diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 0ce72aa..474d920 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -199,6 +199,16 @@ jobs: capabilities: [browser] primary_for: [browser] idle_timeout: 1800 + + browser_learning: + enabled: true + run_on_startup: true + interval_seconds: 2 + extraction: + dedup_enabled: true + variable_extraction_enabled: true + llm: + enabled: false YAML - name: Start Bay API for e2e diff --git a/.github/workflows/nightly-browser-e2e.yml b/.github/workflows/nightly-browser-e2e.yml index ad4b2c9..588455b 100644 --- a/.github/workflows/nightly-browser-e2e.yml +++ b/.github/workflows/nightly-browser-e2e.yml @@ -86,6 +86,16 @@ jobs: capabilities: [browser] primary_for: [browser] idle_timeout: 1800 + + browser_learning: + enabled: true + run_on_startup: true + interval_seconds: 2 + extraction: + dedup_enabled: true + variable_extraction_enabled: true + llm: + enabled: false CFG - name: Start Bay API for nightly e2e diff --git a/demo_evolution.py b/demo_evolution.py new file mode 100644 index 0000000..98dad0c --- /dev/null +++ b/demo_evolution.py @@ -0,0 +1,392 @@ +#!/usr/bin/env python3 +""" +Skill Evolution Demo +==================== +Demonstrates the full evolution pipeline end-to-end: + +1. Declares skill goals (human intent only — "I want a skill that does X") +2. Seeds initial skill candidates with summaries and instructions +3. Reports failure outcomes (the evolution signal feed) +4. Runs the LLM mutation cycle using Claude (or mock if no API key) +5. Shows the evolved skill candidates and their reasoning + +Run from pkgs/bay directory using Bay's venv: + .venv/bin/python ../../demo_evolution.py + +Or set ANTHROPIC_API_KEY for real Claude mutations: + ANTHROPIC_API_KEY=sk-ant-... .venv/bin/python ../../demo_evolution.py +""" + +from __future__ import annotations + +import asyncio +import json +import os +import sys + +import httpx +import structlog +from sqlalchemy.ext.asyncio import AsyncSession, create_async_engine +from sqlalchemy.orm import sessionmaker +from sqlmodel import SQLModel, select + +from app.config import EvolutionConfig, LlmEvolutionConfig +from app.models.skill import ( + ExecutionType, + MetaPrompt, + SkillCandidate, + SkillGoal, + SkillOutcome, + SkillReleaseStage, +) +from app.services.skills import SkillLifecycleService +from app.services.skills.evolution.agent import SkillMutationAgent +from app.services.skills.evolution.llm import LlmEvolutionClient, MutationOutput +from app.services.skills.evolution.meta_prompt import MetaPromptService +from app.services.skills.evolution.scheduler import EvolutionCycleResult, EvolutionScheduler + +structlog.configure(wrapper_class=structlog.make_filtering_bound_logger(30)) # WARNING+ only + +# --------------------------------------------------------------------------- +# Skills we want to evolve (human intent only) +# --------------------------------------------------------------------------- + +SKILLS = [ + { + "skill_key": "github-get-stars", + "goal": "Given a GitHub repo URL, return the exact current star count as an integer.", + "summary": "Navigate to the GitHub repo page and read the star count element.", + "usage_notes": ( + "Open the repo URL in a browser. " + "Look for the star count near the top of the page and return it as an integer." + ), + "preconditions": { + "input": "A valid GitHub repo URL", + "network": "GitHub is accessible", + }, + "postconditions": { + "output": "Integer star count", + }, + "failures": [ + "Element not found at selector '.social-count'. Page may have loaded differently.", + "Star count returned None — element existed but had no text content after JS render.", + "Timeout waiting for page load after 3s. No retry logic was applied.", + ], + }, + { + "skill_key": "hacker-news-top10", + "goal": "Fetch the titles and URLs of the current top 10 stories on Hacker News.", + "summary": "Navigate to news.ycombinator.com and scrape the top 10 story titles with links.", + "usage_notes": ( + "Open the HN homepage. " + "Extract the first 10 .titleline elements and return their text and href attributes." + ), + "preconditions": { + "browser": "Available with JavaScript enabled", + "network": "news.ycombinator.com is accessible", + }, + "postconditions": { + "output": "List of {title, url} dicts with exactly 10 items", + }, + "failures": [ + "Only got 8 items — the page structure may have changed. .titleline missed 2 rows.", + "URLs were relative (item?id=...) instead of absolute. Needed to resolve base URL.", + "Title text included vote/rank numbers. Parsing logic not robust enough.", + ], + }, + { + "skill_key": "pypi-package-info", + "goal": "Given a PyPI package name, return its latest version and one-line description.", + "summary": "Call the PyPI JSON API to fetch package metadata.", + "usage_notes": ( + "Make a GET request to https://pypi.org/pypi/{package}/json. " + "Extract info.version and info.summary from the response." + ), + "preconditions": { + "input": "A valid PyPI package name (str)", + "network": "pypi.org is accessible", + }, + "postconditions": { + "output": "Dict with keys: version (str), summary (str)", + }, + "failures": [ + "KeyError on info.summary for packages that set it to None. Need null check.", + "HTTP 404 for yanked packages — no fallback to previous version attempted.", + "Rate limiting from PyPI (429) not handled; retried immediately and failed again.", + ], + }, +] + +# --------------------------------------------------------------------------- +# Anthropic LLM mutation client +# --------------------------------------------------------------------------- + +ANTHROPIC_KEY = os.environ.get("ANTHROPIC_API_KEY", "") + +_MUTATION_SYSTEM = """\ +You are an expert at improving AI agent skill instructions. +You receive: +1. The current skill content (summary, usage notes, pre/postconditions) +2. Recent failure reports from executions of this skill +3. A mutation strategy to apply + +Your task: rewrite the skill content to address the failures, applying the given strategy. +Return strict JSON only. The mutation_reasoning field MUST explain specifically what you changed and why. +No markdown, no explanation outside the JSON object.\ +""" + +_MUTATION_SCHEMA = { + "type": "object", + "required": ["summary", "usage_notes", "preconditions", "postconditions", "mutation_reasoning"], + "additionalProperties": False, + "properties": { + "summary": {"type": "string"}, + "usage_notes": {"type": "string"}, + "preconditions": {"type": "array", "items": {"type": "string"}}, + "postconditions": {"type": "array", "items": {"type": "string"}}, + "mutation_reasoning": {"type": "string"}, + }, +} + + +class AnthropicMutationClient: + """Calls Claude Haiku for skill mutation via tool_use for structured output.""" + + async def generate_mutation(self, prompt: str) -> MutationOutput: + if not ANTHROPIC_KEY: + return _mock_mutation(prompt) + + async with httpx.AsyncClient(timeout=60) as ac: + r = await ac.post( + "https://api.anthropic.com/v1/messages", + headers={ + "x-api-key": ANTHROPIC_KEY, + "anthropic-version": "2023-06-01", + "content-type": "application/json", + }, + json={ + "model": "claude-haiku-4-5-20251001", + "max_tokens": 1024, + "system": _MUTATION_SYSTEM, + "messages": [{"role": "user", "content": prompt}], + "tools": [ + { + "name": "output_mutation", + "description": "Output the mutated skill content as structured data", + "input_schema": _MUTATION_SCHEMA, + } + ], + "tool_choice": {"type": "tool", "name": "output_mutation"}, + }, + ) + r.raise_for_status() + data = r.json() + for block in data["content"]: + if block.get("type") == "tool_use" and block.get("name") == "output_mutation": + inp = block["input"] + return MutationOutput( + summary=inp["summary"], + usage_notes=inp["usage_notes"], + preconditions=inp["preconditions"], + postconditions=inp["postconditions"], + mutation_reasoning=inp["mutation_reasoning"], + ) + raise RuntimeError("No tool_use block in Claude response") + + +def _mock_mutation(prompt: str) -> MutationOutput: + """Deterministic mock for demo without API key.""" + skill_line = next((l for l in prompt.split("\n") if "SKILL:" in l), "# SKILL: unknown") + skill_key = skill_line.replace("# SKILL:", "").strip() + return MutationOutput( + summary=( + f"[EVOLVED] {skill_key}: Use semantic selectors, explicit waits, and fallback strategies." + ), + usage_notes=( + "1. Navigate to the target URL and wait for DOMContentLoaded (max 10s). " + "2. Use role/aria/text selectors as primary; fall back to CSS class selectors. " + "3. If element not found after 5s, scroll down and retry once. " + "4. Validate the extracted value before returning — log and raise if None or empty." + ), + preconditions=[ + "Browser available with JavaScript enabled", + "Target URL is accessible from the network", + "Page renders within 10 seconds", + ], + postconditions=[ + "Returns a non-None, non-empty value of the expected type", + "Value validated for plausibility before return", + ], + mutation_reasoning=( + f"Original {skill_key} instructions used brittle CSS class selectors with no wait " + "or fallback. Failures showed elements not found due to async rendering and selector " + "changes. Changes: (1) switched to semantic selectors, (2) added explicit 5s wait, " + "(3) added scroll-and-retry fallback, (4) added post-extraction validation." + ), + ) + + +# --------------------------------------------------------------------------- +# Demo runner +# --------------------------------------------------------------------------- + +async def run_demo() -> None: + # Connect to Bay's live SQLite DB (same file Bay server uses) + db_url = "sqlite+aiosqlite:///./bay.db" + engine = create_async_engine(db_url, echo=False) + + # Ensure all tables exist (auto-migrate handles new columns via Bay startup) + async with engine.begin() as conn: + await conn.run_sync(SQLModel.metadata.create_all) + + session_factory = sessionmaker(engine, class_=AsyncSession, expire_on_commit=False) + + print("\n" + "=" * 64) + print(" SKILL EVOLUTION DEMO — AI-native skill improvement loop") + print("=" * 64) + llm_label = "Claude Haiku (Anthropic API)" if ANTHROPIC_KEY else "mock LLM (set ANTHROPIC_API_KEY)" + print(f" LLM backend: {llm_label}") + print(f" Skills to evolve: {len(SKILLS)}") + + async with session_factory() as db: + svc = SkillLifecycleService(db) + meta_svc = MetaPromptService(db) + + # ── 1. Seed MetaPrompt archive ──────────────────────────────────── + count = await meta_svc.seed_defaults() + print(f"\n[1] MetaPrompt archive ready ({count} mutation strategies)\n") + + # ── 2. Declare goals + seed candidates ─────────────────────────── + print("[2] Declaring goals and seeding initial skill candidates") + release_map: dict[str, str] = {} # skill_key → release_id + + for s in SKILLS: + # Declare goal (human intent only) + goal = await svc.declare_goal( + owner="demo", + skill_key=s["skill_key"], + goal=s["goal"], + ) + + # Create a dummy execution to satisfy source_execution_ids + execution = await svc.create_execution( + owner="demo", + sandbox_id="sb-demo", + exec_type=ExecutionType.BROWSER, + code=f"# demo seed for {s['skill_key']}", + success=True, + execution_time_ms=50, + ) + + # Create candidate + candidate = await svc.create_candidate( + owner="demo", + skill_key=s["skill_key"], + source_execution_ids=[execution.id], + summary=s["summary"], + usage_notes=s["usage_notes"], + preconditions=s["preconditions"], + postconditions=s["postconditions"], + created_by="demo:seed", + ) + + # Evaluate and promote to canary + await svc.evaluate_candidate( + owner="demo", candidate_id=candidate.id, passed=True, score=0.80 + ) + release = await svc.promote_candidate( + owner="demo", candidate_id=candidate.id, stage=SkillReleaseStage.CANARY + ) + release_map[s["skill_key"]] = release.id + + print(f" ✓ {s['skill_key']}") + print(f" goal: {s['goal'][:72]}...") + print(f" release: {release.id} (v{release.version}, canary)") + + # ── 3. Report failures ──────────────────────────────────────────── + print("\n[3] Reporting execution failures (evolution signals)") + for s in SKILLS: + release_id = release_map[s["skill_key"]] + for reasoning in s["failures"]: + await svc.record_outcome( + owner="demo", + skill_key=s["skill_key"], + release_id=release_id, + outcome="failure", + reasoning=reasoning, + ) + print(f" ✓ {s['skill_key']} — {len(s['failures'])} failures reported") + + # ── 4. Run evolution cycle ──────────────────────────────────────── + print("\n[4] Running LLM mutation cycle (EvolutionScheduler)") + + config = EvolutionConfig( + enabled=True, + min_failures_to_trigger=2, + max_mutations_per_cycle=10, + max_recent_outcomes=10, + ) + llm_client = AnthropicMutationClient() + agent = SkillMutationAgent( + db_session=db, + llm_client=llm_client, + max_recent_outcomes=10, + ) + scheduler = EvolutionScheduler( + db_session=db, + config=config, + mutation_agent=agent, + ) + + result: EvolutionCycleResult = await scheduler.run_cycle() + print(f" Attempted: {result.mutations_attempted}") + print(f" Succeeded: {result.mutations_succeeded}") + + # ── 5. Show evolved candidates ──────────────────────────────────── + print("\n" + "=" * 64) + print(" EVOLVED SKILL CANDIDATES") + print("=" * 64) + + for s in SKILLS: + # Find the evolution-generated candidate for this skill + result_q = await db.execute( + select(SkillCandidate).where( + SkillCandidate.owner == "demo", + SkillCandidate.skill_key == s["skill_key"], + SkillCandidate.created_by == "system:evolution", + ).order_by(SkillCandidate.created_at.desc()).limit(1) + ) + evolved = result_q.scalars().first() + if evolved is None: + print(f"\n {s['skill_key']}: no mutation generated") + continue + + preconditions = json.loads(evolved.preconditions_json or "[]") + postconditions = json.loads(evolved.postconditions_json or "[]") + + print(f"\n▶ {s['skill_key']} → {evolved.id}") + print(f" Goal: {s['goal']}") + print() + print(f" Summary: {evolved.summary}") + print() + print(f" Usage: {evolved.usage_notes}") + print() + if isinstance(preconditions, list): + print(f" Pre: " + "\n ".join(preconditions)) + if isinstance(postconditions, list): + print(f" Post: " + "\n ".join(postconditions)) + print() + print(f" Reasoning: {evolved.mutation_reasoning}") + print(f" Parent: {evolved.evolution_parent_id}") + print(f" Strategy: {evolved.evolution_meta_prompt_id}") + + print("\n" + "=" * 64) + print(f" {result.mutations_succeeded}/{len(SKILLS)} skills evolved") + print(" Next: evaluate mutated candidates and promote winners to stable.") + print("=" * 64 + "\n") + + await engine.dispose() + + +if __name__ == "__main__": + asyncio.run(run_demo()) diff --git a/deploy/docker/config.yaml b/deploy/docker/config.yaml index dd09853..06da84c 100644 --- a/deploy/docker/config.yaml +++ b/deploy/docker/config.yaml @@ -58,7 +58,6 @@ proxy: # https_proxy: "http://proxy.example.com:7890" # Optional extra entries to append to default NO_PROXY list # no_proxy: "my-internal.service" - # Warm Pool — pre-start standby sandbox instances to reduce cold-start latency. # When a user creates a sandbox, Bay will first try to claim an available warm instance, # delivering near-instant startup instead of waiting for container boot. diff --git a/doc/skills_enhancement_roadmap_zh.md b/doc/skills_enhancement_roadmap_zh.md index bb06b49..5d6eb59 100644 --- a/doc/skills_enhancement_roadmap_zh.md +++ b/doc/skills_enhancement_roadmap_zh.md @@ -14,6 +14,8 @@ ### 1. 技能提取智能化 +> 状态:已规划(OpenSpec change: `skill-extraction-intelligence`) + #### 1.1 现状 `BrowserLearningScheduler`(`app/services/skills/scheduler.py`)采用**规则驱动**的技能提取策略: diff --git a/doc/skills_self_update_guide_zh.md b/doc/skills_self_update_guide_zh.md index 35138d0..78c4874 100644 --- a/doc/skills_self_update_guide_zh.md +++ b/doc/skills_self_update_guide_zh.md @@ -33,6 +33,41 @@ Shipyard Neo 提供的是 **self-update 基建**,而不是固定训练框架 6. canary 健康窗口 24 小时达标后自动升 stable。 7. 若 `success_rate` 下降超过 3% 或 `error_rate` 升至 2x 以上,自动回滚并写审计日志。 +#### 2.1.1 Extraction Pipeline 配置(新增) + +Browser Learning 现在支持可插拔提取策略与可选 LLM 辅助,配置入口为 `browser_learning.extraction`: + +```yaml +browser_learning: + extraction: + dedup_enabled: true + variable_extraction_enabled: true + llm: + enabled: false + api_base: "https://api.openai.com/v1" + api_key: "" + model: "gpt-4.1-mini" + timeout_seconds: 30 + max_tokens: 4096 +``` + +等价环境变量前缀: + +- `BAY_BROWSER_LEARNING__EXTRACTION__DEDUP_ENABLED` +- `BAY_BROWSER_LEARNING__EXTRACTION__VARIABLE_EXTRACTION_ENABLED` +- `BAY_BROWSER_LEARNING__EXTRACTION__LLM__ENABLED` +- `BAY_BROWSER_LEARNING__EXTRACTION__LLM__API_BASE` +- `BAY_BROWSER_LEARNING__EXTRACTION__LLM__API_KEY` +- `BAY_BROWSER_LEARNING__EXTRACTION__LLM__MODEL` +- `BAY_BROWSER_LEARNING__EXTRACTION__LLM__TIMEOUT_SECONDS` +- `BAY_BROWSER_LEARNING__EXTRACTION__LLM__MAX_TOKENS` + +说明: + +1. 默认 `llm.enabled=false`,系统使用纯规则策略。 +2. LLM 调用超时、连接错误或解析失败时,会自动降级到规则策略,不阻断学习流水线。 +3. 候选去重基于 `owner + skill_key + payload_hash`,其中 `payload_hash` 为动作 `cmd` 序列的 SHA-256。 + ## 3. REST API 关键接口 ### 3.1 Execution History diff --git a/pkgs/bay/app/api/v1/skills.py b/pkgs/bay/app/api/v1/skills.py index 57cf1a0..24fcf27 100644 --- a/pkgs/bay/app/api/v1/skills.py +++ b/pkgs/bay/app/api/v1/skills.py @@ -25,8 +25,8 @@ class SkillCandidateCreateRequest(BaseModel): payload_ref: str | None = None summary: str | None = None usage_notes: str | None = None - preconditions: dict[str, Any] | None = None - postconditions: dict[str, Any] | None = None + preconditions: list[str] | dict[str, Any] | None = None + postconditions: list[str] | dict[str, Any] | None = None class SkillCandidateResponse(BaseModel): @@ -41,8 +41,8 @@ class SkillCandidateResponse(BaseModel): auto_release_reason: str | None summary: str | None usage_notes: str | None - preconditions: dict[str, Any] | None - postconditions: dict[str, Any] | None + preconditions: list[str] | dict[str, Any] | None + postconditions: list[str] | dict[str, Any] | None source_execution_ids: list[str] status: str latest_score: float | None @@ -173,6 +173,55 @@ class SkillPayloadResponse(BaseModel): payload: dict[str, Any] | list[Any] +class SkillGoalDeclareRequest(BaseModel): + """Declare goal for a skill key.""" + + skill_key: str + goal: str + + +class SkillGoalDeclareResponse(BaseModel): + """Goal declaration response.""" + + goal_id: str + skill_key: str + goal: str + rubric_summary: str + + +class SkillActiveResponse(BaseModel): + """Active skill view — unified view of the current release.""" + + skill_key: str + release_id: str + version: int + stage: str + goal: str | None + content: str + summary: str | None + preconditions: list[str] + postconditions: list[str] + payload_ref: str | None + + +class SkillOutcomeRequest(BaseModel): + """Report an execution outcome.""" + + skill_key: str + release_id: str + outcome: str = Field(..., pattern="^(success|failure|partial)$") + reasoning: str + execution_id: str | None = None + signals: dict[str, Any] | None = None + + +class SkillOutcomeResponse(BaseModel): + """Outcome record response.""" + + outcome_id: str + accepted: bool + + class SkillDeleteRequest(BaseModel): """Delete request.""" @@ -188,14 +237,23 @@ class SkillDeleteResponse(BaseModel): delete_reason: str | None -def _json_field_to_obj(raw: str | None) -> dict[str, Any] | None: +def _json_field_to_obj(raw: str | None) -> list[str] | dict[str, Any] | None: + """Deserialize a pre/postconditions JSON field. + + Accepts both list (evolution-generated) and dict (API-created) formats. + Returns None on missing or unparseable input. + """ if raw is None: return None try: parsed = json.loads(raw) except Exception: return None - return parsed if isinstance(parsed, dict) else None + if isinstance(parsed, list): + return [str(item) for item in parsed] + if isinstance(parsed, dict): + return parsed + return None def _candidate_to_response(candidate) -> SkillCandidateResponse: @@ -265,6 +323,57 @@ def _release_to_response(release) -> SkillReleaseResponse: ) +@router.post("/goals", response_model=SkillGoalDeclareResponse, status_code=200) +async def declare_skill_goal( + request: SkillGoalDeclareRequest, + skill_svc: SkillLifecycleServiceDep, + owner: AuthDep, +) -> SkillGoalDeclareResponse: + skill_goal = await skill_svc.declare_goal( + owner=owner, + skill_key=request.skill_key, + goal=request.goal, + ) + return SkillGoalDeclareResponse( + goal_id=skill_goal.id, + skill_key=skill_goal.skill_key, + goal=skill_goal.goal, + rubric_summary=skill_goal.rubric_summary, + ) + + +@router.get("/{skill_key}/active", response_model=SkillActiveResponse) +async def get_active_skill( + skill_key: str, + skill_svc: SkillLifecycleServiceDep, + owner: AuthDep, +) -> SkillActiveResponse: + view = await skill_svc.get_active_skill_view(owner=owner, skill_key=skill_key) + if view is None: + from app.errors import NotFoundError + + raise NotFoundError(f"No active release for skill: {skill_key}") + return SkillActiveResponse(**view) + + +@router.post("/outcomes", response_model=SkillOutcomeResponse, status_code=201) +async def report_skill_outcome( + request: SkillOutcomeRequest, + skill_svc: SkillLifecycleServiceDep, + owner: AuthDep, +) -> SkillOutcomeResponse: + outcome = await skill_svc.record_outcome( + owner=owner, + skill_key=request.skill_key, + release_id=request.release_id, + outcome=request.outcome, + reasoning=request.reasoning, + execution_id=request.execution_id, + signals=request.signals, + ) + return SkillOutcomeResponse(outcome_id=outcome.id, accepted=True) + + @router.post("/payloads", response_model=SkillPayloadCreateResponse, status_code=201) async def create_payload( request: SkillPayloadCreateRequest, diff --git a/pkgs/bay/app/config.py b/pkgs/bay/app/config.py index 45b1b39..565cf6a 100644 --- a/pkgs/bay/app/config.py +++ b/pkgs/bay/app/config.py @@ -424,6 +424,83 @@ class BrowserLearningConfig(BaseModel): success_drop_threshold: float = 0.03 error_rate_multiplier_threshold: float = 2.0 + extraction: "ExtractionConfig" = Field(default_factory=lambda: ExtractionConfig()) + + +class LlmExtractionConfig(BaseModel): + """LLM-assisted extraction configuration.""" + + enabled: bool = False + api_base: str = "https://api.openai.com/v1" + api_key: str = "" + model: str = "gpt-4.1-mini" + timeout_seconds: int = 30 + max_tokens: int = 4096 + + +class ExtractionConfig(BaseModel): + """Extraction pipeline configuration.""" + + dedup_enabled: bool = True + variable_extraction_enabled: bool = True + llm: LlmExtractionConfig = Field(default_factory=LlmExtractionConfig) + + +class LlmEvolutionConfig(BaseModel): + """LLM configuration for skill evolution tasks. + + The default fields (api_base, api_key, model) apply to all tasks unless + overridden by a task-specific ModelSpec below. This lets callers use a + fast cheap model for mutation while routing rubric / evaluation to a more + capable (and possibly different provider) model. + """ + + enabled: bool = False + api_base: str = "https://api.openai.com/v1" + api_key: str = "" + model: str = "gpt-4.1-mini" + timeout_seconds: int = 60 + max_tokens: int = 4096 + + # ── Per-task model overrides ────────────────────────────────────────── + # When set, these override the defaults above for that specific task. + # When None, the task inherits the default config above. + + # Model used for rubric generation (declare_goal → LLM generates rubric). + # Recommendation: use a strong reasoning model (e.g. gpt-4.1, claude-opus). + rubric_model: str | None = None + rubric_api_base: str | None = None + rubric_api_key: str | None = None + + # Model used for goal-conditioned evaluation (judge: does mutation satisfy goal?). + # Recommendation: same as rubric_model — quality matters more than speed here. + evaluator_model: str | None = None + evaluator_api_base: str | None = None + evaluator_api_key: str | None = None + + +class EvolutionConfig(BaseModel): + """Skill evolution scheduler configuration.""" + + enabled: bool = False + run_on_startup: bool = False + interval_seconds: int = 600 + + # How many recent failure/partial outcomes to include in mutation context + max_recent_outcomes: int = 10 + + # Minimum failures before triggering mutation for a skill + min_failures_to_trigger: int = 2 + + # Max mutations per scheduler cycle (resource budget) + max_mutations_per_cycle: int = 5 + + # Score threshold above which a mutation candidate is auto-promoted to canary. + # Set to 1.1 to disable auto-promotion (no score can exceed 1.0). + auto_promote_threshold: float = 0.7 + + llm: LlmEvolutionConfig = Field(default_factory=LlmEvolutionConfig) + class WarmPoolConfig(BaseModel): """Warm pool global configuration.""" @@ -486,6 +563,7 @@ class Settings(BaseSettings): browser_learning: BrowserLearningConfig = Field(default_factory=BrowserLearningConfig) proxy: ProxyConfig = Field(default_factory=ProxyConfig) browser_auto_release_enabled: bool = True + evolution: EvolutionConfig = Field(default_factory=EvolutionConfig) # Default profiles profiles: list[ProfileConfig] = Field( diff --git a/pkgs/bay/app/main.py b/pkgs/bay/app/main.py index 1bf6cdd..dba82de 100644 --- a/pkgs/bay/app/main.py +++ b/pkgs/bay/app/main.py @@ -15,6 +15,10 @@ from app.errors import BayError from app.services.gc.lifecycle import init_gc_scheduler, shutdown_gc_scheduler from app.services.http import http_client_manager +from app.services.skills.evolution.lifecycle import ( + init_evolution_scheduler, + shutdown_evolution_scheduler, +) from app.services.skills.lifecycle import ( init_browser_learning_scheduler, shutdown_browser_learning_scheduler, @@ -46,6 +50,10 @@ async def lifespan(app: FastAPI): # Initialize and start GC scheduler await init_gc_scheduler() await init_browser_learning_scheduler() + await init_evolution_scheduler() + + # Initialize warm pool (queue + scheduler) + await init_warm_pool() # Initialize warm pool (queue + scheduler) await init_warm_pool() @@ -58,6 +66,10 @@ async def lifespan(app: FastAPI): # Stop GC scheduler await shutdown_gc_scheduler() await shutdown_browser_learning_scheduler() + await shutdown_evolution_scheduler() + + # Stop warm pool + await shutdown_warm_pool() # Stop warm pool await shutdown_warm_pool() diff --git a/pkgs/bay/app/managers/sandbox/sandbox.py b/pkgs/bay/app/managers/sandbox/sandbox.py index 6edcc45..0bcddc2 100644 --- a/pkgs/bay/app/managers/sandbox/sandbox.py +++ b/pkgs/bay/app/managers/sandbox/sandbox.py @@ -722,7 +722,6 @@ async def claim_warm_sandbox( if cargo is not None: cargo.owner = owner await self._db.commit() - self._log.info( "sandbox.warm_claim.success", sandbox_id=candidate_id, diff --git a/pkgs/bay/app/models/__init__.py b/pkgs/bay/app/models/__init__.py index 9a8a75d..411af9d 100644 --- a/pkgs/bay/app/models/__init__.py +++ b/pkgs/bay/app/models/__init__.py @@ -10,9 +10,12 @@ ExecutionHistory, ExecutionType, LearnStatus, + MetaPrompt, SkillCandidate, SkillCandidateStatus, SkillEvaluation, + SkillGoal, + SkillOutcome, SkillRelease, SkillReleaseMode, SkillReleaseStage, @@ -40,7 +43,10 @@ "SkillCandidate", "SkillCandidateStatus", "SkillType", + "MetaPrompt", "SkillEvaluation", + "SkillGoal", + "SkillOutcome", "SkillRelease", "SkillReleaseStage", "SkillReleaseMode", diff --git a/pkgs/bay/app/models/skill.py b/pkgs/bay/app/models/skill.py index 73a2506..0f4c1e3 100644 --- a/pkgs/bay/app/models/skill.py +++ b/pkgs/bay/app/models/skill.py @@ -123,6 +123,7 @@ class SkillCandidate(SQLModel, table=True): skill_key: str = Field(index=True) scenario_key: str | None = Field(default=None, index=True) payload_ref: str | None = Field(default=None) + payload_hash: str | None = Field(default=None, index=True) skill_type: SkillType = Field(default=SkillType.CODE, index=True) auto_release_eligible: bool = Field(default=False, index=True) auto_release_reason: str | None = Field(default=None) @@ -148,7 +149,10 @@ class SkillCandidate(SQLModel, table=True): last_evaluated_at: datetime | None = Field(default=None) promotion_release_id: str | None = Field(default=None, index=True) - + # Evolution lineage (Phase 2) + evolution_parent_id: str | None = Field(default=None, index=True) + evolution_meta_prompt_id: str | None = Field(default=None, index=True) + mutation_reasoning: str | None = Field(default=None) deleted_at: datetime | None = Field(default=None, index=True) deleted_by: str | None = Field(default=None) delete_reason: str | None = Field(default=None) @@ -173,6 +177,63 @@ class SkillEvaluation(SQLModel, table=True): created_at: datetime = Field(default_factory=utcnow, index=True) +class MetaPrompt(SQLModel, table=True): + """Mutation instruction in the MetaPrompt archive. + + Each record is a strategy for improving a skill (e.g., 'add error handling'). + Selection weight is derived from success_count / (usage_count + 1). + """ + + __tablename__ = "meta_prompts" + + id: str = Field(primary_key=True) + instruction: str + is_default: bool = Field(default=False, index=True) + + usage_count: int = Field(default=0) + success_count: int = Field(default=0) + + created_at: datetime = Field(default_factory=utcnow, index=True) + updated_at: datetime = Field(default_factory=utcnow) + + +class SkillGoal(SQLModel, table=True): + """Declared goal for a skill key — the human's intent input.""" + + __tablename__ = "skill_goals" + + id: str = Field(primary_key=True) + owner: str = Field(index=True) + + skill_key: str = Field(index=True) + + goal: str + rubric_summary: str = Field(default="") + rubric_json: str | None = Field(default=None) # JSON: SkillRubric structure + + created_at: datetime = Field(default_factory=utcnow, index=True) + updated_at: datetime = Field(default_factory=utcnow) + + +class SkillOutcome(SQLModel, table=True): + """Execution outcome report — the evolution signal feed.""" + + __tablename__ = "skill_outcomes" + + id: str = Field(primary_key=True) + owner: str = Field(index=True) + + skill_key: str = Field(index=True) + release_id: str = Field(index=True) + + outcome: str = Field(index=True) # "success" | "failure" | "partial" + reasoning: str + execution_id: str | None = Field(default=None, index=True) + signals_json: str | None = Field(default=None) + + created_at: datetime = Field(default_factory=utcnow, index=True) + + class SkillRelease(SQLModel, table=True): """Promoted, versioned skill release.""" diff --git a/pkgs/bay/app/services/skills/evolution/__init__.py b/pkgs/bay/app/services/skills/evolution/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/pkgs/bay/app/services/skills/evolution/agent.py b/pkgs/bay/app/services/skills/evolution/agent.py new file mode 100644 index 0000000..c07411a --- /dev/null +++ b/pkgs/bay/app/services/skills/evolution/agent.py @@ -0,0 +1,327 @@ +"""SkillMutationAgent — generates mutated skill candidates via LLM. + +Phase 3 additions: +- Injects the declared SkillGoal text into the LLM prompt. +- Generates (or loads cached) SkillRubric via rubric_generator. +- Evaluates the mutated candidate against goal + rubric using an evaluator. +- Auto-promotes the candidate when evaluator score >= auto_promote_threshold. +""" + +from __future__ import annotations + +import json +import uuid + +import structlog +from sqlalchemy.ext.asyncio import AsyncSession +from sqlmodel import select + +from app.models.skill import ( + SkillCandidate, + SkillCandidateStatus, + SkillGoal, + SkillOutcome, + SkillReleaseMode, + SkillReleaseStage, +) +from app.services.skills.evolution.llm import MutationOutput # re-exported +from app.services.skills.evolution.meta_prompt import MetaPromptService +from app.services.skills.evolution.rubric import SkillRubric +from app.services.skills.service import SkillLifecycleService, _assemble_skill_content +from app.utils.datetime import utcnow + +logger = structlog.get_logger() + +__all__ = ["MutationOutput", "SkillMutationAgent"] + + +class SkillMutationAgent: + """Creates mutated skill candidates using LLM + MetaPrompt strategy.""" + + def __init__( + self, + *, + db_session: AsyncSession, + llm_client, + max_recent_outcomes: int = 10, + evaluator=None, + auto_promote_threshold: float = 0.7, + rubric_generator=None, + ) -> None: + self._db = db_session + self._llm = llm_client + self._max_recent_outcomes = max_recent_outcomes + self._evaluator = evaluator + self._auto_promote_threshold = auto_promote_threshold + self._rubric_generator = rubric_generator + self._log = logger.bind(component="skill_mutation_agent") + + async def mutate(self, *, owner: str, skill_key: str) -> str | None: + """Mutate a skill and return the new candidate ID, or None on failure.""" + svc = SkillLifecycleService(self._db) + meta_svc = MetaPromptService(self._db) + + # Get active release — bail early if none + release = await svc.get_active_release(owner=owner, skill_key=skill_key) + if release is None: + self._log.info("skill_mutation.no_active_release", owner=owner, skill_key=skill_key) + return None + + # Load parent candidate directly (bypass soft-delete guard — it's active) + parent_result = await self._db.execute( + select(SkillCandidate).where( + SkillCandidate.id == release.candidate_id, + SkillCandidate.owner == owner, + ) + ) + parent = parent_result.scalars().first() + if parent is None: + return None + + # Collect recent failures as context + failures_result = await self._db.execute( + select(SkillOutcome) + .where( + SkillOutcome.owner == owner, + SkillOutcome.skill_key == skill_key, + SkillOutcome.outcome == "failure", + ) + .order_by(SkillOutcome.created_at.desc()) + .limit(self._max_recent_outcomes) + ) + recent_failures = list(failures_result.scalars().all()) + + # Ensure meta-prompts exist, then sample one + meta_prompt = await meta_svc.sample_prompt() + if meta_prompt is None: + await meta_svc.seed_defaults() + meta_prompt = await meta_svc.sample_prompt() + if meta_prompt is None: + self._log.warning("skill_mutation.no_meta_prompt", owner=owner, skill_key=skill_key) + return None + + # Phase 3: load declared goal and cached rubric (optional) + goal_result = await self._db.execute( + select(SkillGoal).where( + SkillGoal.owner == owner, + SkillGoal.skill_key == skill_key, + ) + ) + skill_goal = goal_result.scalars().first() + goal_text: str | None = skill_goal.goal if skill_goal is not None else None + + # Load or generate rubric when goal + generator are available + rubric: SkillRubric | None = None + if skill_goal is not None and self._rubric_generator is not None: + rubric = await self._load_or_generate_rubric(skill_goal) + + # Decode parent pre/postconditions for the LLM prompt + preconditions = _decode_conditions(parent.preconditions_json) + postconditions = _decode_conditions(parent.postconditions_json) + + # Build LLM prompt from parent content (the "current" state being mutated) + parent_content = _assemble_skill_content( + skill_key=skill_key, + summary=parent.summary, + usage_notes=parent.usage_notes, + preconditions=preconditions, + postconditions=postconditions, + ) + failure_lines = "\n".join( + f"- Failure {i + 1}: {o.reasoning}" for i, o in enumerate(recent_failures) + ) + goal_section = f"## Goal\n\n{goal_text}\n\n" if goal_text else "" + prompt = ( + f"{goal_section}" + f"## Current Skill Content\n\n{parent_content}\n\n" + f"## Recent Failures\n\n{failure_lines or '(none recorded)'}\n\n" + f"## Mutation Strategy\n\n{meta_prompt.instruction}" + ) + + # Call LLM + try: + mutation: MutationOutput = await self._llm.generate_mutation(prompt) + except Exception as exc: + self._log.warning( + "skill_mutation.llm_failed", + owner=owner, + skill_key=skill_key, + error=str(exc), + ) + return None + + # Persist mutated candidate (inherits payload from parent, no new source executions) + now = utcnow() + new_candidate = SkillCandidate( + id=f"sc-{uuid.uuid4().hex[:12]}", + owner=owner, + skill_key=skill_key, + source_execution_ids=parent.source_execution_ids or "", + payload_ref=parent.payload_ref, + payload_hash=parent.payload_hash, + skill_type=parent.skill_type, + summary=mutation.summary, + usage_notes=mutation.usage_notes, + preconditions_json=json.dumps(mutation.preconditions, ensure_ascii=False), + postconditions_json=json.dumps(mutation.postconditions, ensure_ascii=False), + status=SkillCandidateStatus.DRAFT, + created_by="system:evolution", + evolution_parent_id=parent.id, + evolution_meta_prompt_id=meta_prompt.id, + mutation_reasoning=mutation.mutation_reasoning, + created_at=now, + updated_at=now, + ) + self._db.add(new_candidate) + await self._db.commit() + await self._db.refresh(new_candidate) + + # Track meta-prompt usage for weighted sampling + await meta_svc.record_usage(meta_prompt.id) + + self._log.info( + "skill_mutation.created_candidate", + owner=owner, + skill_key=skill_key, + candidate_id=new_candidate.id, + meta_prompt_id=meta_prompt.id, + ) + + # Phase 3: evaluate against mutated content + rubric, then optionally promote + if self._evaluator is not None and goal_text is not None: + # Assemble content from the *mutation output* — not the parent + mutated_content = _assemble_skill_content( + skill_key=skill_key, + summary=mutation.summary, + usage_notes=mutation.usage_notes, + preconditions=mutation.preconditions, + postconditions=mutation.postconditions, + ) + await self._auto_evaluate_and_promote( + svc=svc, + candidate=new_candidate, + goal=goal_text, + rubric=rubric, + skill_content=mutated_content, + failure_context=failure_lines or "(none recorded)", + owner=owner, + ) + + return new_candidate.id + + async def _load_or_generate_rubric(self, skill_goal: SkillGoal) -> SkillRubric | None: + """Return the cached rubric from SkillGoal, or generate+persist a new one.""" + if skill_goal.rubric_json: + try: + rubric = SkillRubric.model_validate_json(skill_goal.rubric_json) + # Backfill legacy rows that cached rubric_json before rubric_summary + # started being persisted alongside it. + if not skill_goal.rubric_summary: + skill_goal.rubric_summary = rubric.summary + skill_goal.updated_at = utcnow() + await self._db.commit() + return rubric + except Exception: + pass # regenerate if cached JSON is malformed + + rubric = await self._rubric_generator.generate(skill_goal.goal) + if rubric is not None: + skill_goal.rubric_json = rubric.model_dump_json() + skill_goal.rubric_summary = rubric.summary + skill_goal.updated_at = utcnow() + await self._db.commit() + self._log.info( + "skill_mutation.rubric_generated", + skill_key=skill_goal.skill_key, + ) + return rubric + + async def _auto_evaluate_and_promote( + self, + *, + svc: SkillLifecycleService, + candidate: SkillCandidate, + goal: str, + rubric: SkillRubric | None, + skill_content: str, + failure_context: str, + owner: str, + ) -> None: + """Evaluate the mutation and auto-promote if it passes the threshold. + + Only records a formal evaluation and promotes when the evaluator returns + a passing score at or above the threshold. Below-threshold candidates + remain DRAFT for human review — we don't mark them REJECTED so they can + still be promoted manually. + """ + try: + result = await self._evaluator.evaluate( + goal=goal, + rubric=rubric, + skill_content=skill_content, + failure_context=failure_context, + ) + except Exception as exc: + self._log.warning( + "skill_mutation.evaluator_failed", + candidate_id=candidate.id, + error=str(exc), + ) + return + + if result is None: + return + + should_promote = result.passed and result.score >= self._auto_promote_threshold + + if should_promote: + # Record evaluation then promote in one sweep + await svc.evaluate_candidate( + owner=owner, + candidate_id=candidate.id, + passed=True, + score=result.score, + report=result.reasoning, + evaluated_by="system:evolution", + ) + try: + await svc.promote_candidate( + owner=owner, + candidate_id=candidate.id, + stage=SkillReleaseStage.CANARY, + release_mode=SkillReleaseMode.AUTO, + ) + self._log.info( + "skill_mutation.auto_promoted", + candidate_id=candidate.id, + score=result.score, + ) + except Exception as exc: + self._log.warning( + "skill_mutation.auto_promote_failed", + candidate_id=candidate.id, + error=str(exc), + ) + else: + self._log.info( + "skill_mutation.evaluation_below_threshold", + candidate_id=candidate.id, + passed=result.passed, + score=result.score, + threshold=self._auto_promote_threshold, + ) + + +def _decode_conditions(json_str: str | None) -> list[str]: + """Parse a preconditions/postconditions JSON string into a list of strings.""" + if not json_str: + return [] + try: + raw = json.loads(json_str) + if isinstance(raw, list): + return [str(x) for x in raw] + if isinstance(raw, dict): + return [f"{k}: {v}" for k, v in raw.items()] + except Exception: + pass + return [] diff --git a/pkgs/bay/app/services/skills/evolution/evaluator.py b/pkgs/bay/app/services/skills/evolution/evaluator.py new file mode 100644 index 0000000..1646b55 --- /dev/null +++ b/pkgs/bay/app/services/skills/evolution/evaluator.py @@ -0,0 +1,84 @@ +"""GoalConditionedEvaluator — LLM-judge for mutation candidates. + +The evaluator wraps an LLM client that implements: + async def evaluate_mutation( + *, + goal: str, + rubric_text: str, + skill_content: str, + failure_context: str, + ) -> EvaluationResult + +EvaluationResult.score is clamped to [0.0, 1.0] regardless of what the LLM returns. +On any LLM failure the evaluator returns None so callers can degrade gracefully. +""" + +from __future__ import annotations + +import structlog +from pydantic import BaseModel + +from app.services.skills.evolution.rubric import SkillRubric + +logger = structlog.get_logger() + + +class EvaluationResult(BaseModel): + """Outcome of a single mutation evaluation.""" + + passed: bool + score: float # always clamped to [0.0, 1.0] + reasoning: str + + def model_post_init(self, __context) -> None: # noqa: ANN001 + object.__setattr__(self, "score", max(0.0, min(1.0, self.score))) + + +class GoalConditionedEvaluator: + """Evaluates a mutation candidate against the declared goal and rubric. + + Parameters + ---------- + llm_client: + Any object with ``async def evaluate_mutation(...)`` → EvaluationResult. + """ + + def __init__(self, *, llm_client) -> None: + self._llm = llm_client + self._log = logger.bind(component="goal_conditioned_evaluator") + + async def evaluate( + self, + *, + goal: str, + rubric: SkillRubric | None, + skill_content: str, + failure_context: str, + ) -> EvaluationResult | None: + """Evaluate *skill_content* against *goal*. + + Returns ``None`` on LLM failure so callers can degrade gracefully. + """ + rubric_text = rubric.to_text() if rubric is not None else "(no rubric provided)" + try: + result: EvaluationResult = await self._llm.evaluate_mutation( + goal=goal, + rubric_text=rubric_text, + skill_content=skill_content, + failure_context=failure_context, + ) + # Clamp score defensively (EvaluationResult.model_post_init also does this, + # but the LLM client may return a plain dict or bypass validation) + clamped = EvaluationResult( + passed=result.passed, + score=max(0.0, min(1.0, result.score)), + reasoning=result.reasoning, + ) + return clamped + except Exception as exc: + self._log.warning( + "goal_conditioned_evaluator.llm_failed", + goal=goal[:80], + error=str(exc), + ) + return None diff --git a/pkgs/bay/app/services/skills/evolution/lifecycle.py b/pkgs/bay/app/services/skills/evolution/lifecycle.py new file mode 100644 index 0000000..6b74174 --- /dev/null +++ b/pkgs/bay/app/services/skills/evolution/lifecycle.py @@ -0,0 +1,172 @@ +"""Evolution scheduler lifecycle — init, run, shutdown.""" + +from __future__ import annotations + +import asyncio + +import structlog + +from app.config import EvolutionConfig, get_settings +from app.db.session import get_async_session +from app.services.skills.evolution.agent import SkillMutationAgent +from app.services.skills.evolution.evaluator import GoalConditionedEvaluator +from app.services.skills.evolution.llm import ( + LlmEvolutionClient, + make_evaluator_client, + make_rubric_client, +) +from app.services.skills.evolution.meta_prompt import MetaPromptService +from app.services.skills.evolution.rubric import RubricGenerator +from app.services.skills.evolution.scheduler import EvolutionCycleResult, EvolutionScheduler + +logger = structlog.get_logger() + + +class EvolutionSchedulerRunner: + """Wraps EvolutionScheduler in a periodic background task.""" + + def __init__(self, config: EvolutionConfig) -> None: + self._config = config + self._running = False + self._task: asyncio.Task | None = None + self._run_lock = asyncio.Lock() + self._log = logger.bind(service="evolution_scheduler_runner") + + @property + def is_running(self) -> bool: + return self._running + + async def run_once(self) -> EvolutionCycleResult: + async with self._run_lock: + return await self._run_cycle() + + async def _run_cycle(self) -> EvolutionCycleResult: + settings = get_settings() + config = settings.evolution + + if not config.enabled: + return EvolutionCycleResult() + + if not config.llm.enabled: + self._log.debug("skills.evolution.llm.disabled") + return EvolutionCycleResult() + + async with get_async_session() as db_session: + # Seed meta-prompts on first run + meta_svc = MetaPromptService(db_session) + await meta_svc.seed_defaults() + + llm_client = LlmEvolutionClient(config.llm) + rubric_generator = RubricGenerator(llm_client=make_rubric_client(config.llm)) + evaluator = GoalConditionedEvaluator(llm_client=make_evaluator_client(config.llm)) + agent = SkillMutationAgent( + db_session=db_session, + llm_client=llm_client, + max_recent_outcomes=config.max_recent_outcomes, + evaluator=evaluator, + auto_promote_threshold=config.auto_promote_threshold, + rubric_generator=rubric_generator, + ) + scheduler = EvolutionScheduler( + db_session=db_session, + config=config, + mutation_agent=agent, + ) + result = await scheduler.run_cycle() + self._log.info( + "skills.evolution.cycle_complete", + attempted=result.mutations_attempted, + succeeded=result.mutations_succeeded, + ) + return result + + async def start(self) -> None: + if self._running: + self._log.warning("skills.evolution.scheduler.already_running") + return + self._running = True + self._task = asyncio.create_task(self._background_loop()) + self._log.info( + "skills.evolution.scheduler.started", + interval_seconds=self._config.interval_seconds, + ) + + async def stop(self) -> None: + if not self._running: + return + self._running = False + if self._task is not None: + self._task.cancel() + try: + await self._task + except asyncio.CancelledError: + pass + self._task = None + self._log.info("skills.evolution.scheduler.stopped") + + async def _background_loop(self) -> None: + while self._running: + try: + await asyncio.sleep(self._config.interval_seconds) + if self._running: + await self._run_cycle() + except asyncio.CancelledError: + break + except Exception as exc: + self._log.exception( + "skills.evolution.scheduler.cycle_error", + error=str(exc), + ) + + +# --------------------------------------------------------------------------- +# Module-level singleton (matches pattern of browser_learning lifecycle) +# --------------------------------------------------------------------------- + +_evolution_runner: EvolutionSchedulerRunner | None = None + + +async def init_evolution_scheduler() -> EvolutionSchedulerRunner: + """Initialize evolution scheduler and optionally start it.""" + global _evolution_runner + + settings = get_settings() + config = settings.evolution + _evolution_runner = EvolutionSchedulerRunner(config=config) + + logger.info( + "skills.evolution.scheduler.init", + enabled=config.enabled, + run_on_startup=config.run_on_startup, + interval_seconds=config.interval_seconds, + min_failures=config.min_failures_to_trigger, + max_mutations=config.max_mutations_per_cycle, + ) + + if not config.enabled: + return _evolution_runner + + if config.run_on_startup: + try: + await _evolution_runner.run_once() + except Exception as exc: + logger.exception( + "skills.evolution.scheduler.startup_cycle_failed", + error=str(exc), + ) + + await _evolution_runner.start() + return _evolution_runner + + +async def shutdown_evolution_scheduler() -> None: + """Stop evolution scheduler.""" + global _evolution_runner + if _evolution_runner is not None: + await _evolution_runner.stop() + _evolution_runner = None + + +def get_evolution_scheduler() -> EvolutionSchedulerRunner | None: + """Get global evolution scheduler instance.""" + return _evolution_runner diff --git a/pkgs/bay/app/services/skills/evolution/llm.py b/pkgs/bay/app/services/skills/evolution/llm.py new file mode 100644 index 0000000..12da2e1 --- /dev/null +++ b/pkgs/bay/app/services/skills/evolution/llm.py @@ -0,0 +1,249 @@ +"""LLM client for skill evolution — thin, mockable boundary. + +Three tasks, one HTTP interface: +- generate_mutation → SkillMutationAgent +- generate_rubric → RubricGenerator +- evaluate_mutation → GoalConditionedEvaluator + +Each task can use a different model via per-task overrides in LlmEvolutionConfig. +Use ``make_rubric_client`` / ``make_evaluator_client`` to get task-specific clients. +""" + +from __future__ import annotations + +import json +from dataclasses import dataclass +from typing import Any + +import httpx +import structlog + +from app.config import LlmEvolutionConfig + +logger = structlog.get_logger() + + +@dataclass +class MutationOutput: + """Structured output from the LLM mutation call.""" + + summary: str + usage_notes: str + preconditions: list[str] + postconditions: list[str] + mutation_reasoning: str + + +_RESPONSE_SCHEMA: dict[str, Any] = { + "type": "object", + "required": ["summary", "usage_notes", "preconditions", "postconditions", "mutation_reasoning"], + "additionalProperties": False, + "properties": { + "summary": {"type": "string"}, + "usage_notes": {"type": "string"}, + "preconditions": {"type": "array", "items": {"type": "string"}}, + "postconditions": {"type": "array", "items": {"type": "string"}}, + "mutation_reasoning": {"type": "string"}, + }, +} + +_SYSTEM_PROMPT = """\ +You are an expert at improving AI agent skill instructions. +You receive: +1. The current skill content (summary, usage notes, pre/postconditions) +2. Recent failure reports from executions of this skill +3. A mutation strategy to apply + +Your task: rewrite the skill content to address the failures, applying the given strategy. +Return strict JSON only. The mutation_reasoning field MUST explain specifically what you changed \ +and why. +No markdown, no explanation outside the JSON object.\ +""" + + +_RUBRIC_RESPONSE_SCHEMA: dict[str, Any] = { + "type": "object", + "required": ["summary", "success_criteria", "failure_indicators", "evaluation_focus"], + "additionalProperties": False, + "properties": { + "summary": {"type": "string"}, + "success_criteria": {"type": "array", "items": {"type": "string"}}, + "failure_indicators": {"type": "array", "items": {"type": "string"}}, + "evaluation_focus": {"type": "string"}, + }, +} + +_RUBRIC_SYSTEM_PROMPT = """\ +You are an expert at writing evaluation rubrics for AI agent skills. +You receive a natural language goal for a skill. +Your task: create a structured rubric with clear success criteria and failure indicators. +Return strict JSON only matching the schema. Be concise and specific.\ +""" + +_EVAL_RESPONSE_SCHEMA: dict[str, Any] = { + "type": "object", + "required": ["passed", "score", "reasoning"], + "additionalProperties": False, + "properties": { + "passed": {"type": "boolean"}, + "score": {"type": "number"}, + "reasoning": {"type": "string"}, + }, +} + +_EVAL_SYSTEM_PROMPT = """\ +You are an expert evaluator of AI agent skill instructions. +You receive: +1. The goal the skill must satisfy +2. An evaluation rubric (success criteria, failure indicators, evaluation focus) +3. The current skill content (the mutated candidate) +4. Recent failure context from previous executions + +Your task: judge whether the mutated skill content is likely to satisfy the goal. +Return strict JSON only. +- passed: true only if the mutation clearly addresses the stated goal and rubric +- score: float between 0.0 (completely wrong) and 1.0 (perfectly correct) +- reasoning: 1-3 sentences explaining your verdict\ +""" + + +class LlmEvolutionClient: + """OpenAI-compatible HTTP client for skill evolution tasks.""" + + def __init__( + self, + config: LlmEvolutionConfig, + *, + model_override: str | None = None, + api_base_override: str | None = None, + api_key_override: str | None = None, + ) -> None: + self._config = config + self._model = model_override or config.model + self._api_base = api_base_override or config.api_base + self._api_key = api_key_override if api_key_override is not None else config.api_key + self._log = logger.bind(component="llm_evolution_client") + + def _headers(self) -> dict[str, str]: + headers = {"Content-Type": "application/json"} + if self._api_key: + headers["Authorization"] = f"Bearer {self._api_key}" + return headers + + async def _post_json_schema( + self, + prompt: str, + system: str, + schema: dict[str, Any], + schema_name: str, + ) -> dict[str, Any]: + endpoint = f"{self._api_base.rstrip('/')}/chat/completions" + request_body = { + "model": self._model, + "max_tokens": self._config.max_tokens, + "temperature": 0.7, + "messages": [ + {"role": "system", "content": system}, + {"role": "user", "content": prompt}, + ], + "response_format": { + "type": "json_schema", + "json_schema": { + "name": schema_name, + "strict": True, + "schema": schema, + }, + }, + } + timeout = httpx.Timeout(self._config.timeout_seconds) + async with httpx.AsyncClient(timeout=timeout) as client: + resp = await client.post(endpoint, headers=self._headers(), json=request_body) + resp.raise_for_status() + data = resp.json() + content = data["choices"][0]["message"]["content"] + if isinstance(content, list): + content = "".join( + item.get("text", "") for item in content if isinstance(item, dict) + ) + return json.loads(content) + + async def generate_mutation(self, prompt: str) -> MutationOutput: + parsed = await self._post_json_schema( + prompt=prompt, + system=_SYSTEM_PROMPT, + schema=_RESPONSE_SCHEMA, + schema_name="skill_mutation_output", + ) + return MutationOutput( + summary=str(parsed["summary"]), + usage_notes=str(parsed["usage_notes"]), + preconditions=[str(p) for p in parsed.get("preconditions", [])], + postconditions=[str(p) for p in parsed.get("postconditions", [])], + mutation_reasoning=str(parsed["mutation_reasoning"]), + ) + + async def generate_rubric(self, goal: str) -> "SkillRubric": # noqa: F821 + """Generate a SkillRubric from a natural language goal.""" + from app.services.skills.evolution.rubric import SkillRubric # avoid circular + + parsed = await self._post_json_schema( + prompt=f"Goal: {goal}", + system=_RUBRIC_SYSTEM_PROMPT, + schema=_RUBRIC_RESPONSE_SCHEMA, + schema_name="skill_rubric_output", + ) + return SkillRubric( + summary=str(parsed["summary"]), + success_criteria=[str(c) for c in parsed.get("success_criteria", [])], + failure_indicators=[str(f) for f in parsed.get("failure_indicators", [])], + evaluation_focus=str(parsed["evaluation_focus"]), + ) + + async def evaluate_mutation( + self, + *, + goal: str, + rubric_text: str, + skill_content: str, + failure_context: str, + ) -> "EvaluationResult": # noqa: F821 + """Judge a mutated skill against goal and rubric.""" + from app.services.skills.evolution.evaluator import EvaluationResult # avoid circular + + user_prompt = ( + f"## Goal\n{goal}\n\n" + f"## Rubric\n{rubric_text}\n\n" + f"## Skill Content\n{skill_content}\n\n" + f"## Recent Failures\n{failure_context}" + ) + parsed = await self._post_json_schema( + prompt=user_prompt, + system=_EVAL_SYSTEM_PROMPT, + schema=_EVAL_RESPONSE_SCHEMA, + schema_name="skill_evaluation_output", + ) + return EvaluationResult( + passed=bool(parsed["passed"]), + score=float(parsed["score"]), + reasoning=str(parsed["reasoning"]), + ) + + +def make_rubric_client(config: LlmEvolutionConfig) -> LlmEvolutionClient: + """Return an LlmEvolutionClient configured for rubric generation.""" + return LlmEvolutionClient( + config, + model_override=config.rubric_model, + api_base_override=config.rubric_api_base, + api_key_override=config.rubric_api_key, + ) + + +def make_evaluator_client(config: LlmEvolutionConfig) -> LlmEvolutionClient: + """Return an LlmEvolutionClient configured for mutation evaluation.""" + return LlmEvolutionClient( + config, + model_override=config.evaluator_model, + api_base_override=config.evaluator_api_base, + api_key_override=config.evaluator_api_key, + ) diff --git a/pkgs/bay/app/services/skills/evolution/meta_prompt.py b/pkgs/bay/app/services/skills/evolution/meta_prompt.py new file mode 100644 index 0000000..90dbba3 --- /dev/null +++ b/pkgs/bay/app/services/skills/evolution/meta_prompt.py @@ -0,0 +1,123 @@ +"""MetaPromptService — manages the archive of mutation instructions.""" + +from __future__ import annotations + +import random +import uuid +from typing import Sequence + +import structlog +from sqlalchemy.ext.asyncio import AsyncSession +from sqlmodel import select + +from app.models.skill import MetaPrompt +from app.utils.datetime import utcnow + +logger = structlog.get_logger() + +# Default mutation instructions seeded on first run. +# Each is a distinct strategy for improving a skill. +DEFAULT_INSTRUCTIONS: list[str] = [ + ( + "Make the instructions clearer and more explicit. " + "Describe each step in plain language a non-expert could follow. " + "Add concrete examples of what to look for on the page." + ), + ( + "Based on the failure context, identify what went wrong and add explicit " + "error-handling or fallback steps. " + "For example: if an element is not found, suggest alternative selectors or wait strategies." + ), + ( + "Simplify the instructions. Remove any unnecessary steps. " + "Make each action more direct and focused on the essential outcome." + ), + ( + "Add more specific selector strategies and fallback approaches for UI elements " + "that may have changed due to a site redesign. " + "Prefer semantic selectors (role, label, text) over brittle CSS class selectors." + ), + ( + "Rewrite the instructions from the perspective of a human expert who has seen " + "the naive approach fail. What would they do differently? " + "Focus on robustness and handling of dynamic or delayed content." + ), +] + + +class MetaPromptService: + """Service for managing the MetaPrompt archive.""" + + def __init__(self, db_session: AsyncSession) -> None: + self._db = db_session + self._log = logger.bind(component="meta_prompt_service") + + async def seed_defaults(self) -> int: + """Seed default instructions if none exist. Returns count of default prompts.""" + result = await self._db.execute( + select(MetaPrompt).where(MetaPrompt.is_default.is_(True)) + ) + existing = result.scalars().all() + if existing: + return len(existing) + + now = utcnow() + for instruction in DEFAULT_INSTRUCTIONS: + prompt = MetaPrompt( + id=f"mp-{uuid.uuid4().hex[:12]}", + instruction=instruction, + is_default=True, + usage_count=0, + success_count=0, + created_at=now, + updated_at=now, + ) + self._db.add(prompt) + + await self._db.commit() + self._log.info("meta_prompt.seeded_defaults", count=len(DEFAULT_INSTRUCTIONS)) + return len(DEFAULT_INSTRUCTIONS) + + async def list_all(self) -> Sequence[MetaPrompt]: + result = await self._db.execute(select(MetaPrompt)) + return result.scalars().all() + + async def get_prompt(self, prompt_id: str) -> MetaPrompt | None: + result = await self._db.execute( + select(MetaPrompt).where(MetaPrompt.id == prompt_id) + ) + return result.scalars().first() + + async def sample_prompt(self) -> MetaPrompt | None: + """Sample a prompt weighted by success rate. + + Weight = (success_count + 1) / (usage_count + 2) + This gives new/unused prompts a fair chance while rewarding effective ones. + """ + result = await self._db.execute(select(MetaPrompt)) + prompts = result.scalars().all() + if not prompts: + return None + + weights = [ + (p.success_count + 1) / (p.usage_count + 2) for p in prompts + ] + return random.choices(prompts, weights=weights, k=1)[0] + + async def record_usage(self, prompt_id: str) -> None: + prompt = await self.get_prompt(prompt_id) + if prompt is None: + return + prompt.usage_count += 1 + prompt.updated_at = utcnow() + self._db.add(prompt) + await self._db.commit() + + async def record_success(self, prompt_id: str) -> None: + prompt = await self.get_prompt(prompt_id) + if prompt is None: + return + prompt.success_count += 1 + prompt.updated_at = utcnow() + self._db.add(prompt) + await self._db.commit() diff --git a/pkgs/bay/app/services/skills/evolution/rubric.py b/pkgs/bay/app/services/skills/evolution/rubric.py new file mode 100644 index 0000000..fd69c3a --- /dev/null +++ b/pkgs/bay/app/services/skills/evolution/rubric.py @@ -0,0 +1,68 @@ +"""SkillRubric dataclass and RubricGenerator. + +RubricGenerator wraps an LLM client that implements: + async def generate_rubric(goal: str) -> SkillRubric + +It is intentionally thin — all strategy lives in the LLM client, +which is constructed in lifecycle.py using per-task model overrides. +""" + +from __future__ import annotations + +import structlog +from pydantic import BaseModel + +logger = structlog.get_logger() + + +class SkillRubric(BaseModel): + """Structured rubric for goal-conditioned evaluation. + + Generated once per SkillGoal and cached in SkillGoal.rubric_json. + """ + + summary: str + success_criteria: list[str] + failure_indicators: list[str] + evaluation_focus: str + + def to_text(self) -> str: + """Render rubric as a plain-text block for inclusion in LLM prompts.""" + lines = [ + f"Summary: {self.summary}", + "", + "Success criteria:", + *[f" - {c}" for c in self.success_criteria], + "", + "Failure indicators:", + *[f" - {f}" for f in self.failure_indicators], + "", + f"Evaluation focus: {self.evaluation_focus}", + ] + return "\n".join(lines) + + +class RubricGenerator: + """Generates a SkillRubric from a natural language goal via an LLM client. + + Parameters + ---------- + llm_client: + Any object with ``async def generate_rubric(goal: str) -> SkillRubric``. + """ + + def __init__(self, *, llm_client) -> None: + self._llm = llm_client + self._log = logger.bind(component="rubric_generator") + + async def generate(self, goal: str) -> SkillRubric | None: + """Generate a rubric for *goal*. + + Returns ``None`` on any LLM failure so callers can proceed without one. + """ + try: + rubric: SkillRubric = await self._llm.generate_rubric(goal) + return rubric + except Exception as exc: + self._log.warning("rubric_generator.llm_failed", goal=goal[:80], error=str(exc)) + return None diff --git a/pkgs/bay/app/services/skills/evolution/scheduler.py b/pkgs/bay/app/services/skills/evolution/scheduler.py new file mode 100644 index 0000000..2b4999f --- /dev/null +++ b/pkgs/bay/app/services/skills/evolution/scheduler.py @@ -0,0 +1,131 @@ +"""EvolutionScheduler — periodic scan and mutation triggering.""" + +from __future__ import annotations + +from dataclasses import dataclass, field + +import structlog +from sqlalchemy.ext.asyncio import AsyncSession +from sqlmodel import select + +from app.config import EvolutionConfig +from app.models.skill import SkillCandidate, SkillGoal, SkillOutcome + +logger = structlog.get_logger() + + +@dataclass +class EvolutionCycleResult: + """Result of a single evolution cycle.""" + + mutations_attempted: int = field(default=0) + mutations_succeeded: int = field(default=0) + + +class EvolutionScheduler: + """Scans for skills with sufficient failures and triggers mutations up to a budget cap.""" + + def __init__( + self, + *, + db_session: AsyncSession, + config: EvolutionConfig, + mutation_agent, + ) -> None: + self._db = db_session + self._config = config + self._agent = mutation_agent + self._log = logger.bind(component="evolution_scheduler") + + async def run_cycle(self) -> EvolutionCycleResult: + """Run one evolution cycle. Returns a summary of what happened.""" + result = EvolutionCycleResult() + + if not self._config.enabled: + self._log.debug("evolution_scheduler.disabled") + return result + + # All declared skill goals define the evolution scope + goals_result = await self._db.execute(select(SkillGoal)) + goals = list(goals_result.scalars().all()) + + budget = self._config.max_mutations_per_cycle + + for goal in goals: + if result.mutations_attempted >= budget: + self._log.debug("evolution_scheduler.budget_exhausted", budget=budget) + break + + # Count recent failures for this skill within the lookback window + failures_result = await self._db.execute( + select(SkillOutcome) + .where( + SkillOutcome.owner == goal.owner, + SkillOutcome.skill_key == goal.skill_key, + SkillOutcome.outcome == "failure", + ) + .order_by(SkillOutcome.created_at.desc()) + .limit(self._config.max_recent_outcomes) + ) + recent_failures = list(failures_result.scalars().all()) + + if len(recent_failures) < self._config.min_failures_to_trigger: + self._log.debug( + "evolution_scheduler.skip_insufficient_failures", + owner=goal.owner, + skill_key=goal.skill_key, + failure_count=len(recent_failures), + min_required=self._config.min_failures_to_trigger, + ) + continue + + # Dedup: skip if an evolution candidate was already created after the latest failure. + # This prevents the scheduler from firing again every cycle on the same failure set. + latest_failure = recent_failures[0] # already sorted desc + latest_mutation_result = await self._db.execute( + select(SkillCandidate) + .where( + SkillCandidate.owner == goal.owner, + SkillCandidate.skill_key == goal.skill_key, + SkillCandidate.created_by == "system:evolution", + SkillCandidate.is_deleted.is_(False), + ) + .order_by(SkillCandidate.created_at.desc()) + .limit(1) + ) + latest_mutation = latest_mutation_result.scalars().first() + already_mutated = ( + latest_mutation is not None + and latest_mutation.created_at >= latest_failure.created_at + ) + if already_mutated: + self._log.debug( + "evolution_scheduler.skip_already_mutated", + owner=goal.owner, + skill_key=goal.skill_key, + last_mutation_at=str(latest_mutation.created_at), + last_failure_at=str(latest_failure.created_at), + ) + continue + + self._log.info( + "evolution_scheduler.triggering_mutation", + owner=goal.owner, + skill_key=goal.skill_key, + failure_count=len(recent_failures), + ) + + result.mutations_attempted += 1 + new_candidate_id = await self._agent.mutate( + owner=goal.owner, + skill_key=goal.skill_key, + ) + if new_candidate_id is not None: + result.mutations_succeeded += 1 + + self._log.info( + "evolution_scheduler.cycle_complete", + attempted=result.mutations_attempted, + succeeded=result.mutations_succeeded, + ) + return result diff --git a/pkgs/bay/app/services/skills/extraction/__init__.py b/pkgs/bay/app/services/skills/extraction/__init__.py new file mode 100644 index 0000000..4959b2f --- /dev/null +++ b/pkgs/bay/app/services/skills/extraction/__init__.py @@ -0,0 +1,26 @@ +"""Browser learning extraction strategies.""" + +from app.services.skills.extraction.base import ( + ExtractionContext, + ExtractionResult, + ExtractionStrategy, + VariableSpec, + compute_payload_hash, +) +from app.services.skills.extraction.llm_strategy import LlmAssistedExtractionStrategy +from app.services.skills.extraction.rule_strategy import ( + READ_ONLY_PREFIXES, + RuleBasedExtractionStrategy, +) + +__all__ = [ + "ExtractionContext", + "ExtractionResult", + "ExtractionStrategy", + "VariableSpec", + "compute_payload_hash", + "READ_ONLY_PREFIXES", + "RuleBasedExtractionStrategy", + "LlmAssistedExtractionStrategy", +] + diff --git a/pkgs/bay/app/services/skills/extraction/base.py b/pkgs/bay/app/services/skills/extraction/base.py new file mode 100644 index 0000000..8a3a13f --- /dev/null +++ b/pkgs/bay/app/services/skills/extraction/base.py @@ -0,0 +1,78 @@ +"""Extraction strategy interfaces and shared data structures.""" + +from __future__ import annotations + +import hashlib +import json +from dataclasses import dataclass +from typing import Any, Protocol, runtime_checkable + + +def compute_payload_hash(*, steps: list[dict[str, Any]]) -> str: + """Compute a deterministic hash from the ordered command sequence.""" + commands = [str(step.get("cmd", "")).strip() for step in steps] + canonical = json.dumps(commands, ensure_ascii=False, separators=(",", ":")) + return hashlib.sha256(canonical.encode("utf-8")).hexdigest() + + +@dataclass(slots=True) +class VariableSpec: + """Variable declaration for a parameterizable command argument.""" + + type: str + default_value: Any + action_index: int + arg_position: int + + def to_payload(self) -> dict[str, Any]: + return { + "type": self.type, + "default_value": self.default_value, + "action_index": self.action_index, + "arg_position": self.arg_position, + } + + +@dataclass(slots=True) +class ExtractionContext: + """Execution context passed into extraction strategies.""" + + owner: str + execution_id: str + sandbox_id: str | None + code: str + description: str | None + tags: str | None + + +@dataclass(slots=True) +class ExtractionResult: + """Structured extraction output consumed by the learning pipeline.""" + + skill_key: str + steps: list[dict[str, Any]] + description: str | None = None + variables: dict[str, VariableSpec] | None = None + payload_hash: str | None = None + + def __post_init__(self) -> None: + if self.payload_hash is None: + self.payload_hash = compute_payload_hash(steps=self.steps) + + def variables_payload(self) -> dict[str, dict[str, Any]] | None: + if not self.variables: + return None + return {name: spec.to_payload() for name, spec in self.variables.items()} + + +@runtime_checkable +class ExtractionStrategy(Protocol): + """Extract structured candidate results from actionable segments.""" + + async def extract( + self, + *, + segments: list[list[dict[str, Any]]], + context: ExtractionContext, + ) -> list[ExtractionResult]: ... + diff --git a/pkgs/bay/app/services/skills/extraction/llm_strategy.py b/pkgs/bay/app/services/skills/extraction/llm_strategy.py new file mode 100644 index 0000000..5734ba2 --- /dev/null +++ b/pkgs/bay/app/services/skills/extraction/llm_strategy.py @@ -0,0 +1,255 @@ +"""LLM-assisted extraction strategy with automatic fallback.""" + +from __future__ import annotations + +import json +from typing import Any + +import httpx +import structlog + +from app.config import LlmExtractionConfig +from app.services.skills.extraction.base import ( + ExtractionContext, + ExtractionResult, + VariableSpec, +) +from app.services.skills.extraction.rule_strategy import RuleBasedExtractionStrategy + + +class LlmAssistedExtractionStrategy: + """Semantic extraction backed by an OpenAI-compatible HTTP API.""" + + def __init__( + self, + *, + config: LlmExtractionConfig, + fallback: RuleBasedExtractionStrategy, + ) -> None: + self._config = config + self._fallback = fallback + self._log = structlog.get_logger().bind(component="browser_learning_extraction_llm") + + async def extract( + self, + *, + segments: list[list[dict[str, Any]]], + context: ExtractionContext, + ) -> list[ExtractionResult]: + if not segments: + return [] + try: + return await self._extract_via_llm(segments=segments, context=context) + except httpx.TimeoutException: + return await self._fallback_with_reason( + reason="timeout", + segments=segments, + context=context, + ) + except (httpx.RequestError, httpx.HTTPStatusError): + return await self._fallback_with_reason( + reason="connection_error", + segments=segments, + context=context, + ) + except (KeyError, TypeError, ValueError, json.JSONDecodeError): + return await self._fallback_with_reason( + reason="parse_error", + segments=segments, + context=context, + ) + + async def _extract_via_llm( + self, + *, + segments: list[list[dict[str, Any]]], + context: ExtractionContext, + ) -> list[ExtractionResult]: + response_payload = await self._call_llm(segments=segments, context=context) + + choices = response_payload["choices"] + first = choices[0] + message = first["message"] + content = message["content"] + + if isinstance(content, list): + content_text = "".join( + str(item.get("text", "")) + for item in content + if isinstance(item, dict) + ) + elif isinstance(content, str): + content_text = content + else: + raise ValueError("Unsupported LLM response content type") + + parsed = json.loads(content_text) + results_raw = parsed["results"] + if not isinstance(results_raw, list): + raise ValueError("Invalid extraction response: results must be a list") + + extracted: list[ExtractionResult] = [] + for item in results_raw: + if not isinstance(item, dict): + raise ValueError("Invalid extraction response: result item must be object") + + skill_key = str(item["skill_key"]).strip() + if not skill_key: + raise ValueError("Invalid extraction response: skill_key cannot be empty") + + steps_raw = item["steps"] + if not isinstance(steps_raw, list): + raise ValueError("Invalid extraction response: steps must be a list") + steps = [step for step in steps_raw if isinstance(step, dict)] + + desc_raw = item.get("description") + description = str(desc_raw).strip() if desc_raw else None + if description == "": + description = None + + variables = self._parse_variables(item.get("variables")) + extracted.append( + ExtractionResult( + skill_key=skill_key, + description=description, + steps=steps, + variables=variables, + ) + ) + + return extracted + + async def _call_llm( + self, + *, + segments: list[list[dict[str, Any]]], + context: ExtractionContext, + ) -> dict[str, Any]: + endpoint = f"{self._config.api_base.rstrip('/')}/chat/completions" + headers: dict[str, str] = {"Content-Type": "application/json"} + if self._config.api_key: + headers["Authorization"] = f"Bearer {self._config.api_key}" + + prompt_payload = { + "owner": context.owner, + "execution_id": context.execution_id, + "sandbox_id": context.sandbox_id, + "description": context.description, + "tags": context.tags, + "segments": segments, + } + request_body = { + "model": self._config.model, + "max_tokens": self._config.max_tokens, + "temperature": 0, + "messages": [ + { + "role": "system", + "content": ( + "You extract browser skill candidates. Return strict JSON only." + ), + }, + { + "role": "user", + "content": json.dumps(prompt_payload, ensure_ascii=False), + }, + ], + "response_format": { + "type": "json_schema", + "json_schema": { + "name": "skill_extraction_results", + "strict": True, + "schema": self._response_schema(), + }, + }, + } + + timeout = httpx.Timeout(self._config.timeout_seconds) + async with httpx.AsyncClient(timeout=timeout) as client: + resp = await client.post(endpoint, headers=headers, json=request_body) + resp.raise_for_status() + data = resp.json() + if not isinstance(data, dict): + raise ValueError("Invalid LLM response payload") + return data + + @staticmethod + def _parse_variables(raw: Any) -> dict[str, VariableSpec] | None: + if raw is None: + return None + if not isinstance(raw, dict): + raise ValueError("Invalid variables payload") + + variables: dict[str, VariableSpec] = {} + for name, spec_raw in raw.items(): + if not isinstance(name, str) or not name.strip(): + raise ValueError("Variable name must be non-empty string") + if not isinstance(spec_raw, dict): + raise ValueError("Variable spec must be object") + variables[name] = VariableSpec( + type=str(spec_raw["type"]), + default_value=spec_raw.get("default_value"), + action_index=int(spec_raw["action_index"]), + arg_position=int(spec_raw["arg_position"]), + ) + return variables or None + + async def _fallback_with_reason( + self, + *, + reason: str, + segments: list[list[dict[str, Any]]], + context: ExtractionContext, + ) -> list[ExtractionResult]: + self._log.warning( + "skills.browser.extraction.llm_fallback", + reason=reason, + execution_id=context.execution_id, + ) + return await self._fallback.extract(segments=segments, context=context) + + @staticmethod + def _response_schema() -> dict[str, Any]: + return { + "type": "object", + "required": ["results"], + "additionalProperties": False, + "properties": { + "results": { + "type": "array", + "items": { + "type": "object", + "required": ["skill_key", "steps"], + "additionalProperties": False, + "properties": { + "skill_key": {"type": "string"}, + "description": {"type": "string"}, + "steps": { + "type": "array", + "items": {"type": "object"}, + }, + "variables": { + "type": "object", + "additionalProperties": { + "type": "object", + "required": [ + "type", + "default_value", + "action_index", + "arg_position", + ], + "additionalProperties": False, + "properties": { + "type": {"type": "string"}, + "default_value": {}, + "action_index": {"type": "integer"}, + "arg_position": {"type": "integer"}, + }, + }, + }, + }, + }, + }, + }, + } + diff --git a/pkgs/bay/app/services/skills/extraction/rule_strategy.py b/pkgs/bay/app/services/skills/extraction/rule_strategy.py new file mode 100644 index 0000000..33f4589 --- /dev/null +++ b/pkgs/bay/app/services/skills/extraction/rule_strategy.py @@ -0,0 +1,176 @@ +"""Rule-based extraction strategy for browser learning.""" + +from __future__ import annotations + +import re +import shlex +from typing import Any + +from app.services.skills.extraction.base import ( + ExtractionContext, + ExtractionResult, + VariableSpec, +) + +READ_ONLY_PREFIXES = ( + "snapshot", + "get ", + "is ", + "wait", + "cookies", + "storage", + "network requests", + "tab", + "frame", + "dialog", +) + + +class RuleBasedExtractionStrategy: + """Heuristic strategy preserving historical extraction behavior.""" + + def __init__(self, *, variable_extraction_enabled: bool = True) -> None: + self._variable_extraction_enabled = variable_extraction_enabled + + async def extract( + self, + *, + segments: list[list[dict[str, Any]]], + context: ExtractionContext, + ) -> list[ExtractionResult]: + skill_key = self.derive_skill_key(tags=context.tags, sandbox_id=context.sandbox_id) + description = context.description.strip() if context.description else None + if description == "": + description = None + + results: list[ExtractionResult] = [] + for segment in segments: + variables = ( + self.extract_variables(segment=segment) + if self._variable_extraction_enabled + else None + ) + results.append( + ExtractionResult( + skill_key=skill_key, + description=description, + steps=segment, + variables=variables, + ) + ) + return results + + @staticmethod + def extract_actionable_segments(*, steps: list[dict[str, Any]]) -> list[list[dict[str, Any]]]: + segments: list[list[dict[str, Any]]] = [] + current: list[dict[str, Any]] = [] + + for step in steps: + cmd = str(step.get("cmd", "")).strip() + failed = int(step.get("exit_code", 1)) != 0 + is_read_only = RuleBasedExtractionStrategy.is_read_only_command(cmd) + kind = str(step.get("kind", "individual_action")) + + if failed or is_read_only or kind != "individual_action" or not cmd: + if len(current) >= 2: + segments.append(current.copy()) + current.clear() + continue + current.append(step) + + if len(current) >= 2: + segments.append(current) + + return segments + + @staticmethod + def is_read_only_command(cmd: str) -> bool: + normalized = cmd.strip().lower() + return any(normalized.startswith(prefix) for prefix in READ_ONLY_PREFIXES) + + @staticmethod + def score_segment(*, segment: list[dict[str, Any]]) -> dict[str, Any]: + steps = len(segment) + samples = steps * 10 + replay_success = 1.0 if steps > 0 else 0.0 + score = min(0.99, 0.75 + 0.08 * steps) + p95_duration = 0 + return { + "score": round(score, 4), + "replay_success": round(replay_success, 4), + "samples": samples, + "error_rate": round(1.0 - replay_success, 4), + "p95_duration": p95_duration, + } + + @staticmethod + def derive_skill_key(*, tags: str | None, sandbox_id: str | None) -> str: + items = [part.strip() for part in (tags or "").split(",") if part.strip()] + for tag in items: + if tag.startswith("skill:") and len(tag) > len("skill:"): + return tag[len("skill:") :] + if sandbox_id: + return f"browser-{sandbox_id}" + return "browser-unknown" + + @staticmethod + def extract_variables( + *, + segment: list[dict[str, Any]], + ) -> dict[str, VariableSpec] | None: + variables: dict[str, VariableSpec] = {} + for action_index, step in enumerate(segment): + cmd = str(step.get("cmd", "")).strip() + if not cmd: + continue + + parsed = RuleBasedExtractionStrategy._parse_type_value(cmd=cmd) + if parsed is None: + continue + + variable_name = RuleBasedExtractionStrategy._derive_variable_name( + value=parsed, + existing=set(variables), + ) + variables[variable_name] = VariableSpec( + type="string", + default_value=parsed, + action_index=action_index, + arg_position=1, + ) + + return variables or None + + @staticmethod + def _parse_type_value(*, cmd: str) -> str | None: + try: + tokens = shlex.split(cmd) + except ValueError: + return None + + if len(tokens) < 3: + return None + if tokens[0].lower() != "type": + return None + + value = " ".join(tokens[2:]).strip() + if not value: + return None + return value + + @staticmethod + def _derive_variable_name(*, value: str, existing: set[str]) -> str: + base = re.sub(r"[^a-zA-Z0-9]+", "_", value).strip("_").lower() + if not base: + base = "value" + if base[0].isdigit(): + base = f"value_{base}" + base = base[:48].rstrip("_") or "value" + + candidate = base + suffix = 2 + while candidate in existing: + candidate = f"{base}_{suffix}" + suffix += 1 + return candidate + diff --git a/pkgs/bay/app/services/skills/scheduler.py b/pkgs/bay/app/services/skills/scheduler.py index c82ec21..a912494 100644 --- a/pkgs/bay/app/services/skills/scheduler.py +++ b/pkgs/bay/app/services/skills/scheduler.py @@ -20,26 +20,18 @@ SkillReleaseStage, SkillType, ) +from app.services.skills.extraction import ( + ExtractionContext, + ExtractionStrategy, + LlmAssistedExtractionStrategy, + RuleBasedExtractionStrategy, +) from app.services.skills.service import SkillLifecycleService from app.utils.datetime import utcnow logger = structlog.get_logger() -READ_ONLY_PREFIXES = ( - "snapshot", - "get ", - "is ", - "wait", - "cookies", - "storage", - "network requests", - "tab", - "frame", - "dialog", -) - - @dataclass class BrowserLearningCycleResult: """Summary of one browser learning scheduler cycle.""" @@ -68,6 +60,16 @@ def __init__( self._config = config self._auto_release_enabled = auto_release_enabled self._log = logger.bind(component="browser_learning") + self._rule_strategy = RuleBasedExtractionStrategy( + variable_extraction_enabled=self._config.extraction.variable_extraction_enabled, + ) + if self._config.extraction.llm.enabled: + self._extraction_strategy: ExtractionStrategy = LlmAssistedExtractionStrategy( + config=self._config.extraction.llm, + fallback=self._rule_strategy, + ) + else: + self._extraction_strategy = self._rule_strategy async def run_cycle(self) -> BrowserLearningCycleResult: result = BrowserLearningCycleResult() @@ -110,7 +112,8 @@ async def _process_execution( entry: ExecutionHistory, result: BrowserLearningCycleResult, ) -> bool: - segments = await self._extract_segments(entry=entry) + steps = await self._load_normalized_steps(entry=entry) + segments = self._rule_strategy.extract_actionable_segments(steps=steps) if not segments: await self._svc.set_execution_learning_status( execution_id=entry.id, @@ -120,28 +123,78 @@ async def _process_execution( ) return False - skill_key = self._derive_skill_key(entry=entry) - scenario_key = self._derive_scenario_key(entry=entry) + extraction_context = ExtractionContext( + owner=entry.owner, + execution_id=entry.id, + sandbox_id=entry.sandbox_id, + code=entry.code, + description=entry.description, + tags=entry.tags, + ) + extraction_results = await self._extraction_strategy.extract( + segments=segments, + context=extraction_context, + ) + if not extraction_results: + await self._svc.set_execution_learning_status( + execution_id=entry.id, + status=LearnStatus.SKIPPED, + error="no_actionable_segments", + processed_at=utcnow(), + ) + return False + + fallback_scenario_key = self._derive_scenario_key(entry=entry) + + for idx, extracted in enumerate(extraction_results): + if self._config.extraction.dedup_enabled: + duplicate = await self._svc.find_candidate_by_payload_hash( + owner=entry.owner, + skill_key=extracted.skill_key, + payload_hash=extracted.payload_hash, + ) + if duplicate is not None: + self._log.info( + "skills.browser.extraction.duplicate_skipped", + owner=entry.owner, + execution_id=entry.id, + skill_key=extracted.skill_key, + payload_hash=extracted.payload_hash, + duplicate_candidate_id=duplicate.id, + ) + continue + + segment_payload: dict[str, Any] = { + "skill_type": "browser", + "segment_index": idx, + "source_execution_id": entry.id, + "commands": [step["cmd"] for step in extracted.steps], + "steps": extracted.steps, + "payload_hash": extracted.payload_hash, + } + if extracted.description is not None: + segment_payload["description"] = extracted.description + variables_payload = extracted.variables_payload() + if variables_payload: + segment_payload["variables"] = variables_payload - for idx, segment in enumerate(segments): segment_blob = await self._svc.create_artifact_blob( owner=entry.owner, kind="browser_segment", - payload={ - "skill_type": "browser", - "segment_index": idx, - "source_execution_id": entry.id, - "commands": [step["cmd"] for step in segment], - "steps": segment, - }, + payload=segment_payload, ) payload_ref = self._svc.make_blob_ref(segment_blob.id) + scenario_key = ( + self._derive_scenario_key_from_text(extracted.description) + or fallback_scenario_key + ) candidate = await self._svc.create_candidate( owner=entry.owner, - skill_key=skill_key, + skill_key=extracted.skill_key, scenario_key=scenario_key, payload_ref=payload_ref, + payload_hash=extracted.payload_hash, source_execution_ids=[entry.id], created_by="system:auto", skill_type=SkillType.BROWSER, @@ -150,7 +203,7 @@ async def _process_execution( ) result.created_candidates += 1 - metrics = self._score_segment(segment=segment) + metrics = self._score_segment(segment=extracted.steps) passed = ( metrics["score"] >= self._config.score_threshold and metrics["replay_success"] >= self._config.replay_success_threshold @@ -196,7 +249,10 @@ async def _process_execution( ) continue - active = await self._svc.get_active_release(owner=entry.owner, skill_key=skill_key) + active = await self._svc.get_active_release( + owner=entry.owner, + skill_key=extracted.skill_key, + ) release = await self._svc.promote_candidate( owner=entry.owner, candidate_id=candidate.id, @@ -232,35 +288,15 @@ async def _process_execution( return True async def _extract_segments(self, *, entry: ExecutionHistory) -> list[list[dict[str, Any]]]: + steps = await self._load_normalized_steps(entry=entry) + return self._rule_strategy.extract_actionable_segments(steps=steps) + + async def _load_normalized_steps(self, *, entry: ExecutionHistory) -> list[dict[str, Any]]: trace_payload = await self._svc.get_payload_by_ref( owner=entry.owner, payload_ref=entry.payload_ref, ) - - steps = self._normalize_steps(entry=entry, trace_payload=trace_payload) - if not steps: - return [] - - segments: list[list[dict[str, Any]]] = [] - current: list[dict[str, Any]] = [] - - for step in steps: - cmd = step.get("cmd", "") - failed = int(step.get("exit_code", 1)) != 0 - is_read_only = self._is_read_only_command(cmd) - kind = str(step.get("kind", "individual_action")) - - if failed or is_read_only or kind != "individual_action" or not cmd: - if len(current) >= 2: - segments.append(current.copy()) - current.clear() - continue - current.append(step) - - if len(current) >= 2: - segments.append(current) - - return segments + return self._normalize_steps(entry=entry, trace_payload=trace_payload) @staticmethod def _normalize_steps( @@ -309,37 +345,28 @@ def _normalize_steps( @staticmethod def _is_read_only_command(cmd: str) -> bool: - normalized = cmd.strip().lower() - return any(normalized.startswith(prefix) for prefix in READ_ONLY_PREFIXES) + return RuleBasedExtractionStrategy.is_read_only_command(cmd) @staticmethod def _score_segment(*, segment: list[dict[str, Any]]) -> dict[str, Any]: - steps = len(segment) - samples = steps * 10 - replay_success = 1.0 if steps > 0 else 0.0 - score = min(0.99, 0.75 + 0.08 * steps) - p95_duration = 0 - return { - "score": round(score, 4), - "replay_success": round(replay_success, 4), - "samples": samples, - "error_rate": round(1.0 - replay_success, 4), - "p95_duration": p95_duration, - } + return RuleBasedExtractionStrategy.score_segment(segment=segment) @staticmethod def _derive_skill_key(*, entry: ExecutionHistory) -> str: - tags = [part.strip() for part in (entry.tags or "").split(",") if part.strip()] - for tag in tags: - if tag.startswith("skill:") and len(tag) > len("skill:"): - return tag[len("skill:") :] - return f"browser-{entry.sandbox_id}" + return RuleBasedExtractionStrategy.derive_skill_key( + tags=entry.tags, + sandbox_id=entry.sandbox_id, + ) @staticmethod def _derive_scenario_key(*, entry: ExecutionHistory) -> str | None: - if not entry.description: + return BrowserLearningProcessor._derive_scenario_key_from_text(entry.description) + + @staticmethod + def _derive_scenario_key_from_text(description: str | None) -> str | None: + if not description: return None - normalized = re.sub(r"[^a-zA-Z0-9._-]+", "-", entry.description.strip().lower()) + normalized = re.sub(r"[^a-zA-Z0-9._-]+", "-", description.strip().lower()) normalized = normalized.strip("-") if not normalized: return None diff --git a/pkgs/bay/app/services/skills/service.py b/pkgs/bay/app/services/skills/service.py index 605fa2c..a48d734 100644 --- a/pkgs/bay/app/services/skills/service.py +++ b/pkgs/bay/app/services/skills/service.py @@ -29,6 +29,8 @@ SkillCandidate, SkillCandidateStatus, SkillEvaluation, + SkillGoal, + SkillOutcome, SkillRelease, SkillReleaseMode, SkillReleaseStage, @@ -37,6 +39,29 @@ from app.utils.datetime import utcnow +def _assemble_skill_content( + *, + skill_key: str, + summary: str | None, + usage_notes: str | None, + preconditions: list[str], + postconditions: list[str], +) -> str: + """Assemble a human/LLM-readable skill content block from candidate fields.""" + lines: list[str] = [f"# SKILL: {skill_key}"] + if summary: + lines += ["", "## Summary", summary] + if usage_notes: + lines += ["", "## Usage Notes", usage_notes] + if preconditions: + lines += ["", "## Preconditions"] + lines += [f"- {p}" for p in preconditions] + if postconditions: + lines += ["", "## Postconditions"] + lines += [f"- {p}" for p in postconditions] + return "\n".join(lines) + + class SkillLifecycleService: """Service for skill learning lifecycle operations.""" @@ -421,10 +446,11 @@ async def create_candidate( payload_ref: str | None = None, summary: str | None = None, usage_notes: str | None = None, - preconditions: dict[str, Any] | None = None, - postconditions: dict[str, Any] | None = None, + preconditions: list[str] | dict[str, Any] | None = None, + postconditions: list[str] | dict[str, Any] | None = None, created_by: str | None = None, skill_type: SkillType = SkillType.CODE, + payload_hash: str | None = None, auto_release_eligible: bool = False, auto_release_reason: str | None = None, ) -> SkillCandidate: @@ -442,6 +468,7 @@ async def create_candidate( skill_key=skill_key.strip(), scenario_key=scenario_key, payload_ref=payload_ref, + payload_hash=payload_hash, source_execution_ids=self._join_csv(source_execution_ids), skill_type=skill_type, auto_release_eligible=auto_release_eligible, @@ -467,6 +494,26 @@ async def create_candidate( await self._db.refresh(candidate) return candidate + async def find_candidate_by_payload_hash( + self, + *, + owner: str, + skill_key: str, + payload_hash: str | None, + ) -> SkillCandidate | None: + if payload_hash is None: + return None + result = await self._db.execute( + select(SkillCandidate) + .where( + SkillCandidate.owner == owner, + SkillCandidate.skill_key == skill_key, + SkillCandidate.payload_hash == payload_hash, + ) + .limit(1) + ) + return result.scalars().first() + async def update_candidate_auto_release( self, *, @@ -993,6 +1040,181 @@ def _aggregate_execution_metrics(items: list[ExecutionHistory]) -> dict[str, Any "p95_duration": p95_duration, } + # --------------------------------------------------------------------- + # Goal registry & outcome reporting (evolution infrastructure) + # --------------------------------------------------------------------- + + async def declare_goal( + self, + *, + owner: str, + skill_key: str, + goal: str, + ) -> SkillGoal: + """Upsert the declared goal for a skill key.""" + result = await self._db.execute( + select(SkillGoal).where( + SkillGoal.owner == owner, + SkillGoal.skill_key == skill_key, + ) + ) + existing = result.scalars().first() + now = utcnow() + if existing is not None: + if existing.goal != goal: + # Goal text changed — cached rubric is stale, clear it so the + # next mutation cycle regenerates it against the new goal. + existing.rubric_json = None + existing.rubric_summary = "" + existing.goal = goal + existing.updated_at = now + self._db.add(existing) + await self._db.commit() + await self._db.refresh(existing) + return existing + + skill_goal = SkillGoal( + id=f"goal-{uuid.uuid4().hex[:12]}", + owner=owner, + skill_key=skill_key, + goal=goal, + rubric_summary="", + created_at=now, + updated_at=now, + ) + self._db.add(skill_goal) + await self._db.commit() + await self._db.refresh(skill_goal) + return skill_goal + + async def get_skill_goal( + self, + *, + owner: str, + skill_key: str, + ) -> SkillGoal | None: + result = await self._db.execute( + select(SkillGoal).where( + SkillGoal.owner == owner, + SkillGoal.skill_key == skill_key, + ) + ) + return result.scalars().first() + + async def get_active_skill_view( + self, + *, + owner: str, + skill_key: str, + ) -> dict[str, Any] | None: + """Return a unified SkillView for the active release of a skill key.""" + release = await self.get_active_release(owner=owner, skill_key=skill_key) + if release is None: + return None + + candidate_result = await self._db.execute( + select(SkillCandidate).where( + SkillCandidate.id == release.candidate_id, + SkillCandidate.owner == owner, + ) + ) + candidate = candidate_result.scalars().first() + if candidate is None: + return None + + goal_record = await self.get_skill_goal(owner=owner, skill_key=skill_key) + + preconditions: list[str] = [] + if candidate.preconditions_json: + try: + raw = json.loads(candidate.preconditions_json) + if isinstance(raw, list): + preconditions = [str(x) for x in raw] + elif isinstance(raw, dict): + preconditions = [f"{k}: {v}" for k, v in raw.items()] + except Exception: + pass + + postconditions: list[str] = [] + if candidate.postconditions_json: + try: + raw = json.loads(candidate.postconditions_json) + if isinstance(raw, list): + postconditions = [str(x) for x in raw] + elif isinstance(raw, dict): + postconditions = [f"{k}: {v}" for k, v in raw.items()] + except Exception: + pass + + content = _assemble_skill_content( + skill_key=skill_key, + summary=candidate.summary, + usage_notes=candidate.usage_notes, + preconditions=preconditions, + postconditions=postconditions, + ) + + return { + "skill_key": skill_key, + "release_id": release.id, + "version": release.version, + "stage": release.stage.value, + "goal": goal_record.goal if goal_record else None, + "content": content, + "summary": candidate.summary, + "preconditions": preconditions, + "postconditions": postconditions, + "payload_ref": candidate.payload_ref, + } + + async def record_outcome( + self, + *, + owner: str, + skill_key: str, + release_id: str, + outcome: str, + reasoning: str, + execution_id: str | None = None, + signals: dict[str, Any] | None = None, + ) -> SkillOutcome: + """Record an execution outcome report — the evolution signal feed.""" + if outcome not in ("success", "failure", "partial"): + raise ValidationError( + f"Invalid outcome: {outcome!r}. Must be success, failure, or partial." + ) + + # Validate release ownership and skill_key consistency to prevent signal pollution. + release_check = await self._db.execute( + select(SkillRelease).where( + SkillRelease.id == release_id, + SkillRelease.owner == owner, + SkillRelease.skill_key == skill_key, + SkillRelease.is_deleted.is_(False), + ) + ) + if release_check.scalars().first() is None: + raise NotFoundError( + f"Release '{release_id}' not found, not owned by '{owner}', " + f"or does not belong to skill '{skill_key}'." + ) + + skill_outcome = SkillOutcome( + id=f"outcome-{uuid.uuid4().hex[:12]}", + owner=owner, + skill_key=skill_key, + release_id=release_id, + outcome=outcome, + reasoning=reasoning, + execution_id=execution_id, + signals_json=json.dumps(signals, ensure_ascii=False) if signals is not None else None, + created_at=utcnow(), + ) + self._db.add(skill_outcome) + await self._db.commit() + await self._db.refresh(skill_outcome) + return skill_outcome + # --------------------------------------------------------------------- # Internal helpers # --------------------------------------------------------------------- @@ -1021,7 +1243,7 @@ async def _get_release(self, *, owner: str, release_id: str) -> SkillRelease: return release async def _sanitize_candidate_promotion_pointer(self, candidate: SkillCandidate) -> None: - """Clear stale promotion release pointer on candidate if target release is deleted/missing.""" + """Clear stale promotion release pointer if target release is deleted/missing.""" release_id = candidate.promotion_release_id if not release_id: return diff --git a/pkgs/bay/tests/integration/core/test_browser_skill_e2e.py b/pkgs/bay/tests/integration/core/test_browser_skill_e2e.py index 5248f49..1cf9840 100644 --- a/pkgs/bay/tests/integration/core/test_browser_skill_e2e.py +++ b/pkgs/bay/tests/integration/core/test_browser_skill_e2e.py @@ -5,7 +5,9 @@ from __future__ import annotations +import asyncio import subprocess +import uuid import httpx import pytest @@ -51,6 +53,34 @@ async def _resolve_browser_profile(client: httpx.AsyncClient) -> str: pytest.skip("No browser-enabled profile available") +async def _wait_for_learning_completion( + client: httpx.AsyncClient, + *, + sandbox_id: str, + execution_id: str, + timeout_seconds: float = 30.0, +) -> str: + deadline = asyncio.get_running_loop().time() + timeout_seconds + last_status = "unknown" + while asyncio.get_running_loop().time() < deadline: + history_resp = await client.get( + f"/v1/sandboxes/{sandbox_id}/history/{execution_id}", + timeout=30.0, + ) + assert history_resp.status_code == 200, history_resp.text + learn_status = str(history_resp.json().get("learn_status") or "").lower() + if learn_status in {"processed", "skipped", "error"}: + return learn_status + if learn_status: + last_status = learn_status + await asyncio.sleep(1.0) + pytest.skip( + "Browser learning scheduler did not process execution within timeout. " + "Configure fast test interval via browser_learning.interval_seconds." + ) + return last_status + + async def test_browser_exec_trace_and_history_round_trip(): _require_browser_runtime() async with httpx.AsyncClient(base_url=BAY_BASE_URL, headers=AUTH_HEADERS) as client: @@ -380,3 +410,145 @@ async def test_browser_run_skill_works_with_payload_created_by_generic_payload_a assert run_data["total_steps"] == 1 assert run_data["completed_steps"] == 1 assert run_data["results"][0]["cmd"] == "open about:blank" + + +async def test_browser_run_skill_works_with_payload_containing_variables_field(): + """Candidate payload with variables metadata should remain runnable end-to-end.""" + _require_browser_runtime() + async with httpx.AsyncClient(base_url=BAY_BASE_URL, headers=AUTH_HEADERS) as client: + browser_profile = await _resolve_browser_profile(client) + async with create_sandbox(client, profile=browser_profile) as sandbox: + sandbox_id = sandbox["id"] + + payload_resp = await client.post( + "/v1/skills/payloads", + json={ + "kind": "candidate_payload", + "payload": { + "commands": ["open about:blank"], + "variables": { + "departure_city": { + "type": "string", + "default_value": "Edinburgh", + "action_index": 0, + "arg_position": 1, + } + }, + }, + }, + timeout=60.0, + ) + assert payload_resp.status_code == 201, payload_resp.text + payload_ref = payload_resp.json()["payload_ref"] + + payload_get_resp = await client.get( + f"/v1/skills/payloads/{payload_ref}", + timeout=60.0, + ) + assert payload_get_resp.status_code == 200, payload_get_resp.text + payload_data = payload_get_resp.json()["payload"] + assert payload_data["variables"]["departure_city"]["default_value"] == "Edinburgh" + + source_exec_resp = await client.post( + f"/v1/sandboxes/{sandbox_id}/browser/exec", + json={"cmd": "open about:blank", "timeout": 60}, + timeout=120.0, + ) + assert source_exec_resp.status_code == 200, source_exec_resp.text + source_execution_id = source_exec_resp.json()["execution_id"] + + candidate_resp = await client.post( + "/v1/skills/candidates", + json={ + "skill_key": "browser-variable-payload", + "source_execution_ids": [source_execution_id], + "payload_ref": payload_ref, + }, + timeout=60.0, + ) + assert candidate_resp.status_code == 201, candidate_resp.text + candidate_id = candidate_resp.json()["id"] + + evaluate_resp = await client.post( + f"/v1/skills/candidates/{candidate_id}/evaluate", + json={"passed": True, "score": 0.92}, + timeout=60.0, + ) + assert evaluate_resp.status_code == 200, evaluate_resp.text + + promote_resp = await client.post( + f"/v1/skills/candidates/{candidate_id}/promote", + json={"stage": "canary"}, + timeout=60.0, + ) + assert promote_resp.status_code == 200, promote_resp.text + + run_resp = await client.post( + f"/v1/sandboxes/{sandbox_id}/browser/skills/browser-variable-payload/run", + json={"timeout": 60, "stop_on_error": True, "include_trace": False}, + timeout=120.0, + ) + assert run_resp.status_code == 200, run_resp.text + run_data = run_resp.json() + assert run_data["execution_id"].startswith("exec-") + assert run_data["total_steps"] == 1 + assert run_data["completed_steps"] == 1 + + +async def test_browser_learning_deduplicates_candidates_by_payload_hash(): + """Automatic learning should skip duplicate candidates for same skill+payload.""" + _require_browser_runtime() + skill_key = f"e2e-browser-learning-dedup-{uuid.uuid4().hex[:8]}" + commands = ["open about:blank", "open about:blank"] + + async with httpx.AsyncClient(base_url=BAY_BASE_URL, headers=AUTH_HEADERS) as client: + browser_profile = await _resolve_browser_profile(client) + async with create_sandbox(client, profile=browser_profile) as sandbox: + sandbox_id = sandbox["id"] + + exec_ids: list[str] = [] + for _ in range(2): + resp = await client.post( + f"/v1/sandboxes/{sandbox_id}/browser/exec_batch", + json={ + "commands": commands, + "timeout": 120, + "stop_on_error": True, + "learn": True, + "include_trace": True, + "tags": f"skill:{skill_key},learn,e2e", + }, + timeout=180.0, + ) + assert resp.status_code == 200, resp.text + exec_ids.append(resp.json()["execution_id"]) + + statuses = [ + await _wait_for_learning_completion( + client, + sandbox_id=sandbox_id, + execution_id=execution_id, + ) + for execution_id in exec_ids + ] + assert statuses == ["processed", "processed"] + + candidates_resp = await client.get( + "/v1/skills/candidates", + params={"skill_key": skill_key, "limit": 50, "offset": 0}, + timeout=30.0, + ) + assert candidates_resp.status_code == 200, candidates_resp.text + candidates_data = candidates_resp.json() + assert candidates_data["total"] == 1 + candidate = candidates_data["items"][0] + assert candidate["skill_key"] == skill_key + + payload_resp = await client.get( + f"/v1/skills/payloads/{candidate['payload_ref']}", + timeout=30.0, + ) + assert payload_resp.status_code == 200, payload_resp.text + payload = payload_resp.json()["payload"] + assert payload["commands"] == commands + assert len(str(payload["payload_hash"])) == 64 diff --git a/pkgs/bay/tests/integration/core/test_skill_evolution_api.py b/pkgs/bay/tests/integration/core/test_skill_evolution_api.py new file mode 100644 index 0000000..67117dc --- /dev/null +++ b/pkgs/bay/tests/integration/core/test_skill_evolution_api.py @@ -0,0 +1,341 @@ +"""Integration tests for skill evolution API endpoints (Phase 1). + +Covers the three new intent-driven endpoints end-to-end against a live Bay: +- POST /v1/skills/goals +- GET /v1/skills/{skill_key}/active +- POST /v1/skills/outcomes + +Prerequisites: Docker, ship:latest, Bay running. +""" + +from __future__ import annotations + +import httpx +import pytest + +from ..conftest import AUTH_HEADERS, BAY_BASE_URL, e2e_skipif_marks + +pytestmark = e2e_skipif_marks + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + + +async def _get_or_create_execution_id(client: httpx.AsyncClient) -> str: + """Create a minimal sandbox, run a shell command, return the execution_id, then delete it.""" + sandbox_resp = await client.post( + "/v1/sandboxes", + json={"profile": "python-default", "ttl": 60}, + timeout=30.0, + ) + if sandbox_resp.status_code != 201: + pytest.skip(f"Cannot create sandbox for execution seed (status {sandbox_resp.status_code})") + sandbox_id = sandbox_resp.json()["id"] + + try: + exec_resp = await client.post( + f"/v1/sandboxes/{sandbox_id}/shell/exec", + json={"command": "echo seed"}, + timeout=30.0, + ) + if exec_resp.status_code != 200: + pytest.skip( + f"Cannot run shell command for execution seed (status {exec_resp.status_code})" + ) + return exec_resp.json()["execution_id"] + finally: + await client.delete(f"/v1/sandboxes/{sandbox_id}", timeout=15.0) + + +async def _create_candidate_and_promote( + client: httpx.AsyncClient, + *, + skill_key: str, + summary: str = "Test skill summary", +) -> tuple[str, str]: + """Create a candidate, evaluate it, promote to canary. Returns (candidate_id, release_id).""" + execution_id = await _get_or_create_execution_id(client) + + payload_resp = await client.post( + "/v1/skills/payloads", + json={"payload": {"commands": ["open about:blank"]}, "kind": "browser_segment"}, + ) + assert payload_resp.status_code == 201 + payload_ref = payload_resp.json()["payload_ref"] + + candidate_resp = await client.post( + "/v1/skills/candidates", + json={ + "skill_key": skill_key, + "source_execution_ids": [execution_id], + "summary": summary, + "usage_notes": "Requires browser sandbox", + "preconditions": {"browser": "available"}, + "postconditions": {"result": "returned"}, + "payload_ref": payload_ref, + }, + ) + assert candidate_resp.status_code == 201 + candidate_id = candidate_resp.json()["id"] + + eval_resp = await client.post( + f"/v1/skills/candidates/{candidate_id}/evaluate", + json={"passed": True, "score": 0.9, "benchmark_id": "test-bench"}, + ) + assert eval_resp.status_code == 200 + + promote_resp = await client.post( + f"/v1/skills/candidates/{candidate_id}/promote", + json={"stage": "canary"}, + ) + assert promote_resp.status_code == 200 + release_id = promote_resp.json()["id"] + + return candidate_id, release_id + + +# --------------------------------------------------------------------------- +# POST /v1/skills/goals +# --------------------------------------------------------------------------- + + +async def test_declare_goal_creates_record(): + skill_key = "evol-test-declare-goal" + async with httpx.AsyncClient(base_url=BAY_BASE_URL, headers=AUTH_HEADERS) as client: + resp = await client.post( + "/v1/skills/goals", + json={"skill_key": skill_key, "goal": "Return the star count of a GitHub repo."}, + ) + + assert resp.status_code == 200 + data = resp.json() + assert data["goal_id"].startswith("goal-") + assert data["skill_key"] == skill_key + assert data["goal"] == "Return the star count of a GitHub repo." + assert "rubric_summary" in data + + +async def test_declare_goal_upsert_updates_goal(): + skill_key = "evol-test-upsert-goal" + async with httpx.AsyncClient(base_url=BAY_BASE_URL, headers=AUTH_HEADERS) as client: + first = await client.post( + "/v1/skills/goals", + json={"skill_key": skill_key, "goal": "Original goal."}, + ) + assert first.status_code == 200 + goal_id = first.json()["goal_id"] + + second = await client.post( + "/v1/skills/goals", + json={"skill_key": skill_key, "goal": "Updated goal."}, + ) + assert second.status_code == 200 + + assert second.json()["goal_id"] == goal_id + assert second.json()["goal"] == "Updated goal." + + +# --------------------------------------------------------------------------- +# GET /v1/skills/{skill_key}/active +# --------------------------------------------------------------------------- + + +async def test_get_active_returns_404_when_no_release(): + async with httpx.AsyncClient(base_url=BAY_BASE_URL, headers=AUTH_HEADERS) as client: + resp = await client.get("/v1/skills/evol-no-such-skill/active") + + assert resp.status_code == 404 + + +async def test_get_active_returns_view_after_promote(): + skill_key = "evol-test-get-active" + async with httpx.AsyncClient(base_url=BAY_BASE_URL, headers=AUTH_HEADERS) as client: + _, release_id = await _create_candidate_and_promote( + client, skill_key=skill_key, summary="Star count skill" + ) + + resp = await client.get(f"/v1/skills/{skill_key}/active") + + assert resp.status_code == 200 + data = resp.json() + assert data["skill_key"] == skill_key + assert data["release_id"] == release_id + assert data["version"] >= 1 + assert data["stage"] == "canary" + assert data["goal"] is None + assert "content" in data + assert skill_key in data["content"] + assert "Star count skill" in data["content"] + + +async def test_get_active_includes_goal_when_declared(): + skill_key = "evol-test-active-with-goal" + async with httpx.AsyncClient(base_url=BAY_BASE_URL, headers=AUTH_HEADERS) as client: + await _create_candidate_and_promote(client, skill_key=skill_key) + + await client.post( + "/v1/skills/goals", + json={"skill_key": skill_key, "goal": "Do something specific."}, + ) + + resp = await client.get(f"/v1/skills/{skill_key}/active") + + assert resp.status_code == 200 + assert resp.json()["goal"] == "Do something specific." + + +async def test_get_active_returns_preconditions_and_postconditions(): + skill_key = "evol-test-active-conds" + async with httpx.AsyncClient(base_url=BAY_BASE_URL, headers=AUTH_HEADERS) as client: + await _create_candidate_and_promote(client, skill_key=skill_key) + resp = await client.get(f"/v1/skills/{skill_key}/active") + + assert resp.status_code == 200 + data = resp.json() + assert isinstance(data["preconditions"], list) + assert isinstance(data["postconditions"], list) + + +# --------------------------------------------------------------------------- +# POST /v1/skills/outcomes +# --------------------------------------------------------------------------- + + +async def test_report_outcome_success_is_accepted(): + skill_key = "evol-test-outcome-success" + async with httpx.AsyncClient(base_url=BAY_BASE_URL, headers=AUTH_HEADERS) as client: + _, release_id = await _create_candidate_and_promote(client, skill_key=skill_key) + + resp = await client.post( + "/v1/skills/outcomes", + json={ + "skill_key": skill_key, + "release_id": release_id, + "outcome": "success", + "reasoning": "Star count element found at .social-count. Value: 12345.", + }, + ) + + assert resp.status_code == 201 + data = resp.json() + assert data["outcome_id"].startswith("outcome-") + assert data["accepted"] is True + + +async def test_report_outcome_failure_with_signals(): + skill_key = "evol-test-outcome-failure" + async with httpx.AsyncClient(base_url=BAY_BASE_URL, headers=AUTH_HEADERS) as client: + _, release_id = await _create_candidate_and_promote(client, skill_key=skill_key) + + resp = await client.post( + "/v1/skills/outcomes", + json={ + "skill_key": skill_key, + "release_id": release_id, + "outcome": "failure", + "reasoning": "GitHub layout changed. Star count element not found at .social-count.", # noqa: E501 + "signals": {"page_load_time_ms": 3200, "element_found": False}, + }, + ) + + assert resp.status_code == 201 + assert resp.json()["accepted"] is True + + +async def test_report_outcome_partial(): + skill_key = "evol-test-outcome-partial" + async with httpx.AsyncClient(base_url=BAY_BASE_URL, headers=AUTH_HEADERS) as client: + _, release_id = await _create_candidate_and_promote(client, skill_key=skill_key) + + resp = await client.post( + "/v1/skills/outcomes", + json={ + "skill_key": skill_key, + "release_id": release_id, + "outcome": "partial", + "reasoning": "Navigation succeeded but data extraction incomplete.", + }, + ) + + assert resp.status_code == 201 + + +async def test_report_outcome_invalid_outcome_rejected(): + async with httpx.AsyncClient(base_url=BAY_BASE_URL, headers=AUTH_HEADERS) as client: + resp = await client.post( + "/v1/skills/outcomes", + json={ + "skill_key": "some-skill", + "release_id": "rel-001", + "outcome": "bad_value", + "reasoning": "This should be rejected.", + }, + ) + + assert resp.status_code == 422 + + +async def test_report_outcome_reasoning_is_required(): + async with httpx.AsyncClient(base_url=BAY_BASE_URL, headers=AUTH_HEADERS) as client: + resp = await client.post( + "/v1/skills/outcomes", + json={ + "skill_key": "some-skill", + "release_id": "rel-001", + "outcome": "success", + }, + ) + + assert resp.status_code == 422 + + +# --------------------------------------------------------------------------- +# Full round-trip: declare → promote → get_active → report_outcome +# --------------------------------------------------------------------------- + + +async def test_full_evolution_round_trip(): + """Simulate one complete evolution cycle from an agent's perspective.""" + skill_key = "evol-test-full-round-trip" + + async with httpx.AsyncClient(base_url=BAY_BASE_URL, headers=AUTH_HEADERS) as client: + # 1. Declare the goal (first thing agent does for a new skill) + goal_resp = await client.post( + "/v1/skills/goals", + json={ + "skill_key": skill_key, + "goal": "Navigate to a GitHub repository page and return the star count as an integer.", # noqa: E501 + }, + ) + assert goal_resp.status_code == 200 + + # 2. Create + promote a candidate (simulating the extraction pipeline) + _, release_id = await _create_candidate_and_promote( + client, skill_key=skill_key, summary="Gets GitHub repo star count" + ) + + # 3. Get the active skill (what the agent reads before executing) + active_resp = await client.get(f"/v1/skills/{skill_key}/active") + assert active_resp.status_code == 200 + active = active_resp.json() + assert active["goal"] == ( + "Navigate to a GitHub repository page and return the star count as an integer." + ) + assert active["release_id"] == release_id + assert skill_key in active["content"] + + # 4. Report the outcome after execution (feeds evolution signal) + outcome_resp = await client.post( + "/v1/skills/outcomes", + json={ + "skill_key": skill_key, + "release_id": release_id, + "outcome": "success", + "reasoning": "Navigated to repo page. Star count found at .social-count: 42000.", + "signals": {"star_count": 42000, "load_time_ms": 800}, + }, + ) + assert outcome_resp.status_code == 201 + assert outcome_resp.json()["accepted"] is True diff --git a/pkgs/bay/tests/integration/core/test_skill_lifecycle_api.py b/pkgs/bay/tests/integration/core/test_skill_lifecycle_api.py index 33d5a8f..9a6e9cf 100644 --- a/pkgs/bay/tests/integration/core/test_skill_lifecycle_api.py +++ b/pkgs/bay/tests/integration/core/test_skill_lifecycle_api.py @@ -390,7 +390,7 @@ async def test_skill_delete_release_and_candidate_flow(): json={"stage": "canary"}, ) assert promote_b.status_code == 200 - release_b = promote_b.json() + promote_b.json() # Deleted release is no longer returned by list endpoint. list_releases = await client.get( @@ -431,3 +431,29 @@ async def test_skill_delete_release_and_candidate_flow(): get_deleted_candidate = await client.get(f"/v1/skills/candidates/{candidate_a['id']}") assert get_deleted_candidate.status_code == 404 + + +async def test_create_candidate_accepts_list_conditions(): + """Candidate create API should accept list[str] pre/postconditions.""" + async with httpx.AsyncClient(base_url=BAY_BASE_URL, headers=AUTH_HEADERS) as client: + async with create_sandbox(client) as sandbox: + sandbox_id = sandbox["id"] + execution_id = await _create_python_execution(client, sandbox_id, "print('candidate')") + + response = await client.post( + "/v1/skills/candidates", + json={ + "skill_key": "browser-login", + "source_execution_ids": [execution_id], + "preconditions": ["browser available", "user authenticated"], + "postconditions": ["dashboard visible"], + }, + ) + + assert response.status_code == 201 + candidate = response.json() + assert candidate["preconditions"] == [ + "browser available", + "user authenticated", + ] + assert candidate["postconditions"] == ["dashboard visible"] diff --git a/pkgs/bay/tests/integration/core/test_version_e2e.py b/pkgs/bay/tests/integration/core/test_version_e2e.py index 7d4486e..7b935d0 100644 --- a/pkgs/bay/tests/integration/core/test_version_e2e.py +++ b/pkgs/bay/tests/integration/core/test_version_e2e.py @@ -14,14 +14,16 @@ import httpx import pytest -from tests.integration.conftest import ( +from ..conftest import ( AUTH_HEADERS, BAY_BASE_URL, DEFAULT_TIMEOUT, create_sandbox, + e2e_skipif_marks, ) SEMVER_RE = re.compile(r"^\d+\.\d+\.\d+(-[a-zA-Z0-9.]+)?$") +pytestmark = e2e_skipif_marks @pytest.mark.asyncio diff --git a/pkgs/bay/tests/scripts/sdk_smoke_test/smoke_test.py b/pkgs/bay/tests/scripts/sdk_smoke_test/smoke_test.py index 0eab6a3..f878401 100644 --- a/pkgs/bay/tests/scripts/sdk_smoke_test/smoke_test.py +++ b/pkgs/bay/tests/scripts/sdk_smoke_test/smoke_test.py @@ -23,6 +23,9 @@ - Idempotency: create_sandbox with idempotency_key """ +# Prevent pytest from collecting this manual smoke script as a test module. +__test__ = False + import asyncio import sys from pathlib import Path @@ -32,7 +35,7 @@ if SDK_PATH.exists(): sys.path.insert(0, str(SDK_PATH)) -from shipyard_neo import BayClient, NotFoundError, SandboxStatus +from shipyard_neo import BayClient, NotFoundError, SandboxStatus # noqa: E402 async def test_sandbox_lifecycle(client: BayClient) -> str: @@ -86,7 +89,7 @@ async def test_sandbox_lifecycle(client: BayClient) -> str: print("\n[7] Extending TTL by 60 seconds...") old_expires = sandbox.expires_at await sandbox.extend_ttl(60) - print(f" ✓ TTL extended") + print(" ✓ TTL extended") print(f" Old expires_at: {old_expires}") print(f" New expires_at: {sandbox.expires_at}") diff --git a/pkgs/bay/tests/unit/api/test_skill_evolution_api.py b/pkgs/bay/tests/unit/api/test_skill_evolution_api.py new file mode 100644 index 0000000..7ff8d01 --- /dev/null +++ b/pkgs/bay/tests/unit/api/test_skill_evolution_api.py @@ -0,0 +1,297 @@ +"""Unit tests for skill evolution API endpoints (Phase 1). + +Tests cover: +- POST /v1/skills/goals (declare_skill_goal) +- GET /v1/skills/{skill_key}/active (get_active_skill) +- POST /v1/skills/outcomes (report_skill_outcome) + +Uses fake service objects — no DB or HTTP involved. +""" + +from __future__ import annotations + +import pytest + +from app.api.v1.skills import ( + SkillActiveResponse, + SkillCandidateCreateRequest, + SkillGoalDeclareRequest, + SkillOutcomeRequest, + declare_skill_goal, + get_active_skill, + report_skill_outcome, +) +from app.errors import NotFoundError + +# --------------------------------------------------------------------------- +# Fake service +# --------------------------------------------------------------------------- + + +class _FakeGoal: + def __init__(self, *, skill_key: str, goal: str, updated: bool = False): + self.id = "goal-abc123" + self.skill_key = skill_key + self.goal = goal + self.rubric_summary = "" + self.updated = updated + + +class _FakeOutcome: + def __init__(self): + self.id = "outcome-xyz789" + + +class _FakeSkillService: + def __init__(self, *, active_view: dict | None = None): + self.declare_goal_calls: list[dict] = [] + self.record_outcome_calls: list[dict] = [] + self._active_view = active_view + self._declare_goal_returns_upserted = False + + async def declare_goal(self, *, owner: str, skill_key: str, goal: str): + self.declare_goal_calls.append( + {"owner": owner, "skill_key": skill_key, "goal": goal} + ) + return _FakeGoal(skill_key=skill_key, goal=goal) + + async def get_active_skill_view(self, *, owner: str, skill_key: str): + if self._active_view is None: + return None + return self._active_view + + async def record_outcome( + self, + *, + owner: str, + skill_key: str, + release_id: str, + outcome: str, + reasoning: str, + execution_id: str | None = None, + signals: dict | None = None, + ): + self.record_outcome_calls.append( + { + "owner": owner, + "skill_key": skill_key, + "release_id": release_id, + "outcome": outcome, + "reasoning": reasoning, + "execution_id": execution_id, + "signals": signals, + } + ) + return _FakeOutcome() + + +# --------------------------------------------------------------------------- +# POST /v1/skills/goals +# --------------------------------------------------------------------------- + + +class TestDeclareSkillGoal: + async def test_returns_goal_id_and_fields(self): + svc = _FakeSkillService() + + response = await declare_skill_goal( + request=SkillGoalDeclareRequest( + skill_key="github-get-stars", + goal="Return the star count of a GitHub repo.", + ), + skill_svc=svc, + owner="default", + ) + + assert response.goal_id == "goal-abc123" + assert response.skill_key == "github-get-stars" + assert response.goal == "Return the star count of a GitHub repo." + assert response.rubric_summary == "" + + async def test_passes_owner_to_service(self): + svc = _FakeSkillService() + + await declare_skill_goal( + request=SkillGoalDeclareRequest(skill_key="sk", goal="goal"), + skill_svc=svc, + owner="alice", + ) + + assert svc.declare_goal_calls[0]["owner"] == "alice" + + async def test_passes_skill_key_and_goal_to_service(self): + svc = _FakeSkillService() + + await declare_skill_goal( + request=SkillGoalDeclareRequest(skill_key="my-skill", goal="My goal."), + skill_svc=svc, + owner="default", + ) + + call = svc.declare_goal_calls[0] + assert call["skill_key"] == "my-skill" + assert call["goal"] == "My goal." + + +def test_skill_candidate_create_request_accepts_list_conditions(): + request = SkillCandidateCreateRequest( + skill_key="browser-login", + source_execution_ids=["exec-1"], + preconditions=["browser available", "user authenticated"], + postconditions=["dashboard visible"], + ) + + assert request.preconditions == ["browser available", "user authenticated"] + assert request.postconditions == ["dashboard visible"] + + +# --------------------------------------------------------------------------- +# GET /v1/skills/{skill_key}/active +# --------------------------------------------------------------------------- + + +class TestGetActiveSkill: + async def test_raises_not_found_when_no_active_release(self): + svc = _FakeSkillService(active_view=None) + + with pytest.raises(NotFoundError): + await get_active_skill(skill_key="missing-skill", skill_svc=svc, owner="default") + + async def test_returns_skill_view_fields(self): + view = { + "skill_key": "github-get-stars", + "release_id": "rel-001", + "version": 3, + "stage": "stable", + "goal": "Return star count.", + "content": "# SKILL: github-get-stars\n\n## Summary\nReturn star count.", + "summary": "Return star count.", + "preconditions": ["browser available"], + "postconditions": ["integer returned"], + "payload_ref": "blob:abc123", + } + svc = _FakeSkillService(active_view=view) + + response = await get_active_skill( + skill_key="github-get-stars", skill_svc=svc, owner="default" + ) + + assert isinstance(response, SkillActiveResponse) + assert response.skill_key == "github-get-stars" + assert response.release_id == "rel-001" + assert response.version == 3 + assert response.stage == "stable" + assert response.goal == "Return star count." + assert "# SKILL:" in response.content + assert response.payload_ref == "blob:abc123" + + async def test_returns_skill_view_without_goal(self): + view = { + "skill_key": "no-goal-skill", + "release_id": "rel-002", + "version": 1, + "stage": "canary", + "goal": None, + "content": "# SKILL: no-goal-skill", + "summary": None, + "preconditions": [], + "postconditions": [], + "payload_ref": None, + } + svc = _FakeSkillService(active_view=view) + + response = await get_active_skill( + skill_key="no-goal-skill", skill_svc=svc, owner="default" + ) + + assert response.goal is None + assert response.payload_ref is None + + +# --------------------------------------------------------------------------- +# POST /v1/skills/outcomes +# --------------------------------------------------------------------------- + + +class TestReportSkillOutcome: + async def test_success_outcome_is_accepted(self): + svc = _FakeSkillService() + + response = await report_skill_outcome( + request=SkillOutcomeRequest( + skill_key="github-get-stars", + release_id="rel-001", + outcome="success", + reasoning="Star count found at expected location.", + ), + skill_svc=svc, + owner="default", + ) + + assert response.outcome_id == "outcome-xyz789" + assert response.accepted is True + + async def test_failure_outcome_with_signals(self): + svc = _FakeSkillService() + + await report_skill_outcome( + request=SkillOutcomeRequest( + skill_key="github-get-stars", + release_id="rel-001", + outcome="failure", + reasoning="Element not found.", + execution_id="exec-123", + signals={"element_found": False}, + ), + skill_svc=svc, + owner="default", + ) + + call = svc.record_outcome_calls[0] + assert call["outcome"] == "failure" + assert call["execution_id"] == "exec-123" + assert call["signals"] == {"element_found": False} + + async def test_partial_outcome(self): + svc = _FakeSkillService() + + response = await report_skill_outcome( + request=SkillOutcomeRequest( + skill_key="ui-skill", + release_id="rel-002", + outcome="partial", + reasoning="Partial completion.", + ), + skill_svc=svc, + owner="default", + ) + + assert response.accepted is True + assert svc.record_outcome_calls[0]["outcome"] == "partial" + + async def test_passes_owner_to_service(self): + svc = _FakeSkillService() + + await report_skill_outcome( + request=SkillOutcomeRequest( + skill_key="sk", + release_id="rel-1", + outcome="success", + reasoning="OK.", + ), + skill_svc=svc, + owner="alice", + ) + + assert svc.record_outcome_calls[0]["owner"] == "alice" + + async def test_invalid_outcome_rejected_by_schema(self): + from pydantic import ValidationError as PydanticValidationError + + with pytest.raises(PydanticValidationError): + SkillOutcomeRequest( + skill_key="sk", + release_id="rel-1", + outcome="invalid_value", + reasoning="This should fail.", + ) diff --git a/pkgs/bay/tests/unit/managers/test_browser_learning_scheduler.py b/pkgs/bay/tests/unit/managers/test_browser_learning_scheduler.py index 0d5793e..3b089b1 100644 --- a/pkgs/bay/tests/unit/managers/test_browser_learning_scheduler.py +++ b/pkgs/bay/tests/unit/managers/test_browser_learning_scheduler.py @@ -3,15 +3,17 @@ from __future__ import annotations import asyncio +import json from datetime import timedelta from types import SimpleNamespace +import httpx import pytest from sqlalchemy.ext.asyncio import AsyncSession, create_async_engine from sqlalchemy.orm import sessionmaker from sqlmodel import SQLModel -from app.config import BrowserLearningConfig +from app.config import BrowserLearningConfig, ExtractionConfig, LlmExtractionConfig from app.models.skill import ( ExecutionType, LearnStatus, @@ -20,6 +22,7 @@ SkillType, ) from app.services.skills import scheduler as scheduler_module +from app.services.skills.extraction import LlmAssistedExtractionStrategy from app.services.skills.scheduler import BrowserLearningProcessor from app.services.skills.service import SkillLifecycleService from app.utils.datetime import utcnow @@ -413,6 +416,391 @@ async def test_processor_marks_execution_error_when_trace_ref_is_invalid( assert "Artifact blob not found" in refreshed.learn_error +@pytest.mark.asyncio +async def test_processor_deduplicates_by_skill_key_and_payload_hash( + skill_service: SkillLifecycleService, + learning_config: BrowserLearningConfig, +): + trace_payload = { + "steps": [ + {"kind": "individual_action", "cmd": "open https://example.com", "exit_code": 0}, + {"kind": "individual_action", "cmd": "click @e1", "exit_code": 0}, + ] + } + first_blob = await skill_service.create_artifact_blob( + owner="default", + kind="browser_trace", + payload=trace_payload, + ) + second_blob = await skill_service.create_artifact_blob( + owner="default", + kind="browser_trace", + payload=trace_payload, + ) + await skill_service.create_execution( + owner="default", + sandbox_id="sandbox-1", + exec_type=ExecutionType.BROWSER_BATCH, + code="batch-1", + success=True, + execution_time_ms=11, + payload_ref=skill_service.make_blob_ref(first_blob.id), + tags="skill:browser-login", + learn_enabled=True, + learn_status=LearnStatus.PENDING, + ) + await skill_service.create_execution( + owner="default", + sandbox_id="sandbox-1", + exec_type=ExecutionType.BROWSER_BATCH, + code="batch-2", + success=True, + execution_time_ms=12, + payload_ref=skill_service.make_blob_ref(second_blob.id), + tags="skill:browser-login", + learn_enabled=True, + learn_status=LearnStatus.PENDING, + ) + + processor = BrowserLearningProcessor( + service=skill_service, + config=learning_config, + auto_release_enabled=False, + ) + cycle = await processor.run_cycle() + assert cycle.processed_executions == 2 + assert cycle.created_candidates == 1 + + same_key_items, same_key_total = await skill_service.list_candidates( + owner="default", + skill_key="browser-login", + ) + assert same_key_total == 1 + assert same_key_items[0].payload_hash is not None + + third_blob = await skill_service.create_artifact_blob( + owner="default", + kind="browser_trace", + payload=trace_payload, + ) + await skill_service.create_execution( + owner="default", + sandbox_id="sandbox-2", + exec_type=ExecutionType.BROWSER_BATCH, + code="batch-3", + success=True, + execution_time_ms=13, + payload_ref=skill_service.make_blob_ref(third_blob.id), + tags="skill:browser-checkout", + learn_enabled=True, + learn_status=LearnStatus.PENDING, + ) + cycle_second = await processor.run_cycle() + assert cycle_second.created_candidates == 1 + + checkout_items, checkout_total = await skill_service.list_candidates( + owner="default", + skill_key="browser-checkout", + ) + assert checkout_total == 1 + assert checkout_items[0].payload_hash == same_key_items[0].payload_hash + + +@pytest.mark.asyncio +async def test_processor_logs_duplicate_skipped_event( + skill_service: SkillLifecycleService, + learning_config: BrowserLearningConfig, +): + trace_payload = { + "steps": [ + {"kind": "individual_action", "cmd": "open about:blank", "exit_code": 0}, + {"kind": "individual_action", "cmd": "open about:blank", "exit_code": 0}, + ] + } + blob_a = await skill_service.create_artifact_blob( + owner="default", + kind="browser_trace", + payload=trace_payload, + ) + blob_b = await skill_service.create_artifact_blob( + owner="default", + kind="browser_trace", + payload=trace_payload, + ) + await skill_service.create_execution( + owner="default", + sandbox_id="sandbox-dup", + exec_type=ExecutionType.BROWSER_BATCH, + code="batch-a", + success=True, + execution_time_ms=9, + payload_ref=skill_service.make_blob_ref(blob_a.id), + tags="skill:browser-dup-log", + learn_enabled=True, + learn_status=LearnStatus.PENDING, + ) + await skill_service.create_execution( + owner="default", + sandbox_id="sandbox-dup", + exec_type=ExecutionType.BROWSER_BATCH, + code="batch-b", + success=True, + execution_time_ms=9, + payload_ref=skill_service.make_blob_ref(blob_b.id), + tags="skill:browser-dup-log", + learn_enabled=True, + learn_status=LearnStatus.PENDING, + ) + + events: list[tuple[str, dict[str, object]]] = [] + processor = BrowserLearningProcessor( + service=skill_service, + config=learning_config, + auto_release_enabled=False, + ) + processor._log = SimpleNamespace( + info=lambda event, **kwargs: events.append((event, kwargs)), + exception=lambda *args, **kwargs: None, + ) + + cycle = await processor.run_cycle() + assert cycle.created_candidates == 1 + duplicate_events = [ + payload + for event, payload in events + if event == "skills.browser.extraction.duplicate_skipped" + ] + assert len(duplicate_events) == 1 + assert duplicate_events[0]["skill_key"] == "browser-dup-log" + assert isinstance(duplicate_events[0]["payload_hash"], str) + assert duplicate_events[0]["duplicate_candidate_id"].startswith("sc-") + + +@pytest.mark.asyncio +async def test_scheduler_uses_llm_strategy_when_enabled( + monkeypatch: pytest.MonkeyPatch, + skill_service: SkillLifecycleService, + learning_config: BrowserLearningConfig, +): + async def _fake_call_llm( + self: LlmAssistedExtractionStrategy, + *, + segments: list[list[dict[str, object]]], + context, + ) -> dict[str, object]: + del self, context + payload = { + "results": [ + { + "skill_key": "semantic-login", + "description": "semantic login flow", + "steps": segments[0], + "variables": { + "username": { + "type": "string", + "default_value": "zenfun", + "action_index": 1, + "arg_position": 1, + } + }, + } + ] + } + return {"choices": [{"message": {"content": json.dumps(payload)}}]} + + monkeypatch.setattr(LlmAssistedExtractionStrategy, "_call_llm", _fake_call_llm) + + blob = await skill_service.create_artifact_blob( + owner="default", + kind="browser_trace", + payload={ + "steps": [ + {"kind": "individual_action", "cmd": "open https://example.com", "exit_code": 0}, + { + "kind": "individual_action", + "cmd": "type @username zenfun", + "exit_code": 0, + }, + {"kind": "individual_action", "cmd": "click @submit", "exit_code": 0}, + ] + }, + ) + await skill_service.create_execution( + owner="default", + sandbox_id="sandbox-llm", + exec_type=ExecutionType.BROWSER_BATCH, + code="batch", + success=True, + execution_time_ms=10, + payload_ref=skill_service.make_blob_ref(blob.id), + tags="skill:browser-login", + learn_enabled=True, + learn_status=LearnStatus.PENDING, + ) + + llm_enabled_config = learning_config.model_copy( + update={ + "extraction": ExtractionConfig( + dedup_enabled=True, + variable_extraction_enabled=True, + llm=LlmExtractionConfig(enabled=True, api_base="https://llm.test/v1", api_key="k"), + ) + } + ) + processor = BrowserLearningProcessor( + service=skill_service, + config=llm_enabled_config, + auto_release_enabled=False, + ) + cycle = await processor.run_cycle() + assert cycle.created_candidates == 1 + + candidates, total = await skill_service.list_candidates(owner="default") + assert total == 1 + assert candidates[0].skill_key == "semantic-login" + + payload = await skill_service.get_payload_by_ref( + owner="default", + payload_ref=candidates[0].payload_ref, + ) + assert isinstance(payload, dict) + assert "variables" in payload + + +@pytest.mark.asyncio +async def test_scheduler_llm_strategy_falls_back_to_rule_based( + monkeypatch: pytest.MonkeyPatch, + skill_service: SkillLifecycleService, + learning_config: BrowserLearningConfig, +): + async def _timeout_call_llm( + self: LlmAssistedExtractionStrategy, + *, + segments: list[list[dict[str, object]]], + context, + ) -> dict[str, object]: + del self, segments, context + raise httpx.TimeoutException("timeout") + + monkeypatch.setattr(LlmAssistedExtractionStrategy, "_call_llm", _timeout_call_llm) + + blob = await skill_service.create_artifact_blob( + owner="default", + kind="browser_trace", + payload={ + "steps": [ + {"kind": "individual_action", "cmd": "open https://example.com", "exit_code": 0}, + {"kind": "individual_action", "cmd": "click @submit", "exit_code": 0}, + ] + }, + ) + await skill_service.create_execution( + owner="default", + sandbox_id="sandbox-fallback", + exec_type=ExecutionType.BROWSER_BATCH, + code="batch", + success=True, + execution_time_ms=9, + payload_ref=skill_service.make_blob_ref(blob.id), + tags="skill:browser-fallback", + learn_enabled=True, + learn_status=LearnStatus.PENDING, + ) + + llm_enabled_config = learning_config.model_copy( + update={ + "extraction": ExtractionConfig( + dedup_enabled=True, + variable_extraction_enabled=True, + llm=LlmExtractionConfig(enabled=True, api_base="https://llm.test/v1", api_key="k"), + ) + } + ) + processor = BrowserLearningProcessor( + service=skill_service, + config=llm_enabled_config, + auto_release_enabled=False, + ) + cycle = await processor.run_cycle() + assert cycle.created_candidates == 1 + + candidates, total = await skill_service.list_candidates(owner="default") + assert total == 1 + assert candidates[0].skill_key == "browser-fallback" + + +@pytest.mark.asyncio +async def test_processor_creates_duplicates_when_dedup_disabled( + skill_service: SkillLifecycleService, + learning_config: BrowserLearningConfig, +): + trace_payload = { + "steps": [ + {"kind": "individual_action", "cmd": "open https://example.com", "exit_code": 0}, + {"kind": "individual_action", "cmd": "click @e1", "exit_code": 0}, + ] + } + first_blob = await skill_service.create_artifact_blob( + owner="default", + kind="browser_trace", + payload=trace_payload, + ) + second_blob = await skill_service.create_artifact_blob( + owner="default", + kind="browser_trace", + payload=trace_payload, + ) + await skill_service.create_execution( + owner="default", + sandbox_id="sandbox-1", + exec_type=ExecutionType.BROWSER_BATCH, + code="batch-1", + success=True, + execution_time_ms=11, + payload_ref=skill_service.make_blob_ref(first_blob.id), + tags="skill:browser-login", + learn_enabled=True, + learn_status=LearnStatus.PENDING, + ) + await skill_service.create_execution( + owner="default", + sandbox_id="sandbox-1", + exec_type=ExecutionType.BROWSER_BATCH, + code="batch-2", + success=True, + execution_time_ms=12, + payload_ref=skill_service.make_blob_ref(second_blob.id), + tags="skill:browser-login", + learn_enabled=True, + learn_status=LearnStatus.PENDING, + ) + + no_dedup_config = learning_config.model_copy( + update={ + "extraction": ExtractionConfig( + dedup_enabled=False, + variable_extraction_enabled=True, + llm=LlmExtractionConfig(enabled=False), + ) + } + ) + processor = BrowserLearningProcessor( + service=skill_service, + config=no_dedup_config, + auto_release_enabled=False, + ) + cycle = await processor.run_cycle() + assert cycle.processed_executions == 2 + assert cycle.created_candidates == 2 + + same_key_items, same_key_total = await skill_service.list_candidates( + owner="default", + skill_key="browser-login", + ) + assert same_key_total == 2 + assert same_key_items[0].payload_hash == same_key_items[1].payload_hash + + @pytest.mark.asyncio async def test_auto_stable_promotion_blocked_when_auto_release_disabled( skill_service: SkillLifecycleService, diff --git a/pkgs/bay/tests/unit/managers/test_evolution_scheduler.py b/pkgs/bay/tests/unit/managers/test_evolution_scheduler.py new file mode 100644 index 0000000..d6ce86a --- /dev/null +++ b/pkgs/bay/tests/unit/managers/test_evolution_scheduler.py @@ -0,0 +1,338 @@ +"""Unit tests for EvolutionScheduler (Phase 2). + +Uses a fake SkillMutationAgent — no real LLM calls. + +Tests cover: +- cycle skips when disabled +- cycle triggers mutation for skills with enough failures +- cycle respects max_mutations_per_cycle budget +- cycle skips skills with too few failures +- cycle result counters are correct +""" + +from __future__ import annotations + +import uuid + +import pytest +from sqlalchemy.ext.asyncio import AsyncSession, create_async_engine +from sqlalchemy.orm import sessionmaker +from sqlmodel import SQLModel + +from app.config import EvolutionConfig +from app.models.skill import ExecutionType, SkillCandidate, SkillCandidateStatus, SkillReleaseStage +from app.services.skills import SkillLifecycleService +from app.services.skills.evolution.scheduler import EvolutionScheduler +from app.utils.datetime import utcnow + +# --------------------------------------------------------------------------- +# Fixtures +# --------------------------------------------------------------------------- + + +@pytest.fixture +async def db_session() -> AsyncSession: + engine = create_async_engine("sqlite+aiosqlite:///:memory:", echo=False) + async with engine.begin() as conn: + await conn.run_sync(SQLModel.metadata.create_all) + factory = sessionmaker(engine, class_=AsyncSession, expire_on_commit=False) + async with factory() as session: + yield session + await engine.dispose() + + +@pytest.fixture +def skill_svc(db_session: AsyncSession) -> SkillLifecycleService: + return SkillLifecycleService(db_session) + + +# --------------------------------------------------------------------------- +# Fake mutation agent +# --------------------------------------------------------------------------- + + +class _FakeMutationAgent: + """Records mutate() calls. + + If ``db_session`` is provided, creates a real SkillCandidate row so the + scheduler's dedup check (which queries the DB) works correctly. + """ + + def __init__(self, *, success: bool = True, db_session: AsyncSession | None = None): + self.calls: list[tuple[str, str]] = [] # (owner, skill_key) + self._success = success + self._db = db_session + + async def mutate(self, *, owner: str, skill_key: str) -> str | None: + self.calls.append((owner, skill_key)) + if not self._success: + return None + candidate_id = f"sc-fake-{uuid.uuid4().hex[:8]}" + if self._db is not None: + candidate = SkillCandidate( + id=candidate_id, + owner=owner, + skill_key=skill_key, + source_execution_ids="", + status=SkillCandidateStatus.DRAFT, + created_by="system:evolution", + created_at=utcnow(), + updated_at=utcnow(), + ) + self._db.add(candidate) + await self._db.commit() + return candidate_id + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + + +async def _setup_skill_with_release_and_failures( + svc: SkillLifecycleService, + *, + owner: str = "default", + skill_key: str, + failure_count: int, +) -> str: + """Returns release_id.""" + execution = await svc.create_execution( + owner=owner, + sandbox_id="sb-1", + exec_type=ExecutionType.BROWSER, + code="open about:blank", + success=True, + execution_time_ms=5, + ) + candidate = await svc.create_candidate( + owner=owner, + skill_key=skill_key, + source_execution_ids=[execution.id], + summary="Test skill", + created_by="system:test", + ) + await svc.evaluate_candidate(owner=owner, candidate_id=candidate.id, passed=True, score=0.9) + release = await svc.promote_candidate( + owner=owner, candidate_id=candidate.id, stage=SkillReleaseStage.CANARY + ) + + # Declare goal so scheduler knows about the skill + await svc.declare_goal(owner=owner, skill_key=skill_key, goal=f"Goal for {skill_key}") + + for i in range(failure_count): + await svc.record_outcome( + owner=owner, + skill_key=skill_key, + release_id=release.id, + outcome="failure", + reasoning=f"Failure {i + 1}", + ) + + return release.id + + +def _make_scheduler( + db_session: AsyncSession, + agent: _FakeMutationAgent, + *, + enabled: bool = True, + min_failures: int = 2, + max_mutations: int = 5, +) -> EvolutionScheduler: + config = EvolutionConfig( + enabled=enabled, + min_failures_to_trigger=min_failures, + max_mutations_per_cycle=max_mutations, + max_recent_outcomes=10, + ) + return EvolutionScheduler( + db_session=db_session, + config=config, + mutation_agent=agent, + ) + + +# --------------------------------------------------------------------------- +# Tests +# --------------------------------------------------------------------------- + + +class TestEvolutionScheduler: + async def test_cycle_returns_empty_when_disabled( + self, db_session: AsyncSession, skill_svc: SkillLifecycleService + ): + agent = _FakeMutationAgent() + scheduler = _make_scheduler(db_session, agent, enabled=False) + + result = await scheduler.run_cycle() + + assert result.mutations_attempted == 0 + assert result.mutations_succeeded == 0 + assert len(agent.calls) == 0 + + async def test_cycle_triggers_mutation_for_skill_with_enough_failures( + self, db_session: AsyncSession, skill_svc: SkillLifecycleService + ): + await _setup_skill_with_release_and_failures( + skill_svc, skill_key="failing-skill", failure_count=3 + ) + + agent = _FakeMutationAgent() + scheduler = _make_scheduler(db_session, agent, min_failures=2) + + result = await scheduler.run_cycle() + + assert any(call[1] == "failing-skill" for call in agent.calls) + assert result.mutations_attempted >= 1 + + async def test_cycle_skips_skill_with_too_few_failures( + self, db_session: AsyncSession, skill_svc: SkillLifecycleService + ): + await _setup_skill_with_release_and_failures( + skill_svc, skill_key="barely-failing", failure_count=1 + ) + + agent = _FakeMutationAgent() + scheduler = _make_scheduler(db_session, agent, min_failures=2) + + await scheduler.run_cycle() + + assert not any(call[1] == "barely-failing" for call in agent.calls) + + async def test_cycle_skips_skill_with_no_goal_declared( + self, db_session: AsyncSession, skill_svc: SkillLifecycleService + ): + """Skills without a declared goal are not in the evolution loop.""" + execution = await skill_svc.create_execution( + owner="default", + sandbox_id="sb-1", + exec_type=ExecutionType.BROWSER, + code="open about:blank", + success=True, + execution_time_ms=5, + ) + candidate = await skill_svc.create_candidate( + owner="default", + skill_key="no-goal-skill", + source_execution_ids=[execution.id], + created_by="system:test", + ) + await skill_svc.evaluate_candidate( + owner="default", candidate_id=candidate.id, passed=True + ) + release = await skill_svc.promote_candidate( + owner="default", candidate_id=candidate.id, stage=SkillReleaseStage.CANARY + ) + # Add failures but NO goal declaration + for _ in range(5): + await skill_svc.record_outcome( + owner="default", + skill_key="no-goal-skill", + release_id=release.id, + outcome="failure", + reasoning="failed", + ) + + agent = _FakeMutationAgent() + scheduler = _make_scheduler(db_session, agent, min_failures=2) + + await scheduler.run_cycle() + + assert not any(call[1] == "no-goal-skill" for call in agent.calls) + + async def test_cycle_respects_max_mutations_budget( + self, db_session: AsyncSession, skill_svc: SkillLifecycleService + ): + for i in range(5): + await _setup_skill_with_release_and_failures( + skill_svc, skill_key=f"skill-{i}", failure_count=3 + ) + + agent = _FakeMutationAgent() + scheduler = _make_scheduler(db_session, agent, min_failures=2, max_mutations=2) + + result = await scheduler.run_cycle() + + assert result.mutations_attempted <= 2 + assert len(agent.calls) <= 2 + + async def test_cycle_counts_succeeded_mutations( + self, db_session: AsyncSession, skill_svc: SkillLifecycleService + ): + await _setup_skill_with_release_and_failures( + skill_svc, skill_key="success-skill", failure_count=3 + ) + + agent = _FakeMutationAgent(success=True) + scheduler = _make_scheduler(db_session, agent, min_failures=2) + + result = await scheduler.run_cycle() + + assert result.mutations_succeeded >= 1 + + async def test_cycle_counts_failed_mutations( + self, db_session: AsyncSession, skill_svc: SkillLifecycleService + ): + await _setup_skill_with_release_and_failures( + skill_svc, skill_key="fail-skill", failure_count=3 + ) + + agent = _FakeMutationAgent(success=False) + scheduler = _make_scheduler(db_session, agent, min_failures=2) + + result = await scheduler.run_cycle() + + assert result.mutations_attempted >= 1 + assert result.mutations_succeeded == 0 + + async def test_cycle_skips_skill_already_mutated_since_last_failure( + self, db_session: AsyncSession, skill_svc: SkillLifecycleService + ): + """Skip mutating when an evolution candidate already exists after the latest failure.""" + await _setup_skill_with_release_and_failures( + skill_svc, skill_key="stable-skill", failure_count=3 + ) + + # Pass db_session so the fake agent creates a real SkillCandidate row + agent = _FakeMutationAgent(success=True, db_session=db_session) + scheduler = _make_scheduler(db_session, agent, min_failures=2) + + # First cycle mutates + first = await scheduler.run_cycle() + assert first.mutations_attempted == 1 + + # Second cycle: mutation candidate already exists after latest failure → skip + second = await scheduler.run_cycle() + assert second.mutations_attempted == 0 + assert len(agent.calls) == 1 # agent called exactly once total + + async def test_cycle_mutates_again_after_new_failures( + self, db_session: AsyncSession, skill_svc: SkillLifecycleService + ): + """New failures that arrive after the last mutation should re-trigger evolution.""" + release_id = await _setup_skill_with_release_and_failures( + skill_svc, skill_key="resurging-skill", failure_count=3 + ) + + agent = _FakeMutationAgent(success=True, db_session=db_session) + scheduler = _make_scheduler(db_session, agent, min_failures=2) + + # First cycle mutates + await scheduler.run_cycle() + assert len(agent.calls) == 1 + + # New failures arrive after the mutation candidate was created + for i in range(3): + await skill_svc.record_outcome( + owner="default", + skill_key="resurging-skill", + release_id=release_id, + outcome="failure", + reasoning=f"New post-mutation failure {i}", + ) + + # Second cycle: new failures detected → mutate again + second = await scheduler.run_cycle() + assert second.mutations_attempted == 1 + assert len(agent.calls) == 2 diff --git a/pkgs/bay/tests/unit/managers/test_extraction_strategies.py b/pkgs/bay/tests/unit/managers/test_extraction_strategies.py new file mode 100644 index 0000000..700e5b2 --- /dev/null +++ b/pkgs/bay/tests/unit/managers/test_extraction_strategies.py @@ -0,0 +1,350 @@ +"""Unit tests for extraction strategies.""" + +from __future__ import annotations + +import json + +import httpx +import pytest + +from app.config import LlmExtractionConfig +from app.services.skills.extraction import ( + ExtractionContext, + ExtractionResult, + LlmAssistedExtractionStrategy, + RuleBasedExtractionStrategy, + compute_payload_hash, +) + + +def _context() -> ExtractionContext: + return ExtractionContext( + owner="default", + execution_id="exec-1", + sandbox_id="sandbox-1", + code="batch", + description="Checkout flow", + tags="skill:browser-checkout,learn", + ) + + +@pytest.mark.asyncio +async def test_rule_strategy_matches_legacy_segmentation_and_scoring(): + strategy = RuleBasedExtractionStrategy() + steps = [ + {"kind": "individual_action", "cmd": "open https://example.com", "exit_code": 0}, + {"kind": "individual_action", "cmd": "click @e1", "exit_code": 0}, + {"kind": "individual_action", "cmd": "snapshot -i", "exit_code": 0}, + {"kind": "individual_action", "cmd": "type @e2 hello", "exit_code": 0}, + ] + segments = strategy.extract_actionable_segments(steps=steps) + assert len(segments) == 1 + assert [step["cmd"] for step in segments[0]] == [ + "open https://example.com", + "click @e1", + ] + + results = await strategy.extract(segments=segments, context=_context()) + assert len(results) == 1 + assert results[0].skill_key == "browser-checkout" + + metrics = strategy.score_segment(segment=results[0].steps) + assert metrics == { + "score": 0.91, + "replay_success": 1.0, + "samples": 20, + "error_rate": 0.0, + "p95_duration": 0, + } + + +@pytest.mark.asyncio +async def test_rule_strategy_extracts_variable_for_type_action(): + strategy = RuleBasedExtractionStrategy(variable_extraction_enabled=True) + segment = [ + {"kind": "individual_action", "cmd": "click @from", "exit_code": 0}, + {"kind": "individual_action", "cmd": 'type @from "San Francisco"', "exit_code": 0}, + ] + results = await strategy.extract(segments=[segment], context=_context()) + variables = results[0].variables + assert variables is not None + assert len(variables) == 1 + spec = next(iter(variables.values())) + assert spec.type == "string" + assert spec.default_value == "San Francisco" + assert spec.action_index == 1 + assert spec.arg_position == 1 + + +@pytest.mark.asyncio +async def test_rule_strategy_variable_extraction_handles_none_and_multiple(): + strategy = RuleBasedExtractionStrategy(variable_extraction_enabled=True) + without_type = [ + {"kind": "individual_action", "cmd": "open https://example.com", "exit_code": 0}, + {"kind": "individual_action", "cmd": "click @btn", "exit_code": 0}, + ] + multiple_type = [ + {"kind": "individual_action", "cmd": "type @from Edinburgh", "exit_code": 0}, + {"kind": "individual_action", "cmd": "click @swap", "exit_code": 0}, + {"kind": "individual_action", "cmd": "type @to Manchester", "exit_code": 0}, + ] + results = await strategy.extract(segments=[without_type, multiple_type], context=_context()) + assert results[0].variables is None + assert results[1].variables is not None + assert len(results[1].variables or {}) == 2 + action_indexes = sorted(spec.action_index for spec in (results[1].variables or {}).values()) + assert action_indexes == [0, 2] + + +def test_extraction_result_computes_payload_hash_from_ordered_cmds(): + steps = [{"cmd": "open https://a.com"}, {"cmd": "click @e1"}] + result = ExtractionResult(skill_key="browser-a", steps=steps) + assert result.payload_hash == compute_payload_hash(steps=steps) + + +@pytest.mark.asyncio +async def test_llm_strategy_successfully_parses_semantic_results(monkeypatch: pytest.MonkeyPatch): + captured_request: dict[str, object] = {} + + async def _fake_post( + self: httpx.AsyncClient, + url: str, + *, + headers: dict[str, str] | None = None, + json: dict[str, object] | None = None, + ) -> httpx.Response: + del self + captured_request["url"] = url + captured_request["headers"] = headers or {} + captured_request["json"] = json or {} + payload = { + "choices": [ + { + "message": { + "content": json_module.dumps( + { + "results": [ + { + "skill_key": "semantic-flight-search", + "description": "Semantic split", + "steps": [ + {"cmd": "open https://flights.example"}, + {"cmd": "click @search"}, + ], + "variables": { + "from_city": { + "type": "string", + "default_value": "Edinburgh", + "action_index": 0, + "arg_position": 1, + } + }, + } + ] + } + ) + } + } + ] + } + request = httpx.Request("POST", url) + return httpx.Response(200, request=request, json=payload) + + json_module = json + monkeypatch.setattr(httpx.AsyncClient, "post", _fake_post) + + strategy = LlmAssistedExtractionStrategy( + config=LlmExtractionConfig( + enabled=True, + api_base="https://llm.test/v1", + api_key="k", + model="test-model", + timeout_seconds=3, + max_tokens=1000, + ), + fallback=RuleBasedExtractionStrategy(), + ) + segment = [ + {"cmd": "open https://flights.example", "exit_code": 0, "kind": "individual_action"}, + {"cmd": "click @search", "exit_code": 0, "kind": "individual_action"}, + ] + results = await strategy.extract(segments=[segment], context=_context()) + + assert len(results) == 1 + assert results[0].skill_key == "semantic-flight-search" + assert results[0].variables is not None + assert "response_format" in (captured_request["json"] or {}) + assert (captured_request["json"] or {}).get("model") == "test-model" + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + "side_effect", + [ + httpx.TimeoutException("timeout"), + httpx.ConnectError("connect fail", request=httpx.Request("POST", "https://llm.test/v1")), + ], +) +async def test_llm_strategy_falls_back_on_timeout_or_connection( + monkeypatch: pytest.MonkeyPatch, + side_effect: Exception, +): + async def _failing_post( + self: httpx.AsyncClient, + url: str, + *, + headers: dict[str, str] | None = None, + json: dict[str, object] | None = None, + ) -> httpx.Response: + del self, url, headers, json + raise side_effect + + monkeypatch.setattr(httpx.AsyncClient, "post", _failing_post) + + strategy = LlmAssistedExtractionStrategy( + config=LlmExtractionConfig(enabled=True, api_base="https://llm.test/v1", api_key="k"), + fallback=RuleBasedExtractionStrategy(), + ) + segment = [ + {"cmd": "open https://example.com", "exit_code": 0, "kind": "individual_action"}, + {"cmd": "click @e1", "exit_code": 0, "kind": "individual_action"}, + ] + results = await strategy.extract(segments=[segment], context=_context()) + assert len(results) == 1 + assert results[0].skill_key == "browser-checkout" + + +@pytest.mark.asyncio +async def test_llm_strategy_falls_back_on_parse_failure(monkeypatch: pytest.MonkeyPatch): + async def _bad_json_post( + self: httpx.AsyncClient, + url: str, + *, + headers: dict[str, str] | None = None, + json: dict[str, object] | None = None, + ) -> httpx.Response: + del self, headers, json + request = httpx.Request("POST", url) + return httpx.Response( + 200, + request=request, + json={"choices": [{"message": {"content": "not-json"}}]}, + ) + + monkeypatch.setattr(httpx.AsyncClient, "post", _bad_json_post) + + strategy = LlmAssistedExtractionStrategy( + config=LlmExtractionConfig(enabled=True, api_base="https://llm.test/v1", api_key="k"), + fallback=RuleBasedExtractionStrategy(), + ) + segment = [ + {"cmd": "open https://example.com", "exit_code": 0, "kind": "individual_action"}, + {"cmd": "click @e1", "exit_code": 0, "kind": "individual_action"}, + ] + results = await strategy.extract(segments=[segment], context=_context()) + assert len(results) == 1 + assert results[0].skill_key == "browser-checkout" + + +@pytest.mark.asyncio +async def test_llm_strategy_maps_http_status_error_to_connection_reason( + monkeypatch: pytest.MonkeyPatch, +): + request = httpx.Request("POST", "https://llm.test/v1/chat/completions") + response = httpx.Response(503, request=request, json={"error": "upstream unavailable"}) + + async def _raise_status_error( + self: LlmAssistedExtractionStrategy, + *, + segments: list[list[dict[str, object]]], + context: ExtractionContext, + ) -> list[ExtractionResult]: + del self, segments, context + raise httpx.HTTPStatusError("upstream error", request=request, response=response) + + captured: dict[str, str] = {} + + async def _capture_reason( + self: LlmAssistedExtractionStrategy, + *, + reason: str, + segments: list[list[dict[str, object]]], + context: ExtractionContext, + ) -> list[ExtractionResult]: + captured["reason"] = reason + return await RuleBasedExtractionStrategy().extract(segments=segments, context=context) + + monkeypatch.setattr(LlmAssistedExtractionStrategy, "_extract_via_llm", _raise_status_error) + monkeypatch.setattr(LlmAssistedExtractionStrategy, "_fallback_with_reason", _capture_reason) + + strategy = LlmAssistedExtractionStrategy( + config=LlmExtractionConfig(enabled=True, api_base="https://llm.test/v1", api_key="k"), + fallback=RuleBasedExtractionStrategy(), + ) + segment = [ + {"cmd": "open https://example.com", "exit_code": 0, "kind": "individual_action"}, + {"cmd": "click @e1", "exit_code": 0, "kind": "individual_action"}, + ] + results = await strategy.extract(segments=[segment], context=_context()) + + assert captured["reason"] == "connection_error" + assert len(results) == 1 + assert results[0].skill_key == "browser-checkout" + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + ("side_effect", "expected_reason"), + [ + (httpx.TimeoutException("timeout"), "timeout"), + ( + httpx.ConnectError( + "connect fail", + request=httpx.Request("POST", "https://llm.test/v1/chat/completions"), + ), + "connection_error", + ), + (ValueError("bad parse"), "parse_error"), + ], +) +async def test_llm_strategy_logs_structured_fallback_event( + monkeypatch: pytest.MonkeyPatch, + side_effect: Exception, + expected_reason: str, +): + async def _raise( + self: LlmAssistedExtractionStrategy, + *, + segments: list[list[dict[str, object]]], + context: ExtractionContext, + ) -> list[ExtractionResult]: + del self, segments, context + raise side_effect + + events: list[tuple[str, dict[str, object]]] = [] + + class _FakeLog: + def warning(self, event: str, **kwargs: object) -> None: + events.append((event, kwargs)) + + monkeypatch.setattr(LlmAssistedExtractionStrategy, "_extract_via_llm", _raise) + + strategy = LlmAssistedExtractionStrategy( + config=LlmExtractionConfig(enabled=True, api_base="https://llm.test/v1", api_key="k"), + fallback=RuleBasedExtractionStrategy(), + ) + strategy._log = _FakeLog() + + segment = [ + {"cmd": "open https://example.com", "exit_code": 0, "kind": "individual_action"}, + {"cmd": "click @e1", "exit_code": 0, "kind": "individual_action"}, + ] + results = await strategy.extract(segments=[segment], context=_context()) + + assert len(results) == 1 + assert events == [ + ( + "skills.browser.extraction.llm_fallback", + {"reason": expected_reason, "execution_id": "exec-1"}, + ) + ] diff --git a/pkgs/bay/tests/unit/managers/test_goal_conditioned_evaluator.py b/pkgs/bay/tests/unit/managers/test_goal_conditioned_evaluator.py new file mode 100644 index 0000000..32695b0 --- /dev/null +++ b/pkgs/bay/tests/unit/managers/test_goal_conditioned_evaluator.py @@ -0,0 +1,182 @@ +"""Unit tests for GoalConditionedEvaluator (Phase 3). + +All tests use a FakeLlmClient — no real HTTP calls. + +Tests cover: +- evaluates passing mutation candidate +- evaluates failing mutation candidate +- includes goal, rubric, and failure context in the LLM prompt +- returns None (graceful failure) when LLM call fails +- score is clamped to [0.0, 1.0] +""" + +from __future__ import annotations + +import pytest + +from app.services.skills.evolution.evaluator import EvaluationResult, GoalConditionedEvaluator +from app.services.skills.evolution.rubric import SkillRubric + +# --------------------------------------------------------------------------- +# Fake LLM client +# --------------------------------------------------------------------------- + + +class _FakeLlmClient: + def __init__( + self, + output: EvaluationResult | None = None, + raises: Exception | None = None, + ): + self._output = output or EvaluationResult( + passed=True, + score=0.85, + reasoning=( + "The mutation adds explicit waits and semantic selectors that address " + "the reported async rendering failures." + ), + ) + self._raises = raises + self.calls: list[dict] = [] # records kwargs passed to evaluate + + async def evaluate_mutation( + self, + *, + goal: str, + rubric_text: str, + skill_content: str, + failure_context: str, + ) -> EvaluationResult: + self.calls.append( + { + "goal": goal, + "rubric_text": rubric_text, + "skill_content": skill_content, + "failure_context": failure_context, + } + ) + if self._raises is not None: + raise self._raises + return self._output + + +_SAMPLE_RUBRIC = SkillRubric( + summary="Skill returns star count as an integer.", + success_criteria=["Returns an integer", "Handles async loading"], + failure_indicators=["Returns None", "Timeout"], + evaluation_focus="Check robustness of selectors and wait strategies.", +) + +_SAMPLE_CONTENT = "# SKILL: github-get-stars\n\n## Summary\nImproved wait logic." +_SAMPLE_FAILURES = "- Element not found\n- Timeout after 3s" + + +# --------------------------------------------------------------------------- +# Tests +# --------------------------------------------------------------------------- + + +class TestGoalConditionedEvaluator: + def _make_evaluator(self, llm: _FakeLlmClient | None = None) -> GoalConditionedEvaluator: + return GoalConditionedEvaluator(llm_client=llm or _FakeLlmClient()) + + async def test_returns_evaluation_result(self): + ev = self._make_evaluator() + result = await ev.evaluate( + goal="Return star count as integer.", + rubric=_SAMPLE_RUBRIC, + skill_content=_SAMPLE_CONTENT, + failure_context=_SAMPLE_FAILURES, + ) + + assert isinstance(result, EvaluationResult) + + async def test_passing_candidate_returns_passed_true(self): + llm = _FakeLlmClient(EvaluationResult(passed=True, score=0.9, reasoning="Good.")) + ev = self._make_evaluator(llm) + + result = await ev.evaluate( + goal="Return star count.", + rubric=_SAMPLE_RUBRIC, + skill_content=_SAMPLE_CONTENT, + failure_context=_SAMPLE_FAILURES, + ) + + assert result.passed is True + assert result.score == pytest.approx(0.9) + + async def test_failing_candidate_returns_passed_false(self): + llm = _FakeLlmClient(EvaluationResult(passed=False, score=0.2, reasoning="Still broken.")) + ev = self._make_evaluator(llm) + + result = await ev.evaluate( + goal="Return star count.", + rubric=_SAMPLE_RUBRIC, + skill_content=_SAMPLE_CONTENT, + failure_context=_SAMPLE_FAILURES, + ) + + assert result.passed is False + assert result.score < 0.5 + + async def test_goal_included_in_llm_call(self): + llm = _FakeLlmClient() + ev = self._make_evaluator(llm) + goal = "Return the exact star count as an integer." + + await ev.evaluate( + goal=goal, + rubric=_SAMPLE_RUBRIC, + skill_content=_SAMPLE_CONTENT, + failure_context=_SAMPLE_FAILURES, + ) + + assert len(llm.calls) == 1 + assert goal in llm.calls[0]["goal"] + + async def test_failure_context_included_in_llm_call(self): + llm = _FakeLlmClient() + ev = self._make_evaluator(llm) + + await ev.evaluate( + goal="Return star count.", + rubric=_SAMPLE_RUBRIC, + skill_content=_SAMPLE_CONTENT, + failure_context="- Element not found at .star-count", + ) + + assert "Element not found" in llm.calls[0]["failure_context"] + + async def test_works_without_rubric(self): + ev = self._make_evaluator() + result = await ev.evaluate( + goal="Return star count.", + rubric=None, + skill_content=_SAMPLE_CONTENT, + failure_context=_SAMPLE_FAILURES, + ) + + assert result is not None + + async def test_returns_none_when_llm_fails(self): + llm = _FakeLlmClient(raises=RuntimeError("LLM unavailable")) + ev = self._make_evaluator(llm) + + result = await ev.evaluate( + goal="Return star count.", + rubric=_SAMPLE_RUBRIC, + skill_content=_SAMPLE_CONTENT, + failure_context=_SAMPLE_FAILURES, + ) + + assert result is None + + async def test_score_clamped_to_zero_one(self): + llm = _FakeLlmClient(EvaluationResult(passed=True, score=1.5, reasoning="Overclaim.")) + ev = self._make_evaluator(llm) + + result = await ev.evaluate( + goal="g", rubric=None, skill_content="c", failure_context="f" + ) + + assert 0.0 <= result.score <= 1.0 diff --git a/pkgs/bay/tests/unit/managers/test_meta_prompt_service.py b/pkgs/bay/tests/unit/managers/test_meta_prompt_service.py new file mode 100644 index 0000000..3709d5c --- /dev/null +++ b/pkgs/bay/tests/unit/managers/test_meta_prompt_service.py @@ -0,0 +1,126 @@ +"""Unit tests for MetaPromptService (Phase 2). + +Tests cover: +- seed_defaults: creates default prompts when DB is empty, idempotent on re-run +- sample_prompt: returns a prompt from the archive +- record_usage / record_success: updates counters +""" + +from __future__ import annotations + +import pytest +from sqlalchemy.ext.asyncio import AsyncSession, create_async_engine +from sqlalchemy.orm import sessionmaker +from sqlmodel import SQLModel + +from app.services.skills.evolution.meta_prompt import MetaPromptService + + +@pytest.fixture +async def db_session() -> AsyncSession: + engine = create_async_engine("sqlite+aiosqlite:///:memory:", echo=False) + async with engine.begin() as conn: + await conn.run_sync(SQLModel.metadata.create_all) + factory = sessionmaker(engine, class_=AsyncSession, expire_on_commit=False) + async with factory() as session: + yield session + await engine.dispose() + + +@pytest.fixture +def svc(db_session: AsyncSession) -> MetaPromptService: + return MetaPromptService(db_session) + + +class TestSeedDefaults: + async def test_creates_default_prompts_on_empty_db(self, svc: MetaPromptService): + count = await svc.seed_defaults() + + assert count > 0 + + async def test_each_default_prompt_has_instruction_text(self, svc: MetaPromptService): + await svc.seed_defaults() + prompts = await svc.list_all() + + assert len(prompts) > 0 + for p in prompts: + assert p.instruction.strip() != "" + + async def test_default_prompts_are_flagged_is_default(self, svc: MetaPromptService): + await svc.seed_defaults() + prompts = await svc.list_all() + + assert all(p.is_default for p in prompts) + + async def test_seed_is_idempotent(self, svc: MetaPromptService): + count_first = await svc.seed_defaults() + count_second = await svc.seed_defaults() + + assert count_first == count_second + # Total number of prompts should not have doubled + all_prompts = await svc.list_all() + assert len(all_prompts) == count_first + + +class TestSamplePrompt: + async def test_sample_returns_a_prompt_after_seeding(self, svc: MetaPromptService): + await svc.seed_defaults() + + prompt = await svc.sample_prompt() + + assert prompt is not None + assert prompt.instruction.strip() != "" + + async def test_sample_returns_none_when_empty(self, svc: MetaPromptService): + prompt = await svc.sample_prompt() + + assert prompt is None + + +class TestRecordUsage: + async def test_increments_usage_count(self, svc: MetaPromptService): + await svc.seed_defaults() + prompts = await svc.list_all() + prompt = prompts[0] + assert prompt.usage_count == 0 + + await svc.record_usage(prompt.id) + + updated = await svc.get_prompt(prompt.id) + assert updated is not None + assert updated.usage_count == 1 + + async def test_multiple_usages_accumulate(self, svc: MetaPromptService): + await svc.seed_defaults() + prompt = (await svc.list_all())[0] + + await svc.record_usage(prompt.id) + await svc.record_usage(prompt.id) + await svc.record_usage(prompt.id) + + updated = await svc.get_prompt(prompt.id) + assert updated.usage_count == 3 + + +class TestRecordSuccess: + async def test_increments_success_count(self, svc: MetaPromptService): + await svc.seed_defaults() + prompt = (await svc.list_all())[0] + assert prompt.success_count == 0 + + await svc.record_success(prompt.id) + + updated = await svc.get_prompt(prompt.id) + assert updated.success_count == 1 + + async def test_success_count_independent_from_usage_count(self, svc: MetaPromptService): + await svc.seed_defaults() + prompt = (await svc.list_all())[0] + + await svc.record_usage(prompt.id) + await svc.record_usage(prompt.id) + await svc.record_success(prompt.id) + + updated = await svc.get_prompt(prompt.id) + assert updated.usage_count == 2 + assert updated.success_count == 1 diff --git a/pkgs/bay/tests/unit/managers/test_rubric_generator.py b/pkgs/bay/tests/unit/managers/test_rubric_generator.py new file mode 100644 index 0000000..cee44cb --- /dev/null +++ b/pkgs/bay/tests/unit/managers/test_rubric_generator.py @@ -0,0 +1,111 @@ +"""Unit tests for RubricGenerator (Phase 3). + +All tests use a FakeLlmClient — no real HTTP calls. + +Tests cover: +- generates rubric with all required fields from a goal +- gracefully returns None when LLM call fails +- rubric fields are non-empty strings / non-empty lists +""" + +from __future__ import annotations + +from app.services.skills.evolution.rubric import RubricGenerator, SkillRubric + +# --------------------------------------------------------------------------- +# Fake LLM client +# --------------------------------------------------------------------------- + + +class _FakeLlmClient: + """Returns a fixed SkillRubric or raises.""" + + def __init__(self, output: SkillRubric | None = None, raises: Exception | None = None): + self._output = output or SkillRubric( + summary="Skill returns the exact star count as an integer.", + success_criteria=[ + "Returns an integer value greater than or equal to 0", + "Value matches the current star count displayed on the page", + ], + failure_indicators=[ + "Returns None or raises an exception", + "Returns a string instead of integer", + "Value is 0 for a popular repository", + ], + evaluation_focus=( + "Focus on whether the mutated instructions add explicit waits " + "and robust selectors that survive page layout changes." + ), + ) + self._raises = raises + self.calls: list[str] = [] + + async def generate_rubric(self, goal: str) -> SkillRubric: + self.calls.append(goal) + if self._raises is not None: + raise self._raises + return self._output + + +# --------------------------------------------------------------------------- +# Tests +# --------------------------------------------------------------------------- + + +class TestRubricGenerator: + def _make_generator(self, llm: _FakeLlmClient | None = None) -> RubricGenerator: + return RubricGenerator(llm_client=llm or _FakeLlmClient()) + + async def test_generates_rubric_for_goal(self): + gen = self._make_generator() + rubric = await gen.generate( + "Navigate to a GitHub repo page and return the current star count as an integer." + ) + + assert rubric is not None + assert isinstance(rubric, SkillRubric) + + async def test_rubric_summary_is_nonempty(self): + gen = self._make_generator() + rubric = await gen.generate("Return the PyPI package version.") + + assert rubric.summary.strip() != "" + + async def test_rubric_success_criteria_nonempty_list(self): + gen = self._make_generator() + rubric = await gen.generate("Return the PyPI package version.") + + assert isinstance(rubric.success_criteria, list) + assert len(rubric.success_criteria) >= 1 + assert all(isinstance(c, str) and c.strip() for c in rubric.success_criteria) + + async def test_rubric_failure_indicators_nonempty_list(self): + gen = self._make_generator() + rubric = await gen.generate("Return the PyPI package version.") + + assert isinstance(rubric.failure_indicators, list) + assert len(rubric.failure_indicators) >= 1 + + async def test_rubric_evaluation_focus_nonempty(self): + gen = self._make_generator() + rubric = await gen.generate("Return the PyPI package version.") + + assert rubric.evaluation_focus.strip() != "" + + async def test_llm_receives_the_goal_text(self): + llm = _FakeLlmClient() + gen = self._make_generator(llm) + goal = "Fetch the top 10 Hacker News story titles." + + await gen.generate(goal) + + assert len(llm.calls) == 1 + assert llm.calls[0] == goal + + async def test_returns_none_when_llm_fails(self): + llm = _FakeLlmClient(raises=RuntimeError("LLM unavailable")) + gen = self._make_generator(llm) + + result = await gen.generate("Some goal") + + assert result is None diff --git a/pkgs/bay/tests/unit/managers/test_skill_evolution_service.py b/pkgs/bay/tests/unit/managers/test_skill_evolution_service.py new file mode 100644 index 0000000..2371305 --- /dev/null +++ b/pkgs/bay/tests/unit/managers/test_skill_evolution_service.py @@ -0,0 +1,467 @@ +"""Unit tests for skill evolution service methods (Phase 1). + +Tests cover: +- declare_goal: create, upsert, owner isolation +- get_skill_goal: returns None when absent +- record_outcome: success/failure/partial, invalid outcome, signals +- get_active_skill_view: no release, with release + goal, content assembly +""" + +from __future__ import annotations + +import json + +import pytest +from sqlalchemy.ext.asyncio import AsyncSession, create_async_engine +from sqlalchemy.orm import sessionmaker +from sqlmodel import SQLModel + +from app.errors import NotFoundError, ValidationError +from app.models.skill import ( + ExecutionType, + SkillReleaseStage, +) +from app.services.skills import SkillLifecycleService + + +@pytest.fixture +async def db_session() -> AsyncSession: + engine = create_async_engine("sqlite+aiosqlite:///:memory:", echo=False) + async with engine.begin() as conn: + await conn.run_sync(SQLModel.metadata.create_all) + factory = sessionmaker(engine, class_=AsyncSession, expire_on_commit=False) + async with factory() as session: + yield session + await engine.dispose() + + +@pytest.fixture +def svc(db_session: AsyncSession) -> SkillLifecycleService: + return SkillLifecycleService(db_session) + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + + +async def _make_candidate_with_release( + svc: SkillLifecycleService, + *, + owner: str = "default", + skill_key: str = "test-skill", + summary: str | None = "Test summary", + usage_notes: str | None = "Test usage notes", + preconditions: dict | None = None, + postconditions: dict | None = None, +) -> tuple: + """Create a candidate + passing evaluation + active release. Returns (candidate, release).""" + execution = await svc.create_execution( + owner=owner, + sandbox_id="sandbox-1", + exec_type=ExecutionType.BROWSER, + code="open about:blank", + success=True, + execution_time_ms=10, + ) + candidate = await svc.create_candidate( + owner=owner, + skill_key=skill_key, + source_execution_ids=[execution.id], + summary=summary, + usage_notes=usage_notes, + preconditions=preconditions, + postconditions=postconditions, + created_by="system:test", + ) + await svc.evaluate_candidate( + owner=owner, + candidate_id=candidate.id, + passed=True, + score=0.9, + ) + release = await svc.promote_candidate( + owner=owner, + candidate_id=candidate.id, + stage=SkillReleaseStage.CANARY, + ) + return candidate, release + + +# --------------------------------------------------------------------------- +# declare_goal +# --------------------------------------------------------------------------- + + +class TestDeclareGoal: + async def test_creates_new_record(self, svc: SkillLifecycleService): + goal = await svc.declare_goal( + owner="default", + skill_key="github-get-stars", + goal="Return the star count of a GitHub repo.", + ) + + assert goal.id.startswith("goal-") + assert goal.skill_key == "github-get-stars" + assert goal.goal == "Return the star count of a GitHub repo." + assert goal.owner == "default" + assert goal.rubric_summary == "" + + async def test_upserts_existing_goal(self, svc: SkillLifecycleService): + first = await svc.declare_goal( + owner="default", + skill_key="github-get-stars", + goal="Original goal.", + ) + second = await svc.declare_goal( + owner="default", + skill_key="github-get-stars", + goal="Updated goal.", + ) + + assert second.id == first.id + assert second.goal == "Updated goal." + + async def test_owner_isolation(self, svc: SkillLifecycleService): + goal_a = await svc.declare_goal( + owner="alice", + skill_key="shared-skill", + goal="Alice's goal.", + ) + goal_b = await svc.declare_goal( + owner="bob", + skill_key="shared-skill", + goal="Bob's goal.", + ) + + assert goal_a.id != goal_b.id + assert goal_a.goal == "Alice's goal." + assert goal_b.goal == "Bob's goal." + + async def test_different_skill_keys_are_independent(self, svc: SkillLifecycleService): + g1 = await svc.declare_goal(owner="default", skill_key="skill-a", goal="Goal A") + g2 = await svc.declare_goal(owner="default", skill_key="skill-b", goal="Goal B") + + assert g1.id != g2.id + assert g1.skill_key == "skill-a" + assert g2.skill_key == "skill-b" + + async def test_goal_change_clears_rubric_cache(self, svc: SkillLifecycleService): + """When goal text changes, rubric_json and rubric_summary must be cleared. + + This prevents the evaluator from using a stale rubric that was derived + from the old goal text. + """ + goal = await svc.declare_goal( + owner="default", + skill_key="cache-skill", + goal="Original goal.", + ) + # Simulate rubric having been generated and cached + goal.rubric_json = '{"summary": "old rubric", "success_criteria": [], "failure_indicators": [], "evaluation_focus": ""}' # noqa: E501 + goal.rubric_summary = "old rubric" + svc._db.add(goal) + await svc._db.commit() + + # Update goal to a new text + updated = await svc.declare_goal( + owner="default", + skill_key="cache-skill", + goal="Completely different goal.", + ) + + assert updated.rubric_json is None + assert updated.rubric_summary == "" + + async def test_same_goal_text_preserves_rubric_cache(self, svc: SkillLifecycleService): + """Re-declaring with the exact same goal text must NOT clear the rubric cache.""" + goal = await svc.declare_goal( + owner="default", + skill_key="stable-skill", + goal="Stable goal.", + ) + goal.rubric_json = '{"summary": "valid rubric", "success_criteria": [], "failure_indicators": [], "evaluation_focus": ""}' # noqa: E501 + goal.rubric_summary = "valid rubric" + svc._db.add(goal) + await svc._db.commit() + + # Re-declare with the same goal text + updated = await svc.declare_goal( + owner="default", + skill_key="stable-skill", + goal="Stable goal.", + ) + + assert updated.rubric_json is not None + assert updated.rubric_summary == "valid rubric" + + +# --------------------------------------------------------------------------- +# get_skill_goal +# --------------------------------------------------------------------------- + + +class TestGetSkillGoal: + async def test_returns_none_when_not_declared(self, svc: SkillLifecycleService): + result = await svc.get_skill_goal(owner="default", skill_key="nonexistent") + assert result is None + + async def test_returns_goal_after_declare(self, svc: SkillLifecycleService): + await svc.declare_goal(owner="default", skill_key="found-skill", goal="A goal") + result = await svc.get_skill_goal(owner="default", skill_key="found-skill") + + assert result is not None + assert result.goal == "A goal" + + async def test_owner_isolation_on_get(self, svc: SkillLifecycleService): + await svc.declare_goal(owner="alice", skill_key="skill-x", goal="Alice goal") + + result = await svc.get_skill_goal(owner="bob", skill_key="skill-x") + assert result is None + + +# --------------------------------------------------------------------------- +# record_outcome +# --------------------------------------------------------------------------- + + +class TestRecordOutcome: + async def test_success_outcome(self, svc: SkillLifecycleService): + _, release = await _make_candidate_with_release(svc, skill_key="github-get-stars") + outcome = await svc.record_outcome( + owner="default", + skill_key="github-get-stars", + release_id=release.id, + outcome="success", + reasoning="Page loaded, star count found at expected selector.", + ) + + assert outcome.id.startswith("outcome-") + assert outcome.owner == "default" + assert outcome.skill_key == "github-get-stars" + assert outcome.release_id == release.id + assert outcome.outcome == "success" + assert outcome.reasoning == "Page loaded, star count found at expected selector." + assert outcome.execution_id is None + assert outcome.signals_json is None + + async def test_failure_outcome_with_signals(self, svc: SkillLifecycleService): + _, release = await _make_candidate_with_release(svc, skill_key="github-get-stars") + signals = {"page_load_time_ms": 5000, "element_found": False} + outcome = await svc.record_outcome( + owner="default", + skill_key="github-get-stars", + release_id=release.id, + outcome="failure", + reasoning="Star count element no longer at .social-count — layout changed.", + execution_id="exec-123", + signals=signals, + ) + + assert outcome.outcome == "failure" + assert outcome.execution_id == "exec-123" + parsed_signals = json.loads(outcome.signals_json) + assert parsed_signals["element_found"] is False + assert parsed_signals["page_load_time_ms"] == 5000 + + async def test_partial_outcome(self, svc: SkillLifecycleService): + _, release = await _make_candidate_with_release(svc, skill_key="ui-beautify") + outcome = await svc.record_outcome( + owner="default", + skill_key="ui-beautify", + release_id=release.id, + outcome="partial", + reasoning="UI partially updated; footer styles were skipped.", + ) + + assert outcome.outcome == "partial" + + async def test_invalid_outcome_raises_validation_error(self, svc: SkillLifecycleService): + # ValidationError fires before release lookup — no real release needed + with pytest.raises(ValidationError, match="Invalid outcome"): + await svc.record_outcome( + owner="default", + skill_key="test-skill", + release_id="release-001", + outcome="unknown", + reasoning="some reasoning", + ) + + async def test_release_id_not_found_raises_not_found_error(self, svc: SkillLifecycleService): + with pytest.raises(NotFoundError): + await svc.record_outcome( + owner="default", + skill_key="test-skill", + release_id="nonexistent-release", + outcome="success", + reasoning="some reasoning", + ) + + async def test_wrong_owner_raises_not_found_error(self, svc: SkillLifecycleService): + _, release = await _make_candidate_with_release( + svc, owner="alice", skill_key="shared-skill" + ) + with pytest.raises(NotFoundError): + await svc.record_outcome( + owner="bob", # wrong owner + skill_key="shared-skill", + release_id=release.id, + outcome="success", + reasoning="trying to pollute alice's signal feed", + ) + + async def test_wrong_skill_key_raises_not_found_error(self, svc: SkillLifecycleService): + _, release = await _make_candidate_with_release(svc, skill_key="real-skill") + with pytest.raises(NotFoundError): + await svc.record_outcome( + owner="default", + skill_key="other-skill", # mismatched skill_key + release_id=release.id, + outcome="failure", + reasoning="trying to tag wrong skill", + ) + + async def test_multiple_outcomes_are_independent_records(self, svc: SkillLifecycleService): + _, release = await _make_candidate_with_release(svc, skill_key="skill-z") + o1 = await svc.record_outcome( + owner="default", + skill_key="skill-z", + release_id=release.id, + outcome="success", + reasoning="First run OK.", + ) + o2 = await svc.record_outcome( + owner="default", + skill_key="skill-z", + release_id=release.id, + outcome="failure", + reasoning="Second run failed.", + ) + + assert o1.id != o2.id + + async def test_owner_isolation(self, svc: SkillLifecycleService): + _, release_a = await _make_candidate_with_release( + svc, owner="alice", skill_key="shared-skill" + ) + _, release_b = await _make_candidate_with_release( + svc, owner="bob", skill_key="shared-skill" + ) + oa = await svc.record_outcome( + owner="alice", + skill_key="shared-skill", + release_id=release_a.id, + outcome="success", + reasoning="Alice's run.", + ) + ob = await svc.record_outcome( + owner="bob", + skill_key="shared-skill", + release_id=release_b.id, + outcome="failure", + reasoning="Bob's run.", + ) + + assert oa.owner == "alice" + assert ob.owner == "bob" + + +# --------------------------------------------------------------------------- +# get_active_skill_view +# --------------------------------------------------------------------------- + + +class TestGetActiveSkillView: + async def test_returns_none_when_no_release(self, svc: SkillLifecycleService): + result = await svc.get_active_skill_view( + owner="default", skill_key="nonexistent-skill" + ) + assert result is None + + async def test_returns_view_with_active_release(self, svc: SkillLifecycleService): + candidate, release = await _make_candidate_with_release( + svc, + skill_key="view-skill", + summary="Loads CSV data", + usage_notes="Requires valid credentials", + ) + + view = await svc.get_active_skill_view(owner="default", skill_key="view-skill") + + assert view is not None + assert view["skill_key"] == "view-skill" + assert view["release_id"] == release.id + assert view["version"] == release.version + assert view["stage"] == "canary" + assert view["summary"] == "Loads CSV data" + assert view["goal"] is None # not declared yet + + async def test_includes_goal_when_declared(self, svc: SkillLifecycleService): + await _make_candidate_with_release(svc, skill_key="goal-skill") + await svc.declare_goal( + owner="default", + skill_key="goal-skill", + goal="Load CSV and validate schema.", + ) + + view = await svc.get_active_skill_view(owner="default", skill_key="goal-skill") + + assert view is not None + assert view["goal"] == "Load CSV and validate schema." + + async def test_content_contains_skill_key(self, svc: SkillLifecycleService): + await _make_candidate_with_release(svc, skill_key="content-skill") + + view = await svc.get_active_skill_view(owner="default", skill_key="content-skill") + + assert view is not None + assert "content-skill" in view["content"] + + async def test_content_assembles_summary_and_usage_notes(self, svc: SkillLifecycleService): + await _make_candidate_with_release( + svc, + skill_key="rich-skill", + summary="Opens GitHub repo page", + usage_notes="Requires sandbox with browser capability", + ) + + view = await svc.get_active_skill_view(owner="default", skill_key="rich-skill") + + assert view is not None + assert "Opens GitHub repo page" in view["content"] + assert "Requires sandbox with browser capability" in view["content"] + + async def test_content_assembles_preconditions_and_postconditions( + self, svc: SkillLifecycleService + ): + await _make_candidate_with_release( + svc, + skill_key="cond-skill", + summary="Star counter", + preconditions={"browser": "available"}, + postconditions={"result": "integer"}, + ) + + view = await svc.get_active_skill_view(owner="default", skill_key="cond-skill") + + assert view is not None + assert "browser" in view["content"] + assert "result" in view["content"] + + async def test_returns_payload_ref_from_candidate(self, svc: SkillLifecycleService): + candidate, release = await _make_candidate_with_release( + svc, skill_key="ref-skill" + ) + + view = await svc.get_active_skill_view(owner="default", skill_key="ref-skill") + + assert view is not None + assert view["payload_ref"] == candidate.payload_ref + + async def test_owner_isolation(self, svc: SkillLifecycleService): + await _make_candidate_with_release( + svc, owner="alice", skill_key="iso-skill", summary="Alice's skill" + ) + + view = await svc.get_active_skill_view(owner="bob", skill_key="iso-skill") + assert view is None diff --git a/pkgs/bay/tests/unit/managers/test_skill_lifecycle_service.py b/pkgs/bay/tests/unit/managers/test_skill_lifecycle_service.py index 354415e..006e1fb 100644 --- a/pkgs/bay/tests/unit/managers/test_skill_lifecycle_service.py +++ b/pkgs/bay/tests/unit/managers/test_skill_lifecycle_service.py @@ -2,6 +2,8 @@ from __future__ import annotations +import json + import pytest from sqlalchemy.ext.asyncio import AsyncSession, create_async_engine from sqlalchemy.orm import sessionmaker @@ -336,6 +338,32 @@ async def test_create_candidate_validates_inputs(self, skill_service: SkillLifec source_execution_ids=["exec-missing"], ) + async def test_create_candidate_persists_list_conditions( + self, skill_service: SkillLifecycleService + ): + entry = await skill_service.create_execution( + owner="default", + sandbox_id="sandbox-1", + exec_type=ExecutionType.SHELL, + code="echo ok", + success=True, + execution_time_ms=1, + ) + + candidate = await skill_service.create_candidate( + owner="default", + skill_key="browser-skill", + source_execution_ids=[entry.id], + preconditions=["browser available", "js enabled"], + postconditions=["integer returned"], + ) + + assert json.loads(candidate.preconditions_json or "null") == [ + "browser available", + "js enabled", + ] + assert json.loads(candidate.postconditions_json or "null") == ["integer returned"] + async def test_promote_requires_passing_evaluation(self, skill_service: SkillLifecycleService): entry = await skill_service.create_execution( owner="default", @@ -465,6 +493,77 @@ async def test_list_candidates_validates_pagination(self, skill_service: SkillLi offset=-1, ) + async def test_find_candidate_by_payload_hash(self, skill_service: SkillLifecycleService): + entry_a = await skill_service.create_execution( + owner="default", + sandbox_id="sandbox-1", + exec_type=ExecutionType.BROWSER, + code="open https://example.com", + success=True, + execution_time_ms=1, + ) + entry_b = await skill_service.create_execution( + owner="default", + sandbox_id="sandbox-1", + exec_type=ExecutionType.BROWSER, + code="open https://example.com", + success=True, + execution_time_ms=1, + ) + candidate = await skill_service.create_candidate( + owner="default", + skill_key="browser-login", + source_execution_ids=[entry_a.id], + payload_hash="abc123", + ) + await skill_service.create_candidate( + owner="default", + skill_key="browser-checkout", + source_execution_ids=[entry_b.id], + payload_hash="abc123", + ) + + found = await skill_service.find_candidate_by_payload_hash( + owner="default", + skill_key="browser-login", + payload_hash="abc123", + ) + assert found is not None + assert found.id == candidate.id + + not_found = await skill_service.find_candidate_by_payload_hash( + owner="default", + skill_key="browser-login", + payload_hash="missing", + ) + assert not_found is None + + async def test_find_candidate_by_payload_hash_returns_none_when_hash_not_provided( + self, + skill_service: SkillLifecycleService, + ): + entry = await skill_service.create_execution( + owner="default", + sandbox_id="sandbox-1", + exec_type=ExecutionType.BROWSER, + code="open https://example.com", + success=True, + execution_time_ms=1, + ) + await skill_service.create_candidate( + owner="default", + skill_key="browser-login", + source_execution_ids=[entry.id], + payload_hash="abc123", + ) + + found = await skill_service.find_candidate_by_payload_hash( + owner="default", + skill_key="browser-login", + payload_hash=None, + ) + assert found is None + async def test_promote_deactivates_previous_release(self, skill_service: SkillLifecycleService): entry_v1 = await skill_service.create_execution( owner="default", diff --git a/pkgs/bay/tests/unit/managers/test_skill_mutation_agent.py b/pkgs/bay/tests/unit/managers/test_skill_mutation_agent.py new file mode 100644 index 0000000..aff28ad --- /dev/null +++ b/pkgs/bay/tests/unit/managers/test_skill_mutation_agent.py @@ -0,0 +1,614 @@ +"""Unit tests for SkillMutationAgent (Phase 2 + Phase 3). + +All tests use a FakeLlmClient — no real HTTP calls. + +Tests cover: +- mutate: happy path creates new candidate with mutation fields set +- mutate: links to parent candidate and meta-prompt +- mutate: stores mutation_reasoning on candidate +- mutate: graceful failure when LLM call fails +- mutate: returns None when no active release exists +- mutate: records meta-prompt usage +Phase 3: +- mutate: includes goal text in LLM prompt when SkillGoal exists +- mutate: calls evaluator after creating candidate when injected +- mutate: auto-promotes when evaluator passes and score >= threshold +- mutate: does not promote when evaluator score < threshold +""" + +from __future__ import annotations + +import pytest +from sqlalchemy.ext.asyncio import AsyncSession, create_async_engine +from sqlalchemy.orm import sessionmaker +from sqlmodel import SQLModel, select + +from app.models.skill import ExecutionType, SkillCandidate, SkillCandidateStatus, SkillReleaseStage +from app.services.skills import SkillLifecycleService +from app.services.skills.evolution.agent import MutationOutput, SkillMutationAgent +from app.services.skills.evolution.evaluator import EvaluationResult +from app.services.skills.evolution.meta_prompt import MetaPromptService + +# --------------------------------------------------------------------------- +# Fixtures +# --------------------------------------------------------------------------- + + +@pytest.fixture +async def db_session() -> AsyncSession: + engine = create_async_engine("sqlite+aiosqlite:///:memory:", echo=False) + async with engine.begin() as conn: + await conn.run_sync(SQLModel.metadata.create_all) + factory = sessionmaker(engine, class_=AsyncSession, expire_on_commit=False) + async with factory() as session: + yield session + await engine.dispose() + + +@pytest.fixture +def skill_svc(db_session: AsyncSession) -> SkillLifecycleService: + return SkillLifecycleService(db_session) + + +@pytest.fixture +def meta_svc(db_session: AsyncSession) -> MetaPromptService: + return MetaPromptService(db_session) + + +# --------------------------------------------------------------------------- +# Fake LLM client +# --------------------------------------------------------------------------- + + +class _FakeLlmClient: + """Synchronously returns a fixed MutationOutput.""" + + def __init__(self, output: MutationOutput | None = None, raises: Exception | None = None): + self._output = output or MutationOutput( + summary="Improved: wait for dynamic content before reading