diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index ceeac9b9..7f53556a 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -668,6 +668,32 @@ jobs: name: ctrf-test-results-gateway path: ctrf/ + gepa: + name: "GEPA Optimizer: test" + needs: [detect-changes] + if: > + github.event_name == 'workflow_dispatch' || + needs.detect-changes.outputs.gepa == 'true' + runs-on: ubuntu-latest + + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Install uv + uses: astral-sh/setup-uv@v6 + with: + enable-cache: true + cache-dependency-glob: apps/gepa-optimizer/uv.lock + + - name: Install locked dependencies + working-directory: apps/gepa-optimizer + run: uv sync --locked + + - name: Run tests + working-directory: apps/gepa-optimizer + run: uv run pytest + notice-check: name: Third-party NOTICE # Runs on every PR with no detect-changes gate: the NOTICE / NOTICE-REVIEW @@ -782,6 +808,7 @@ jobs: db-migrations: ${{ steps.changes.outputs.db-migrations }} shared: ${{ steps.changes.outputs.shared }} gateway: ${{ steps.changes.outputs.gateway }} + gepa: ${{ steps.changes.outputs.gepa }} coder-acp-copilot-windows: ${{ steps.changes.outputs.coder-acp-copilot-windows }} model-scanning: ${{ steps.changes.outputs.model-scanning }} typescript: ${{ steps.changes.outputs.typescript }} @@ -834,6 +861,10 @@ jobs: - 'packages/db-migrations/**' gateway: - 'apps/gateway/**' + gepa: + - 'apps/gepa-optimizer/**' + - 'package.json' + - '.github/workflows/ci.yml' coder-acp-copilot-windows: - 'apps/workers/coder-acp-copilot-windows/**' - 'apps/workers/coder-acp-copilot/versions.env' @@ -874,7 +905,7 @@ jobs: ci-summary: name: CI Summary runs-on: ubuntu-latest - needs: [test, integration-test, integration-test-queue, windows-build, lint, build, cli-bundle-test, gateway, llm-evals, notice-check, license-headers] + needs: [test, integration-test, integration-test-queue, windows-build, lint, build, cli-bundle-test, gateway, gepa, llm-evals, notice-check, license-headers] if: always() steps: @@ -890,7 +921,8 @@ jobs: [ "${{ needs.llm-evals.result }}" = "success" -o "${{ needs.llm-evals.result }}" = "skipped" ] && \ [ "${{ needs.notice-check.result }}" = "success" -o "${{ needs.notice-check.result }}" = "skipped" ] && \ [ "${{ needs.license-headers.result }}" = "success" ] && \ - [ "${{ needs.gateway.result }}" = "success" -o "${{ needs.gateway.result }}" = "skipped" ]; then + [ "${{ needs.gateway.result }}" = "success" -o "${{ needs.gateway.result }}" = "skipped" ] && \ + [ "${{ needs.gepa.result }}" = "success" -o "${{ needs.gepa.result }}" = "skipped" ]; then echo "✅ All checks passed!" exit 0 else @@ -906,5 +938,6 @@ jobs: echo " Third-party NOTICE: ${{ needs.notice-check.result }}" echo " License Headers: ${{ needs.license-headers.result }}" echo " Gateway: ${{ needs.gateway.result }}" + echo " GEPA Optimizer: ${{ needs.gepa.result }}" exit 1 fi diff --git a/AGENTS.md b/AGENTS.md index 326cc3ed..aa9eb276 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -23,6 +23,7 @@ apps/ model-scanners/ # Feature detection for Copilot and Anthropic models version-checkers/ # Poll for new agent/tool releases key-updaters/ # GitHub auth cookie management for VS Code Web + gepa-optimizer/ # Python+uv GEPA driver — optimizes AGENTS.md via Scope runs packages/ shared/ # Monorepo foundation — types, DB models, queue/blob/redis clients db-migrations/ # MongoDB migration framework (mongo-migrate-ts) @@ -141,6 +142,20 @@ Express service for centralized token storage, validation, and round-robin distr - Architecture: [docs/architecture/token-manager.md](docs/architecture/token-manager.md) +### GEPA Optimizer (`apps/gepa-optimizer/`) + +Python + uv app (outside the pnpm workspace, like the Rust `apps/gateway`) that +runs the [GEPA](https://github.com/gepa-ai/gepa) reflective prompt-optimization +algorithm to optimize an **AGENTS.md** file. Each candidate is scored by a real +Scope run (Copilot CLI worker + Claude Haiku 4.5) via a custom `ScopeAdapter`: +submit standalone requests with `agentsMd`, add them through canonical Experiment +membership revisions, poll exact requests/executions/reports, and score from the +run's `criteriaResults`. Scoring is derived, never persisted. Run tests with +`pnpm test:gepa` (`uv run pytest`); CI runs `uv sync --locked && uv run pytest`. + +- README: [apps/gepa-optimizer/README.md](apps/gepa-optimizer/README.md) +- Experiment integration: [docs/architecture/experiments.md](docs/architecture/experiments.md) + ### Shared Package (`packages/shared/`) Monorepo foundation. Exports types (runs, iterations, criteria, scenarios, personas), Mongoose models, and service clients (queue, blob, redis, config loader, criteria-store, criteria-provider). All apps and workers depend on it — breaking changes here affect everything. diff --git a/ENV_VARIABLES.md b/ENV_VARIABLES.md index c79b005f..a15dd708 100644 --- a/ENV_VARIABLES.md +++ b/ENV_VARIABLES.md @@ -1369,6 +1369,73 @@ URL of the gateway/DevProxy REST API. Used to start/stop recording, check status Host port mapping for the Copilot DevProxy REST API in Docker Compose. +## GEPA Optimizer Configuration (`apps/gepa-optimizer`) + +The Python + uv GEPA app optimizes an AGENTS.md file by scoring each candidate +with real Scope runs. Configure it through `apps/gepa-optimizer/.env`; see the +co-located `.env.example`. + +### SCOPE_API_URL +**Default:** `http://localhost:3100` + +Base URL of the Scope REST API used to submit and poll candidate runs. + +### SCOPE_PROJECT_ID +**Default:** none (required) + +Project that owns the criteria and candidate runs. Scope's catalog and run +creation APIs are project-scoped, so the optimizer fails fast when this is not +configured. + +### SCOPE_EXPERIMENT_ID / SCOPE_EXPERIMENT_NAME +**Defaults:** empty / `GEPA AGENTS.md optimization ` + +With no ID, the optimizer creates one empty canonical Experiment and uses its +membership revisions for every evaluated candidate batch. Set +`SCOPE_EXPERIMENT_ID` to target an existing Experiment; the optimizer reads its +current revision before adding requests. `SCOPE_EXPERIMENT_NAME` controls the +metadata name only when creating a new Experiment. + +### SCOPE_WORKER / SCOPE_MODEL +**Defaults:** `coder-acp-copilot` / `claude-haiku-4.5` + +Worker and model used to evaluate each candidate. + +### SCOPE_MAX_CONCURRENCY +**Default:** `10` +**Type:** integer + +Maximum number of candidate runs kept in flight concurrently. + +### SCOPE_RUN_TIMEOUT_SECONDS / SCOPE_POLL_INTERVAL_SECONDS +**Defaults:** `1800` / `15` + +Per-run timeout and polling interval, in seconds. + +### SCOPE_MAX_ITERATIONS +**Default:** `10` + +Maximum agent/judge iterations for each candidate run. + +### SCOPE_SCORE_STRATEGY +**Default:** `mean` +**Options:** `mean` | `final` | `weighted` + +Aggregation applied to per-turn criteria pass fractions. `weighted` computes +`0.8 * final + 0.2 * mean`. + +### GEPA_MAX_METRIC_CALLS / GEPA_REFLECTION_MINIBATCH_SIZE +**Defaults:** `12` / `2` + +Optimizer budget. Each metric call is a complete Scope agent run. + +### GEPA_REFLECTION_LM +**Default:** `azure/gpt-5.5` + +litellm model string for the reflection model that rewrites AGENTS.md. +Provider-specific credentials such as `AZURE_API_KEY`, `AZURE_API_BASE`, and +`AZURE_API_VERSION` are consumed by litellm. + ## Observability ### OTEL_COLLECTOR_ENDPOINT diff --git a/apps/cli/src/run-get-action.ts b/apps/cli/src/run-get-action.ts index e3f83515..e05cb6a1 100644 --- a/apps/cli/src/run-get-action.ts +++ b/apps/cli/src/run-get-action.ts @@ -5,7 +5,7 @@ * Action handler for `run get` subcommand — extracted for testability. */ import { colorLevel, dimTimestamp, errorText, successText, label, value, banner, warnBanner, criterionIcon } from "./utils/style.js"; -import { TAXONOMY_ELEMENT_METADATA } from "shared"; +import { TAXONOMY_ELEMENT_METADATA } from "shared/observation-taxonomy"; import { GATE_METADATA, GATE_ORDER, type ConversationTurn, type GateId, type GateRunSummary, type RequestDocument, type TaxonomyElementId } from "shared/types"; import { formatData, isMachineReadable } from "./utils/formatters.js"; import type { OutputFormat, DisplayField } from "./utils/types.js"; diff --git a/apps/gepa-optimizer/.env.example b/apps/gepa-optimizer/.env.example new file mode 100644 index 00000000..0a0985a0 --- /dev/null +++ b/apps/gepa-optimizer/.env.example @@ -0,0 +1,41 @@ +# Scope REST API base URL (no trailing slash). +SCOPE_API_URL=http://localhost:3100 +SCOPE_PROJECT_ID= + +# Leave empty to create one canonical Experiment for this optimization. Set an +# existing empty/current Experiment id to append evaluation revisions there. +SCOPE_EXPERIMENT_ID= +SCOPE_EXPERIMENT_NAME= + +# Scope worker + model used to evaluate each AGENTS.md candidate. +SCOPE_WORKER=coder-acp-copilot +SCOPE_MODEL=claude-haiku-4.5 + +# Max number of Scope runs kept in-flight at once. Defaults to 10 to match the +# Linux coder-acp-copilot KEDA ceiling (maxReplicaCount: 10). +SCOPE_MAX_CONCURRENCY=10 + +# Per-run polling: how long to wait for a run to reach status=done, and how +# often to poll (seconds). +SCOPE_RUN_TIMEOUT_SECONDS=1800 +SCOPE_POLL_INTERVAL_SECONDS=15 + +# Multi-turn iterations per Scope run. +SCOPE_MAX_ITERATIONS=10 + +# Scoring strategy over a run's turns: mean | final | weighted. +SCOPE_SCORE_STRATEGY=mean + +# GEPA budget. Each metric call is ONE full (slow) Scope agent run, so keep +# this small. See README for budgeting math. +GEPA_MAX_METRIC_CALLS=12 +GEPA_REFLECTION_MINIBATCH_SIZE=2 + +# Reflection LM passed to gepa.optimize (litellm model string). The reflection +# LM rewrites the AGENTS.md; it is NOT the agent under test. +GEPA_REFLECTION_LM=azure/gpt-5.5 + +# Azure OpenAI credentials for the reflection LM (litellm reads these). +AZURE_API_KEY= +AZURE_API_BASE= +AZURE_API_VERSION=2024-12-01-preview diff --git a/apps/gepa-optimizer/.gitignore b/apps/gepa-optimizer/.gitignore new file mode 100644 index 00000000..971241aa --- /dev/null +++ b/apps/gepa-optimizer/.gitignore @@ -0,0 +1,6 @@ +.venv/ +__pycache__/ +*.pyc +.env +runs/ +.pytest_cache/ diff --git a/apps/gepa-optimizer/.python-version b/apps/gepa-optimizer/.python-version new file mode 100644 index 00000000..e4fba218 --- /dev/null +++ b/apps/gepa-optimizer/.python-version @@ -0,0 +1 @@ +3.12 diff --git a/apps/gepa-optimizer/README.md b/apps/gepa-optimizer/README.md new file mode 100644 index 00000000..49da3049 --- /dev/null +++ b/apps/gepa-optimizer/README.md @@ -0,0 +1,116 @@ +# GEPA × Scope Optimizer (`apps/gepa-optimizer`) + +Optimizes an **`AGENTS.md`** file with the [GEPA](https://github.com/gepa-ai/gepa) +reflective prompt-optimization algorithm, where