | None, + best_score: float | None, +) -> float | None: + explicit = _optional_float(score_summary.get("delta_best_vs_initial")) + if explicit is not None: + return explicit + if initial_score is None or best_score is None: + return None + return round(best_score - initial_score, 6) + + +def _warn( + warnings: list[dict[str, str]], + kind: str, + message: str, + recommendation: str, +) -> None: + warnings.append({ + "kind": kind, + "message": message, + "recommendation": recommendation, + }) + + +def _diff_summary(seed_text: str, best_text: str) -> dict[str, Any]: + diff_lines = list( + difflib.unified_diff( + seed_text.splitlines(), + best_text.splitlines(), + fromfile="seed", + tofile="optimized", + lineterm="", + ) + ) + added = sum(1 for line in diff_lines if line.startswith("+") and not line.startswith("+++")) + removed = sum(1 for line in diff_lines if line.startswith("-") and not line.startswith("---")) + return { + "changed": seed_text != best_text, + "added": added, + "removed": removed, + "preview": diff_lines[:40], + } + + +def _validate_heldout( + seed_text: str, + best_text: str, + command: list[str], + threshold: float, +) -> dict[str, Any]: + seed_result = _run_evaluator(command, seed_text) + best_result = _run_evaluator(command, best_text) + seed_score = _optional_float(seed_result.get("score")) + best_score = _optional_float(best_result.get("score")) + score_delta = ( + None + if seed_score is None or best_score is None + else round(best_score - seed_score, 6) + ) + accepted = score_delta is not None and score_delta >= threshold + return { + "command": command, + "threshold": threshold, + "seed_score": seed_score, + "best_score": best_score, + "score_delta": score_delta, + "accepted": accepted, + "seed_feedback": seed_result.get("feedback", []), + "best_feedback": best_result.get("feedback", []), + } + + +def _run_evaluator(command: list[str], candidate: str) -> dict[str, Any]: + payload = json.dumps({"candidate": candidate}) + proc = subprocess.run( + command, + input=payload, + capture_output=True, + text=True, + cwd=str(REPO_ROOT), + timeout=60, + ) + if proc.returncode != 0: + return { + "score": None, + "error": f"Evaluator exited with code {proc.returncode}", + "stderr": proc.stderr.strip()[:500], + } + try: + result = json.loads(proc.stdout) + except json.JSONDecodeError as exc: + return { + "score": None, + "error": f"Evaluator output is not valid JSON: {exc}", + "stdout": proc.stdout.strip()[:500], + } + score = _optional_float(result.get("score")) + if score is None: + result["error"] = "Evaluator did not return a finite numeric score" + return result + + +def _optimizer_artifacts(run_dir: Path) -> list[str]: + core = {"seed.txt", "best_artifact.txt", "summary.json"} + return [ + str(path) + for path in sorted(run_dir.iterdir()) + if path.name not in core + ] + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/src/optimize_anything/cli.py b/src/optimize_anything/cli.py index 9d7f45f..5930876 100644 --- a/src/optimize_anything/cli.py +++ b/src/optimize_anything/cli.py @@ -94,14 +94,21 @@ def main(argv: list[str] | None = None) -> int: "--run-dir", help=( "Directory to save run artifacts. Creates /run-/ " - "containing seed.txt, best_artifact.txt, and summary.json." + "with seed.txt, best_artifact.txt, summary.json, and GEPA runtime artifacts." ), ) opt_parser.add_argument( "--parallel", + dest="parallel", action="store_true", - default=False, - help="Enable parallel evaluator calls.", + default=None, + help="Enable parallel evaluator calls (default).", + ) + opt_parser.add_argument( + "--no-parallel", + dest="parallel", + action="store_false", + help="Run evaluator calls serially.", ) opt_parser.add_argument( "--workers", diff --git a/src/optimize_anything/cli_optimize.py b/src/optimize_anything/cli_optimize.py index fb49a7e..d22dc7e 100644 --- a/src/optimize_anything/cli_optimize.py +++ b/src/optimize_anything/cli_optimize.py @@ -163,13 +163,21 @@ def _build_optimize_runtime( ) ] - engine = EngineConfig( - max_metric_calls=args.budget, - run_dir=gepa_run_dir, - parallel=args.parallel or (args.workers is not None), - max_workers=args.workers, - cache_evaluation=args.cache, - ) + parallel = args.parallel + if parallel is None: + parallel = True + if args.workers is not None: + parallel = True + + engine_kwargs = { + "max_metric_calls": args.budget, + "run_dir": gepa_run_dir, + "parallel": parallel, + "cache_evaluation": args.cache, + } + if args.workers is not None: + engine_kwargs["max_workers"] = args.workers + engine = EngineConfig(**engine_kwargs) if model: config = GEPAConfig( engine=engine, @@ -288,6 +296,11 @@ def _validate_optimize_args(args: argparse.Namespace) -> str | None: return "Error: --cache-from requires --cache" if args.valset and not args.dataset: return "Error: --valset requires --dataset" + if args.parallel is False and args.workers is not None: + return ( + "Error: --workers requires parallel execution; " + "spec workers require parallel execution too." + ) return None @@ -345,10 +358,11 @@ def _apply_spec_to_args( "evaluator_command", "budget", "judge_model", - "workers", ) _apply_spec_alias_if_missing(args, spec, arg_key="model", spec_key="proposer_model") - _apply_true_flags_from_spec(args, spec, "parallel", "cache", "early_stop") + _apply_parallel_from_spec(args, spec) + _apply_spec_values_if_missing(args, spec, "workers") + _apply_true_flags_from_spec(args, spec, "cache", "early_stop") _apply_spec_value_if_default(args, spec, "early_stop_window", default=10) _apply_spec_value_if_default(args, spec, "early_stop_threshold", default=0.005) @@ -393,6 +407,15 @@ def _apply_true_flags_from_spec( setattr(args, key, True) +def _apply_parallel_from_spec(args: argparse.Namespace, spec: dict[str, Any]) -> None: + if ( + getattr(args, "parallel", None) is None + and getattr(args, "workers", None) is None + and spec.get("parallel") is not None + ): + setattr(args, "parallel", spec["parallel"]) + + def _apply_spec_value_if_default( args: argparse.Namespace, spec: dict[str, Any], diff --git a/tests/test_cli.py b/tests/test_cli.py index ab20fc5..e6eeb2e 100644 --- a/tests/test_cli.py +++ b/tests/test_cli.py @@ -1560,6 +1560,91 @@ def fake_optimize(**kwargs): class TestEngineConfigWiring: + def _run_optimize_with_config(self, tmp_path: Path, monkeypatch, *extra_args: str): + seed_file = tmp_path / "seed.txt" + seed_file.write_text("test") + captured = {} + + class DummyResult: + best_candidate = "x" + total_metric_calls = 1 + + def fake_optimize(**kwargs): + captured["config"] = kwargs["config"] + return DummyResult() + + monkeypatch.setattr( + "optimize_anything.cli._preflight_command_evaluator", + lambda command, cwd=None: None, + ) + monkeypatch.setattr( + "optimize_anything.evaluators.command_evaluator", + lambda command, cwd=None, **kwargs: lambda c: (0.5, {}), + ) + monkeypatch.setattr("gepa.optimize_anything.optimize_anything", fake_optimize) + + result = main([ + "optimize", str(seed_file), + "--evaluator-command", "bash", "eval.sh", + "--budget", "1", + *extra_args, + ]) + return result, captured + + def test_default_sets_engine_parallel_without_worker_override( + self, tmp_path: Path, monkeypatch + ): + captured_engine_kwargs = {} + + class FakeEngineConfig: + def __init__(self, **kwargs): + captured_engine_kwargs.update(kwargs) + self.parallel = kwargs["parallel"] + self.max_workers = kwargs.get("max_workers") + self.cache_evaluation = kwargs["cache_evaluation"] + self.run_dir = kwargs["run_dir"] + + class FakeGEPAConfig: + def __init__(self, **kwargs): + self.engine = kwargs["engine"] + self.stop_callbacks = kwargs.get("stop_callbacks") + + monkeypatch.setattr("gepa.optimize_anything.EngineConfig", FakeEngineConfig) + monkeypatch.setattr("gepa.optimize_anything.GEPAConfig", FakeGEPAConfig) + + result, captured = self._run_optimize_with_config(tmp_path, monkeypatch) + + assert result == 0 + assert captured["config"].engine.parallel is True + assert "max_workers" not in captured_engine_kwargs + + def test_no_parallel_flag_sets_engine_parallel_false( + self, tmp_path: Path, monkeypatch + ): + result, captured = self._run_optimize_with_config( + tmp_path, monkeypatch, "--no-parallel" + ) + + assert result == 0 + assert captured["config"].engine.parallel is False + + def test_no_parallel_rejects_worker_count( + self, tmp_path: Path, capsys + ): + seed_file = tmp_path / "seed.txt" + seed_file.write_text("test") + + result = main([ + "optimize", str(seed_file), + "--evaluator-command", "bash", "eval.sh", + "--no-parallel", + "--workers", "4", + "--budget", "1", + ]) + + assert result == 1 + assert "--workers requires parallel execution" in capsys.readouterr().err + def test_parallel_flag_sets_engine_parallel(self, tmp_path: Path, monkeypatch): seed_file = tmp_path / "seed.txt" seed_file.write_text("test") diff --git a/tests/test_dependency_safety.py b/tests/test_dependency_safety.py new file mode 100644 index 0000000..aea0b67 --- /dev/null +++ b/tests/test_dependency_safety.py @@ -0,0 +1,44 @@ +from __future__ import annotations + +from pathlib import Path +import tomllib + +from packaging.requirements import Requirement +from packaging.version import Version + + +REPO_ROOT = Path(__file__).resolve().parents[1] +COMPROMISED_LITELLM_VERSIONS = {Version("1.82.7"), Version("1.82.8")} + + +def _project_dependencies() -> dict[str, Requirement]: + data = tomllib.loads((REPO_ROOT / "pyproject.toml").read_text()) + requirements = [Requirement(value) for value in data["project"]["dependencies"]] + return {requirement.name: requirement for requirement in requirements} + + +def _locked_version(package_name: str) -> Version: + data = tomllib.loads((REPO_ROOT / "uv.lock").read_text()) + for package in data["package"]: + if package["name"] == package_name: + return Version(package["version"]) + raise AssertionError(f"{package_name} is not present in uv.lock") + + +def test_optimizer_dependency_constraints_exclude_compromised_litellm_versions(): + dependencies = _project_dependencies() + + assert Version("0.1.1") in dependencies["gepa"].specifier + assert Version("0.2.0") not in dependencies["gepa"].specifier + + litellm_specifier = dependencies["litellm"].specifier + assert Version("1.83.0") in litellm_specifier + for version in COMPROMISED_LITELLM_VERSIONS: + assert version not in litellm_specifier + + +def test_uv_lock_pins_litellm_after_compromised_versions(): + locked_litellm = _locked_version("litellm") + + assert locked_litellm >= Version("1.83.0") + assert locked_litellm not in COMPROMISED_LITELLM_VERSIONS diff --git a/tests/test_optimization_observer.py b/tests/test_optimization_observer.py new file mode 100644 index 0000000..2d96ee4 --- /dev/null +++ b/tests/test_optimization_observer.py @@ -0,0 +1,254 @@ +"""Tests for the realistic optimization observer workflow.""" + +from __future__ import annotations + +import json +import subprocess +import sys +from pathlib import Path + +import pytest + +from scripts import optimization_observer + + +def _write_run_dir( + path: Path, + *, + seed: str, + best: str, + initial: float | None = 0.4, + best_score: float | None = 