1. Introduzione
In questo codelab, creerai un sistema multi-agente in cui tre agenti AI specializzati collaborano per pianificare una maratona cittadina. Un agente Marathon Planner orchestra la progettazione, un agente Evaluator valuta la qualità del piano utilizzando Vertex AI Evaluation e un agente Simulation Controller convalida la preparazione, il tutto comunicando tramite il protocollo Agent-to-Agent (A2A).
In questo lab proverai a:
- Creare agenti AI con Agent Development Kit (ADK) utilizzando i modelli Gemini su Vertex AI
- Definire output strutturati con schemi Pydantic per risposte affidabili degli agenti
- Creare metriche Vertex AI Evaluation personalizzate utilizzando
MetricPromptBuilder(LLM-as-Judge) - Collegare gli agenti come sub-agenti con
AgentToole connettere gli agenti remoti tramite il protocollo A2A - Utilizzare ADK Skills per la conoscenza procedurale e Memory Bank per l'apprendimento tra le sessioni
- Pubblicare gli agenti localmente con i server A2A e testare la collaborazione multi-agente
Che cosa ti serve
- Un browser web come Chrome
- Un progetto cloud Google Cloud con la fatturazione abilitata
Questo codelab è destinato a sviluppatori di livello intermedio che hanno familiarità con Python e i concetti di AI.
Tempo stimato per completare il corso: 90 minuti.
Le risorse create in questo codelab dovrebbero costare meno di 5 $.
2. Prima di iniziare
Creare un progetto Google Cloud
- Nella console Google Cloud, seleziona o crea un progetto Google Cloud.
- Verifica che la fatturazione sia attivata per il tuo progetto Cloud.
Avvia Cloud Shell
- Fai clic su Attiva Cloud Shell nella parte superiore della console Google Cloud.
- Verifica l'autenticazione:
gcloud auth list
- Conferma il progetto:
gcloud config get project
- Impostalo se necessario:
export PROJECT_ID=<YOUR_PROJECT_ID>
gcloud config set project $PROJECT_ID
Abilita API
gcloud services enable aiplatform.googleapis.com
Installa il gestore dei pacchetti Python
Installa uv, un gestore dei pacchetti Python veloce:
curl -LsSf https://astral.sh/uv/install.sh | sh
source $HOME/.local/bin/env
3. Configura il progetto
In questo passaggio, creerai la struttura del progetto, installerai le dipendenze e configurerai i servizi condivisi utilizzati da tutti gli agenti.
Crea la directory del progetto
mkdir -p marathon-agents && cd marathon-agents
Crea la configurazione del progetto
Crea pyproject.toml:
[project]
name = "marathon-agents"
version = "0.1.0"
description = "Multi-agent marathon planning system with ADK, A2A, and Vertex AI Evaluation"
readme = "README.md"
requires-python = ">=3.10"
dependencies = [
"google-cloud-aiplatform[agent_engines,adk,evaluation]>=1.121.0",
"google-adk>=1.25.0",
"a2a-sdk>=0.3.9",
"pydantic>=2.12.0",
"python-dotenv>=1.0.0",
"httpx>=0.27.0",
"uvicorn>=0.30.0",
"google-auth>=2.0.0",
"pandas>=2.0.0",
]
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
[tool.hatch.build.targets.wheel]
packages = ["src"]
[tool.pytest.ini_options]
asyncio_mode = "auto"
testpaths = ["tests"]
Installa le dipendenze
uv sync
Configura le variabili di ambiente
Crea un file .env:
cat > .env << 'EOF'
GOOGLE_CLOUD_PROJECT=<YOUR_PROJECT_ID>
GOOGLE_CLOUD_LOCATION=us-central1
GOOGLE_GENAI_USE_VERTEXAI=true
EOF
Crea la struttura della directory di origine
Il progetto segue la convenzione multi-agente di ADK: ogni agente è un pacchetto autonomo in src/:
mkdir -p src/planner_agent/{agent,evaluator/{services},skills/{route-planning/references,plan-evaluation/references},services,runtime}
mkdir -p src/simulator_agent/{agent,skills/review-marathon-plan/references,services,runtime}
Inizializza i pacchetti Python
touch src/__init__.py
touch src/planner_agent/__init__.py src/planner_agent/agent/__init__.py
touch src/planner_agent/evaluator/__init__.py src/planner_agent/evaluator/services/__init__.py
touch src/planner_agent/services/__init__.py src/planner_agent/runtime/__init__.py
touch src/simulator_agent/__init__.py src/simulator_agent/agent/__init__.py
touch src/simulator_agent/services/__init__.py src/simulator_agent/runtime/__init__.py
Crea la configurazione condivisa
Crea src/config.py : configurazione GCP condivisa per tutti gli agenti:
"""Shared configuration for the multi-agent system."""
import os
from dotenv import load_dotenv
load_dotenv()
# GCP Configuration
GOOGLE_CLOUD_PROJECT = os.environ.get("GOOGLE_CLOUD_PROJECT")
GOOGLE_CLOUD_LOCATION = os.environ.get("GOOGLE_CLOUD_LOCATION", "us-central1")
BUCKET_URI = os.environ.get("BUCKET_URI")
def validate_config():
"""Validate required configuration is set."""
required = {
"GOOGLE_CLOUD_PROJECT": GOOGLE_CLOUD_PROJECT,
}
missing = [k for k, v in required.items() if not v]
if missing:
raise ValueError(
f"Missing required environment variables: {', '.join(missing)}. "
"Check your .env file."
)
return True
Crea Memory Manager
Ogni agente utilizza un Memory Bank per l'apprendimento tra le sessioni. Crea src/planner_agent/services/memory_manager.py:
"""Memory Manager for Marathon Planner Agent.