every candidate is scored by a +**real Scope benchmark run** — the Copilot CLI worker (Linux) with the +**Claude Haiku 4.5** model. + +This mirrors the reference experiment (`geo-experiments/gepa-prompt-optimization`) +but, instead of optimizing a system prompt evaluated by a single LLM completion, +it optimizes an `AGENTS.md` evaluated by a multi-turn Scope agent run and judged +against a criteria DAG. + +```mermaid +flowchart LR + Candidate[GEPA AGENTS.md candidate] --> Submit[Submit standalone Scope requests] + Submit --> Membership[Publish one Experiment membership revision] + Membership --> Runs[Poll exact request runs] + Runs --> Score[Derive criteria scores] + Membership --> Execution[Poll exact Experiment execution] + Execution --> Reports[Read exact execution reports] + Score --> Reflection[Build reflective evidence] + Reports --> Reflection + Reflection --> Candidate +``` + +One optimization owns one canonical Scope Experiment. By default the entry +point creates an empty Experiment, then every `evaluate()` call adds its newly +submitted requests through `POST /experiments/:id/requests`. That publishes a +new immutable revision and execution, whose exact identities and reports are +stored in the GEPA outputs and reflective trajectories. Set +`SCOPE_EXPERIMENT_ID` to target an existing Experiment instead. + +## Polyglot exception + +This is a **Python** app (managed by [`uv`](https://docs.astral.sh/uv/)) living in +a TypeScript/pnpm monorepo, exactly like the Rust `apps/gateway`. It is +intentionally **outside** the pnpm workspace. `uv.lock` is committed and CI runs +`uv sync --locked && uv run pytest`. Root scripts: `pnpm test:gepa`, +`pnpm build:gepa`. + +## Setup + +```bash +cd apps/gepa-optimizer +uv sync # create .venv and install deps from uv.lock +cp .env.example .env # then edit SCOPE_API_URL, SCOPE_PROJECT_ID, criteria ids, reflection LM creds +``` + +## How scoring works + +The GEPA score is **derived, never persisted in Scope**. For a finished run: + +- The **denominator is the number of requested criteria** (not whatever Scope + returned), so a missing/skipped criterion always counts as failed. +- A criterion only counts as passed when it was **evaluated and passed**; + `evaluated == false` (skipped because a DAG ancestor failed) counts as failed. +- Per-turn score = `passed / requested`. The run score aggregates turns via + `SCOPE_SCORE_STRATEGY`: + - `mean` (default) — average across all turns; + - `final` — last turn only (closest to "task success"); + - `weighted` — `0.8 * final + 0.2 * mean`. +- A run that did not succeed, has no turns, or errored scores `0.0` (never `NaN`). + +## Budget + +GEPA counts **one metric call per dataset example**, and each metric call is a +full (minutes-long) Scope agent run. Rough spend per optimization: + +```text +len(valset) # seed validation ++ 2 * reflection_minibatch_size # parent + child minibatch per proposal ++ len(valset) # full re-validation of each accepted candidate +``` + +Keep `valset` tiny and `GEPA_MAX_METRIC_CALLS` small. + +## Run + +Edit `src/gepa_optimizer/dataset.py` so the criterion ids exist in your Scope +instance (`pnpm cli criteria list`), then: + +```bash +uv run gepa-optimizer +# or +uv run python -m gepa_optimizer.main +``` + +Results (best candidate, Pareto front, and a best-effort lineage DAG +reconstructed from GEPA's parent indices) are written to `runs//results.json`. +The artifact also records the canonical `experimentId` and final +`experimentRevisionId`. Every evaluated request remains visible in the normal +Scope Runs, Experiment Detail, CLI, and API surfaces. + +## Tests + +```bash +uv run pytest # or, from repo root: pnpm test:gepa +``` + +Unit tests cover scoring, reflective-dataset construction, lineage +reconstruction, the REST client (via `httpx.MockTransport`), and the adapter +(with a stubbed client). No live Scope instance is required. + +## Concurrency + +`SCOPE_MAX_CONCURRENCY` (default **10**) caps in-flight runs to match the Linux +`coder-acp-copilot` KEDA ceiling (`maxReplicaCount: 10`). Submitting more is +harmless — extras just queue — but yields no speedup. + +## Lineage + +Parent→child lineage is reconstructed **after** optimization from GEPA's public +`result.parents` / `result.candidates` and saved into `results.json` +(`root` / `mutation` / `merge`). Candidate evaluation requests are grouped by +canonical Experiment membership revisions rather than a caller-provided +correlation field. diff --git a/apps/gepa-optimizer/pyproject.toml b/apps/gepa-optimizer/pyproject.toml new file mode 100644 index 00000000..2973ea92 --- /dev/null +++ b/apps/gepa-optimizer/pyproject.toml @@ -0,0 +1,32 @@ +[project] +name = "gepa-optimizer" +version = "0.1.0" +description = "GEPA prompt-optimization driver that optimizes AGENTS.md files evaluated by real Scope benchmark runs." +readme = "README.md" +requires-python = ">=3.12" +dependencies = [ + "gepa>=0.1.1", + "httpx>=0.27", + "python-dotenv>=1.0", + "litellm>=1.0", +] + +[project.scripts] +gepa-optimizer = "gepa_optimizer.main:main" + +[dependency-groups] +dev = [ + "pytest>=8.0", + "pytest-asyncio>=0.23", +] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["src/gepa_optimizer"] + +[tool.pytest.ini_options] +testpaths = ["tests"] +asyncio_mode = "auto" diff --git a/apps/gepa-optimizer/src/gepa_optimizer/__init__.py b/apps/gepa-optimizer/src/gepa_optimizer/__init__.py new file mode 100644 index 00000000..675ffd0a --- /dev/null +++ b/apps/gepa-optimizer/src/gepa_optimizer/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. + +"""GEPA × Scope optimizer package.""" + +from .scope_adapter import ScopeAdapter +from .scope_client import ScopeClient + +__all__ = ["ScopeAdapter", "ScopeClient"] diff --git a/apps/gepa-optimizer/src/gepa_optimizer/dataset.py b/apps/gepa-optimizer/src/gepa_optimizer/dataset.py new file mode 100644 index 00000000..8f5b476c --- /dev/null +++ b/apps/gepa-optimizer/src/gepa_optimizer/dataset.py @@ -0,0 +1,90 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. + +"""Dataset + preflight helpers for the optimizer. + +The dataset is intentionally tiny: every example is one full (minutes-long) +Scope agent run, so a handful of tasks is plenty for a demonstration. Each +example references **existing** Scope criterion ids; ``preflight_criteria`` +validates them before any expensive run starts. +""" + +from __future__ import annotations + +import asyncio +from typing import Any, Mapping, Sequence + +import httpx + +from .scope_client import ScopeClient, SystemicScopeError + +DataInst = dict[str, Any] + + +# The seed AGENTS.md GEPA mutates. It starts EMPTY on purpose: the whole point of +# this experiment is to discover, from scratch, the AGENTS.md guidance that makes +# the agent reliably satisfy the task's criteria. +SEED_AGENTS_MD = "" + +# The single task we optimize the AGENTS.md for. +BLOG_TASK = ( + "Create a blogging platform inspired by Medium using Python and Django, " + "utilizing a nonrelational database for data storage and management. " + "Must run in the Cloud." +) + +# Criteria requested per run. We request the FULL ancestor closure of the target +# leaf (uses_azure_documentdb), not just the leaf, so scoring is graded: +# uses_database -> uses_document_oriented_db -> uses_azure_documentdb +# uses_azure ----------------------------------^ +# Partial credit (e.g. "uses a document DB but not on Azure") gives GEPA a much +# denser gradient than a single 0/1 leaf, while the leaf remains the real goal. +BLOG_CRITERIA = [ + "uses_database", + "uses_document_oriented_db", + "uses_azure", + "uses_azure_documentdb", +] + + +def default_dataset() -> tuple[list[DataInst], list[DataInst]]: + """Return ``(trainset, valset)``. + + A single seed task (the Medium-style Django blog on Azure Cosmos DB). Both + splits point at the same example: GEPA reflects on it (train) and selects on + it (val). ``preflight_criteria`` validates the ids before any expensive run. + """ + example: DataInst = {"task": BLOG_TASK, "criteria": list(BLOG_CRITERIA)} + trainset: list[DataInst] = [example] + valset: list[DataInst] = [dict(example)] + return trainset, valset + + +def collect_criteria(datasets: Sequence[Sequence[Mapping[str, Any]]]) -> set[str]: + ids: set[str] = set() + for ds in datasets: + for ex in ds: + for cid in ex.get("criteria") or []: + ids.add(cid) + return ids + + +async def _preflight_async(client: ScopeClient, criteria: Sequence[str]) -> list[str]: + missing: list[str] = [] + async with httpx.AsyncClient(timeout=client.timeout_seconds) as http: + for cid in criteria: + doc = await client.get_criterion(http, cid) + if doc is None: + missing.append(cid) + return missing + + +def preflight_criteria(client: ScopeClient, criteria: Sequence[str]) -> None: + """Raise ``SystemicScopeError`` if any criterion id does not exist.""" + if not criteria: + raise SystemicScopeError("Dataset has no criteria; every example needs >=1.") + missing = asyncio.run(_preflight_async(client, list(criteria))) + if missing: + raise SystemicScopeError( + f"Unknown criterion ids (create them or fix the dataset): {sorted(missing)}" + ) diff --git a/apps/gepa-optimizer/src/gepa_optimizer/lineage.py b/apps/gepa-optimizer/src/gepa_optimizer/lineage.py new file mode 100644 index 00000000..427efdb8 --- /dev/null +++ b/apps/gepa-optimizer/src/gepa_optimizer/lineage.py @@ -0,0 +1,55 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. + +"""Post-hoc lineage reconstruction from a GEPA result. + +The rubber-duck critique flagged that capturing parent→child lineage *during* +evaluation (via a custom ``propose_new_texts``) is fragile: it requires +reimplementing GEPA's reflection proposer and races on shared state. Instead we +reconstruct the candidate DAG **after** ``gepa.optimize`` returns, from the +public ``result.parents`` (a ``list[list[int]]`` of parent indices per +candidate) and ``result.candidates``. This uses only GEPA's documented result +surface and cannot corrupt the optimization. + +The reconstructed edges are written to ``results.json`` for inspection. The +Scope ``requests.agentsMdParentIds`` field remains available for callers that +*do* know parentage at submit time, but the optimizer does not depend on it. +""" + +from __future__ import annotations + +from typing import Any, Mapping, Sequence + +COMPONENT = "AGENTS.md" + + +def reconstruct_lineage( + candidates: Sequence[Mapping[str, str]], + parents: Sequence[Sequence[int] | None], +) -> list[dict[str, Any]]: + """Build a serializable lineage DAG from GEPA candidates + parent indices. + + Each node: ``{index, parents: [int...], kind, agentsMdPreview}`` where + ``kind`` is ``root`` (no parents), ``mutation`` (one parent) or ``merge`` + (two+ parents). + """ + nodes: list[dict[str, Any]] = [] + for idx, cand in enumerate(candidates): + raw_parents = parents[idx] if idx < len(parents) else None + parent_list = [p for p in (raw_parents or []) if p is not None and p >= 0] + if not parent_list: + kind = "root" + elif len(parent_list) == 1: + kind = "mutation" + else: + kind = "merge" + text = (cand or {}).get(COMPONENT, "") if isinstance(cand, Mapping) else "" + nodes.append( + { + "index": idx, + "parents": parent_list, + "kind": kind, + "agentsMdPreview": text[:200], + } + ) + return nodes diff --git a/apps/gepa-optimizer/src/gepa_optimizer/main.py b/apps/gepa-optimizer/src/gepa_optimizer/main.py new file mode 100644 index 00000000..0fbfc7fc --- /dev/null +++ b/apps/gepa-optimizer/src/gepa_optimizer/main.py @@ -0,0 +1,155 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. + +"""Entry point: optimize an AGENTS.md file with GEPA against Scope. + +Budgeting note (``GEPA_MAX_METRIC_CALLS``): GEPA counts **one metric call per +dataset example evaluated**, and each metric call here is a full, slow Scope +agent run. A single optimization spends roughly:: + + len(valset) # initial validation of the seed + + 2 * reflection_minibatch_size # parent + child minibatch per proposal + + len(valset) # full re-validation of each accepted candidate + +So keep ``valset`` tiny and ``GEPA_MAX_METRIC_CALLS`` small. +""" + +from __future__ import annotations + +import asyncio +import json +import os +from datetime import datetime +from pathlib import Path + +import httpx +from dotenv import load_dotenv + +import gepa + +from .dataset import ( + SEED_AGENTS_MD, + collect_criteria, + default_dataset, + preflight_criteria, +) +from .lineage import reconstruct_lineage +from .scope_adapter import ScopeAdapter +from .scope_client import ScopeClient + + +def _env(name: str, default: str) -> str: + return os.getenv(name, default) + + +def main() -> None: + load_dotenv() + + base_url = _env("SCOPE_API_URL", "http://localhost:3100") + project_id = _env("SCOPE_PROJECT_ID", "") + if not project_id: + raise ValueError("SCOPE_PROJECT_ID is required") + worker = _env("SCOPE_WORKER", "coder-acp-copilot") + model = _env("SCOPE_MODEL", "claude-haiku-4.5") + max_concurrency = int(_env("SCOPE_MAX_CONCURRENCY", "10")) + run_timeout = float(_env("SCOPE_RUN_TIMEOUT_SECONDS", "1800")) + poll_interval = float(_env("SCOPE_POLL_INTERVAL_SECONDS", "15")) + max_iterations = int(_env("SCOPE_MAX_ITERATIONS", "10")) + score_strategy = _env("SCOPE_SCORE_STRATEGY", "mean") + max_metric_calls = int(_env("GEPA_MAX_METRIC_CALLS", "12")) + reflection_minibatch_size = int(_env("GEPA_REFLECTION_MINIBATCH_SIZE", "2")) + reflection_lm = _env("GEPA_REFLECTION_LM", "azure/gpt-5.5") + configured_experiment_id = _env("SCOPE_EXPERIMENT_ID", "") + experiment_name = _env( + "SCOPE_EXPERIMENT_NAME", + f"GEPA AGENTS.md optimization {datetime.now().isoformat(timespec='seconds')}", + ) + + run_dir = Path(f"runs/{datetime.now().strftime('%Y%m%d_%H%M%S')}") + run_dir.mkdir(parents=True, exist_ok=True) + + trainset, valset = default_dataset() + + client = ScopeClient(base_url, project_id=project_id, worker=worker) + + # Preflight: fail fast on unknown criterion ids before burning agent runs. + all_criteria = collect_criteria([trainset, valset]) + preflight_criteria(client, sorted(all_criteria)) + + async def prepare_experiment() -> tuple[str, str]: + async with httpx.AsyncClient(timeout=client.timeout_seconds) as http: + if configured_experiment_id: + payload = await client.get_experiment( + http, configured_experiment_id + ) + experiment = payload + else: + payload = await client.create_experiment( + http, + name=experiment_name, + description=( + "Canonical Scope Experiment containing every request " + "evaluated by one GEPA optimization." + ), + ) + experiment = payload.get("experiment") or {} + experiment_id = experiment.get("experimentId") + revision_id = (experiment.get("revision") or {}).get( + "experimentRevisionId" + ) + if not isinstance(experiment_id, str) or not isinstance(revision_id, str): + raise ValueError( + "Experiment response omitted experiment/revision identity" + ) + return experiment_id, revision_id + + experiment_id, experiment_revision_id = asyncio.run(prepare_experiment()) + + adapter = ScopeAdapter( + client, + model=model, + experiment_id=experiment_id, + experiment_revision_id=experiment_revision_id, + max_iterations=max_iterations, + max_concurrency=max_concurrency, + run_timeout_seconds=run_timeout, + poll_interval_seconds=poll_interval, + score_strategy=score_strategy, + ) + + print( + f"Experiment id: {experiment_id} " + f"(starting revision {experiment_revision_id})" + ) + print(f"Worker={worker} model={model} strategy={score_strategy}") + + result = gepa.optimize( + seed_candidate={"AGENTS.md": SEED_AGENTS_MD}, + trainset=trainset, + valset=valset, + adapter=adapter, + reflection_lm=reflection_lm, + max_metric_calls=max_metric_calls, + reflection_minibatch_size=reflection_minibatch_size, + use_merge=False, + run_dir=str(run_dir), + ) + + payload = result.to_dict() + # Best-effort lineage DAG reconstructed from GEPA's public parent indices. + candidates = getattr(result, "candidates", None) or [] + parents = getattr(result, "parents", None) or [] + payload["lineage"] = reconstruct_lineage(candidates, parents) + payload["experimentId"] = experiment_id + payload["experimentRevisionId"] = adapter.experiment_revision_id + + (run_dir / "results.json").write_text(json.dumps(payload, indent=2, default=str)) + + best = result.best_candidate.get("AGENTS.md", "") + print(f"\nRun saved to: {run_dir}/") + print(f"Best score: {result.val_aggregate_scores[result.best_idx]}") + print(f"Best AGENTS.md (first 200 chars):\n{best[:200]}") + + +if __name__ == "__main__": + main() diff --git a/apps/gepa-optimizer/src/gepa_optimizer/reflective.py b/apps/gepa-optimizer/src/gepa_optimizer/reflective.py new file mode 100644 index 00000000..136b3706 --- /dev/null +++ b/apps/gepa-optimizer/src/gepa_optimizer/reflective.py @@ -0,0 +1,86 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. + +"""Build GEPA reflective-dataset records from a Scope run trajectory. + +Kept as a pure function (no GEPA / network imports) so it is trivially testable. +A reflective record is ``{Inputs, "Generated Outputs", Feedback}`` per the GEPA +``make_reflective_dataset`` contract. The *Feedback* string is the actionable +gradient handed to the reflection LM, so it captures the full multi-turn +trajectory: every turn's score, judge narrative, and per-criterion pass/fail +with the judge's feedback text — emphasizing failed and skipped criteria. +""" + +from __future__ import annotations + +from typing import Any, Mapping, Sequence + +from .scoring import compute_turn_scores + + +def _format_criteria(results: Sequence[Mapping[str, Any]]) -> str: + lines: list[str] = [] + for r in results: + cid = r.get("criterionId", "") + evaluated = bool(r.get("evaluated", True)) + passed = bool(r.get("passed")) and evaluated + if not evaluated: + status = "SKIPPED (ancestor failed)" + elif passed: + status = "PASS" + else: + status = "FAIL" + feedback = (r.get("feedback") or "").strip() + suffix = f" — {feedback}" if feedback else "" + lines.append(f" - [{status}] {cid}{suffix}") + return "\n".join(lines) + + +def build_reflective_record( + trajectory: Mapping[str, Any], + requested_criteria: Sequence[str], + *, + max_chars: int | None = None, +) -> dict[str, Any]: + """Turn one Scope trajectory into a single GEPA reflective record.""" + task = trajectory.get("task", "") + turns = trajectory.get("turns") or [] + overall_score = trajectory.get("score", 0.0) + error = trajectory.get("error") + + turn_scores = compute_turn_scores({"turns": turns}, requested_criteria) + + output_parts: list[str] = [] + feedback_parts: list[str] = [f"Overall score: {overall_score:.3f}"] + if trajectory.get("runOutcome") and trajectory["runOutcome"] != "succeeded": + feedback_parts.append(f"Run outcome: {trajectory['runOutcome']} (scored 0).") + if error: + feedback_parts.append(f"Evaluation error: {error}") + + for i, turn in enumerate(turns): + iteration = turn.get("iteration", i) + response = (turn.get("response") or turn.get("codingAgentResponse") or "").strip() + output_parts.append(f"--- Turn {iteration} ---\n{response or '(no response)'}") + + t_score = turn_scores[i] if i < len(turn_scores) else 0.0 + seg = [f"Turn {iteration} score: {t_score:.3f}"] + judge = (turn.get("judgeFeedback") or "").strip() + if judge: + seg.append(f"Judge: {judge}") + results = turn.get("criteriaResults") or [] + if results: + seg.append("Criteria:\n" + _format_criteria(results)) + feedback_parts.append("\n".join(seg)) + + generated_outputs = "\n\n".join(output_parts) if output_parts else "(no turns)" + feedback = "\n\n".join(feedback_parts) + + if max_chars is not None and len(generated_outputs) > max_chars: + # Prioritize the newest turns by keeping the tail. + generated_outputs = "...(truncated)...