star count.", + usage_notes="Use explicit wait after navigation.", + preconditions=["browser available", "JavaScript enabled"], + postconditions=["integer returned"], + mutation_reasoning=( + "The failure indicated that the star count element loads asynchronously. " + "Added explicit wait instructions." + ), + ) + self._raises = raises + self.calls: list[str] = [] + + async def generate_mutation(self, prompt: str) -> MutationOutput: + self.calls.append(prompt) + if self._raises is not None: + raise self._raises + return self._output + + +# --------------------------------------------------------------------------- +# Fake evaluator (Phase 3) +# --------------------------------------------------------------------------- + + +class _FakeEvaluator: + """Synchronously returns a fixed EvaluationResult and records call kwargs.""" + + def __init__(self, output: EvaluationResult | None = None): + self._output = output or EvaluationResult(passed=True, score=0.85, reasoning="Looks good.") + self.call_count = 0 + self.last_kwargs: dict = {} + + async def evaluate( + self, + *, + goal: str, + rubric, + skill_content: str, + failure_context: str, + ) -> EvaluationResult | None: + self.call_count += 1 + self.last_kwargs = { + "goal": goal, + "rubric": rubric, + "skill_content": skill_content, + "failure_context": failure_context, + } + return self._output + + +class _FakeRubricGenerator: + """Returns a fixed rubric string-ish object and records calls.""" + + def __init__(self, rubric=None): + from app.services.skills.evolution.rubric import SkillRubric + + self._rubric = rubric or SkillRubric( + summary="Return star count as integer.", + success_criteria=["Returns an integer"], + failure_indicators=["Returns None"], + evaluation_focus="Selector robustness.", + ) + self.call_count = 0 + + async def generate(self, goal: str): + self.call_count += 1 + return self._rubric + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + + +async def _setup_skill_with_release( + svc: SkillLifecycleService, + *, + owner: str = "default", + skill_key: str = "github-get-stars", +) -> tuple: + """Creates candidate → evaluation → release. Returns (candidate, release).""" + execution = await svc.create_execution( + owner=owner, + sandbox_id="sb-1", + exec_type=ExecutionType.BROWSER, + code="open github.com", + success=True, + execution_time_ms=10, + ) + candidate = await svc.create_candidate( + owner=owner, + skill_key=skill_key, + source_execution_ids=[execution.id], + summary="Gets GitHub repo star count", + usage_notes="Navigate to repo page and read star count", + preconditions={"browser": "available"}, + postconditions={"result": "integer"}, + created_by="system:test", + ) + await svc.evaluate_candidate(owner=owner, candidate_id=candidate.id, passed=True, score=0.9) + release = await svc.promote_candidate( + owner=owner, candidate_id=candidate.id, stage=SkillReleaseStage.CANARY + ) + return candidate, release + + +async def _add_failure_outcomes( + svc: SkillLifecycleService, + *, + owner: str = "default", + skill_key: str = "github-get-stars", + release_id: str, + count: int = 3, +) -> None: + for i in range(count): + await svc.record_outcome( + owner=owner, + skill_key=skill_key, + release_id=release_id, + outcome="failure", + reasoning=f"Failure #{i + 1}: element not found at expected selector.", + ) + + +# --------------------------------------------------------------------------- +# Tests +# --------------------------------------------------------------------------- + + +class TestSkillMutationAgent: + def _make_agent( + self, + db_session: AsyncSession, + llm: _FakeLlmClient | None = None, + max_recent_outcomes: int = 10, + evaluator: _FakeEvaluator | None = None, + auto_promote_threshold: float = 0.7, + rubric_generator: _FakeRubricGenerator | None = None, + ) -> SkillMutationAgent: + return SkillMutationAgent( + db_session=db_session, + llm_client=llm or _FakeLlmClient(), + max_recent_outcomes=max_recent_outcomes, + evaluator=evaluator, + auto_promote_threshold=auto_promote_threshold, + rubric_generator=rubric_generator, + ) + + async def test_mutate_returns_none_when_no_active_release( + self, db_session: AsyncSession, skill_svc: SkillLifecycleService + ): + agent = self._make_agent(db_session) + + result = await agent.mutate(owner="default", skill_key="no-such-skill") + + assert result is None + + async def test_mutate_creates_new_candidate( + self, + db_session: AsyncSession, + skill_svc: SkillLifecycleService, + meta_svc: MetaPromptService, + ): + await meta_svc.seed_defaults() + parent, release = await _setup_skill_with_release(skill_svc) + await _add_failure_outcomes(skill_svc, release_id=release.id) + + agent = self._make_agent(db_session) + new_candidate_id = await agent.mutate(owner="default", skill_key="github-get-stars") + + assert new_candidate_id is not None + + result = await db_session.execute( + select(SkillCandidate).where(SkillCandidate.id == new_candidate_id) + ) + new_candidate = result.scalars().first() + assert new_candidate is not None + assert new_candidate.skill_key == "github-get-stars" + + async def test_mutate_links_to_parent_candidate( + self, + db_session: AsyncSession, + skill_svc: SkillLifecycleService, + meta_svc: MetaPromptService, + ): + await meta_svc.seed_defaults() + parent, release = await _setup_skill_with_release(skill_svc) + await _add_failure_outcomes(skill_svc, release_id=release.id) + + agent = self._make_agent(db_session) + new_id = await agent.mutate(owner="default", skill_key="github-get-stars") + + result = await db_session.execute( + select(SkillCandidate).where(SkillCandidate.id == new_id) + ) + new_candidate = result.scalars().first() + assert new_candidate.evolution_parent_id == parent.id + + async def test_mutate_stores_mutation_reasoning( + self, + db_session: AsyncSession, + skill_svc: SkillLifecycleService, + meta_svc: MetaPromptService, + ): + await meta_svc.seed_defaults() + parent, release = await _setup_skill_with_release(skill_svc) + await _add_failure_outcomes(skill_svc, release_id=release.id) + + llm = _FakeLlmClient() + agent = self._make_agent(db_session, llm=llm) + new_id = await agent.mutate(owner="default", skill_key="github-get-stars") + + result = await db_session.execute( + select(SkillCandidate).where(SkillCandidate.id == new_id) + ) + new_candidate = result.scalars().first() + assert new_candidate.mutation_reasoning is not None + assert "failure" in new_candidate.mutation_reasoning.lower() or len( + new_candidate.mutation_reasoning + ) > 10 + + async def test_mutate_applies_llm_output_to_candidate_fields( + self, + db_session: AsyncSession, + skill_svc: SkillLifecycleService, + meta_svc: MetaPromptService, + ): + await meta_svc.seed_defaults() + parent, release = await _setup_skill_with_release(skill_svc) + await _add_failure_outcomes(skill_svc, release_id=release.id) + + llm = _FakeLlmClient( + MutationOutput( + summary="Wait for dynamic content", + usage_notes="Use explicit wait", + preconditions=["browser available"], + postconditions=["integer returned"], + mutation_reasoning="Added wait for async loading.", + ) + ) + agent = self._make_agent(db_session, llm=llm) + new_id = await agent.mutate(owner="default", skill_key="github-get-stars") + + result = await db_session.execute( + select(SkillCandidate).where(SkillCandidate.id == new_id) + ) + new_candidate = result.scalars().first() + assert new_candidate.summary == "Wait for dynamic content" + assert new_candidate.usage_notes == "Use explicit wait" + + async def test_mutate_stores_meta_prompt_id( + self, + db_session: AsyncSession, + skill_svc: SkillLifecycleService, + meta_svc: MetaPromptService, + ): + await meta_svc.seed_defaults() + parent, release = await _setup_skill_with_release(skill_svc) + await _add_failure_outcomes(skill_svc, release_id=release.id) + + agent = self._make_agent(db_session) + new_id = await agent.mutate(owner="default", skill_key="github-get-stars") + + result = await db_session.execute( + select(SkillCandidate).where(SkillCandidate.id == new_id) + ) + new_candidate = result.scalars().first() + assert new_candidate.evolution_meta_prompt_id is not None + + async def test_mutate_marks_candidate_created_by_evolution( + self, + db_session: AsyncSession, + skill_svc: SkillLifecycleService, + meta_svc: MetaPromptService, + ): + await meta_svc.seed_defaults() + parent, release = await _setup_skill_with_release(skill_svc) + await _add_failure_outcomes(skill_svc, release_id=release.id) + + agent = self._make_agent(db_session) + new_id = await agent.mutate(owner="default", skill_key="github-get-stars") + + result = await db_session.execute( + select(SkillCandidate).where(SkillCandidate.id == new_id) + ) + new_candidate = result.scalars().first() + assert new_candidate.created_by == "system:evolution" + + async def test_mutate_calls_llm_with_context_including_failures( + self, + db_session: AsyncSession, + skill_svc: SkillLifecycleService, + meta_svc: MetaPromptService, + ): + await meta_svc.seed_defaults() + parent, release = await _setup_skill_with_release(skill_svc) + await _add_failure_outcomes(skill_svc, release_id=release.id, count=2) + + llm = _FakeLlmClient() + agent = self._make_agent(db_session, llm=llm) + await agent.mutate(owner="default", skill_key="github-get-stars") + + assert len(llm.calls) == 1 + prompt = llm.calls[0] + # prompt should contain failure context + assert "failure" in prompt.lower() or "Failure" in prompt + + async def test_mutate_returns_none_and_logs_when_llm_fails( + self, + db_session: AsyncSession, + skill_svc: SkillLifecycleService, + meta_svc: MetaPromptService, + ): + await meta_svc.seed_defaults() + parent, release = await _setup_skill_with_release(skill_svc) + await _add_failure_outcomes(skill_svc, release_id=release.id) + + llm = _FakeLlmClient(raises=RuntimeError("LLM unavailable")) + agent = self._make_agent(db_session, llm=llm) + + result = await agent.mutate(owner="default", skill_key="github-get-stars") + + assert result is None + + async def test_mutate_records_meta_prompt_usage( + self, + db_session: AsyncSession, + skill_svc: SkillLifecycleService, + meta_svc: MetaPromptService, + ): + await meta_svc.seed_defaults() + parent, release = await _setup_skill_with_release(skill_svc) + await _add_failure_outcomes(skill_svc, release_id=release.id) + + agent = self._make_agent(db_session) + new_id = await agent.mutate(owner="default", skill_key="github-get-stars") + + result = await db_session.execute( + select(SkillCandidate).where(SkillCandidate.id == new_id) + ) + new_candidate = result.scalars().first() + prompt_id = new_candidate.evolution_meta_prompt_id + + updated_prompt = await meta_svc.get_prompt(prompt_id) + assert updated_prompt.usage_count >= 1 + + # ------------------------------------------------------------------------- + # Phase 3: Goal-conditioned evolution + # ------------------------------------------------------------------------- + + async def test_mutate_includes_goal_in_llm_prompt( + self, + db_session: AsyncSession, + skill_svc: SkillLifecycleService, + meta_svc: MetaPromptService, + ): + """When a SkillGoal is declared, the goal text appears in the LLM prompt.""" + await meta_svc.seed_defaults() + parent, release = await _setup_skill_with_release(skill_svc) + await _add_failure_outcomes(skill_svc, release_id=release.id) + await skill_svc.declare_goal( + owner="default", + skill_key="github-get-stars", + goal="Return the exact star count as an integer.", + ) + + llm = _FakeLlmClient() + agent = self._make_agent(db_session, llm=llm) + await agent.mutate(owner="default", skill_key="github-get-stars") + + assert len(llm.calls) == 1 + assert "Return the exact star count as an integer." in llm.calls[0] + + async def test_mutate_calls_evaluator_after_creating_candidate( + self, + db_session: AsyncSession, + skill_svc: SkillLifecycleService, + meta_svc: MetaPromptService, + ): + """When an evaluator is injected, it's invoked after the mutation candidate is created.""" + await meta_svc.seed_defaults() + parent, release = await _setup_skill_with_release(skill_svc) + await _add_failure_outcomes(skill_svc, release_id=release.id) + await skill_svc.declare_goal( + owner="default", + skill_key="github-get-stars", + goal="Return star count.", + ) + + evaluator = _FakeEvaluator(EvaluationResult(passed=True, score=0.9, reasoning="Good.")) + agent = self._make_agent(db_session, evaluator=evaluator) + await agent.mutate(owner="default", skill_key="github-get-stars") + + assert evaluator.call_count == 1 + + async def test_mutate_auto_promotes_when_evaluator_passes( + self, + db_session: AsyncSession, + skill_svc: SkillLifecycleService, + meta_svc: MetaPromptService, + ): + """If evaluator returns passed=True with score >= threshold, candidate is auto-promoted.""" + await meta_svc.seed_defaults() + parent, release = await _setup_skill_with_release(skill_svc) + await _add_failure_outcomes(skill_svc, release_id=release.id) + await skill_svc.declare_goal( + owner="default", + skill_key="github-get-stars", + goal="Return star count.", + ) + + evaluator = _FakeEvaluator(EvaluationResult(passed=True, score=0.9, reasoning="Excellent.")) + agent = self._make_agent(db_session, evaluator=evaluator, auto_promote_threshold=0.7) + new_id = await agent.mutate(owner="default", skill_key="github-get-stars") + + result = await db_session.execute( + select(SkillCandidate).where(SkillCandidate.id == new_id) + ) + new_candidate = result.scalars().first() + assert new_candidate.status == SkillCandidateStatus.PROMOTED + + async def test_mutate_does_not_promote_when_score_below_threshold( + self, + db_session: AsyncSession, + skill_svc: SkillLifecycleService, + meta_svc: MetaPromptService, + ): + """Candidate stays DRAFT when evaluator score is below the auto-promote threshold.""" + await meta_svc.seed_defaults() + parent, release = await _setup_skill_with_release(skill_svc) + await _add_failure_outcomes(skill_svc, release_id=release.id) + await skill_svc.declare_goal( + owner="default", + skill_key="github-get-stars", + goal="Return star count.", + ) + + evaluator = _FakeEvaluator(EvaluationResult(passed=False, score=0.4, reasoning="Too slow.")) + agent = self._make_agent(db_session, evaluator=evaluator, auto_promote_threshold=0.7) + new_id = await agent.mutate(owner="default", skill_key="github-get-stars") + + result = await db_session.execute( + select(SkillCandidate).where(SkillCandidate.id == new_id) + ) + new_candidate = result.scalars().first() + assert new_candidate.status == SkillCandidateStatus.DRAFT + + async def test_mutate_evaluator_receives_mutated_content_not_parent( + self, + db_session: AsyncSession, + skill_svc: SkillLifecycleService, + meta_svc: MetaPromptService, + ): + """Evaluator must see the mutation output fields, not the parent candidate's fields.""" + await meta_svc.seed_defaults() + parent, release = await _setup_skill_with_release(skill_svc) + await _add_failure_outcomes(skill_svc, release_id=release.id) + await skill_svc.declare_goal( + owner="default", + skill_key="github-get-stars", + goal="Return star count.", + ) + + # LLM produces a mutation with a distinctive summary + mutated_summary = "MUTATED: wait 3s for dynamic star count element" + llm = _FakeLlmClient( + MutationOutput( + summary=mutated_summary, + usage_notes="Use explicit wait", + preconditions=["browser available"], + postconditions=["integer returned"], + mutation_reasoning="Addressed async loading failure.", + ) + ) + evaluator = _FakeEvaluator(EvaluationResult(passed=True, score=0.9, reasoning="Good.")) + agent = self._make_agent(db_session, llm=llm, evaluator=evaluator) + await agent.mutate(owner="default", skill_key="github-get-stars") + + # The skill_content passed to evaluator must contain the mutated summary + assert evaluator.call_count == 1 + assert mutated_summary in evaluator.last_kwargs["skill_content"] + # And must NOT be the parent's summary + assert "Gets GitHub repo star count" not in evaluator.last_kwargs["skill_content"] + + async def test_mutate_evaluator_receives_rubric_when_generator_provided( + self, + db_session: AsyncSession, + skill_svc: SkillLifecycleService, + meta_svc: MetaPromptService, + ): + """When a rubric_generator is injected, the evaluator receives the generated rubric.""" + await meta_svc.seed_defaults() + parent, release = await _setup_skill_with_release(skill_svc) + await _add_failure_outcomes(skill_svc, release_id=release.id) + await skill_svc.declare_goal( + owner="default", + skill_key="github-get-stars", + goal="Return star count.", + ) + + rubric_gen = _FakeRubricGenerator() + evaluator = _FakeEvaluator(EvaluationResult(passed=True, score=0.9, reasoning="Good.")) + agent = self._make_agent(db_session, evaluator=evaluator, rubric_generator=rubric_gen) + await agent.mutate(owner="default", skill_key="github-get-stars") + + assert evaluator.call_count == 1 + assert evaluator.last_kwargs["rubric"] is not None + assert rubric_gen.call_count >= 1 + + async def test_mutate_backfills_rubric_summary_from_cached_rubric_json( + self, + db_session: AsyncSession, + skill_svc: SkillLifecycleService, + meta_svc: MetaPromptService, + ): + """Legacy cached rubrics should self-heal rubric_summary on read.""" + await meta_svc.seed_defaults() + _, release = await _setup_skill_with_release(skill_svc) + await _add_failure_outcomes(skill_svc, release_id=release.id) + goal = await skill_svc.declare_goal( + owner="default", + skill_key="github-get-stars", + goal="Return star count.", + ) + goal.rubric_json = ( + '{"summary":"Cached rubric summary","success_criteria":["Returns an integer"],' + '"failure_indicators":["Returns None"],"evaluation_focus":"Selector robustness."}' + ) + goal.rubric_summary = "" + db_session.add(goal) + await db_session.commit() + + rubric_gen = _FakeRubricGenerator() + evaluator = _FakeEvaluator(EvaluationResult(passed=True, score=0.9, reasoning="Good.")) + agent = self._make_agent(db_session, evaluator=evaluator, rubric_generator=rubric_gen) + await agent.mutate(owner="default", skill_key="github-get-stars") + + updated_goal = await skill_svc.get_skill_goal( + owner="default", + skill_key="github-get-stars", + ) + assert evaluator.call_count == 1 + assert evaluator.last_kwargs["rubric"] is not None + assert evaluator.last_kwargs["rubric"].summary == "Cached rubric summary" + assert updated_goal is not None + assert updated_goal.rubric_summary == "Cached rubric summary" + assert rubric_gen.call_count == 0 diff --git a/pkgs/bay/tests/unit/test_browser_learning_config.py b/pkgs/bay/tests/unit/test_browser_learning_config.py new file mode 100644 index 0000000..1ae662b --- /dev/null +++ b/pkgs/bay/tests/unit/test_browser_learning_config.py @@ -0,0 +1,39 @@ +"""Unit tests for browser learning extraction config loading.""" + +from __future__ import annotations + +from app.config import Settings + + +def test_browser_learning_extraction_defaults(): + settings = Settings() + extraction = settings.browser_learning.extraction + assert extraction.dedup_enabled is True + assert extraction.variable_extraction_enabled is True + assert extraction.llm.enabled is False + + +def test_browser_learning_extraction_env_prefix(monkeypatch): + monkeypatch.setenv("BAY_BROWSER_LEARNING__EXTRACTION__DEDUP_ENABLED", "false") + monkeypatch.setenv( + "BAY_BROWSER_LEARNING__EXTRACTION__VARIABLE_EXTRACTION_ENABLED", + "false", + ) + monkeypatch.setenv("BAY_BROWSER_LEARNING__EXTRACTION__LLM__ENABLED", "true") + monkeypatch.setenv( + "BAY_BROWSER_LEARNING__EXTRACTION__LLM__API_BASE", + "https://llm.example/v1", + ) + monkeypatch.setenv("BAY_BROWSER_LEARNING__EXTRACTION__LLM__API_KEY", "sk-test") + monkeypatch.setenv("BAY_BROWSER_LEARNING__EXTRACTION__LLM__MODEL", "gpt-test") + monkeypatch.setenv("BAY_BROWSER_LEARNING__EXTRACTION__LLM__TIMEOUT_SECONDS", "9") + + settings = Settings() + extraction = settings.browser_learning.extraction + assert extraction.dedup_enabled is False + assert 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browser execution trace 中提取,提取质量取决于提取规则的准确性 +- Skill 的评估是规则打分(`_score_segment`),不理解 skill 的语义 +- 网站 UI 改版后,skill 中的步骤会静默失效,没有感知机制 +- 多个语义相似的 skill 版本同时存在,没有竞争/淘汰机制 +- Skill 的好坏标准由人在代码里定义,无法适应不同类型的 skill + +更深层的问题:**当前系统是单向流水线,不是演化系统。** + +``` +现状:execution → extract → evaluate → release + ↑ 终点,无反馈 + +目标:execution → extract → [演化循环] → 持续改进的 skill 库 +``` + +--- + +## 三、研究基础 + +以下论文构成本方案的理论支撑,按重要性排序。 + +### AlphaEvolve(Google DeepMind, 2025) +**最直接的参考框架** + +核心机制:LLM 作为变异算子,读取历史版本和评估结果,生成有依据的 diff(而非随机变异)。维护两个并行演化的数据库:solution archive(skill 变体)和 meta-prompt archive("如何生成更好 skill"的指令本身)。 + +对本项目的贡献: +- **两数据库并行演化**的结构:skill 和生成 skill 的策略同时改进 +- **MAP-Elites + island model**:不只保留"最优",保留"多样性" +- **LLM 生成 feedback 作为额外评估维度**:对无法自动评估的任务的补充方案 + +局限:假设评估函数是自动化、可量化的。UI 美化、项目设计等创造性任务不在其原始范围内。本项目通过 goal-conditioned rubric 生成机制扩展了这个边界。 + +### Darwin Gödel Machine(UBC / Sakana AI, 2025) +**Agent 修改自身策略的极限形态** + +DGM 不优化某个解,而是优化 agent 自己的代码。它维护一个 agent 变体的 archive,不断让 agent 修改自身,并在真实任务上评估。关键发现:agent 自发地给自己加上了记录历史尝试的 memory、生成多解取最优的 ensemble 策略——这些都不是人设计的。 + +对本项目的贡献: +- **策略本身是可演化的**,不只是 skill 内容 +- **open-ended 探索比收敛到单一最优更有价值**,特别是对创造性任务 +- **Archive 保留 stepping stones**(当前非最优但未来有用的中间状态) + +### TextGrad(Stanford / CZ Biohub, 2024) +**LLM 系统的自动微分** + +将 LLM 系统建模为计算图,文本反馈作为"梯度",沿计算图反向传播更新 prompt/instruction。SKILL.md 是可优化的变量,执行结果是 loss 的来源。 + +对本项目的贡献: +- 把 SKILL.md 优化问题明确地建模为**梯度优化的自然语言类比** +- 每次执行失败都产生一个"文本梯度":描述为什么失败、应该怎么改 + +### Reflexion(NeurIPS, 2023) +**失败比成功更有价值** + +不更新模型权重,而是把失败轨迹转换为自然语言反思,存入 episodic memory,作为下次尝试的上下文。实验表明这比单纯的 episodic memory 多 8% 绝对性能提升。 + +对本项目的贡献: +- **每次 skill 失败应生成 verbal reflection,而不只是记录 fail 状态** +- 反思记忆跨 session 积累,下一个同类 skill candidate 到达时注入上下文 + +--- + +## 四、核心架构 + +### 基本原则 + +人只做一件事:**表达意图(goal)**。 + +系统暴露三类硬性约束(基础设施,不可演化): + +``` +1. 执行能力 execute(skill, sandbox) → signals +2. 存储接口 store/retrieve(archive) +3. 变异边界 什么可以被改(SKILL.md 内容) + 什么不可以被改(执行沙箱、其他 skill、安全边界) +``` + +**边界内的一切,包括策略本身,都由 AI 决定并留下 reasoning。** + +### 系统分层 + +``` +┌─────────────────────────────────────────────────────────────┐ +│ Layer 0: Human Intent │ +│ goal: str ← 人唯一的输入 │ +└────────────────────────┬────────────────────────────────────┘ + │ +┌────────────────────────▼────────────────────────────────────┐ +│ Layer 1: AI Strategy Engine(可演化) │ +│ │ +│ ┌─────────────────┐ ┌───────────────────────────────┐ │ +│ │ Skill Archive │ │ Meta-Prompt Archive │ │ +│ │ MAP-Elites │ │ "如何改进 skill 的指令" │ │ +│ │ (skill_key × │ │ 同样用 MAP-Elites 管理 │ │ +│ │ behavior_dim) │ │ 好的指令 → 更高选中概率 │ │ +│ └────────┬────────┘ └──────────────┬────────────────┘ │ +│ │ sample skill │ sample mutation │ +│ └──────────────┬─────────────┘ │ +│ ▼ │ +│ LLM Mutation Agent │ +│ 生成 SKILL.md diff │ +│ 留下 reasoning │ +└────────────────────────┬────────────────────────────────────┘ + │ new candidate +┌────────────────────────▼────────────────────────────────────┐ +│ Layer 2: Infrastructure(固定) │ +│ execute → signals → evaluate → archive update │ +└─────────────────────────────────────────────────────────────┘ +``` + +### 评估机制:goal-conditioned rubric + +评估标准不是预先定义的,而是从 goal 中动态生成: + +``` +goal (str) + ↓ LLM +rubric: [ + {criterion: str, check_type: "deterministic|llm_judge", weight: float} +] + ↓ 执行 skill +execution signals + ↓ LLM judge(按 rubric 逐条评估) +score_vector: {c1: 0.9, c2: 0.7, c3: 1.0} + ↓ +fitness: float(加权聚合) +``` + +对确定性任务(获取数据、执行操作):rubric 大部分是 `check_type: deterministic`, +AI 自动识别并使用外部信号验证。 + +对创造性任务(UI 设计、架构规划):rubric 以 `check_type: llm_judge` 为主, +`target` 不是收敛到最优,而是维持多样性 archive。 + +**Rubric 本身也被存储并演化**:如果某个 rubric 和后续外部反馈一致性高, +它在 meta-prompt archive 里的分数上升;如果它系统性地漏掉某个维度,分数下降。 + +### 演化循环 + +```python +# 这不是实现代码,是逻辑描述 +# 具体参数全部由 AI 在运行时决定 + +while True: + # 1. 采样:从 archive 选一个 skill + 一条 mutation instruction + skill = archive.sample(strategy=AI_DECIDES) + instruction = meta_prompt_archive.sample(strategy=AI_DECIDES) + + # 2. 变异:LLM 生成 diff + reasoning + new_skill, reasoning = llm.mutate(skill, instruction, history) + + # 3. 执行并评估 + signals = execute(new_skill) + rubric = llm.generate_rubric(skill.goal) # 从 goal 生成 + fitness = llm.evaluate(signals, rubric) + + # 4. 更新 archive + archive.update(new_skill, fitness, reasoning) # MAP-Elites 竞争 + meta_prompt_archive.update(instruction, outcome=fitness_delta) + + # 5. 失败时生成 reflection(Reflexion 机制) + if fitness < incumbent_fitness: + reflection = llm.reflect(skill, new_skill, signals, rubric) + reflection_memory.store(skill.key, reflection) +``` + +--- + +## 五、与现有系统的关系 + +### 当前 Bay 已有的(保留并扩展) + +| 现有组件 | 在新系统中的角色 | +|---|---| +| `SkillCandidate` 表 | Skill Archive 的持久化层 | +| `SkillEvaluation` 表 | 评估结果记录,扩展为存储 rubric 和 reasoning | +| `SkillRelease` 表 | Archive incumbent 的生产版本 | +| `BrowserLearningScheduler` | 演化循环的调度基础设施 | +| `ArtifactBlob` | Skill payload 和 reflection memory 的存储 | +| `LlmAssistedExtractionStrategy` | 保留,作为 skill 初始生成的入口 | + +### 需要新建的 + +| 新组件 | 职责 | +|---|---| +| `SkillMutationAgent` | 读取 archive + meta-prompt,调用 LLM 生成 diff | +| `GoalConditionedEvaluator` | 从 goal 生成 rubric,执行评估 | +| `ReflectionMemory` | per-skill-key 的失败反思存储 | +| `MetaPromptArchive` | mutation instruction 的演化数据库 | +| behavior descriptor 列 | `SkillCandidate` 表新增,用于 MAP-Elites 维度 | + +### 不需要重写的 + +Bay 的数据模型骨架是合适的。演化逻辑是叠加在现有调度层之上的新阶段, +不替换现有的 extraction → evaluation → release 流水线, +而是在流水线末尾增加一个**演化反馈环**。 + +--- + +## 六、关于"度"的把握 + +放权 AI 不等于没有约束。需要保持清醒的几个边界: + +**必须硬编码的**(基础设施层,不交给 AI): +- SKILL.md 格式规范(Anthropic Agent Skills 标准,行业互操作基础) +- 执行沙箱的安全边界 +- 变异不能修改其他 skill 或基础设施代码 +- 每轮演化的最大 LLM 调用预算(资源约束) + +**不应该硬编码的**(策略层,交给 AI): +- 评估标准和权重 +- 何时触发变异、变异幅度 +- Archive 组织方式(behavior descriptor 的语义) +- 收敛 vs 多样化的取舍 +- Meta-prompt 的内容和演化方向 + +**关键设计原则**:AI 做的每个策略决策必须附带自然语言 reasoning, +存入 archive。这不是监控,而是为了让 AI 自己能在下一轮读取历史并质疑自己的判断。 +**没有 reasoning 的策略决策不被接受。** + +--- + +## 七、开发方向与阶段划分 + +### Phase 1:建立演化基础设施 +- 为 `SkillCandidate` 增加 behavior descriptor 维度 +- 实现 `ReflectionMemory`(基于现有 `ArtifactBlob`) +- 实现 `GoalConditionedEvaluator` 替换 `_score_segment` +- 在 `BrowserLearningScheduler` 末尾加入 reflection 生成步骤 + +### Phase 2:引入 LLM 变异循环 +- 实现 `SkillMutationAgent` +- 实现 MAP-Elites archive 竞争逻辑 +- 演化循环接入调度器(独立于 extraction 流水线) + +### Phase 3:Meta-Prompt 演化 +- 实现 `MetaPromptArchive` +- Mutation instruction 开始随使用结果更新评分 +- 观察 AI 自发涌现的 mutation 策略类型 + +### Phase 4:策略引擎自演化(DGM 方向) +- 评估 AI 是否能修改自己的演化策略代码 +- 引入 open-ended exploration 机制 +- 评估 rubric 演化的可靠性 + +--- + +## 八、一个值得记住的判断标准 + +**如果一个参数需要人来设定它的数值,那它就不应该是参数,它应该是 AI 的一个判断。** + +系统设计中每出现一个 magic number,就问自己: +"为什么是这个数而不是另一个数?" +如果答案是"因为我觉得合适",那它就应该被 AI 的 reasoning 替代。 + +--- + +*last updated: 2026-03-06* +*status: living document* diff --git a/plans/skill-evolution-sdk-integration.md b/plans/skill-evolution-sdk-integration.md new file mode 100644 index 0000000..33d26b1 --- /dev/null +++ b/plans/skill-evolution-sdk-integration.md @@ -0,0 +1,526 @@ +# Skill Evolution: SDK & MCP Integration Design + +> 配套文档 · 与 `skill-evolution-intelligence.md` 联合阅读 + +--- + +## 一、设计原则 + +**中心思想文档**已确立:人只提供意图,AI 负责策略。 + +本文档回答:这个原则如何在 SDK 和 MCP 层落地? + +具体地: + +- SDK 的 `SkillManager` 当前是显式生命周期管理(CRUD),需要演变为**意图驱动接口** +- MCP 的 skill 工具当前是手动操作封装,需要演变为**AI agent 的自然语言接口** +- Bay 需要新增**目标注册**和**演化调度**能力作为内部基础设施 + +--- + +## 二、当前状态(Before) + +### SDK 层 + +```python +# 当前使用方式:agent 必须管理完整的生命周期 +payload = await skill_manager.create_payload(data={...}) +candidate = await skill_manager.create_candidate( + skill_key="github-get-stars", + payload_ref=payload.payload_ref, + skill_type="browser", +) +eval_result = await skill_manager.evaluate_candidate( + candidate_id=candidate.candidate_id, + passed=True, + score=0.92, + benchmark_id="browser-replay-v1", +) +release = await skill_manager.promote_candidate( + candidate_id=candidate.candidate_id, + stage="canary", +) +``` + +问题:agent 必须理解系统内部状态,手动协调每一步。评估标准由人硬编码(`passed=True`, `score=0.92`),没有演化反馈机制。 + +### MCP 层 + +``` +tools available: + - create_skill_candidate # 创建候选 + - evaluate_skill_candidate # 评估 + - promote_skill_candidate # 晋升 + - rollback_skill_release # 回滚 +``` + +问题:工具是操作(operation),不是意图(intent)。AI agent 必须知道"我现在应该调用哪一步"。 + +--- + +## 三、目标状态(After) + +### 3.1 SDK 新接口 + +新增三个意图驱动方法,保留现有 CRUD 接口(向后兼容): + +```python +class SkillManager: + + # ──────────────────────────────────────────────── + # 意图接口(新增) + # ──────────────────────────────────────────────── + + async def declare_goal( + self, + skill_key: str, + goal: str, + ) -> GoalDeclaration: + """ + 声明某个 skill 的目标。 + + Bay 收到后: + 1. 存储 goal,生成初始 rubric(goal-conditioned evaluator) + 2. 如果该 skill_key 已有 goal,更新并触发重新评估 + 3. 如果尚无 active release,标记为"待演化" + + 返回 GoalDeclaration:包含 goal_id 和 Bay 生成的初始 rubric 摘要。 + + agent 不需要关心 rubric 的内容,rubric 是 AI 的策略,不是 API 参数。 + """ + + async def get_active( + self, + skill_key: str, + ) -> SkillView | None: + """ + 获取当前 active 的 skill release。 + + 返回 SkillView:包含 skill 的自然语言描述、preconditions、 + postconditions、以及 SKILL.md 格式内容(如果 release 存在)。 + + agent 读取这个内容作为执行指导,不需要理解 release_id、 + candidate_id 等内部状态。 + """ + + async def report_outcome( + self, + skill_key: str, + release_id: str, + outcome: Literal["success", "failure", "partial"], + reasoning: str, + execution_id: str | None = None, + signals: dict[str, Any] | None = None, + ) -> None: + """ + 向演化系统汇报 skill 执行结果。 + + reasoning 是必填项——agent 必须解释为什么这次执行成功或失败。 + 这条 reasoning 会进入演化循环: + - 成功案例 → 作为 MAP-Elites 的正向信号 + - 失败案例 → 触发 ReflectionMemory 存储,下次突变时注入上下文 + + signals 是可选的定量信号,例如: + {"page_load_time_ms": 1200, "element_found": true, "retry_count": 0} + Bay 的 goal-conditioned evaluator 会根据 rubric 决定如何使用这些信号。 + + agent 不需要知道: + - 如何计算 score + - 是否触发突变 + - 是否更新 archive + 这些都是 Bay 的策略,由 Bay 的演化调度器决定。 + """ + + # ──────────────────────────────────────────────── + # 现有 CRUD 接口(保留,向后兼容) + # ──────────────────────────────────────────────── + + async def create_payload(self, ...) -> PayloadRef: ... + async def create_candidate(self, ...) -> SkillCandidateRef: ... + async def evaluate_candidate(self, ...) -> SkillEvaluationRef: ... + async def promote_candidate(self, ...) -> SkillReleaseRef: ... + async def rollback_release(self, ...) -> SkillReleaseRef: ... + async def get_release_health(self, ...) -> ReleaseHealth: ... +``` + +### 3.2 新增数据类型 + +```python +@dataclass +class GoalDeclaration: + goal_id: str + skill_key: str + goal: str + rubric_summary: str # Bay 生成的初始 rubric 的自然语言摘要 + # 不是完整 rubric,是给人/agent 看的概要 + +@dataclass +class SkillView: + skill_key: str + release_id: str + version: int + stage: str # "canary" | "stable" + goal: str | None # 如果 goal 已声明 + content: str # SKILL.md 格式内容,agent 直接使用 + summary: str | None + preconditions: list[str] + postconditions: list[str] +``` + +--- + +## 四、MCP 新工具 + +### 4.1 工具定义 + +```python +# 现有工具(保留) +create_skill_candidate → 用于手动/高级场景 +evaluate_skill_candidate → 用于手动评估 +promote_skill_candidate → 用于手动晋升 +rollback_skill_release → 用于手动回滚 + +# 新增工具(意图驱动) +get_active_skill → 获取当前 active skill 的内容 +declare_skill_goal → 声明 skill 目标 +report_skill_outcome → 汇报执行结果 +``` + +### 4.2 工具描述(面向 AI agent) + +```python +TOOLS = [ + { + "name": "get_active_skill", + "description": ( + "Get the currently active skill for a given skill_key. " + "Returns the skill content in SKILL.md format that the agent " + "should follow when executing the skill. " + "Returns null if no active skill exists yet." + ), + "input_schema": { + "type": "object", + "properties": { + "skill_key": { + "type": "string", + "description": "The skill identifier, e.g. 