Manages Memory Bank integration with custom topics.
Enables cross-session learning.
"""
import os
from typing import TYPE_CHECKING
from google.adk.memory import VertexAiMemoryBankService
from vertexai._genai.types import (
MemoryBankCustomizationConfig,
MemoryBankCustomizationConfigMemoryTopic as MemoryTopic,
MemoryBankCustomizationConfigMemoryTopicCustomMemoryTopic as CustomMemoryTopic,
)
if TYPE_CHECKING:
from google.adk.agents.callback_context import CallbackContext
# Custom memory topics for cross-session learning
PLANNING_HISTORY = MemoryTopic(
custom_memory_topic=CustomMemoryTopic(
label="planning_history",
description="""Track marathon plans generated across sessions.
Extract: City, date, key route decisions, final score, iteration count.
Format: "Plan: city={city}, date={date}, score={score}, iterations={n}"
""",
)
)
USER_PREFERENCES = MemoryTopic(
custom_memory_topic=CustomMemoryTopic(
label="user_preferences",
description="""Track user preferences and constraints across sessions.
Extract: Preferred themes, budget priorities, scale, city preferences.
Format: "Preference: type={type}, value={value}"
""",
)
)
ROUTE_PATTERNS = MemoryTopic(
custom_memory_topic=CustomMemoryTopic(
label="route_patterns",
description="""Track successful route patterns and lessons learned.
Extract: Route segments with positive feedback, landmark combinations.
Format: "Route: city={city}, pattern={pattern}, outcome={outcome}"
""",
)
)
LOGISTICS_INSIGHTS = MemoryTopic(
custom_memory_topic=CustomMemoryTopic(
label="logistics_insights",
description="""Track logistics decisions and capacity learnings.
Extract: Water station placement, venue capacity, crowd management.
Format: "Logistics: category={cat}, insight={insight}"
""",
)
)
def create_marathon_planner_memory_topics() -> MemoryBankCustomizationConfig:
"""Create Memory Bank customization config with custom topics."""
return MemoryBankCustomizationConfig(
memory_topics=[
PLANNING_HISTORY, USER_PREFERENCES,
ROUTE_PATTERNS, LOGISTICS_INSIGHTS,
]
)
def create_memory_service(
project: str | None = None,
location: str | None = None,
agent_engine_id: str | None = None,
) -> VertexAiMemoryBankService | None:
"""Create a VertexAiMemoryBankService.
Returns None if agent_engine_id is not set (local development).
"""
project = project or os.environ.get("GOOGLE_CLOUD_PROJECT")
location = location or (
os.environ.get("AGENT_ENGINE_LOCATION")
or os.environ.get("GOOGLE_CLOUD_LOCATION", "us-central1")
)
agent_engine_id = agent_engine_id or os.environ.get("AGENT_ENGINE_ID")
if not project:
raise ValueError("GOOGLE_CLOUD_PROJECT environment variable required")
if not agent_engine_id:
return None
return VertexAiMemoryBankService(
project=project, location=location,
agent_engine_id=agent_engine_id,
)
async def auto_save_memories(callback_context: "CallbackContext") -> None:
"""Automatically save session to Memory Bank after agent responds."""
project_id = os.environ.get("GOOGLE_CLOUD_PROJECT")
location = (
os.environ.get("AGENT_ENGINE_LOCATION")
or os.environ.get("GOOGLE_CLOUD_LOCATION", "us-central1")
)
agent_engine_id = os.environ.get("AGENT_ENGINE_ID")
if not agent_engine_id:
return
try:
memory_service = VertexAiMemoryBankService(
project=project_id, location=location,
agent_engine_id=agent_engine_id,
)
await memory_service.add_session_to_memory(
callback_context._invocation_context.session
)
except Exception as e:
print(f"Warning: Failed to save memories: {e}")
Il callback auto_save_memories viene eseguito dopo ogni risposta dell'agente, salvando la conversazione in Memory Bank quando viene eseguito il deployment in Agent Engine. Durante lo sviluppo locale, viene ignorato se AGENT_ENGINE_ID non è impostato.
Crea Session Manager
Crea src/planner_agent/services/session_manager.py : gestisce il ciclo di vita della sessione con la memorizzazione nella cache TTL:
"""Session Manager — manages session lifecycle with TTL-based caching."""
import os
import time
from typing import Any
from google.adk.sessions import InMemorySessionService, VertexAiSessionService
class TTLCache:
"""Simple TTL cache for mapping A2A context_id to session_id."""
def __init__(self, maxsize: int = 1000, ttl: int = 3600):
self._cache: dict[str, tuple[Any, float]] = {}
self._maxsize = maxsize
self._ttl = ttl
def get(self, key: str) -> Any | None:
if key in self._cache:
value, timestamp = self._cache[key]
if time.time() - timestamp < self._ttl:
return value
else:
del self._cache[key]
return None
def set(self, key: str, value: Any) -> None:
if len(self._cache) >= self._maxsize:
self._evict_oldest()
self._cache[key] = (value, time.time())
def _evict_oldest(self) -> None:
if not self._cache:
return
sorted_keys = sorted(
self._cache.keys(), key=lambda k: self._cache[k][1]
)
for key in sorted_keys[: max(1, len(sorted_keys) // 10)]:
del self._cache[key]
def __contains__(self, key: str) -> bool:
return self.get(key) is not None
def create_session_service(
project: str | None = None,
location: str | None = None,
agent_engine_id: str | None = None,
use_vertex: bool | None = None,
) -> VertexAiSessionService | InMemorySessionService:
"""Create appropriate session service based on environment.
Returns VertexAiSessionService for production, InMemorySessionService for local dev.