\n" + generated_outputs[-max_chars:] + + return { + "Inputs": task, + "Generated Outputs": generated_outputs, + "Feedback": feedback, + } diff --git a/apps/gepa-optimizer/src/gepa_optimizer/scope_adapter.py b/apps/gepa-optimizer/src/gepa_optimizer/scope_adapter.py new file mode 100644 index 00000000..5d8b345d --- /dev/null +++ b/apps/gepa-optimizer/src/gepa_optimizer/scope_adapter.py @@ -0,0 +1,323 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. + +"""``ScopeAdapter`` — a GEPA adapter that scores AGENTS.md candidates with Scope. + +Each GEPA candidate is ``{"AGENTS.md": ""}``. ``evaluate`` submits one +standalone Scope run per dataset example, publishes those requests as one +canonical Experiment membership revision, polls the exact requests and +execution, and scores from persisted criteria results. Submissions/polls run +concurrently under a semaphore capped at the Linux ``coder-acp-copilot`` KEDA +ceiling (default 10). + +Failure model (per the rubber-duck critique): + +* A *per-example* failure (HTTP transport error, run timeout, a single rejected + submission) yields a ``0.0`` score with an error trajectory — GEPA keeps going. +* A *systemic* failure (auth, bad schema, repeated 5xx) raised by the client + aborts the batch if it affects a majority of examples, so we never optimize + toward infrastructure noise. +""" + +from __future__ import annotations + +import asyncio +from typing import Any, Mapping, Sequence + +import httpx +from gepa.core.adapter import EvaluationBatch, GEPAAdapter + +from .reflective import build_reflective_record +from .scope_client import ( + RequestFailedError, + RequestTimeout, + ScopeClient, + SystemicScopeError, +) +from .scoring import compute_run_score + +COMPONENT = "AGENTS.md" + + +class ScopeAdapter(GEPAAdapter): + def __init__( + self, + client: ScopeClient, + *, + model: str, + experiment_id: str, + experiment_revision_id: str, + max_iterations: int, + max_concurrency: int = 10, + run_timeout_seconds: float = 1800.0, + poll_interval_seconds: float = 15.0, + score_strategy: str = "mean", + reflective_max_chars: int | None = None, + ) -> None: + self.client = client + self.model = model + self.experiment_id = experiment_id + self.experiment_revision_id = experiment_revision_id + self.max_iterations = max_iterations + self.max_concurrency = max_concurrency + self.run_timeout_seconds = run_timeout_seconds + self.poll_interval_seconds = poll_interval_seconds + self.score_strategy = score_strategy + self.reflective_max_chars = reflective_max_chars + + # -- GEPAAdapter.evaluate --------------------------------------------- + + def evaluate( + self, + batch: Sequence[Mapping[str, Any]], + candidate: Mapping[str, str], + capture_traces: bool = False, + ) -> EvaluationBatch: + agents_md = candidate.get(COMPONENT, "") + results = asyncio.run(self._evaluate_async(batch, agents_md)) + + # Abort only if a *majority* of examples hit systemic failures — a few + # transient per-example errors should not poison the run. + systemic = [r for r in results if isinstance(r.get("error_kind"), str) and r["error_kind"] == "systemic"] + if batch and len(systemic) > len(batch) / 2: + raise SystemicScopeError( + f"{len(systemic)}/{len(batch)} evaluations failed systemically; " + "aborting to avoid optimizing toward infrastructure failure." + ) + + outputs = [r["output"] for r in results] + scores = [r["score"] for r in results] + trajectories = [r["trajectory"] for r in results] if capture_traces else None + return EvaluationBatch(outputs=outputs, scores=scores, trajectories=trajectories) + + async def _evaluate_async( + self, batch: Sequence[Mapping[str, Any]], agents_md: str + ) -> list[dict[str, Any]]: + sem = asyncio.Semaphore(self.max_concurrency) + async with httpx.AsyncClient(timeout=self.client.timeout_seconds) as http: + async def submit_one(example: Mapping[str, Any]) -> dict[str, Any]: + async with sem: + return await self._submit_one(http, example, agents_md) + + submitted = await asyncio.gather(*(submit_one(ex) for ex in batch)) + request_ids = [ + result["request_id"] + for result in submitted + if result.get("request_id") is not None + ] + if not request_ids: + return submitted + + membership = await self.client.add_requests( + http, + experiment_id=self.experiment_id, + expected_experiment_revision_id=self.experiment_revision_id, + request_ids=request_ids, + ) + experiment = membership.get("experiment") or {} + revision = experiment.get("revision") or {} + execution = experiment.get("currentExecution") or {} + revision_id = revision.get("experimentRevisionId") + execution_id = execution.get("experimentExecutionId") + if not isinstance(revision_id, str) or not isinstance(execution_id, str): + raise SystemicScopeError( + "Experiment membership response omitted revision/execution identity" + ) + self.experiment_revision_id = revision_id + + async def finish_one(result: dict[str, Any]) -> dict[str, Any]: + if result.get("request_id") is None: + return result + async with sem: + return await self._finish_one( + http, + result, + revision_id=revision_id, + execution_id=execution_id, + ) + + results = await asyncio.gather(*(finish_one(result) for result in submitted)) + execution_error = None + try: + exact_execution = await self.client.wait_for_execution( + http, + self.experiment_id, + execution_id, + timeout_seconds=self.run_timeout_seconds, + poll_interval_seconds=self.poll_interval_seconds, + ) + except RequestTimeout as exc: + execution_error = str(exc) + exact_execution = await self.client.get_execution( + http, self.experiment_id, execution_id + ) + reports = await self.client.list_execution_reports( + http, self.experiment_id, execution_id + ) + report_refs = [ + { + "reportId": report.get("id") or report.get("_id"), + "status": report.get("status"), + "reportType": report.get("reportType"), + } + for report in reports + ] + for result in results: + result["output"].update( + { + "experimentId": self.experiment_id, + "experimentRevisionId": revision_id, + "experimentExecutionId": execution_id, + "experimentReadiness": exact_execution.get("readiness"), + "reports": report_refs, + **( + {"experimentExecutionError": execution_error} + if execution_error + else {} + ), + } + ) + result["trajectory"].update(result["output"]) + return results + + async def _submit_one( + self, + http: httpx.AsyncClient, + example: Mapping[str, Any], + agents_md: str, + ) -> dict[str, Any]: + task = example.get("task", "") + criteria = list(example.get("criteria") or []) + try: + created = await self.client.submit_request( + http, + task=task, + criteria=criteria, + agents_md=agents_md or None, + model=self.model, + max_iterations=self.max_iterations, + ) + request_id = created.get("_id") or created.get("id") + if not isinstance(request_id, str): + raise SystemicScopeError("Request submission omitted request identity") + return { + "request_id": request_id, + "task": task, + "criteria": criteria, + } + except SystemicScopeError as exc: + return self._failure(task, criteria, str(exc), "systemic") + except (RequestFailedError, RequestTimeout) as exc: + return self._failure(task, criteria, str(exc), "per_example") + except Exception as exc: # never raise for a single example + return self._failure(task, criteria, repr(exc), "per_example") + + async def _finish_one( + self, + http: httpx.AsyncClient, + submitted: Mapping[str, Any], + *, + revision_id: str, + execution_id: str, + ) -> dict[str, Any]: + task = str(submitted["task"]) + criteria = list(submitted["criteria"]) + request_id = str(submitted["request_id"]) + try: + doc = await self.client.wait_for_completion( + http, + request_id, + timeout_seconds=self.run_timeout_seconds, + poll_interval_seconds=self.poll_interval_seconds, + ) + run = doc.get("run") or {} + score = compute_run_score(run, criteria, self.score_strategy) + trajectory = self._build_trajectory( + task, criteria, request_id, run, score + ) + identity = { + "requestId": request_id, + "score": score, + "experimentId": self.experiment_id, + "experimentRevisionId": revision_id, + "experimentExecutionId": execution_id, + } + return { + "score": score, + "output": identity, + "trajectory": {**trajectory, **identity}, + "error_kind": None, + } + except SystemicScopeError as exc: + return self._failure(task, criteria, str(exc), "systemic") + except (RequestFailedError, RequestTimeout) as exc: + return self._failure(task, criteria, str(exc), "per_example") + except Exception as exc: # never raise for a single example + return self._failure(task, criteria, repr(exc), "per_example") + + def _build_trajectory( + self, + task: str, + criteria: Sequence[str], + request_id: str | None, + run: Mapping[str, Any], + score: float, + ) -> dict[str, Any]: + turns_out = [] + for t in run.get("turns") or []: + turns_out.append( + { + "iteration": t.get("iteration"), + "response": t.get("codingAgentResponse"), + "judgeFeedback": t.get("judgeFeedback"), + "criteriaResults": t.get("criteriaResults") or [], + } + ) + return { + "task": task, + "criteria": list(criteria), + "requestId": request_id, + "runOutcome": run.get("outcome"), + "turns": turns_out, + "score": score, + } + + def _failure( + self, task: str, criteria: Sequence[str], message: str, kind: str + ) -> dict[str, Any]: + trajectory = { + "task": task, + "criteria": list(criteria), + "requestId": None, + "runOutcome": None, + "turns": [], + "score": 0.0, + "error": message, + } + return { + "request_id": None, + "score": 0.0, + "output": {"error": message}, + "trajectory": trajectory, + "error_kind": kind, + } + + # -- GEPAAdapter.make_reflective_dataset ------------------------------ + + def make_reflective_dataset( + self, + candidate: Mapping[str, str], + eval_batch: EvaluationBatch, + components_to_update: Sequence[str], + ) -> Mapping[str, Sequence[Mapping[str, Any]]]: + trajectories = eval_batch.trajectories or [] + records = [ + build_reflective_record( + traj, + traj.get("criteria") or [], + max_chars=self.reflective_max_chars, + ) + for traj in trajectories + ] + # We only ever optimize the single AGENTS.md component. + return {component: records for component in components_to_update} diff --git a/apps/gepa-optimizer/src/gepa_optimizer/scope_client.py b/apps/gepa-optimizer/src/gepa_optimizer/scope_client.py new file mode 100644 index 00000000..dffe8f55 --- /dev/null +++ b/apps/gepa-optimizer/src/gepa_optimizer/scope_client.py @@ -0,0 +1,313 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. + +"""Async REST client for the Scope API used by the GEPA adapter. + +Only the handful of endpoints the optimizer needs are implemented: + +* ``create_experiment`` — POST one canonical empty Experiment. +* ``add_requests`` — publish a membership revision for evaluated requests. +* ``submit_request`` — POST a standalone request (task + criteria + AGENTS.md). +* ``get_request`` — GET a request doc (including its ``run`` state). +* ``wait_for_completion`` — poll ``get_request`` until ``run.status == "done"``. +* ``wait_for_execution`` — poll one exact Experiment execution. +* ``list_execution_reports`` — read reports for that exact execution. +* ``get_criterion`` — used for preflight validation of criterion ids. + +Error model (see the rubber-duck critique): a ``SystemicScopeError`` signals a +problem that would poison the whole optimization (auth failure, malformed +request schema, repeated 5xx) and should abort the run. A ``RequestFailedError`` +or ``RequestTimeout`` is a *per-example* failure that the adapter turns into a +``0.0`` score without aborting. Idempotent GETs are retried with exponential +backoff + jitter; POSTs are **not** retried (a duplicate POST would launch a +second real agent run). +""" + +from __future__ import annotations + +import asyncio +import random +from typing import Any, Mapping, Sequence + +import httpx + +DONE_STATUS = "done" +TERMINAL_EXECUTION_READINESS = {"blocked", "done", "failed"} + + +class ScopeError(Exception): + """Base class for Scope client errors.""" + + +class SystemicScopeError(ScopeError): + """A failure that should abort the whole optimization (auth, schema, 5xx).""" + + +class RequestFailedError(ScopeError): + """A single submission failed in a way that