0.7, + candidates: int = 3, + diagnostics: list[dict] | None = None, +) -> Path: + path.mkdir() + (path / "seed.txt").write_text(seed, encoding="utf-8") + (path / "best_artifact.txt").write_text(best, encoding="utf-8") + summary = { + "best_artifact": best, + "total_metric_calls": candidates + 1, + "score_summary": { + "initial": initial, + "latest": best_score, + "best": best_score, + "delta_best_vs_initial": ( + None + if initial is None or best_score is None + else round(best_score - initial, 6) + ), + "num_candidates": candidates, + }, + "top_diagnostics": diagnostics + if diagnostics is not None + else [{"name": "contract_completeness", "value": best_score or 0.0}], + "plateau_guidance": "No strong plateau detected.", + } + (path / "summary.json").write_text(json.dumps(summary), encoding="utf-8") + return path + + +def _scorer(path: Path) -> Path: + path.write_text( + "\n".join( + [ + "#!/usr/bin/env bash", + "payload=$(cat)", + "python3 - \"$payload\" <<'PY'", + "import json", + "import sys", + "data = json.loads(sys.argv[1])", + "candidate = data.get('candidate', '')", + "score = 0.9 if 'feedback' in candidate.lower() else 0.6", + "print(json.dumps({'score': score, 'feedback': ['ok']}))", + "PY", + ] + ), + encoding="utf-8", + ) + path.chmod(0o755) + return path + + +def test_setup_benchmark_copies_seed_without_overwriting_source(tmp_path: Path): + result = optimization_observer.setup_benchmark( + optimization_observer.DEFAULT_BENCHMARK_PATH, + tmp_path, + ) + + seed_file = Path(result["seed_file"]) + assert seed_file.exists() + assert seed_file != optimization_observer.REPO_ROOT / result["seed_source"] + assert seed_file.read_text(encoding="utf-8") == ( + optimization_observer.REPO_ROOT / result["seed_source"] + ).read_text(encoding="utf-8") + assert "generate-evaluator" in result["why"] + assert result["training_evaluator_command"][-1].endswith( + "evaluator_generation_training.sh" + ) + + +def test_training_scorer_returns_score_dimensions_and_feedback(): + candidate = (optimization_observer.REPO_ROOT / "skills/generate-evaluator/SKILL.md").read_text( + encoding="utf-8" + ) + proc = subprocess.run( + ["bash", "evaluators/evaluator_generation_training.sh"], + input=json.dumps({"candidate": candidate}), + capture_output=True, + text=True, + timeout=30, + ) + + assert proc.returncode == 0, proc.stderr + result = json.loads(proc.stdout) + assert 0.0 <= result["score"] <= 1.0 + assert result["contract_completeness"] > 0 + assert result["runnable_examples"] > 0 + assert result["feedback"] + + +def test_training_scorer_rejects_malformed_payload(): + proc = subprocess.run( + ["bash", "evaluators/evaluator_generation_training.sh"], + input=json.dumps({"not_candidate": "missing field"}), + capture_output=True, + text=True, + timeout=30, + ) + + assert proc.returncode == 0 + result = json.loads(proc.stdout) + assert result["score"] == 0.0 + assert "candidate" in result["error"] + + +def test_observation_report_summarizes_successful_run(tmp_path: Path): + run_dir = _write_run_dir( + tmp_path / "run", + seed="# Skill\n\n- Add evaluator code.", + best="# Skill\n\n- Add evaluator code.\n- Return JSON score and feedback.", + ) + scorer = _scorer(tmp_path / "heldout.sh") + + report = optimization_observer.build_observation_report( + run_dir, + validation_command=["bash", str(scorer)], + meaningful_delta=0.05, + validation_delta=0.01, + ) + + assert report["training"]["initial_score"] == 0.4 + assert report["training"]["best_score"] == 0.7 + assert report["training"]["score_delta"] == 0.3 + assert report["training"]["candidate_count"] == 3 + assert report["training"]["metric_calls"] == 4 + assert report["diff_summary"]["added"] > 0 + assert