"""
project = project or os.environ.get("GOOGLE_CLOUD_PROJECT")
location = location or (
os.environ.get("AGENT_ENGINE_LOCATION")
or os.environ.get("GOOGLE_CLOUD_LOCATION", "us-central1")
)
agent_engine_id = agent_engine_id or os.environ.get("AGENT_ENGINE_ID")
if use_vertex is None:
use_vertex = agent_engine_id is not None
if use_vertex:
if not project:
raise ValueError("GOOGLE_CLOUD_PROJECT environment variable required")
return VertexAiSessionService(
project=project, location=location,
agent_engine_id=agent_engine_id,
)
else:
return InMemorySessionService()
class SessionManager:
"""Manages sessions with A2A context mapping and TTL caching."""
def __init__(self, session_service=None, cache_maxsize=1000, cache_ttl=3600):
self.session_service = session_service or create_session_service()
self.session_cache = TTLCache(maxsize=cache_maxsize, ttl=cache_ttl)
async def get_or_create_session(self, context_id, app_name, user_id):
cached = self.session_cache.get(context_id)
if cached:
return cached
session = await self.session_service.create_session(
app_name=app_name, user_id=user_id,
)
self.session_cache.set(context_id, session.id)
return session.id
Ora copia questi file di servizio per gli altri agenti: seguono lo stesso pattern di scaffolding con argomenti di memoria specifici per l'agente:
cp src/planner_agent/services/session_manager.py src/simulator_agent/services/session_manager.py
cp src/planner_agent/services/session_manager.py src/planner_agent/evaluator/services/session_manager.py
Verifica la struttura del progetto
find src -type f -name "*.py" | sort
Dovresti visualizzare i file di configurazione condivisa, i gestori di memoria, i gestori di sessione e __init__.py in tutti e tre i pacchetti di agenti.
4. Crea l'agente Evaluator
L'agente Evaluator è il giudice di qualità del sistema. Valuta i piani di maratona in base a 7 criteri utilizzando Vertex AI Evaluation con metriche LLM-as-Judge personalizzate. Qui puoi vedere la potenza della combinazione del giudizio di Gemini con le rubriche di valutazione strutturate.
Definisci gli schemi di valutazione
Crea src/planner_agent/evaluator/schemas.py : i modelli Pydantic definiscono il formato di output strutturato:
"""Schemas for the Evaluator Agent.
Defines structured output formats for plan evaluation.
"""
from pydantic import BaseModel, Field
EVALUATION_CRITERIA = [
"safety_compliance",
"community_impact",
"logistics_completeness",
"financial_viability",
"participant_experience",
"intent_alignment",
"distance_compliance",
]
class EvaluationFinding(BaseModel):
"""A specific finding from plan evaluation."""
criterion: str = Field(
description=(
f"Which evaluation criterion triggered this finding. "
f"Must be one of: {', '.join(EVALUATION_CRITERIA)}"
),
)
severity: str = Field(
description="Severity of the finding: 'high', 'medium', or 'low'",
)
description: str = Field(
description="Description of the finding and why it matters",
)
class EvaluationResult(BaseModel):
"""Structured output from the Evaluator Agent."""
passed: bool = Field(
description="Whether the plan passes evaluation (overall_score >= 85)",
)
overall_score: float = Field(
ge=0.0, le=100.0,
description="Weighted average score across all criteria (0.0 to 100.0)",
)
scores: dict[str, float] = Field(
default_factory=dict,
description="Per-criterion scores: {criterion_name: score}",
)
findings: list[EvaluationFinding] = Field(
default_factory=list,
description="List of specific evaluation findings",
)
improvement_suggestions: list[str] = Field(
default_factory=list,
description="Actionable suggestions to improve the plan score",
)
iteration_number: int = Field(
default=1, ge=1,
description="Which evaluation iteration this is",
)
summary: str = Field(
default="",
description="Brief natural-language summary of the evaluation",
)
L'utilizzo di output_schema=EvaluationResult sull'agente garantisce che ogni risposta corrisponda a questa struttura, senza bisogno di analisi.
Scrivi le istruzioni di valutazione
Crea src/planner_agent/evaluator/instruction.md:
You are the Evaluator Agent — the quality judge for marathon plans.
Your role is to evaluate marathon plans across multiple criteria and provide actionable feedback so the planning team can iteratively improve the plan. You use a multi-step "Chain of Thought" process to ensure your evaluation is thorough, fair, and constructive.
## Phase 1: Evaluation Methodology
When you receive a plan, evaluate it across the following 7 criteria. For each, you must derive a raw score (1-100) based on the specific checks below:
1. **Participant Experience (15% weight)**
- **Checks**: Route scenic quality, landmarks, surface variety, amenities.
- **Rubric**: 100: Outstanding landmarks/view; 1: Boring/unpleasant experience.
2. **Intent Alignment (10% weight)**
- **Checks**: City match, theme match (scenic/fast), scale, budget goals.
- **Rubric**: 100: Perfectly aligned; 1: Completely misaligned.
3. **Distance Compliance (5% weight)**
- **Checks**: Exactly 26.2 miles or 42.195 km.
- **Rubric**: 100: Exactly correct; 1: Wrong distance.
4. **Safety Compliance (20% weight)**
- **Checks**: Emergency vehicle access, crowd management.
- **Rubric**: 100: Fully safe, hospital access prioritized; 60: Some concerns; 1: Dangerous blockages.
5. **Logistics Completeness (20% weight)**
- **Checks**: Timing systems, course marshals, infrastructure.
- **Rubric**: 100: Comprehensive; 60: Significant gaps; 1: Critical logistics missing.
6. **Community Impact (15% weight)**
- **Checks**: Noise disruption, neighborhood equity, residential/business access.