is local to that example.""" + + +class RequestTimeout(ScopeError): + """A run did not reach a terminal state within the timeout.""" + + +def _is_systemic_status(status: int) -> bool: + # 401/403 = auth; 400/422 = our payload is wrong; 5xx after retries = infra. + return status in (400, 401, 403, 422) or status >= 500 + + +class ScopeClient: + def __init__( + self, + base_url: str, + *, + project_id: str, + worker: str = "coder-acp-copilot", + timeout_seconds: float = 30.0, + max_get_retries: int = 4, + ) -> None: + self.base_url = base_url.rstrip("/") + self.project_id = project_id + self.worker = worker + self.timeout_seconds = timeout_seconds + self.max_get_retries = max_get_retries + + # -- low-level helpers ------------------------------------------------- + + async def _get_json( + self, + client: httpx.AsyncClient, + path: str, + *, + allow_not_found: bool = False, + ) -> Any: + """GET with retry/backoff on 429/5xx/network errors.""" + last_exc: Exception | None = None + for attempt in range(self.max_get_retries): + try: + resp = await client.get(f"{self.base_url}{path}") + if allow_not_found and resp.status_code == 404: + return None + if resp.status_code == 429 or resp.status_code >= 500: + last_exc = SystemicScopeError( + f"GET {path} -> {resp.status_code}" + ) + elif resp.status_code >= 400: + raise SystemicScopeError( + f"GET {path} -> {resp.status_code}: {resp.text[:500]}" + ) + else: + return resp.json() + except (httpx.TransportError, httpx.TimeoutException) as exc: + last_exc = exc + if attempt + 1 < self.max_get_retries: + await asyncio.sleep( + min(2**attempt, 10) * (0.5 + random.random()) + ) + raise SystemicScopeError(f"GET {path} failed after retries: {last_exc}") + + # -- public API -------------------------------------------------------- + + async def submit_request( + self, + client: httpx.AsyncClient, + *, + task: str, + criteria: Sequence[str], + agents_md: str | None = None, + model: str | None = None, + agents_md_parent_ids: Sequence[str] | None = None, + max_iterations: int | None = None, + ) -> Mapping[str, Any]: + """Submit one request. Returns the created request doc (with ``_id``).""" + body: dict[str, Any] = {"scenario": {"task": task, "criteria": list(criteria)}} + if model: + body["model"] = model + if max_iterations: + body["maxIterations"] = max_iterations + if agents_md: + body["agentsMd"] = agents_md + if agents_md_parent_ids: + body["agentsMdParentIds"] = list(agents_md_parent_ids) + + url = f"{self.base_url}/api/v1/requests" + try: + resp = await client.post( + url, + params={"worker": self.worker, "projectId": self.project_id}, + json=body, + ) + except (httpx.TransportError, httpx.TimeoutException) as exc: + # The outcome is ambiguous, so never retry and risk a duplicate run. + raise RequestFailedError(f"POST /requests transport error: {exc}") from exc + + if resp.status_code >= 400: + text = resp.text[:500] + if _is_systemic_status(resp.status_code): + raise SystemicScopeError( + f"POST /requests -> {resp.status_code}: {text}" + ) + raise RequestFailedError(f"POST /requests -> {resp.status_code}: {text}") + return resp.json() + + async def create_experiment( + self, + client: httpx.AsyncClient, + *, + name: str, + description: str | None = None, + ) -> Mapping[str, Any]: + """Create one empty canonical Experiment. This POST is never retried.""" + metadata = {"name": name} + if description: + metadata["description"] = description + return await self._systemic_post_json( + client, + "/api/v1/experiments", + { + "mode": "empty", + "metadata": metadata, + "requests": [], + "requestIds": [], + }, + ) + + async def add_requests( + self, + client: httpx.AsyncClient, + *, + experiment_id: str, + expected_experiment_revision_id: str, + request_ids: Sequence[str], + ) -> Mapping[str, Any]: + """Publish one canonical membership revision. This POST is never retried.""" + return await self._systemic_post_json( + client, + f"/api/v1/experiments/{experiment_id}/requests", + { + "expectedExperimentRevisionId": expected_experiment_revision_id, + "requestIds": list(request_ids), + }, + ) + + async def _systemic_post_json( + self, + client: httpx.AsyncClient, + path: str, + body: Mapping[str, Any], + ) -> Mapping[str, Any]: + try: + resp = await client.post( + f"{self.base_url}{path}", + params={"projectId": self.project_id}, + json=body, + ) + except (httpx.TransportError, httpx.TimeoutException) as exc: + raise SystemicScopeError(f"POST {path} transport error: {exc}") from exc + if resp.status_code >= 400: + raise SystemicScopeError( + f"POST {path} -> {resp.status_code}: {resp.text[:500]}" + ) + payload = resp.json() + if not isinstance(payload, Mapping): + raise SystemicScopeError(f"POST {path} returned a non-object response") + return payload + + async def get_request( + self, client: httpx.AsyncClient, request_id: str + ) -> Mapping[str, Any]: + return await self._get_json(client, f"/api/v1/requests/{request_id}") + + async def get_experiment( + self, client: httpx.AsyncClient, experiment_id: str + ) -> Mapping[str, Any]: + return await self._get_json( + client, + f"/api/v1/experiments/{experiment_id}?projectId={self.project_id}", + ) + + async def get_execution( + self, + client: httpx.AsyncClient, + experiment_id: str, + experiment_execution_id: str, + ) -> Mapping[str, Any]: + return await self._get_json( + client, + f"/api/v1/experiments/{experiment_id}/executions/" + f"{experiment_execution_id}?projectId={self.project_id}", + ) + + async def list_execution_reports( + self, + client: httpx.AsyncClient, + experiment_id: str, + experiment_execution_id: str, + ) -> Sequence[Mapping[str, Any]]: + payload = await self._get_json( + client, + f"/api/v1/experiments/{experiment_id}/executions/" + f"{experiment_execution_id}/reports?projectId={self.project_id}", + ) + if not isinstance(payload, list): + raise SystemicScopeError("Experiment reports response was not an array") + return payload + + async def get_criterion( + self, client: httpx.AsyncClient, criterion_id: str + ) -> Mapping[str, Any] | None: + """Return the criterion doc, or ``None`` if it does not exist (404).""" + return await self._get_json( + client, + f"/api/v1/criteria/{criterion_id}?projectId={self.project_id}", + allow_not_found=True, + ) + + async def wait_for_completion( + self, + client: httpx.AsyncClient, + request_id: str, + *, + timeout_seconds: float, + poll_interval_seconds: float, + ) -> Mapping[str, Any]: + """Poll until ``run.status == "done"``; raise ``RequestTimeout`` if not.""" + loop = asyncio.get_event_loop() + deadline = loop.time() + timeout_seconds + while True: + doc = await self.get_request(client, request_id) + run = doc.get("run") or {} + if run.get("status") == DONE_STATUS: + return doc + if loop.time() >= deadline: + raise RequestTimeout( + f"request {request_id} not done within {timeout_seconds}s " + f"(last status={run.get('status')})" + ) + await asyncio.sleep(poll_interval_seconds) + + async def wait_for_execution( + self, + client: httpx.AsyncClient, + experiment_id: str, + experiment_execution_id: str, + *, + timeout_seconds: float, + poll_interval_seconds: float, + ) -> Mapping[str, Any]: + """Poll one exact execution until its handlers finish or fail.""" + loop = asyncio.get_event_loop() + deadline = loop.time() + timeout_seconds + while True: + execution = await self.get_execution( + client, experiment_id, experiment_execution_id + ) + readiness = execution.get("readiness") + if readiness in TERMINAL_EXECUTION_READINESS: + return execution + if loop.time() >= deadline: + raise RequestTimeout( + f"experiment execution {experiment_execution_id} did not finish " + f"within {timeout_seconds}s (last readiness={readiness})" + ) + await asyncio.sleep(poll_interval_seconds) diff --git a/apps/gepa-optimizer/src/gepa_optimizer/scoring.py b/apps/gepa-optimizer/src/gepa_optimizer/scoring.py new file mode 100644 index 00000000..9b25c3e1 --- /dev/null +++ b/apps/gepa-optimizer/src/gepa_optimizer/scoring.py @@ -0,0 +1,109 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. + +"""Pure, side-effect-free scoring of a finished Scope run. + +The GEPA metric is **derived, never persisted in Scope**: Scope stores the raw +``run.turns[].criteriaResults`` and the run ``outcome``; we compute the +fraction of requested criteria that passed and aggregate it across turns. + +Design decisions (see plan §C2/§D and the rubber-duck critique): + +* The denominator is the number of **requested** criteria, not the number of + results Scope happened to return. This is robust even if Scope ever omits + skipped criteria from ``criteriaResults`` — a requested criterion with no + passing result always counts as failed. +* ``evaluated == False`` (a DAG ancestor failed, so the criterion was skipped) + counts as **failed**, amplifying the penalty for early failures. +* A run is scored by its criteria fraction whenever it **completed and was + judged** — i.e. ``outcome`` is ``"succeeded"`` (all criteria passed) **or** + ``"finished"`` (ran to ``maxIterations`` and was judged, but not all criteria + passed). Scoring only ``"succeeded"`` runs would collapse the optimization + landscape to ``0.0`` until a candidate passes *every* criterion, destroying + the partial-credit gradient GEPA relies on. A run that ``"failed"`` (errored + or abandoned the task before being judged to completion), has no turns, or has + no judged criteria scores ``0.0``. The function never returns ``NaN``. +""" + +from __future__ import annotations + +from typing import Any, Mapping, Sequence + +SUCCEEDED_OUTCOME = "succeeded" +FINISHED_OUTCOME = "finished" +DONE_STATUS = "done" + +# Outcomes that mean "the run completed and was judged", so its criteria +# fraction is a meaningful score. ``"failed"`` is deliberately excluded — it +# signals an error or an abandoned run with no trustworthy judgement. +SCOREABLE_OUTCOMES = frozenset({SUCCEEDED_OUTCOME, FINISHED_OUTCOME}) + +ScoreStrategy = str # "mean" | "final" | "weighted" + + +def _turn_score(turn: Mapping[str, Any], requested_criteria: Sequence[str]) -> float: + """Fraction of *requested* criteria that passed in a single turn.""" + results = turn.get("criteriaResults") or [] + passed_by_id: dict[str, bool] = {} + for r in results: + cid = r.get("criterionId") + if cid is None: + continue + # A criterion only counts as passed when it was actually evaluated and + # passed. Skipped (evaluated == False) criteria count as failed. + passed_by_id[cid] = bool(r.get("passed")) and bool(r.get("evaluated", True)) + + if requested_criteria: + denominator = len(requested_criteria) + passed = sum(1 for cid in requested_criteria if