report["validation"]["accepted"] is True + assert report["accepted"] is True + + +def test_observation_report_flags_seed_only_and_insufficient_improvement(tmp_path: Path): + run_dir = _write_run_dir( + tmp_path / "run", + seed="same", + best="same", + initial=0.5, + best_score=0.5, + candidates=1, + diagnostics=[], + ) + + report = optimization_observer.build_observation_report( + run_dir, + meaningful_delta=0.05, + ) + + kinds = {warning["kind"] for warning in report["warnings"]} + assert "seed_only" in kinds + assert "missing_diagnostics" in kinds + assert "insufficient_improvement" in kinds + assert "seed_and_best_identical" in kinds + assert report["accepted"] is False + + +def test_observation_report_rejects_score_only_diagnostics(tmp_path: Path): + run_dir = _write_run_dir( + tmp_path / "run", + seed="old", + best="new feedback", + initial=0.4, + best_score=0.6, + candidates=3, + diagnostics=[{"name": "overall_score", "value": 0.6}], + ) + scorer = _scorer(tmp_path / "heldout.sh") + + report = optimization_observer.build_observation_report( + run_dir, + validation_command=["bash", str(scorer)], + meaningful_delta=0.05, + validation_delta=0.01, + ) + + assert any(w["kind"] == "missing_diagnostics" for w in report["warnings"]) + assert report["accepted"] is False + + +def test_held_out_regression_prevents_acceptance(tmp_path: Path): + run_dir = _write_run_dir( + tmp_path / "run", + seed="# Skill\n\nReturn JSON score.", + best="# Skill\n\nReturn JSON score.\nRepeat score score score score.", + initial=0.4, + best_score=0.8, + candidates=4, + ) + scorer = tmp_path / "heldout.sh" + scorer.write_text( + "\n".join( + [ + "#!/usr/bin/env bash", + "payload=$(cat)", + "python3 - \"$payload\" <<'PY'", + "import json", + "import sys", + "data = json.loads(sys.argv[1])", + "score = 0.3 if 'Repeat' in data.get('candidate', '') else 0.8", + "print(json.dumps({'score': score, 'feedback': ['heldout']}))", + "PY", + ] + ), + encoding="utf-8", + ) + scorer.chmod(0o755) + + report = optimization_observer.build_observation_report( + run_dir, + validation_command=["bash", str(scorer)], + meaningful_delta=0.05, + validation_delta=0.01, + ) + + assert report["training"]["score_delta"] == 0.4 + assert report["validation"]["accepted"] is False + assert report["accepted"] is False + assert any(w["kind"] == "heldout_regression" for w in report["warnings"]) + + +def test_main_report_strict_returns_nonzero_for_unaccepted_run(tmp_path: Path, capsys): + run_dir = _write_run_dir( + tmp_path / "run", + seed="same", + best="same", + initial=0.5, + best_score=0.5, + candidates=1, + diagnostics=[], + ) + + rc = optimization_observer.main(["report", "--run-dir", str(run_dir), "--strict"]) + + assert rc == 1 + result = json.loads(capsys.readouterr().out) + assert result["accepted"] is False diff --git a/tests/test_spec_loader.py b/tests/test_spec_loader.py index b024b7e..bdd1b34 100644 --- a/tests/test_spec_loader.py +++ b/tests/test_spec_loader.py @@ -125,6 +125,47 @@ def test_empty_spec_returns_all_none(self, tmp_path: Path): class TestSpecCliIntegration: + def _run_with_spec( + self, + tmp_path: Path, + monkeypatch, + spec_text: str, + *extra_args: str, + ): + from optimize_anything.cli import main + + seed_file = tmp_path / "seed.txt" + seed_file.write_text("test") + spec_file = tmp_path / "opt.toml" + spec_file.write_text(spec_text) + captured_config: dict = {} + + class DummyResult: + best_candidate = "best" + total_metric_calls = 1 + + def fake_optimize(**kwargs): + captured_config["config"] = kwargs.get("config") + return DummyResult() + + monkeypatch.setattr( + "optimize_anything.cli._preflight_command_evaluator", + lambda command, cwd=None: None, + ) + monkeypatch.setattr( + "optimize_anything.evaluators.command_evaluator", + lambda command, cwd=None, **kwargs: lambda c: (0.5, {}), + ) + monkeypatch.setattr("gepa.optimize_anything.optimize_anything", fake_optimize) + + result = main([ + "optimize", str(seed_file), + "--evaluator-command", "bash", "eval.sh", + "--spec-file", str(spec_file), + *extra_args, + ]) + return result, captured_config + def test_cli_flags_take_precedence_over_spec( self, tmp_path: Path, capsys, monkeypatch ): @@ -206,6 +247,69 @@ def fake_optimize(**kwargs): assert cfg is not None assert cfg.engine.max_metric_calls == 7 + def test_spec_parallel_false_disables_engine_parallel( + self, tmp_path: Path, monkeypatch + ): + result, captured = self._run_with_spec( + tmp_path, + monkeypatch, + '[optimization]\nobjective = "test"\nparallel = false\n', + ) + + assert result == 0 + assert captured["config"].engine.parallel is False + + def test_spec_parallel_true_enables_engine_parallel( + self, tmp_path: Path, monkeypatch + ): + result, captured = self._run_with_spec( + tmp_path, + monkeypatch, + '[optimization]\nobjective = "test"\nparallel = true\n', + ) + + assert result == 0 + assert captured["config"].engine.parallel is True + + def test_cli_parallel_overrides_spec_parallel_false( + self, tmp_path: Path, monkeypatch + ): + result, captured = self._run_with_spec( + tmp_path, + monkeypatch, + '[optimization]\nobjective = "test"\nparallel = false\n', + "--parallel", + ) + + assert result == 0 + assert captured["config"].engine.parallel is True + + def test_cli_no_parallel_overrides_spec_parallel_true( + self, tmp_path: Path, monkeypatch + ): + result, captured = self._run_with_spec( + tmp_path, + monkeypatch, + '[optimization]\nobjective = "test"\nparallel = true\n', + "--no-parallel", + ) + + assert result == 0 + assert captured["config"].engine.parallel is False + + def test_spec_parallel_false_with_workers_returns_error( + self, tmp_path: Path, capsys, monkeypatch + ): + result, captured = self._run_with_spec( + tmp_path, + monkeypatch, + '[optimization]\nobjective = "test"\nparallel = false\nworkers = 4\n', + ) + + assert result == 1 + assert "config" not in captured + assert "workers require parallel execution" in capsys.readouterr().err + def test_invalid_spec_file_returns_1(self, tmp_path: Path, capsys): from optimize_anything.cli import main diff --git a/uv.lock b/uv.lock index 50a4f77..cb09c4d 100644 --- a/uv.lock +++ b/uv.lock @@ -1,14 +1,14 @@ version = 1 -revision = 1 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size = 10276, upload-time = "2025-06-08T17:06:38.034Z" }, ] From 64cb77c3043af2afdd93c91db07e18071b83f425 Mon Sep 17 00:00:00 2001 From: Ahmad Ragab Date: Sun, 5 Jul 2026 07:02:21 -0400 Subject: [PATCH 2/2] Adding CLAUDE.md notes --- CLAUDE.md | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/CLAUDE.md b/CLAUDE.md index d4cc26e..e641cc1 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -107,3 +107,17 @@ The intake specification (`--intake-json` / `--intake-file`) normalizes these fi - `evaluation_pattern` — scoring strategy (`verification`, `judge`, `simulation`, `composite`) - `execution_mode` — evaluator transport (`command`, `http`) - `evaluator_cwd` — working directory for command evaluators + +## Agent skills + +### Issue tracker + +Issues and PRDs live as GitHub issues in `ASRagab/optimize-anything`, managed via the `gh` CLI. See `docs/agents/issue-tracker.md`. + +### Triage labels + +Five canonical triage roles, with `needs-triage`→`triage`, `ready-for-agent`→`dev`, `ready-for-human`→`review` remapped. See `docs/agents/triage-labels.md`. + +### Domain docs + +Single-context: one `CONTEXT.md` + `docs/adr/` at the repo root. See `docs/agents/domain.md`.