- **Rubric**: 100: Community benefits/event integration; 1: Major negative impact.
7. **Financial Viability (15% weight)**
- **Checks**: Budget balance, revenue (sponsorships), realistic cost estimates.
- **Rubric**: 100: Strong/sustainable; 1: Major financial gaps/un-viable.
**Action**: Use the `evaluate_plan` tool to get computed scores from the **Vertex AI Evaluation API**. Use these scores as your baseline.
## Phase 2: Score Interpretation
- **Pass Threshold**: A plan passes ONLY if `overall_score >= 85` AND there are no High-Severity findings.
- **Severity Mapping**: Score < 40 → High, < 60 → Medium, < 80 → Low.
## Phase 3: Improvement Strategy
Generate actionable suggestions for any criterion scoring below 80. Provide 3-5 prioritized improvements. Be specific: "Add course marshals at miles 12, 14, and 16" instead of "Add more marshals".
Return a structured `EvaluationResult` with your assessment.
Crea src/planner_agent/evaluator/prompts.py:
"""Prompts for the Evaluator Agent."""
import pathlib
_PROMPT_DIR = pathlib.Path(__file__).parent
INSTRUCTION = (_PROMPT_DIR / "instruction.md").read_text()
Crea gli strumenti di valutazione con Vertex AI
Questo è il cuore di Evaluator: metriche personalizzate che utilizzano MetricPromptBuilder. Crea src/planner_agent/evaluator/tools.py:
"""Tools for the Evaluator Agent.
Provides tools for evaluating marathon plans using Vertex AI Evaluation
with custom metrics. Uses a hybrid approach: LLM-based evaluation with
custom rubrics, plus deterministic checks, with heuristic fallback.
"""
import asyncio
import json
import os
import re
from typing import Any
import logging
import pandas as pd
import vertexai
from google.genai import types as genai_types
from vertexai import types
from .agent import CRITERION_WEIGHTS, SEVERITY_THRESHOLDS, MODEL
from .schemas import EvaluationResult
logger = logging.getLogger(__name__)
def _get_model_resource() -> str:
"""Get the full resource path for the Vertex AI evaluation model."""
project_id = os.environ.get("GOOGLE_CLOUD_PROJECT")
location = os.environ.get("GOOGLE_CLOUD_LOCATION", "us-central1")
return (
f"projects/{project_id}/locations/{location}/publishers/google/models/{MODEL}"
if project_id else MODEL
)
# ============================================================================
# CUSTOM METRICS — each uses MetricPromptBuilder for structured rubrics
# ============================================================================
def _create_safety_compliance_metric() -> types.LLMMetric:
"""Evaluate emergency corridor access and crowd safety."""
builder = types.MetricPromptBuilder(
metric_definition=(
"Evaluate whether the proposed marathon route maintains emergency "
"vehicle access and crowd safety. Check for blocked hospitals, fire stations, "
"and major emergency corridors."
),
criteria={
"Emergency corridor access": (
"The route does not permanently block access to hospitals, "
"fire stations, or police stations without providing clear detour routes."
),
"Evacuation routes": (
"Major evacuation routes remain accessible or have documented alternatives."
),
"Emergency vehicle passage": (
"The plan includes provisions for emergency vehicle crossing "
"at regular intervals along the route."
),
},
rating_scores={
"1": "Dangerous - Major emergency corridors blocked with no detours",
"25": "Unsafe - Several emergency access points compromised",
"50": "Concerning - Some emergency access issues that need resolution",
"75": "Mostly safe - Minor emergency access concerns, easy to fix",
"100": "Fully safe - Emergency access maintained throughout",
},
)
return types.LLMMetric(
name="safety_compliance",
prompt_template=str(builder),
judge_model=_get_model_resource(),
)
def _create_community_impact_metric() -> types.LLMMetric:
"""Evaluate community disruption, inclusivity, and neighborhood equity."""
builder = types.MetricPromptBuilder(
metric_definition=(
"Evaluate the marathon plan's impact on the local community. "
"Check for noise disruption, residential access, business impact, "
"inclusivity, and equitable neighborhood treatment."
),
criteria={
"Residential disruption": (
"The plan minimizes disruption to residential areas, with "
"reasonable timing and notification plans."
),
"Business access": (
"Local businesses along the route can still operate or have "
"been accommodated with alternative access."
),
"Neighborhood equity": (
"The route and plan are accessible to diverse communities "
"and do not disproportionately burden any demographic group."
),
"Community engagement": (
"The plan includes cheer zones, community events, or other "
"ways to involve and benefit local residents."
),
},
rating_scores={
"1": "Harmful - Major negative impact on community with no mitigation",
"25": "Disruptive - Significant community issues that need resolution",
"50": "Mixed - Some community concerns that need attention",
"75": "Considerate - Minor community impacts with good mitigation",
"100": "Excellent - Community benefits from the event",
},
)
return types.LLMMetric(
name="community_impact",
prompt_template=str(builder),
judge_model=_get_model_resource(),
)
def _create_logistics_completeness_metric() -> types.LLMMetric:
"""Evaluate logistics coverage: water stations, timing, marshals."""
builder = types.MetricPromptBuilder(
metric_definition=(
"Evaluate whether the marathon plan's logistics are complete and "
"adequate for the expected scale."