passed_by_id.get(cid, False)) + else: + # No explicit request list — fall back to whatever the turn reported. + denominator = len(passed_by_id) + passed = sum(1 for v in passed_by_id.values() if v) + + if denominator == 0: + return 0.0 + return passed / denominator + + +def compute_turn_scores( + run: Mapping[str, Any], requested_criteria: Sequence[str] +) -> list[float]: + """Per-turn scores for a finished run, in turn order.""" + turns = run.get("turns") or [] + return [_turn_score(t, requested_criteria) for t in turns] + + +def compute_run_score( + run: Mapping[str, Any] | None, + requested_criteria: Sequence[str], + strategy: ScoreStrategy = "mean", +) -> float: + """Aggregate a finished run into a single GEPA score in ``[0.0, 1.0]``. + + Returns ``0.0`` (never ``NaN``) for any run that did not complete-and-judge + (``outcome`` not in :data:`SCOREABLE_OUTCOMES`) or produced no judged turns. + A ``"finished"`` run (ran to completion, partial criteria) is scored by its + criteria fraction so GEPA keeps a partial-credit gradient. + """ + if not run: + return 0.0 + if run.get("status") != DONE_STATUS: + return 0.0 + if run.get("outcome") not in SCOREABLE_OUTCOMES: + return 0.0 + + turn_scores = compute_turn_scores(run, requested_criteria) + if not turn_scores: + return 0.0 + + if strategy == "final": + return turn_scores[-1] + if strategy == "weighted": + # Anchor on the final turn (the objective) but keep a small gradient + # from the whole trajectory so partial progress is still rewarded. + final = turn_scores[-1] + mean = sum(turn_scores) / len(turn_scores) + return 0.8 * final + 0.2 * mean + # default: mean across all turns + return sum(turn_scores) / len(turn_scores) diff --git a/apps/gepa-optimizer/tests/test_lineage.py b/apps/gepa-optimizer/tests/test_lineage.py new file mode 100644 index 00000000..56fb7748 --- /dev/null +++ b/apps/gepa-optimizer/tests/test_lineage.py @@ -0,0 +1,41 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. + +from gepa_optimizer.lineage import reconstruct_lineage + + +def test_root_mutation_merge_classification(): + candidates = [ + {"AGENTS.md": "seed"}, + {"AGENTS.md": "child of seed"}, + {"AGENTS.md": "merge of 0 and 1"}, + ] + parents = [[], [0], [0, 1]] + nodes = reconstruct_lineage(candidates, parents) + assert nodes[0]["kind"] == "root" + assert nodes[1]["kind"] == "mutation" + assert nodes[1]["parents"] == [0] + assert nodes[2]["kind"] == "merge" + assert nodes[2]["parents"] == [0, 1] + + +def test_handles_none_and_missing_parents(): + candidates = [{"AGENTS.md": "a"}, {"AGENTS.md": "b"}] + parents = [None] # shorter than candidates; second has no entry + nodes = reconstruct_lineage(candidates, parents) + assert nodes[0]["kind"] == "root" + assert nodes[1]["kind"] == "root" + + +def test_preview_truncated(): + candidates = [{"AGENTS.md": "x" * 500}] + nodes = reconstruct_lineage(candidates, [[]]) + assert len(nodes[0]["agentsMdPreview"]) == 200 + + +def test_negative_parent_indices_ignored(): + candidates = [{"AGENTS.md": "a"}, {"AGENTS.md": "b"}] + parents = [[], [-1]] + nodes = reconstruct_lineage(candidates, parents) + assert nodes[1]["kind"] == "root" + assert nodes[1]["parents"] == [] diff --git a/apps/gepa-optimizer/tests/test_reflective.py b/apps/gepa-optimizer/tests/test_reflective.py new file mode 100644 index 00000000..20f0d1b0 --- /dev/null +++ b/apps/gepa-optimizer/tests/test_reflective.py @@ -0,0 +1,80 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. + +from gepa_optimizer.reflective import build_reflective_record + + +def test_reflective_record_shape_and_content(): + traj = { + "task": "Build an Express server", + "runOutcome": "succeeded", + "score": 0.5, + "turns": [ + { + "iteration": 0, + "response": "first attempt", + "judgeFeedback": "missing route", + "criteriaResults": [ + {"criterionId": "server_starts", "passed": True, "evaluated": True, "feedback": "ok"}, + {"criterionId": "hello", "passed": False, "evaluated": True, "feedback": "no /"}, + ], + }, + ], + } + rec = build_reflective_record(traj, ["server_starts", "hello"]) + assert set(rec.keys()) == {"Inputs", "Generated Outputs", "Feedback"} + assert rec["Inputs"] == "Build an Express server" + assert "first attempt" in rec["Generated Outputs"] + assert "Turn 0" in rec["Generated Outputs"] + # Feedback surfaces score, judge narrative and per-criterion pass/fail. + assert "Overall score: 0.500" in rec["Feedback"] + assert "missing route" in rec["Feedback"] + assert "[PASS] server_starts" in rec["Feedback"] + assert "[FAIL] hello" in rec["Feedback"] + + +def test_skipped_criterion_flagged(): + traj = { + "task": "t", + "runOutcome": "succeeded", + "score": 0.0, + "turns": [ + { + "iteration": 0, + "response": "x", + "judgeFeedback": "", + "criteriaResults": [ + {"criterionId": "a", "passed": False, "evaluated": True, "feedback": ""}, + {"criterionId": "b", "passed": True, "evaluated": False, "feedback": ""}, + ], + } + ], + } + rec = build_reflective_record(traj, ["a", "b"]) + assert "SKIPPED" in rec["Feedback"] + + +def test_error_trajectory_surfaces_error(): + traj = {"task": "t", "score": 0.0, "turns": [], "error": "timeout"} + rec = build_reflective_record(traj, ["a"]) + assert "Evaluation error: timeout" in rec["Feedback"] + assert rec["Generated Outputs"] == "(no turns)" + + +def test_failed_outcome_noted(): + traj = {"task": "t", "runOutcome": "failed", "score": 0.0, "turns": []} + rec = build_reflective_record(traj, ["a"]) + assert "Run outcome: failed" in rec["Feedback"] + + +def test_truncation_keeps_tail(): + long_resp = "A" * 50 + "TAILMARK" + traj = { + "task": "t", + "runOutcome": "succeeded", + "score": 1.0, + "turns": [{"iteration": 0, "response": long_resp, "judgeFeedback": "", "criteriaResults": []}], + } + rec = build_reflective_record(traj, ["a"], max_chars=20) + assert rec["Generated Outputs"].startswith("...(truncated)...") + assert "TAILMARK" in rec["Generated Outputs"] diff --git a/apps/gepa-optimizer/tests/test_scope_adapter.py b/apps/gepa-optimizer/tests/test_scope_adapter.py new file mode 100644 index 00000000..0a02d2cd --- /dev/null +++ b/apps/gepa-optimizer/tests/test_scope_adapter.py @@ -0,0 +1,188 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. + +import pytest + +from gepa_optimizer.scope_adapter import ScopeAdapter +from gepa_optimizer.scope_client import RequestTimeout, SystemicScopeError + + +class FakeClient: + """Stand-in for ScopeClient that returns canned run docs without network.""" + + def __init__(self, runs_by_task, *, fail=None): + # runs_by_task: task -> run dict; fail: task -> exception to raise + self.runs_by_task = runs_by_task + self.fail = fail or {} + self.timeout_seconds = 1.0 + self.submitted = [] + self.memberships = [] + self.revision_number = 1 + + async def submit_request(self, http, *, task, criteria, agents_md=None, model=None, + agents_md_parent_ids=None, max_iterations=None): + self.submitted.append({"task": task, "agents_md": agents_md}) + if task in self.fail: + raise self.fail[task] + return {"_id": f"req-{task}"} + + async def add_requests( + self, + http, + *, + experiment_id, + expected_experiment_revision_id, + request_ids, + ): + self.revision_number += 1 + revision_id = f"rev-{self.revision_number}" + execution_id = f"execution-{self.revision_number}" + self.memberships.append( + { + "experiment_id": experiment_id, + "expected_revision_id": expected_experiment_revision_id, + "request_ids": request_ids, + } + ) + return { + "experiment": { + "revision": {"experimentRevisionId": revision_id}, + "currentExecution": { + "experimentExecutionId": execution_id + }, + } + } + + async def wait_for_completion(self, http, request_id, *, timeout_seconds, poll_interval_seconds): + task = request_id.removeprefix("req-") + return {"_id": request_id, "run": self.runs_by_task[task]} + + async def wait_for_execution( + self, + http, + experiment_id, + experiment_execution_id, + *, + timeout_seconds, + poll_interval_seconds, + ): + return { + "experimentExecutionId": experiment_execution_id, + "readiness": "done", + } + + async def list_execution_reports( + self, http, experiment_id, experiment_execution_id + ): + return [ + { + "id": f"report-{experiment_execution_id}", + "status": "done", + "reportType": "pp-readout", + } + ] + + +def _good_run(passed=True): + return { + "status": "done", + "outcome": "succeeded", + "turns": [ + { + "iteration": 0, + "codingAgentResponse": "did the work", + "judgeFeedback": "looks good", + "criteriaResults": [{"criterionId": "a", "passed": passed, "evaluated": True, "feedback": "f"}], + } + ], + } + + +def make_adapter(client): + return ScopeAdapter( + client, + model="claude-haiku-4.5", + experiment_id="exp-1", + experiment_revision_id="rev-1", + max_iterations=3, + max_concurrency=2, + run_timeout_seconds=1, + poll_interval_seconds=0, + ) + + +def test_evaluate_scores_runs(): + client = FakeClient({"t1": _good_run(True), "t2": _good_run(False)}) + adapter = make_adapter(client) + batch = [{"task": "t1", "criteria": ["a"]}, {"task": "t2", "criteria": ["a"]}] + result = adapter.evaluate(batch, {"AGENTS.md": "# guide"}, capture_traces=True) + assert result.scores == [1.0, 0.0] + assert len(result.trajectories) == 2 + # AGENTS.md was forwarded to the submission. + assert client.submitted[0]["agents_md"] == "# guide" + assert client.memberships == [ + { + "experiment_id": "exp-1", + "expected_revision_id": "rev-1", + "request_ids": ["req-t1", "req-t2"], + } + ] + assert result.outputs[0]["experimentRevisionId"] == "rev-2" + assert result.outputs[0]["experimentExecutionId"] == "execution-2" + assert result.outputs[0]["reports"][0]["reportType"] == "pp-readout" + + +def test_evaluate_advances_membership_revision_between_candidates(): + client = FakeClient({"t1": _good_run(True)}) + adapter = make_adapter(client) + batch = [{"task": "t1", "criteria": ["a"]}] + + adapter.evaluate(batch, {"AGENTS.md": "first"}) + adapter.evaluate(batch, {"AGENTS.md": "second"}) + + assert [entry["expected_revision_id"] for entry in client.memberships] == [ + "rev-1", + "rev-2", + ] + assert adapter.experiment_revision_id == "rev-3" + + +def test_per_example_timeout_scores_zero_without_raising(): + client = FakeClient({"t1": _good_run(True)}, fail={"t2": RequestTimeout("nope")}) + adapter = make_adapter(client) + batch = [{"task": "t1", "criteria": ["a"]}, {"task": "t2", "criteria": ["a"]}] + result = adapter.evaluate(batch, {"AGENTS.md": "x"}, capture_traces=True) + assert result.scores == [1.0, 0.0] + # The failed example carries an error trajectory. + assert result.trajectories[1]["error"] == "nope" + + +def test_systemic_majority_aborts(): + client = FakeClient( + {"t1": _good_run(True)}, + fail={"t1": SystemicScopeError("auth"), "t2": SystemicScopeError("auth")}, + ) + adapter = make_adapter(client) + batch = [{"task": "t1", "criteria": ["a"]}, {"task": "t2", "criteria": ["a"]}] + with pytest.raises(SystemicScopeError): + adapter.evaluate(batch, {"AGENTS.md": "x"}, capture_traces=False) + + +def test_single_systemic_failure_does_not_abort(): + # 1 of 2 systemic is not a majority, so the batch survives. + client = FakeClient({"t1": _good_run(True)}, fail={"t2": SystemicScopeError("blip")}) + adapter = make_adapter(client) + batch = [{"task": "t1", "criteria": ["a"]}, {"task": "t2", "criteria": ["a"]}] + result = adapter.evaluate(batch, {"AGENTS.md": "x"}, capture_traces=False) + assert result.scores == [1.0, 0.0] + + +def test_make_reflective_dataset_keys_component(): + client = FakeClient({"t1": _good_run(False)}) + adapter = make_adapter(client) + batch = [{"task": "t1", "criteria": ["a"]}] + eval_batch = adapter.evaluate(batch, {"AGENTS.md": "x"}, capture_traces=True) + ds = adapter.make_reflective_dataset({"AGENTS.md": "x"}, eval_batch, ["AGENTS.md"]) + assert set(ds.keys()) == {"AGENTS.md"} + assert len(ds["AGENTS.md"]) == 1 + assert "FAIL" in ds["AGENTS.md"][0]["Feedback"] diff --git a/apps/gepa-optimizer/tests/test_scope_client.py b/apps/gepa-optimizer/tests/test_scope_client.py new file mode 100644 index 00000000..77f2c52f --- /dev/null +++ b/apps/gepa-optimizer/tests/test_scope_client.py @@ -0,0 +1,311 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. + +import httpx +import pytest + +from gepa_optimizer.scope_client import ( + RequestFailedError, + RequestTimeout, + ScopeClient, + SystemicScopeError, +) + + +def make_client_and_http(handler): + transport = httpx.MockTransport(handler) + http = httpx.AsyncClient(transport=transport, base_url="http://test") + client = ScopeClient( + "http://test", + project_id="project-1", + worker="coder-acp-copilot", + max_get_retries=2, + ) + return client, http + + +async def test_submit_request_builds_body_and_url(): + captured = {} + + def handler(request: httpx.Request) -> httpx.Response: + captured["url"] = str(request.url) + captured["body"] = request.read().decode() + return httpx.Response(200, json={"_id": "req-1"}) + + client, http = make_client_and_http(handler) + async with http: + doc = await client.submit_request( + http, + task="do it", + criteria=["a", "b"], + agents_md="# guide", + model="claude-haiku-4.5", + agents_md_parent_ids=["p1"], + max_iterations=5, + ) + assert doc["_id"] == "req-1" + assert "worker=coder-acp-copilot" in captured["url"] + assert "projectId=project-1" in captured["url"] + import json + + body = json.loads(captured["body"]) + assert body["scenario"] == {"task": "do it", "criteria": ["a", "b"]} + assert body["agentsMd"] == "# guide" + assert body["model"] == "claude-haiku-4.5" + assert "experimentId" not in body + assert body["agentsMdParentIds"] == ["p1"] + assert body["maxIterations"] == 5 + + +async def test_submit_systemic_on_auth_error(): + def handler(request: httpx.Request) -> httpx.Response: + return httpx.Response(401, text="no auth") + + client, http = make_client_and_http(handler) + async with http: + with pytest.raises(SystemicScopeError): + await client.submit_request(http, task="t", criteria=["a"]) + + +async def test_submit_systemic_on_bad_schema(): + def handler(request: httpx.Request) -> httpx.Response: + return httpx.Response(400, text="bad body") + + client, http = make_client_and_http(handler) + async with http: + with pytest.raises(SystemicScopeError): + await client.submit_request(http, task="t", criteria=["a"]) + + +async def test_submit_per_example_on_404(): + def handler(request: httpx.Request) -> httpx.Response: + return httpx.Response(404, text="not found") + + client, http = make_client_and_http(handler) + async with http: + with pytest.raises(RequestFailedError): + await client.submit_request(http, task="t", criteria=["a"]) + + +async def test_get_request_retries_then_succeeds(): + calls = {"n": 0} + + def handler(request: httpx.Request) -> httpx.Response: + calls["n"] += 1 + if calls["n"] == 1: + return httpx.Response(503, text="busy") + return httpx.Response(200, json={"_id": "r", "run": {"status": "done"}}) + + client, http = make_client_and_http(handler) + async with http: + doc = await client.get_request(http, "r") + assert doc["run"]["status"] == "done" + assert calls["n"] == 2 + + +async def test_get_request_systemic_after_retries_exhausted(): + def handler(request: httpx.Request) -> httpx.Response: + return httpx.Response(503, text="busy") + + client, http = make_client_and_http(handler) + async with http: + with pytest.raises(SystemicScopeError): + await client.get_request(http, "r") + + +async def test_create_empty_experiment_uses_canonical_endpoint_without_retry(): + captured = {} + + def handler(request: httpx.Request) -> httpx.Response: + captured["url"] = str(request.url) + captured["body"] = request.read().decode() + return httpx.Response( + 201, + json={ + "experiment": { + "experimentId": "exp-1", + "revision": {"experimentRevisionId": "rev-1"}, + }, + "requestIds": [], + }, + ) + + client, http = make_client_and_http(handler) + async with http: + payload = await client.create_experiment( + http, name="GEPA run", description="optimization" + ) + + import json + + assert "projectId=project-1" in captured["url"] + assert json.loads(captured["body"]) == { + "mode": "empty", + "metadata": {"name": "GEPA run", "description": "optimization"}, + "requests": [], + "requestIds": [], + } + assert payload["experiment"]["experimentId"] == "exp-1" + + +async def test_add_requests_publishes_expected_membership_revision(): + captured = {} + + def handler(request: httpx.Request) -> httpx.Response: + captured["url"] = str(request.url) + captured["body"] = request.read().decode() + return httpx.Response( + 201, + json={ + "experiment": { + "experimentId": "exp-1", + "revision": {"experimentRevisionId": "rev-2"}, + "currentExecution": { + "experimentExecutionId": "execution-2" + }, + }, + "attachedRequestIds": ["req-1"], + "detachedRequestIds": [], + }, + ) + + client, http = make_client_and_http(handler) + async with http: + payload = await client.add_requests( + http, + experiment_id="exp-1", + expected_experiment_revision_id="rev-1", + request_ids=["req-1"], + ) + + import json + + assert "/api/v1/experiments/exp-1/requests" in captured["url"] + assert "projectId=project-1" in captured["url"] + assert json.loads(captured["body"]) == { + "expectedExperimentRevisionId": "rev-1", + "requestIds": ["req-1"], + } + assert ( + payload["experiment"]["currentExecution"]["experimentExecutionId"] + == "execution-2" + ) + + +async def test_experiment_posts_are_not_retried(): + calls = {"n": 0} + + def handler(request: httpx.Request) -> httpx.Response: + calls["n"] += 1 + return httpx.Response(503, text="busy") + + client, http = make_client_and_http(handler) + async with http: + with pytest.raises(SystemicScopeError): + await client.create_experiment(http, name="GEPA run") + assert calls["n"] == 1 + + +async def test_wait_for_completion_polls_until_done(): + statuses = ["queued", "processing", "done"] + + def handler(request: httpx.Request) -> httpx.Response: + s = statuses.pop(0) if statuses else "done" + return httpx.Response(200, json={"_id": "r", "run": {"status": s}}) + + client, http = make_client_and_http(handler) + async with http: + doc = await client.wait_for_completion( + http, "r", timeout_seconds=10, poll_interval_seconds=0 + ) + assert doc["run"]["status"] == "done" + + +async def test_wait_for_completion_times_out(): + def handler(request: httpx.Request) -> httpx.Response: + return httpx.Response(200, json={"_id": "r", "run": {"status": "processing"}}) + + client, http = make_client_and_http(handler) + async with http: + with pytest.raises(RequestTimeout): + await client.wait_for_completion( + http, "r", timeout_seconds=0, poll_interval_seconds=0 + ) + + +async def test_wait_for_exact_execution_and_list_reports(): + execution_calls = {"n": 0} + + def handler(request: httpx.Request) -> httpx.Response: + if request.url.path.endswith("/reports"): + return httpx.Response( + 200, + json=[{"id": "report-1", "status": "done"}], + ) + execution_calls["n"] += 1 + readiness = "processing" if execution_calls["n"] == 1 else "done" + return httpx.Response( + 200, + json={ + "experimentExecutionId": "execution-2", + "readiness": readiness, + }, + ) + + client, http = make_client_and_http(handler) + async with http: + execution = await client.wait_for_execution( + http, + "exp-1", + "execution-2", + timeout_seconds=10, + poll_interval_seconds=0, + ) + reports = await client.list_execution_reports( + http, "exp-1", "execution-2" + ) + + assert execution["readiness"] == "done" + assert reports == [{"id": "report-1", "status": "done"}] + assert execution_calls["n"] == 2 + + +async def test_get_criterion_returns_none_on_404(): + def handler(request: httpx.Request) -> httpx.Response: + return httpx.Response(404) + + client, http = make_client_and_http(handler) + async with http: + assert await client.get_criterion(http, "missing") is None + + +async def test_get_criterion_is_project_scoped(): + captured_url = "" + + def handler(request: httpx.Request) -> httpx.Response: + nonlocal captured_url + captured_url = str(request.url) + return httpx.Response(200, json={"id": "criterion-1"}) + + client, http = make_client_and_http(handler) + async with http: + await client.get_criterion(http, "criterion-1") + + assert "projectId=project-1" in captured_url + + +async def test_get_criterion_retries_idempotent_read(): + calls = {"n": 0} + + def handler(request: httpx.Request) -> httpx.Response: + calls["n"] += 1 + if calls["n"] == 1: + return httpx.Response(503, text="busy") + return httpx.Response(200, json={"id": "criterion-1"}) + + client, http = make_client_and_http(handler) + async with http: + criterion = await client.get_criterion(http, "criterion-1") + + assert criterion == {"id": "criterion-1"} + assert calls["n"] == 2 diff --git a/apps/gepa-optimizer/tests/test_scoring.py b/apps/gepa-optimizer/tests/test_scoring.py new file mode 100644 index 00000000..72b6c6cd --- /dev/null +++ b/apps/gepa-optimizer/tests/test_scoring.py @@ -0,0 +1,112 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. + +from gepa_optimizer.scoring import compute_run_score, compute_turn_scores + + +def _turn(iteration, results): + return {"iteration": iteration, "criteriaResults": results} + + +def _r(cid, passed, evaluated=True): + return {"criterionId": cid, "passed": passed, "evaluated": evaluated, "feedback": ""} + + +def