'github-get-stars'" + } + }, + "required": ["skill_key"] + } + }, + { + "name": "declare_skill_goal", + "description": ( + "Declare the goal for a skill. The system will use this goal to " + "automatically generate evaluation criteria and drive skill evolution. " + "Call this once when introducing a new skill type, or when the goal changes. " + "The agent does not need to specify how to evaluate — the system handles that." + ), + "input_schema": { + "type": "object", + "properties": { + "skill_key": {"type": "string"}, + "goal": { + "type": "string", + "description": ( + "Natural language description of what this skill should accomplish. " + "Be specific about the desired outcome, not the steps. " + "Example: 'Navigate to a GitHub repository page and return the current star count as an integer.'" + ) + } + }, + "required": ["skill_key", "goal"] + } + }, + { + "name": "report_skill_outcome", + "description": ( + "Report the outcome of executing a skill. This feeds into the skill " + "evolution system — failures generate reflection memory that improves " + "future mutations, successes reinforce the current approach. " + "The reasoning field is required: explain why the execution succeeded or failed." + ), + "input_schema": { + "type": "object", + "properties": { + "skill_key": {"type": "string"}, + "release_id": {"type": "string"}, + "outcome": { + "type": "string", + "enum": ["success", "failure", "partial"], + }, + "reasoning": { + "type": "string", + "description": ( + "Why did this execution succeed or fail? " + "Be specific: what worked, what broke, what was unexpected. " + "This reasoning is stored and used in future evolution cycles." + ) + }, + "execution_id": {"type": "string"}, + "signals": { + "type": "object", + "description": "Optional quantitative signals from the execution" + } + }, + "required": ["skill_key", "release_id", "outcome", "reasoning"] + } + } +] +``` + +--- + +## 五、全栈集成图 + +``` +┌──────────────────────────────────────────────────────────────────┐ +│ AI Agent (Claude / camel WebVoyager / any MCP client) │ +│ │ +│ 1. declare_skill_goal("github-get-stars", goal="...") │ +│ 2. skill = get_active_skill("github-get-stars") │ +│ 3. execute skill.content against sandbox │ +│ 4. report_skill_outcome(..., outcome="success", reasoning="...") │ +└────────────────────────────┬─────────────────────────────────────┘ + │ MCP protocol +┌────────────────────────────▼─────────────────────────────────────┐ +│ shipyard-neo-mcp │ +│ handlers/skills.py │ +│ handle_declare_skill_goal() │ +│ handle_get_active_skill() │ +│ handle_report_skill_outcome() │ +└────────────────────────────┬─────────────────────────────────────┘ + │ HTTP / SDK +┌────────────────────────────▼─────────────────────────────────────┐ +│ shipyard-neo-sdk │ +│ SkillManager │ +│ .declare_goal() .get_active() .report_outcome() │ +└────────────────────────────┬─────────────────────────────────────┘ + │ REST API +┌────────────────────────────▼─────────────────────────────────────┐ +│ Bay (FastAPI) │ +│ │ +│ POST /v1/skills/goals ← declare_goal │ +│ GET /v1/skills/{key}/active ← get_active │ +│ POST /v1/skills/outcomes ← report_outcome │ +│ │ +│ ┌──────────────────────┐ ┌──────────────────────────────────┐ │ +│ │ GoalRegistry │ │ EvolutionScheduler │ │ +│ │ goal → rubric │ │ 收集 outcome → │ │ +│ │ (goal-conditioned │ │ 触发 SkillMutationAgent │ │ +│ │ evaluator) │ │ 更新 MAP-Elites archive │ │ +│ └──────────────────────┘ └──────────────────────────────────┘ │ +│ │ +│ ┌──────────────────────────────────────────────────────────────┐ │ +│ │ 现有基础设施(不变) │ │ +│ │ SkillCandidate · SkillEvaluation · SkillRelease │ │ +│ │ ArtifactBlob · BrowserLearningScheduler │ │ +│ └──────────────────────────────────────────────────────────────┘ │ +└──────────────────────────────────────────────────────────────────┘ +``` + +--- + +## 六、Bay 新增内部组件 + +### GoalRegistry + +职责:存储 goal,生成并缓存 rubric,响应 goal 变更。 + +``` +POST /v1/skills/goals + body: { skill_key, goal } + → 存储 goal + → 调用 LLM 生成 rubric(goal-conditioned evaluator) + → rubric 存储在 ArtifactBlob(kind="skill_rubric") + → 返回 goal_id + rubric_summary +``` + +rubric 的生成逻辑由 AI 决定,不由 API 参数控制。Bay 的 `GoalConditionedEvaluator` 读取 goal,自主决定: +- 哪些维度需要确定性检查(`check_type: deterministic`) +- 哪些维度需要 LLM judge(`check_type: llm_judge`) +- 各维度权重 + +### EvolutionScheduler + +职责:从 `report_outcome` 的信号流中驱动演化循环。 + +行为由 AI 自主决定,人只控制触发频率上限(资源约束): + +``` +outcome 流入 + → GoalConditionedEvaluator 评估(对照 rubric + signals) + → fitness 更新 MAP-Elites archive + → 如果 outcome == "failure" + → ReflectionMemory.store(skill_key, reasoning, execution_trace) + → 达到演化触发条件(AI 决定) + → SkillMutationAgent.generate_mutation( + current_skill, + reflection_memory, + meta_prompt_archive, + ) + → 新 candidate → 走现有 evaluation → release 流程 +``` + +--- + +## 七、camel 集成点 + +camel 的 `TaskVerifier` 是天然的 `report_skill_outcome` 数据源。 + +```python +# camel/examples/toolkits/browser_skills_example/run_webvoyager_tasks.py +# TaskVerifier 已有的逻辑: +class TaskVerifier: + async def verify(self, task: str, trajectory: list) -> VerificationResult: + # LLM judge: 判断任务是否完成 + ... + +# 集成后,在 WebVoyagerRunner 的结束处: +async def run_task(self, task: WebVoyagerTask) -> TaskResult: + # ... 执行 ... + result = await self.execute(task) + + # 新增:汇报结果给演化系统 + skill = await self.skill_manager.get_active(task.skill_key) + if skill: + verification = await self.verifier.verify(task.goal, result.trajectory) + await self.skill_manager.report_outcome( + skill_key=task.skill_key, + release_id=skill.release_id, + outcome="success" if verification.passed else "failure", + reasoning=verification.explanation, # TaskVerifier 已有的自然语言解释 + execution_id=result.execution_id, + signals={ + "steps_taken": len(result.trajectory), + "retry_count": result.retry_count, + "time_ms": result.execution_time_ms, + }, + ) +``` + +`TaskVerifier` 的 `explanation` 字段(LLM 对任务完成情况的自然语言描述)直接成为 `ReflectionMemory` 的输入——零额外工作。 + +--- + +## 八、Developer Experience 对比 + +### 8.1 首次引入新技能 + +**Before(当前):** +```python +# 开发者必须自己决定:评估逻辑、阈值、晋升时机 +payload = await sm.create_payload(data={"steps": [...]}) +candidate = await sm.create_candidate(skill_key="github-get-stars", ...) +# 手动写评估逻辑 +await sm.evaluate_candidate(candidate_id=..., passed=True, score=0.9) +await sm.promote_candidate(candidate_id=..., stage="canary") +``` + +**After(新):** +```python +# 开发者只声明意图 +await sm.declare_goal( + skill_key="github-get-stars", + goal="Navigate to a GitHub repository page and return the current star count as an integer." +) +# 系统自动生成 rubric、等待演化产生候选、自动评估晋升 +``` + +### 8.2 日常执行 + +**Before:** +```python +# 每次执行都要手动查询 release,手动获取 payload +release = await sm.get_active_release(skill_key="github-get-stars") +candidate = await sm.get_candidate(candidate_id=release.candidate_id) +payload = await sm.get_payload(payload_ref=candidate.payload_ref) +# 然后执行 payload["steps"] +``` + +**After:** +```python +# 一次调用获取所有执行所需信息 +skill = await sm.get_active(skill_key="github-get-stars") +if skill: + # skill.content 是 SKILL.md 格式,直接给 LLM 执行 + result = await agent.execute_skill(skill.content, sandbox_id=...) +``` + +### 8.3 执行结果反馈 + +**Before:** +无机制。执行完就结束,没有反馈回路。 + +**After:** +```python +await sm.report_outcome( + skill_key="github-get-stars", + release_id=skill.release_id, + outcome="failure", + reasoning=( + "The repository page layout has changed. " + "The star count element previously at '.social-count' " + "is now inside a React component that renders asynchronously. " + "The skill needs to wait for dynamic content to load." + ), + execution_id=result.execution_id, +) +# 系统自动:生成 reflection,下次突变时注入上下文 +# 开发者不需要做任何其他事情 +``` + +--- + +## 九、API 端点设计 + +新增三个端点,挂载在 `/v1/skills/` 下: + +``` +POST /v1/skills/goals + Request: { skill_key: str, goal: str } + Response: { goal_id: str, rubric_summary: str } + +GET /v1/skills/{skill_key}/active + Response: { + skill_key: str, + release_id: str, + version: int, + stage: "canary" | "stable", + goal: str | null, + content: str, # SKILL.md 格式 + summary: str | null, + preconditions: list[str], + postconditions: list[str], + } | null + +POST /v1/skills/outcomes + Request: { + skill_key: str, + release_id: str, + outcome: "success" | "failure" | "partial", + reasoning: str, # 必填 + execution_id: str | null, + signals: object | null, + } + Response: { accepted: true } +``` + +Bay 内部对 `outcome` 的处理策略(何时触发突变、如何聚合信号)由演化调度器的 AI 决定,不通过 API 暴露为可配置参数。 + +--- + +## 十、openspec 更新方向 + +以下能力需要新增到 openspec specs 中(Phase 1 落地时): + +1. **`skill-goal-registry`** — 新 spec:声明 goal、生成 rubric、goal 演化追踪 +2. **`skill-outcome-reporting`** — 新 spec:outcome 汇报、reflection memory 行为 +3. **`agent-skill-lifecycle`** — 现有 spec 扩展:`get_active_skill` 端点的合约 + +--- + +*last updated: 2026-03-06* +*status: living document* +*paired with: plans/skill-evolution-intelligence.md* diff --git a/shipyard-neo-mcp/src/shipyard_neo_mcp/handlers/__init__.py b/shipyard-neo-mcp/src/shipyard_neo_mcp/handlers/__init__.py index 7b7a230..b9518b7 100644 --- a/shipyard-neo-mcp/src/shipyard_neo_mcp/handlers/__init__.py +++ b/shipyard-neo-mcp/src/shipyard_neo_mcp/handlers/__init__.py @@ -33,6 +33,9 @@ handle_delete_skill_release, handle_delete_skill_candidate, handle_rollback_skill_release, + handle_declare_skill_goal, + handle_get_active_skill, + handle_report_skill_outcome, ) from shipyard_neo_mcp.handlers.browser import ( handle_execute_browser, @@ -65,6 +68,9 @@ "handle_delete_skill_release", "handle_delete_skill_candidate", "handle_rollback_skill_release", + "handle_declare_skill_goal", + "handle_get_active_skill", + "handle_report_skill_outcome", "handle_execute_browser", "handle_execute_browser_batch", "handle_list_profiles", @@ -94,6 +100,9 @@ "delete_skill_release": handle_delete_skill_release, "delete_skill_candidate": handle_delete_skill_candidate, "rollback_skill_release": handle_rollback_skill_release, + "declare_skill_goal": handle_declare_skill_goal, + "get_active_skill": handle_get_active_skill, + "report_skill_outcome": handle_report_skill_outcome, "execute_browser": handle_execute_browser, "execute_browser_batch": handle_execute_browser_batch, "upload_file": handle_upload_file, diff --git a/shipyard-neo-mcp/src/shipyard_neo_mcp/handlers/skills.py b/shipyard-neo-mcp/src/shipyard_neo_mcp/handlers/skills.py index 7a35787..272f672 100644 --- a/shipyard-neo-mcp/src/shipyard_neo_mcp/handlers/skills.py +++ b/shipyard-neo-mcp/src/shipyard_neo_mcp/handlers/skills.py @@ -14,6 +14,7 @@ optional_str, read_bool, read_int, + read_optional_conditions, read_optional_number, read_release_stage, require_str, @@ -77,16 +78,8 @@ async def handle_create_skill_candidate( payload_ref=optional_str(arguments, "payload_ref"), summary=optional_str(arguments, "summary"), usage_notes=optional_str(arguments, "usage_notes"), - preconditions=( - arguments.get("preconditions") - if isinstance(arguments.get("preconditions"), dict) - else None - ), - postconditions=( - arguments.get("postconditions") - if isinstance(arguments.get("postconditions"), dict) - else None - ), + preconditions=read_optional_conditions(arguments, "preconditions"), + postconditions=read_optional_conditions(arguments, "postconditions"), ) return [ TextContent( @@ -277,3 +270,84 @@ async def handle_rollback_skill_release( ), ) ] + + +async def handle_declare_skill_goal( + arguments: dict[str, Any], +) -> list[TextContent]: + """Declare the goal for a skill key to drive evolution.""" + client = get_client() + skill_key = require_str(arguments, "skill_key") + goal = require_str(arguments, "goal") + async with asyncio.timeout(_config.SDK_CALL_TIMEOUT): + result = await client.skills.declare_goal(skill_key=skill_key, goal=goal) + return [ + TextContent( + type="text", + text=( + f"Goal declared for skill '{result.skill_key}'.\n" + f"goal_id: {result.goal_id}\n" + f"goal: {result.goal}" + ), + ) + ] + + +async def handle_get_active_skill( + arguments: dict[str, Any], +) -> list[TextContent]: + """Get the currently active skill and its content.""" + client = get_client() + skill_key = require_str(arguments, "skill_key") + async with asyncio.timeout(_config.SDK_CALL_TIMEOUT): + skill = await client.skills.get_active(skill_key) + if skill is None: + return [TextContent(type="text", text=f"No active release found for skill '{skill_key}'.")] + return [ + TextContent( + type="text", + text=( + f"skill_key: {skill.skill_key}\n" + f"release_id: {skill.release_id}\n" + f"version: {skill.version}\n" + f"stage: {skill.stage}\n" + f"goal: {skill.goal or '(not declared)'}\n" + f"\n{skill.content}" + ), + ) + ] + + +async def handle_report_skill_outcome( + arguments: dict[str, Any], +) -> list[TextContent]: + """Report the outcome of executing a skill to feed the evolution system.""" + client = get_client() + skill_key = require_str(arguments, "skill_key") + release_id = require_str(arguments, "release_id") + outcome_raw = require_str(arguments, "outcome") + if outcome_raw not in ("success", "failure", "partial"): + raise ValueError("field 'outcome' must be 'success', 'failure', or 'partial'") + reasoning = require_str(arguments, "reasoning") + execution_id = optional_str(arguments, "execution_id") + signals = arguments.get("signals") if isinstance(arguments.get("signals"), dict) else None + + async with asyncio.timeout(_config.SDK_CALL_TIMEOUT): + await client.skills.report_outcome( + skill_key=skill_key, + release_id=release_id, + outcome=outcome_raw, # type: ignore[arg-type] + reasoning=reasoning, + execution_id=execution_id, + signals=signals, + ) + return [ + TextContent( + type="text", + text=( + f"Outcome reported for skill '{skill_key}'.\n" + f"outcome: {outcome_raw}\n" + f"reasoning recorded and queued for evolution processing." + ), + ) + ] diff --git a/shipyard-neo-mcp/src/shipyard_neo_mcp/tool_defs.py b/shipyard-neo-mcp/src/shipyard_neo_mcp/tool_defs.py index 7df487b..ba69b85 100644 --- a/shipyard-neo-mcp/src/shipyard_neo_mcp/tool_defs.py +++ b/shipyard-neo-mcp/src/shipyard_neo_mcp/tool_defs.py @@ -338,12 +338,22 @@ def get_tool_definitions() -> list[Tool]: "description": "Optional usage notes for operators/agents.", }, "preconditions": { - "type": "object", - "description": "Optional JSON object describing preconditions.", + "anyOf": [ + {"type": "object"}, + {"type": "array", "items": {"type": "string"}}, + ], + "description": ( + "Optional JSON object or string array describing preconditions." + ), }, "postconditions": { - "type": "object", - "description": "Optional JSON object describing postconditions.", + "anyOf": [ + {"type": "object"}, + {"type": "array", "items": {"type": "string"}}, + ], + "description": ( + "Optional JSON object or string array describing postconditions." + ), }, }, "required": ["skill_key", "source_execution_ids"], @@ -515,6 +525,95 @@ def get_tool_definitions() -> list[Tool]: "required": ["release_id"], }, ), + Tool( + name="declare_skill_goal", + description=( + "Declare the goal for a skill. The system uses this goal to automatically " + "generate evaluation criteria and drive skill evolution. Call this once when " + "introducing a new skill type, or when the goal changes. " + "The agent does not need to specify how to evaluate — the system handles that." + ), + inputSchema={ + "type": "object", + "properties": { + "skill_key": { + "type": "string", + "description": "The skill identifier, e.g. 'github-get-stars'.", + }, + "goal": { + "type": "string", + "description": ( + "Natural language description of what this skill should accomplish. " + "Be specific about the desired outcome, not the steps. " + "Example: 'Navigate to a GitHub repository page and return the current star count as an integer.'" + ), + }, + }, + "required": ["skill_key", "goal"], + }, + ), + Tool( + name="get_active_skill", + description=( + "Get the currently active skill for a given skill_key. " + "Returns the skill content that the agent should follow when executing the skill. " + "Returns a message indicating no active skill if none exists." + ), + inputSchema={ + "type": "object", + "properties": { + "skill_key": { + "type": "string", + "description": "The skill identifier, e.g. 'github-get-stars'.", + }, + }, + "required": ["skill_key"], + }, + ), + Tool( + name="report_skill_outcome", + description=( + "Report the outcome of executing a skill. This feeds into the skill evolution system — " + "failures generate reflection memory that improves future mutations, successes reinforce " + "the current approach. The reasoning field is required: explain why the execution " + "succeeded or failed." + ), + inputSchema={ + "type": "object", + "properties": { + "skill_key": { + "type": "string", + "description": "The skill identifier.", + }, + "release_id": { + "type": "string", + "description": "The release_id from get_active_skill.", + }, + "outcome": { + "type": "string", + "enum": ["success", "failure", "partial"], + "description": "Whether the skill execution succeeded.", + }, + "reasoning": { + "type": "string", + "description": ( + "Why did this execution succeed or fail? Be specific: what worked, " + "what broke, what was unexpected. This reasoning is stored and used " + "in future evolution cycles." + ), + }, + "execution_id": { + "type": "string", + "description": "Optional execution_id from the browser or code execution.", + }, + "signals": { + "type": "object", + "description": "Optional quantitative signals from the execution, e.g. {page_load_time_ms: 1200}.", + }, + }, + "required": ["skill_key", "release_id", "outcome", "reasoning"], + }, + ), Tool( name="execute_browser", description=( diff --git a/shipyard-neo-mcp/src/shipyard_neo_mcp/validators.py b/shipyard-neo-mcp/src/shipyard_neo_mcp/validators.py index ff0de52..4064691 100644 --- a/shipyard-neo-mcp/src/shipyard_neo_mcp/validators.py +++ b/shipyard-neo-mcp/src/shipyard_neo_mcp/validators.py @@ -165,3 +165,22 @@ def require_str_list(arguments: dict[str, Any], key: str) -> list[str]: raise ValueError(f"field '{key}' must be a non-empty array of strings") normalized.append(item) return normalized + + +def read_optional_conditions( + arguments: dict[str, Any], key: str +) -> list[str] | dict[str, Any] | None: + """Extract optional pre/postconditions as object or string array.""" + value = arguments.get(key) + if value is None: + return None + if isinstance(value, dict): + return value + if isinstance(value, list): + normalized: list[str] = [] + for item in value: + if not isinstance(item, str): + raise ValueError(f"field '{key}' must be an object or an array of strings") + normalized.append(item) + return normalized + raise ValueError(f"field '{key}' must be an object or an array of strings") diff --git a/shipyard-neo-mcp/tests/test_server.py b/shipyard-neo-mcp/tests/test_server.py index 664e219..f1611e2 100644 --- a/shipyard-neo-mcp/tests/test_server.py +++ b/shipyard-neo-mcp/tests/test_server.py @@ -237,6 +237,7 @@ async def delete(self) -> None: class FakeSkills: def __init__(self) -> None: self.last_promote_stage: str | None = None + self.last_create_candidate_args: dict[str, object] | None = None async def create_payload( self, @@ -263,17 +264,19 @@ async def create_candidate( payload_ref: str | None = None, summary: str | None = None, usage_notes: str | None = None, - preconditions: dict | None = None, - postconditions: dict | None = None, + preconditions: list[str] | dict | None = None, + postconditions: list[str] | dict | None = None, ): - _ = ( - scenario_key, - payload_ref, - summary, - usage_notes, - preconditions, - postconditions, - ) + self.last_create_candidate_args = { + "skill_key": skill_key, + "source_execution_ids": source_execution_ids, + "scenario_key": scenario_key, + "payload_ref": payload_ref, + "summary": summary, + "usage_notes": usage_notes, + "preconditions": preconditions, + "postconditions": postconditions, + } return SimpleNamespace( id="sc-1", skill_key=skill_key, @@ -437,6 +440,21 @@ async def test_list_tools_contains_history_and_skill_tools(): assert "list_profiles" in names +@pytest.mark.asyncio +async def test_create_skill_candidate_schema_accepts_list_conditions(): + tools = await mcp_server.list_tools() + candidate_tool = next(tool for tool in tools if tool.name == "create_skill_candidate") + + preconditions_schema = candidate_tool.inputSchema["properties"]["preconditions"] + postconditions_schema = candidate_tool.inputSchema["properties"]["postconditions"] + + assert preconditions_schema["anyOf"][1] == {"type": "array", "items": {"type": "string"}} + assert postconditions_schema["anyOf"][1] == { + "type": "array", + "items": {"type": "string"}, + } + + @pytest.mark.asyncio async def test_call_tool_requires_initialized_client(): response = await mcp_server.call_tool("unknown", {}) @@ -525,6 +543,29 @@ async def test_create_skill_candidate_tool_calls_sdk_manager(): assert "source_execution_ids: exec-1, exec-2" in text +@pytest.mark.asyncio +async def test_create_skill_candidate_tool_accepts_list_conditions(): + skills = FakeSkills() + mcp_server._client = FakeClient(skills=skills) + + await mcp_server.call_tool( + "create_skill_candidate", + { + "skill_key": "browser-login", + "source_execution_ids": ["exec-1"], + "preconditions": ["browser available", "user authenticated"], + "postconditions": ["dashboard visible"], + }, + ) + + assert skills.last_create_candidate_args is not None + assert skills.last_create_candidate_args["preconditions"] == [ + "browser available", + "user authenticated", + ] + assert skills.last_create_candidate_args["postconditions"] == ["dashboard visible"] + + @pytest.mark.asyncio async def test_create_skill_payload_tool_calls_sdk_manager(): skills = FakeSkills() @@ -1269,8 +1310,47 @@ async def test_delete_sandbox_logs_info(caplog): mcp_server._client = FakeClient() with caplog.at_level(logging.INFO, logger="shipyard_neo_mcp"): - response = await mcp_server.call_tool("delete_sandbox", {"sandbox_id": "sbx-1"}) + await mcp_server.call_tool("delete_sandbox", {"sandbox_id": "sbx-1"}) assert "sandbox_deleted" in caplog.text assert "sbx-1" in caplog.text - assert "deleted successfully" in response[0].text + + +# --------------------------------------------------------------------------- +# pre/postconditions dual-format: list_skill_candidates with evolution candidates +# --------------------------------------------------------------------------- + + +@pytest.mark.asyncio +async def test_list_skill_candidates_handles_evolution_candidates_with_list_preconditions(): + """list_skill_candidates must not crash when evolution candidates have list preconditions. + + The evolution agent writes pre/postconditions as list[str]. + The SDK must accept both list[str] and dict[str, Any] without raising ValidationError. + """ + from datetime import datetime, timezone + from shipyard_neo.types import SkillCandidateInfo, SkillCandidateList + + evolution_candidate = SkillCandidateInfo( + id="sc-evo-1", + skill_key="github-get-stars", + source_execution_ids=["exec-1"], + status=SkillCandidateStatus.DRAFT, + preconditions=["browser available", "JS enabled"], + postconditions=["integer returned"], + summary="Improved wait logic", + latest_pass=None, + created_at=datetime(2026, 3, 1, tzinfo=timezone.utc), + updated_at=datetime(2026, 3, 1, tzinfo=timezone.utc), + ) + + class _FakeSkillsWithEvolution: + async def list_candidates(self, *, status=None, skill_key=None, limit=50, offset=0): + return SkillCandidateList(items=[evolution_candidate], total=1) + + mcp_server._client = FakeClient(skills=_FakeSkillsWithEvolution()) + response = await mcp_server.call_tool("list_skill_candidates", {}) + + text = response[0].text + assert "sc-evo-1" in text + assert "github-get-stars" in text diff --git a/shipyard-neo-mcp/uv.lock b/shipyard-neo-mcp/uv.lock index 29e5418..1f33e58 100644 --- a/shipyard-neo-mcp/uv.lock +++ b/shipyard-neo-mcp/uv.lock @@ -45,11 +45,11 @@ wheels = [ [[package]] name = "certifi" -version = "2026.1.4" +version = "2026.2.25" source = { registry = "https://pypi.org/simple" } -sdist = { url = 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a/shipyard-neo-sdk/shipyard_neo/skills.py +++ b/shipyard-neo-sdk/shipyard_neo/skills.py @@ -3,11 +3,10 @@ from __future__ import annotations import json -from typing import Any, cast - -from typing import TYPE_CHECKING +from typing import TYPE_CHECKING, Any, Literal, cast from shipyard_neo.types import ( + GoalDeclaration, SkillCandidateInfo, SkillCandidateList, SkillCandidateStatus, @@ -18,6 +17,7 @@ SkillReleaseInfo, SkillReleaseList, SkillReleaseStage, + SkillView, _SkillPayloadCreateRequest, ) @@ -74,8 +74,8 @@ async def create_candidate( payload_ref: str | None = None, summary: str | None = None, usage_notes: str | None = None, - preconditions: dict[str, Any] | None = None, - postconditions: dict[str, Any] | None = None, + preconditions: list[str] | dict[str, Any] | None = None, + postconditions: list[str] | dict[str, Any] | None = None, ) -> SkillCandidateInfo: body = { "skill_key": skill_key, @@ -221,3 +221,69 @@ async def rollback_release(self, release_id: str) -> SkillReleaseInfo: async def get_release_health(self, release_id: str) -> SkillReleaseHealth: response = await self._http.get(f"/v1/skills/releases/{release_id}/health") return SkillReleaseHealth.model_validate(response) + + # ------------------------------------------------------------------ + # Intent-driven interface (evolution system) + # ------------------------------------------------------------------ + + async def declare_goal( + self, + *, + skill_key: str, + goal: str, + ) -> GoalDeclaration: + """Declare the goal for a skill key. + + The system uses this goal to drive skill evolution and generate + evaluation criteria. Call once when introducing a new skill, or + whenever the goal changes. This is an upsert operation. + """ + response = await self._http.post( + "/v1/skills/goals", + json={"skill_key": skill_key, "goal": goal}, + ) + return GoalDeclaration.model_validate(response) + + async def get_active(self, skill_key: str) -> SkillView | None: + """Get the currently active skill release. + + Returns a SkillView whose ``content`` field can be passed directly + to an LLM as execution instructions. Returns None if no active + release exists yet. + """ + from shipyard_neo.errors import NotFoundError + + try: + response = await self._http.get(f"/v1/skills/{skill_key}/active") + except NotFoundError: + return None + return SkillView.model_validate(response) + + async def report_outcome( + self, + *, + skill_key: str, + release_id: str, + outcome: Literal["success", "failure", "partial"], + reasoning: str, + execution_id: str | None = None, + signals: dict[str, Any] | None = None, + ) -> None: + """Report the outcome of executing a skill. + + ``reasoning`` is required — explain why the execution succeeded or + failed. This text is stored as the evolution signal feed: failures + generate reflection memory that improves future mutations, successes + reinforce the current approach. + """ + body: dict[str, Any] = { + "skill_key": skill_key, + "release_id": release_id, + "outcome": outcome, + "reasoning": reasoning, + } + if execution_id is not None: + body["execution_id"] = execution_id + if signals is not None: + body["signals"] = signals + await self._http.post("/v1/skills/outcomes", json=body) diff --git a/shipyard-neo-sdk/shipyard_neo/types.py b/shipyard-neo-sdk/shipyard_neo/types.py index b9fbc96..eb610b6 100644 --- a/shipyard-neo-sdk/shipyard_neo/types.py +++ b/shipyard-neo-sdk/shipyard_neo/types.py @@ -275,8 +275,8 @@ class SkillCandidateInfo(BaseModel): auto_release_reason: str | None = None summary: str | None = None usage_notes: str | None = None - preconditions: dict[str, Any] | None = None - postconditions: dict[str, Any] | None = None + preconditions: list[str] | dict[str, Any] | None = None + postconditions: list[str] | dict[str, Any] | None = None source_execution_ids: list[str] status: SkillCandidateStatus latest_score: float | None = None @@ -382,6 +382,30 @@ class SkillPayloadInfo(BaseModel): payload: dict[str, Any] | list[Any] +class GoalDeclaration(BaseModel): + """Response from declaring a skill goal.""" + + goal_id: str + skill_key: str + goal: str + rubric_summary: str + + +class SkillView(BaseModel): + """Unified view of the currently active skill release.""" + + skill_key: str + release_id: str + version: int + stage: str + goal: str | None = None + content: str + summary: str | None = None + preconditions: list[str] = Field(default_factory=list) + postconditions: list[str] = Field(default_factory=list) + payload_ref: str | None = None + + # Internal request models (not exported) # # These are SDK-internal helpers to build request bodies consistently and diff --git a/shipyard-neo-sdk/tests/test_skills_and_history.py b/shipyard-neo-sdk/tests/test_skills_and_history.py index 4d72b61..b2ba2bf 100644 --- a/shipyard-neo-sdk/tests/test_skills_and_history.py +++ b/shipyard-neo-sdk/tests/test_skills_and_history.py @@ -486,4 +486,139 @@ async def test_skills_get_release_health_parses_rollback_signals(self, httpx_moc health = await client.skills.get_release_health("sr-risky") assert health.should_rollback is True assert health.rollback_reasons == ["success_rate_drop", "error_rate_regression"] - assert health.thresholds["error_rate_multiplier"] == 2.0 + + # ------------------------------------------------------------------------- + # pre/postconditions dual-format compatibility (dict vs list) + # ------------------------------------------------------------------------- + + def _candidate_payload( + self, + *, + preconditions=None, + postconditions=None, + ) -> dict: + return { + "id": "sc-evo-1", + "skill_key": "github-get-stars", + "source_execution_ids": ["exec-1"], + "status": "draft", + "created_at": "2026-03-01T00:00:00Z", + "updated_at": "2026-03-01T00:00:00Z", + "preconditions": preconditions, + "postconditions": postconditions, + } + + @pytest.mark.asyncio + async def test_get_candidate_parses_list_preconditions(self, httpx_mock): + """get_candidate must not raise when server returns list[str] pre/postconditions.""" + httpx_mock.add_response( + method="GET", + url="http://localhost:8000/v1/skills/candidates/sc-evo-1", + json=self._candidate_payload( + preconditions=["browser available", "JS enabled"], + postconditions=["integer returned"], + ), + status_code=200, + ) + async with BayClient( + endpoint_url="http://localhost:8000", + access_token="test-token", + ) as client: + c = await client.skills.get_candidate("sc-evo-1") + assert c.preconditions == ["browser available", "JS enabled"] + assert c.postconditions == ["integer returned"] + + @pytest.mark.asyncio + async def test_get_candidate_parses_dict_preconditions(self, httpx_mock): + """get_candidate must still work when server returns dict pre/postconditions.""" + httpx_mock.add_response( + method="GET", + url="http://localhost:8000/v1/skills/candidates/sc-evo-1", + json=self._candidate_payload( + preconditions={"browser": "available"}, + postconditions={"result": "integer"}, + ), + status_code=200, + ) + async with BayClient( + endpoint_url="http://localhost:8000", + access_token="test-token", + ) as client: + c = await client.skills.get_candidate("sc-evo-1") + assert c.preconditions == {"browser": "available"} + assert c.postconditions == {"result": "integer"} + + @pytest.mark.asyncio + async def test_get_candidate_parses_null_preconditions(self, httpx_mock): + """get_candidate must handle null pre/postconditions (no conditions set).""" + httpx_mock.add_response( + method="GET", + url="http://localhost:8000/v1/skills/candidates/sc-evo-1", + json=self._candidate_payload(), + status_code=200, + ) + async with BayClient( + endpoint_url="http://localhost:8000", + access_token="test-token", + ) as client: + c = await client.skills.get_candidate("sc-evo-1") + assert c.preconditions is None + assert c.postconditions is None + + @pytest.mark.asyncio + async def test_list_candidates_parses_list_preconditions(self, httpx_mock): + """list_candidates must not raise when evolution candidates have list preconditions.""" + httpx_mock.add_response( + method="GET", + url="http://localhost:8000/v1/skills/candidates?limit=100&offset=0", + json={ + "items": [ + self._candidate_payload( + preconditions=["browser available"], + postconditions=["integer returned"], + ) + ], + "total": 1, + }, + status_code=200, + ) + async with BayClient( + endpoint_url="http://localhost:8000", + access_token="test-token", + ) as client: + result = await client.skills.list_candidates() + assert result.total == 1 + assert result.items[0].preconditions == ["browser available"] + + @pytest.mark.asyncio + async def test_create_candidate_accepts_list_preconditions(self, httpx_mock): + """create_candidate should pass list[str] conditions through unchanged.""" + httpx_mock.add_response( + method="POST", + url="http://localhost:8000/v1/skills/candidates", + json=self._candidate_payload( + preconditions=["browser available", "JS enabled"], + postconditions=["integer returned"], + ), + status_code=201, + ) + + async with BayClient( + endpoint_url="http://localhost:8000", + access_token="test-token", + ) as client: + candidate = await client.skills.create_candidate( + skill_key="github-get-stars", + source_execution_ids=["exec-1"], + preconditions=["browser available", "JS enabled"], + postconditions=["integer returned"], + ) + + request = httpx_mock.get_requests()[0] + assert request.read().decode() == ( + '{"skill_key":"github-get-stars","source_execution_ids":["exec-1"],' + '"preconditions":["browser available","JS enabled"],' + '"postconditions":["integer returned"]}' + ) + assert candidate.preconditions == ["browser available", "JS enabled"] + assert candidate.postconditions == ["integer returned"] 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