),
criteria={
"Water and nutrition": "Hydration and nutrition are considered for the expected participant count.",
"Timing and tracking": "Timing systems (chip timing, mats) are specified.",
"Course management": "Sufficient course marshals, signage, barriers, and traffic control are planned.",
"Start/finish infrastructure": "Start/finish areas have adequate capacity for the participant count.",
},
rating_scores={
"1": "Incomplete - Critical logistics missing",
"25": "Sparse - Major logistics gaps",
"50": "Partial - Some logistics covered but significant gaps remain",
"75": "Mostly complete - Minor logistics details to finalize",
"100": "Comprehensive - All logistics thoroughly planned",
},
)
return types.LLMMetric(
name="logistics_completeness",
prompt_template=str(builder),
judge_model=_get_model_resource(),
)
def _create_financial_viability_metric() -> types.LLMMetric:
"""Evaluate budget balance, revenue sources, and cost control."""
builder = types.MetricPromptBuilder(
metric_definition=(
"Evaluate the financial viability of the marathon plan. "
"Check budget balance, revenue sources, cost estimates, and sustainability."
),
criteria={
"Budget balance": "Revenue projections meet or exceed estimated costs.",
"Cost estimates": "Cost estimates are realistic and cover major expense categories.",
"Revenue diversity": "Revenue comes from multiple sources, not just registration fees.",
},
rating_scores={
"1": "Unviable - Major financial gaps",
"25": "Risky - Budget concerns that could threaten viability",
"50": "Uncertain - Financial plan needs more detail",
"75": "Viable - Sound financial plan with minor gaps",
"100": "Strong - Well-balanced budget with diverse revenue streams",
},
)
return types.LLMMetric(
name="financial_viability",
prompt_template=str(builder),
judge_model=_get_model_resource(),
)
def _create_participant_experience_metric() -> types.LLMMetric:
"""Evaluate route quality, scenic value, and runner amenities."""
builder = types.MetricPromptBuilder(
metric_definition=(
"Evaluate the participant experience of a proposed marathon plan."
),
criteria={
"Route Quality": "The route surface is suitable and elevation profile appropriate.",
"Scenic Value": "The route passes through interesting or landmark areas.",
"Runner Amenities": "Post-race amenities (food, medals, recovery area) are planned.",
},
rating_scores={
"1": "Poor experience - Boring route, no amenities",
"50": "Average - Adequate but unremarkable",
"100": "Excellent - Outstanding experience that runners will remember",
},
)
return types.LLMMetric(
name="participant_experience",
prompt_template=str(builder),
judge_model=_get_model_resource(),
)
def _create_intent_alignment_metric() -> types.LLMMetric:
"""Evaluate whether the plan matches the user's original request."""
builder = types.MetricPromptBuilder(
metric_definition=(
"Evaluate whether a proposed marathon plan matches the user's original intent."
),
criteria={
"City Match": "The plan takes place in the requested city.",
"Theme Match": "The plan matches the desired theme (scenic, fast, charity, etc.).",
"Scale Match": "The plan's scale matches the user's vision.",
},
rating_scores={
"1": "Completely misaligned",
"50": "Partially aligned - Key requirements missed",
"100": "Perfectly aligned - All requirements addressed",
},
)
return types.LLMMetric(
name="intent_alignment",
prompt_template=str(builder),
judge_model=_get_model_resource(),
)
def _check_distance_compliance_logic(response_text: str) -> dict:
"""Deterministic check for 26.2 mile (42.195 km) marathon distance."""
text_lower = response_text.lower()
score = 100.0
issues = []
for distance_str in re.findall(r'(\d+(?:\.\d+)?)\s*(?:miles?|mi)\b', text_lower):
distance = float(distance_str)
if 20 <= distance <= 30:
deviation = abs(distance - 26.2)
if deviation > 0.5:
score = 1.0
issues.append(f"Route distance {distance} miles deviates from 26.2 mile standard")
elif deviation > 0.1:
score = 60.0
issues.append(f"Route distance {distance} miles is close but not exactly 26.2 miles")
for distance_str in re.findall(r'(\d+(?:\.\d+)?)\s*(?:kilometers?|km)\b', text_lower):
distance = float(distance_str)
if 35 <= distance <= 50 and abs(distance - 42.195) > 1.0:
score = 1.0
issues.append(f"Route distance {distance} km deviates from 42.195 km standard")
return {"score": score, "explanation": "; ".join(issues) if issues else "No distance issues detected"}
def _create_distance_compliance_metric() -> types.Metric:
"""Deterministic check for marathon distance — no LLM needed."""
def check_distance_compliance(instance: dict) -> dict:
response_text = instance["response"]["parts"][0]["text"]
return _check_distance_compliance_logic(response_text)
return types.Metric(
name="distance_compliance",
custom_function=check_distance_compliance,
)
# ============================================================================
# MAIN EVALUATION TOOL
# ============================================================================
async def evaluate_plan(evaluation_request: str) -> dict[str, Any]:
"""Evaluate a proposed marathon plan across quality criteria.
Uses Vertex AI Evaluation with 7 custom metrics.
Falls back to heuristic evaluation if API fails.
Args:
evaluation_request: JSON string with user_intent and proposed_plan
Returns:
dict with evaluation results
"""
try:
request_data = json.loads(evaluation_request)
except json.JSONDecodeError as e:
return {
"passed": False, "scores": {}, "overall_score": 0.0,
"findings": [{"criterion": "intent_alignment",
"description": f"Invalid JSON input: {e}", "severity": "high"}],
"improvement_suggestions": ["Provide valid JSON with 'user_intent' and 'proposed_plan' fields."],
"eval_method": "error",
}
user_intent_raw = request_data.get("user_intent", "Unknown intent")
proposed_plan_raw = request_data.get("proposed_plan", "No plan provided")
user_intent = json.dumps(user_intent_raw) if not isinstance(user_intent_raw, str) else user_intent_raw
proposed_plan = json.dumps(proposed_plan_raw) if not isinstance(proposed_plan_raw, str) else proposed_plan_raw
project_id = os.environ.get("GOOGLE_CLOUD_PROJECT")
location = os.environ.get("GOOGLE_CLOUD_LOCATION", "us-central1")
if project_id:
try:
scores, details = await _run_custom_eval(
project_id=project_id, location=location,
user_intent=user_intent, proposed_plan=proposed_plan,
)
return _build_result(scores, details, eval_method="vertex_ai_eval")
except Exception as e:
logger.warning(f"Vertex AI Eval failed, using heuristics: {e}")
scores, details = _heuristic_eval(user_intent, proposed_plan)
return _build_result(scores, details, eval_method="heuristic")
async def _run_custom_eval(
project_id: str, location: str,
user_intent: str, proposed_plan: str,
) -> tuple[dict[str, float], dict[str, str]]:
"""Run Vertex AI Evaluation with custom metrics."""