test_non_done_status_scores_zero(): + run = {"status": "processing", "outcome": "succeeded", "turns": [_turn(0, [_r("a", True)])]} + assert compute_run_score(run, ["a"]) == 0.0 + + +def test_failed_outcome_scores_zero(): + run = {"status": "done", "outcome": "failed", "turns": [_turn(0, [_r("a", True)])]} + assert compute_run_score(run, ["a"]) == 0.0 + + +def test_finished_outcome_scores_partial_credit(): + # A run that ran to completion but did not pass every criterion has + # outcome "finished" — it must still earn its criteria fraction, not 0.0, + # otherwise GEPA loses its gradient. + run = { + "status": "done", + "outcome": "finished", + "turns": [_turn(0, [_r("a", True), _r("b", True), _r("c", True), _r("d", False)])], + } + assert compute_run_score(run, ["a", "b", "c", "d"]) == 0.75 + + +def test_no_turns_scores_zero(): + run = {"status": "done", "outcome": "succeeded", "turns": []} + assert compute_run_score(run, ["a"]) == 0.0 + + +def test_none_run_scores_zero(): + assert compute_run_score(None, ["a"]) == 0.0 + + +def test_all_passed_single_turn(): + run = {"status": "done", "outcome": "succeeded", "turns": [_turn(0, [_r("a", True), _r("b", True)])]} + assert compute_run_score(run, ["a", "b"]) == 1.0 + + +def test_half_passed(): + run = {"status": "done", "outcome": "succeeded", "turns": [_turn(0, [_r("a", True), _r("b", False)])]} + assert compute_run_score(run, ["a", "b"]) == 0.5 + + +def test_skipped_counts_as_failed(): + # b was skipped (evaluated False) because ancestor a failed. + run = {"status": "done", "outcome": "succeeded", "turns": [_turn(0, [_r("a", False), _r("b", True, evaluated=False)])]} + assert compute_run_score(run, ["a", "b"]) == 0.0 + + +def test_denominator_is_requested_criteria_not_returned(): + # Scope omitted 'b' from results entirely; it must still count in the denominator as failed. + run = {"status": "done", "outcome": "succeeded", "turns": [_turn(0, [_r("a", True)])]} + assert compute_run_score(run, ["a", "b"]) == 0.5 + + +def test_mean_across_turns(): + run = { + "status": "done", + "outcome": "succeeded", + "turns": [ + _turn(0, [_r("a", False), _r("b", False)]), # 0.0 + _turn(1, [_r("a", True), _r("b", True)]), # 1.0 + ], + } + assert compute_run_score(run, ["a", "b"], "mean") == 0.5 + + +def test_final_strategy_uses_last_turn(): + run = { + "status": "done", + "outcome": "succeeded", + "turns": [ + _turn(0, [_r("a", False), _r("b", False)]), # 0.0 + _turn(1, [_r("a", True), _r("b", True)]), # 1.0 + ], + } + assert compute_run_score(run, ["a", "b"], "final") == 1.0 + + +def test_weighted_strategy(): + run = { + "status": "done", + "outcome": "succeeded", + "turns": [ + _turn(0, [_r("a", False), _r("b", False)]), # 0.0 + _turn(1, [_r("a", True), _r("b", True)]), # 1.0 + ], + } + # 0.8*final(1.0) + 0.2*mean(0.5) = 0.9 + assert compute_run_score(run, ["a", "b"], "weighted") == 0.9 + + +def test_empty_requested_falls_back_to_returned(): + run = {"status": "done", "outcome": "succeeded", "turns": [_turn(0, [_r("a", True), _r("b", False)])]} + assert compute_run_score(run, []) == 0.5 + + +def test_compute_turn_scores_order(): + run = 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+describe("ExperimentDetail", { timeout: 10_000 }, () => { it("starts an asynchronous deletion from the confirmation dialog", async () => { - const user = userEvent.setup(); vi.mocked(api.deleteExperiment).mockResolvedValue({ operationId: "operation-delete", phase: "deleting", @@ -263,11 +262,11 @@ describe("ExperimentDetail", () => { }); renderDetail(); - await user.click(await screen.findByRole("button", { name: "Delete" })); + fireEvent.click(await screen.findByRole("button", { name: "Delete" })); expect( screen.getByRole("heading", { name: "Delete this Experiment?" }), ).toBeTruthy(); - await user.click( + fireEvent.click( screen.getByRole("button", { name: /Delete Experiment/ }), ); diff --git a/docs/architecture/app-design.md b/docs/architecture/app-design.md index aadad061..e11e84cb 100644 --- a/docs/architecture/app-design.md +++ b/docs/architecture/app-design.md @@ -321,6 +321,20 @@ pre-asserts no composite duplicates, reuses 025's Cosmos-safe helpers (composite non-unique on Cosmos, unique on real MongoDB), and its `down()` unsets `slug`/`ref` (index changes log-only). The dead `prompt-feature-extractions` drop is guarded against a missing namespace. +### GEPA optimization + +The Python optimizer in `apps/gepa-optimizer` is a client of the canonical +Experiment API described in [experiments.md](experiments.md). One optimization +creates or targets one Experiment. Each candidate batch first creates +standalone requests, then publishes those exact request IDs through the +membership API, producing a new immutable revision and execution. The optimizer +records those identities and reads reports through the exact execution route. + +**GEPA scoring is not persisted.** Its metric (fraction of criteria passed, +aggregated across turns) is derived from `run.turns[].criteriaResults` and +remains algorithm-specific. Candidate lineage is reconstructed from GEPA's +public parent indices in the optimizer result artifact. + ## Judge Pipeline The judge evaluates coding agent output against criteria. Two strategies are supported: diff --git a/docs/architecture/experiments.md b/docs/architecture/experiments.md index 103bee35..f936332f 100644 --- a/docs/architecture/experiments.md +++ b/docs/architecture/experiments.md @@ -429,6 +429,27 @@ confirmation dialog; a deleted Experiment exposes **Restore**. Membership, retry, and report publication controls stay disabled while lifecycle work is active. +## GEPA optimizer integration + +`apps/gepa-optimizer` treats the Experiment model as the durable record of one +prompt-optimization run: + +1. It creates one empty Experiment, or targets `SCOPE_EXPERIMENT_ID` and reads + its current revision. +2. Each candidate batch submits standalone requests without retrying the + submission POSTs. +3. It adds the successful request IDs in one revision-guarded membership POST. +4. It polls the exact request runs and exact `experimentExecutionId` using + idempotent GET retries, then reads reports from that exact execution. +5. It stores `experimentId`, `experimentRevisionId`, + `experimentExecutionId`, and report references in GEPA outputs and + reflective trajectories. + +The Experiment therefore remains operable through the normal API, CLI, and +Portal. Membership revisions provide the durable candidate-batch boundaries; +the algorithm-specific criteria score and post-hoc candidate lineage remain in +the optimizer's `results.json` rather than extending Scope persistence. + ## Permanent request creation and composition deprecation `POST /api/v1/requests`, `scope run submit`, and the Portal's **New Run** flow diff --git a/package.json b/package.json index d12b0082..4d8641e3 100644 --- a/package.json +++ b/package.json @@ -60,6 +60,8 @@ "test:gateway": "cd apps/gateway && cargo test", "lint:gateway": "cd apps/gateway && cargo clippy -- -D warnings", "fmt:gateway": "cd apps/gateway && cargo fmt --check", + "build:gepa": "cd apps/gepa-optimizer && uv sync --locked", + "test:gepa": "cd apps/gepa-optimizer && uv run pytest", "dev:token-manager": "pnpm --filter token-manager dev", "dev:model-scanner-copilot": "pnpm --filter model-scanner-copilot dev", "dev:model-scanner-anthropic": "pnpm --filter model-scanner-anthropic dev", diff --git a/packages/shared/package.json b/packages/shared/package.json index 155cb564..13337432 100644 --- a/packages/shared/package.json +++ b/packages/shared/package.json @@ -45,6 +45,11 @@ "types": "./dist/criteria/criteria-provider-factory.d.ts", "import": "./dist/criteria/criteria-provider-factory.js" }, + "./observation-taxonomy": { + "source": "./src/criteria/observation-taxonomy.ts", + "types": "./dist/criteria/observation-taxonomy.d.ts", + "import": "./dist/criteria/observation-taxonomy.js" + }, "./experiments": { "source": "./src/experiments/index.ts", "types": "./dist/experiments/index.d.ts", diff --git a/packages/shared/src/experiments/experiment-store.test.ts b/packages/shared/src/experiments/experiment-store.test.ts index 3e7c991b..dc0dcb3c 100644 --- a/packages/shared/src/experiments/experiment-store.test.ts +++ b/packages/shared/src/experiments/experiment-store.test.ts @@ -591,7 +591,17 @@ describe("ExperimentStore CAS contracts", () => { const payloadStore = new FixedPayloadStore( chunks as unknown as Collection, ); - const currentRequests = requestVector(4_000); + const chunkedRequests = (count: number) => + requestVector(count).map((request) => ({ + ...request, + dependencies: Array.from({ length: 4 }, (_, index) => ({ + handlerId: `handler-${index}`.padEnd(200, "x"), + state: "ready" as const, + version: 3, + })), + blockers: [], + })); + const currentRequests = chunkedRequests(320); const root = experiment(); root.revision.memberCount = currentRequests.length; root.revision.membershipFingerprint = @@ -621,7 +631,7 @@ describe("ExperimentStore CAS contracts", () => { historyCollection() as unknown as Collection, chunks: chunks as unknown as Collection, }); - const grownRequests = requestVector(4_200); + const grownRequests = chunkedRequests(330); const grown = await grow.replaceMembership({ projectId: "project-1", experimentId: "experiment-1", diff --git a/scripts/ci-workflow.test.ts b/scripts/ci-workflow.test.ts index 81afcfe2..be3ae746 100644 --- a/scripts/ci-workflow.test.ts +++ b/scripts/ci-workflow.test.ts @@ -84,6 +84,18 @@ describe("CI execution prerequisites", () => { expect(step("integration-test", "Pre-build Docker test images").run).toContain("${{ matrix.worker.images }}"); }); + it("runs GEPA tests when the optimizer changes and includes them in the summary", () => { + const filtersStep = jobs["detect-changes"].steps.find((candidate) => candidate.uses?.startsWith("dorny/paths-filter@")); + const filters = parse(filtersStep!.with!.filters) as Record; + expect(filters.gepa).toContain("apps/gepa-optimizer/**"); + expect(jobs["detect-changes"].outputs?.gepa).toBe("${{ steps.changes.outputs.gepa }}"); + expect(jobs.gepa.if).toContain("needs.detect-changes.outputs.gepa == 'true'"); + expect(step("gepa", "Install locked dependencies").run).toBe("uv sync --locked"); + expect(step("gepa", "Run tests").run).toBe("uv run pytest"); + expect(jobs["ci-summary"].needs).toContain("gepa"); + expect(step("ci-summary", "Check overall status").run).toContain("needs.gepa.result"); + }); + it("parses the remaining inline shell scripts", () => { for (const job of Object.values(jobs)) { for (const command of job.steps.filter((candidate) => candidate.run && candidate.shell !== "pwsh")) {