vertexai.init(project=project_id, location=location)
client = vertexai.Client(
project=project_id, location=location,
http_options=genai_types.HttpOptions(api_version="v1beta1"),
)
df = pd.DataFrame({"prompt": [user_intent], "response": [proposed_plan]})
metrics = [
_create_safety_compliance_metric(),
_create_community_impact_metric(),
_create_logistics_completeness_metric(),
_create_financial_viability_metric(),
_create_participant_experience_metric(),
_create_intent_alignment_metric(),
_create_distance_compliance_metric(),
]
result = client.evals.evaluate(dataset=df, metrics=metrics)
scores, details = {}, {}
for case in result.eval_case_results:
for cand in case.response_candidate_results:
for metric_name, metric_result in cand.metric_results.items():
raw_score = metric_result.score if hasattr(metric_result, "score") and metric_result.score is not None else 50.0
scores[metric_name] = round(float(raw_score), 2)
if hasattr(metric_result, "explanation") and metric_result.explanation:
details[metric_name] = metric_result.explanation
return scores, details
def _heuristic_eval(user_intent: str, proposed_plan: str) -> tuple[dict[str, float], dict[str, str]]:
"""Heuristic evaluation fallback when Vertex AI Eval is unavailable."""
plan_lower = proposed_plan.lower()
scores, details = {}, {}
# Safety
safety_score = 80.0
if "emergency" in plan_lower and "no detour" in plan_lower:
safety_score = 20.0
if "emergency vehicle" in plan_lower:
safety_score = min(safety_score + 10.0, 100.0)
scores["safety_compliance"] = safety_score
details["safety_compliance"] = "Heuristic safety check"
# Community
community_score = 75.0
if any(kw in plan_lower for kw in ["cheer zone", "community"]):
community_score = min(community_score + 10.0, 100.0)
scores["community_impact"] = community_score
details["community_impact"] = "Heuristic community check"
# Logistics
logistics_score = 60.0
for kw in ["hydration", "timing", "chip", "marshal"]:
if kw in plan_lower:
logistics_score += 10.0
scores["logistics_completeness"] = min(logistics_score, 100.0)
details["logistics_completeness"] = "Heuristic logistics check"
# Financial
financial_score = 65.0
for kw in ["budget", "cost", "revenue", "sponsor"]:
if kw in plan_lower:
financial_score += 8.0
scores["financial_viability"] = min(financial_score, 100.0)
details["financial_viability"] = "Heuristic financial check"
# Experience
experience_score = 70.0
for kw in ["scenic", "landmark", "medal", "post-race"]:
if kw in plan_lower:
experience_score += 8.0
scores["participant_experience"] = min(experience_score, 100.0)
details["participant_experience"] = "Heuristic experience check"
# Intent
scores["intent_alignment"] = 70.0
details["intent_alignment"] = "Heuristic intent check"
# Distance
distance_result = _check_distance_compliance_logic(proposed_plan)
scores["distance_compliance"] = float(distance_result["score"])
details["distance_compliance"] = distance_result["explanation"]
return scores, details
def _build_result(scores, details, eval_method):
"""Build the final evaluation result from scores and details."""
findings, improvement_suggestions = [], []
for criterion, score in scores.items():
if score < 80.0:
severity = "high" if score < SEVERITY_THRESHOLDS["high"] else "medium" if score < SEVERITY_THRESHOLDS["medium"] else "low"
findings.append({"criterion": criterion, "description": details.get(criterion, f"Score: {score}"), "severity": severity})
improvement_suggestions.append(_suggest_improvement(criterion, score, details.get(criterion, "")))
overall_score = round(sum(scores.get(c, 50.0) * w for c, w in CRITERION_WEIGHTS.items()), 2)
passed = overall_score >= 85.0 and not any(f["severity"] == "high" for f in findings)
return {"passed": passed, "scores": scores, "findings": findings,
"improvement_suggestions": improvement_suggestions,
"overall_score": overall_score, "eval_method": eval_method}
def _suggest_improvement(criterion, score, detail):
suggestions = {
"safety_compliance": "Add emergency vehicle crossing points every 2 miles and plan detour routes around hospitals.",
"community_impact": "Include cheer zones, notify affected residents, and ensure equitable route distribution.",
"logistics_completeness": "Add timing chip details, plan course marshal positions, and detail start/finish infrastructure.",
"financial_viability": "Add budget breakdown with cost estimates and identify 3+ revenue sources.",
"participant_experience": "Highlight scenic landmarks and detail post-race amenities (medals, food, recovery).",
"intent_alignment": "Review the user's original request and ensure the plan matches their stated requirements.",
"distance_compliance": "Verify the route is exactly 26.2 miles (42.195 km) for marathon certification.",
}
return suggestions.get(criterion, f"Improve {criterion} (current score: {score:.2f})")
Il pattern chiave qui è la valutazione ibrida: lo strumento prova prima Vertex AI Eval con le metriche LLM-as-Judge, per poi ricorrere a controlli euristici se l'API non è disponibile.
Collega l'agente Evaluator
Crea src/planner_agent/evaluator/agent.py:
"""Evaluator Agent — scores marathon plans using Vertex AI Evaluation.
Uses gemini-3.1-pro-preview for best evaluation quality.
"""
import os
import vertexai
from google.adk.agents import LlmAgent
from google.adk.tools.preload_memory_tool import PreloadMemoryTool
from .prompts import INSTRUCTION
from .services.memory_manager import auto_save_memories
from .schemas import EvaluationResult
# Agent identity
AGENT_NAME = "evaluator_agent"
AGENT_DESCRIPTION = (
"Evaluates marathon plans across multiple quality criteria using Vertex AI "
"Evaluation with custom metrics. Acts as LLM-as-Judge to score plans and "
"provide actionable feedback for iterative improvement."
)
# Model — use gemini-3.1-pro-preview for evaluation accuracy
MODEL = os.getenv("EVALUATOR_MODEL", "gemini-3.1-pro-preview")
# Structured output schema — guarantees every response matches EvaluationResult
OUTPUT_SCHEMA = EvaluationResult
# Criterion weights must sum to 1.0
CRITERION_WEIGHTS = {
"safety_compliance": 0.20,
"community_impact": 0.15,
"logistics_completeness": 0.20,
"financial_viability": 0.15,
"participant_experience": 0.15,
"intent_alignment": 0.10,
"distance_compliance": 0.05,
}
SEVERITY_THRESHOLDS = {"high": 40.0, "medium": 60.0, "low": 80.0}
# Initialize Vertex AI
project_id = os.environ.get("GOOGLE_CLOUD_PROJECT")
location = os.environ.get("GOOGLE_CLOUD_LOCATION", "us-central1")
if project_id:
vertexai.init(project=project_id, location=location)
from google.genai.types import GenerateContentConfig, ThinkingConfig
from .tools import evaluate_plan
# Enable thinking for better evaluation reasoning
evaluator_config = GenerateContentConfig(max_output_tokens=4096)
if "pro" in MODEL:
evaluator_config.thinking_config = ThinkingConfig(thinking_budget=1024)
evaluator_agent = LlmAgent(
name="evaluator_agent",
model=MODEL,
description=AGENT_DESCRIPTION,
static_instruction=INSTRUCTION,
output_schema=OUTPUT_SCHEMA,
generate_content_config=evaluator_config,
include_contents='none',
tools=[PreloadMemoryTool(), evaluate_plan],
after_agent_callback=auto_save_memories,
)
root_agent = evaluator_agent
Ora crea il gestore di memoria di Evaluator in src/planner_agent/evaluator/services/memory_manager.py : tiene traccia della cronologia delle valutazioni e delle tendenze di punteggio:
"""Memory Manager for Evaluator Agent."""
import os
from typing import TYPE_CHECKING
from google.adk.memory import VertexAiMemoryBankService
from vertexai._genai.types import (
MemoryBankCustomizationConfig,
MemoryBankCustomizationConfigMemoryTopic as MemoryTopic,
MemoryBankCustomizationConfigMemoryTopicCustomMemoryTopic as CustomMemoryTopic,
)
if TYPE_CHECKING:
from google.adk.agents.callback_context import CallbackContext
EVALUATION_HISTORY = MemoryTopic(
custom_memory_topic=CustomMemoryTopic(
label="evaluation_history",
description="""Track evaluation results: scores, pass/fail verdicts,
findings count, and iteration numbers.""",
)
)
SCORING_TRENDS = MemoryTopic(
custom_memory_topic=CustomMemoryTopic(
label="scoring_trends",
description="""Track which criteria consistently score low and
average scores by category.""",
)
)
def create_memory_service(project=None, location=None, agent_engine_id=None):
project = project or os.environ.get("GOOGLE_CLOUD_PROJECT")
location = location or os.environ.get("AGENT_ENGINE_LOCATION") or os.environ.get("GOOGLE_CLOUD_LOCATION", "us-central1")
agent_engine_id = agent_engine_id or os.environ.get("AGENT_ENGINE_ID")
if not project:
raise ValueError("GOOGLE_CLOUD_PROJECT required")
if not agent_engine_id:
return None
return VertexAiMemoryBankService(project=project, location=location, agent_engine_id=agent_engine_id)
async def auto_save_memories(callback_context: "CallbackContext") -> None:
agent_engine_id = os.environ.get("AGENT_ENGINE_ID")
if not agent_engine_id:
return
try:
svc = VertexAiMemoryBankService(
project=os.environ.get("GOOGLE_CLOUD_PROJECT"),
location=os.environ.get("AGENT_ENGINE_LOCATION") or os.environ.get("GOOGLE_CLOUD_LOCATION", "us-central1"),
agent_engine_id=agent_engine_id,
)
await svc.add_session_to_memory(callback_context._invocation_context.session)
except Exception as e:
print(f"Warning: Failed to save memories: {e}")
Infine, configura le esportazioni del pacchetto di valutazione. Sostituisci src/planner_agent/evaluator/__init__.py:
try:
from .agent import root_agent, AGENT_NAME, MODEL
__all__ = ["root_agent", "AGENT_NAME", "MODEL"]
except Exception:
__all__ = ["root_agent"]
5. Crea l'agente Simulation Controller
Simulation Controller è il gatekeeper finale: esegue un rapido controllo dei prerequisiti per verificare che un piano abbia dati sufficienti per eseguire una simulazione, senza rivalutare la qualità.
Definisci lo schema di approvazione
Crea src/simulator_agent/agent/schemas.py:
"""Schemas for the Simulation Controller Agent."""
from pydantic import BaseModel, Field
class SimulationApproval(BaseModel):
"""Structured output from the Simulation Controller Agent."""
approved: bool = Field(description="Whether the plan is approved for simulation")
overall_readiness: float = Field(ge=0.0, le=1.0, description="Readiness score (0-1)")
route_feasibility: str = Field(description="'feasible', 'marginal', or 'infeasible'")
logistics_readiness: str = Field(description="'ready', 'partial', or 'not_ready'")
safety_clearance: str = Field(description="'cleared', 'conditional', or 'blocked'")
blockers: list[str] = Field(default_factory=list, description="Blocking issues")
recommendations: list[str] = Field(default_factory=list, description="Non-blocking suggestions")
summary: str = Field(default="", description="Summary of the decision")
Crea lo strumento di controllo della preparazione
Crea src/simulator_agent/agent/tools.py : un elenco di controllo deterministico, non è necessario un LLM:
"""Tools for the Simulation Controller Agent."""
import re
from typing import Any
async def check_plan_readiness(plan_text: str) -> dict[str, Any]:
"""Check a marathon plan for simulation readiness.
Performs deterministic checks on the plan text to identify
missing elements and assess completeness.
"""
plan_lower = plan_text.lower()
checklist = {
"distance_specified": bool(
re.search(r'(\d+(?:\.\d+)?)\s*(?:miles?|mi|kilometers?|km)\b', plan_lower)
),
"water_stations": "water station" in plan_lower,
"medical_tents": any(kw in plan_lower for kw in ["medical tent", "first aid", "medical station"]),
"timing_system": any(kw in plan_lower for kw in ["timing", "chip", "tracking"]),
"start_finish": any(kw in plan_lower for kw in ["start line", "finish line", "start/finish", "starting area"]),
"emergency_access": any(kw in plan_lower for kw in ["emergency", "ambulance", "evacuation"]),
"budget_included": any(kw in plan_lower for kw in ["budget", "cost", "revenue", "expense"]),
"timeline_included": any(kw in plan_lower for kw in ["schedule", "timeline", "race day", "setup"]),
}
passed = sum(checklist.values())
total = len(checklist)
return {
"checklist": checklist,
"missing_elements": [k for k, v in checklist.items() if not v],
"readiness_score": round(passed / total, 2),
"passed_checks": passed,
"total_checks": total,
}
def get_tools() -> list:
return [check_plan_readiness]
Crea la skill ADK
Le skill ADK forniscono conoscenze procedurali che l'agente carica in fase di runtime. Crea src/simulator_agent/skills/review-marathon-plan/SKILL.md:
---
name: review-marathon-plan
description: Step-by-step methodology for reviewing marathon plans for simulation readiness, covering route feasibility, logistics completeness, and safety clearance.
---
# Review Marathon Plan
## Purpose
Systematically confirm that a marathon plan is ready for simulation execution by verifying the presence of all required prerequisite data.
## Procedure
### Step 1: Prerequisite Data Verification
Use the `check_plan_readiness` tool to confirm the presence of:
1. **Route Data**: Waypoints, landmarks, distance (26.2 mi / 42.195 km), start/finish locations.
2. **Logistics Data**: Water stations, medical tents, timing systems, participant scale.
3. **Safety Data**: Emergency access routes, evacuation plans, crowd management.
### Step 2: Approval Decision
1. Set `approved=true` ONLY if all three data categories are present.
2. If any critical data is missing, set `approved=false` and list missing elements in `blockers`.
3. Provide `recommendations` for minor data gaps.
Collega l'agente Simulation Controller
Crea src/simulator_agent/agent/config.py:
"""Configuration for the Simulation Controller Agent."""
import os
from .schemas import SimulationApproval
AGENT_NAME = "simulator_agent"
AGENT_DESCRIPTION = (
"Simulation Controller Agent. Reviews marathon plans for simulation readiness, "
"assessing route feasibility, logistics completeness, and safety clearance."
)
MODEL = os.getenv("SIMULATOR_MODEL", "gemini-3-flash-preview")
OUTPUT_SCHEMA = SimulationApproval
Crea src/simulator_agent/agent/prompts.py:
"""Prompts for the Simulation Controller Agent."""
INSTRUCTION = """You are the Simulation Controller Agent — the final gatekeeper before a marathon plan enters simulation.
Your role is to perform a fast "Simulation Prerequisite Check" on marathon plans. You do NOT evaluate quality (the Evaluator Agent does that). You simply confirm the plan has all data required for the simulation engine.
## Available ADK Skills
1. **review-marathon-plan** — Check for simulation prerequisites (Route, Logistics, Safety).
## Prerequisite Check
1. **Route Data**: Waypoints, distance (26.2 mi / 42.195 km), start/finish locations.
2. **Logistics Data**: Timing and registration infrastructure, participant count.
3. **Safety Clearance**: Emergency access, evacuation plan, crowd management.
## Approval Decision
- **Approved** (`approved=true`): All prerequisite data present.
- **Rejected** (`approved=false`): Critical data missing. List in `blockers`.
Always return a structured SimulationApproval."""
Crea src/simulator_agent/agent/agent.py:
"""Simulation Controller Agent — reviews plans for simulation readiness."""
import os
import pathlib
import vertexai
from google.adk.agents import LlmAgent
from google.adk.skills import load_skill_from_dir
from google.adk.tools.preload_memory_tool import PreloadMemoryTool
from google.adk.tools.skill_toolset import SkillToolset
from ..services.memory_manager import auto_save_memories
from .config import AGENT_DESCRIPTION, AGENT_NAME, MODEL, OUTPUT_SCHEMA
from .prompts import INSTRUCTION
from .tools import get_tools
# Initialize Vertex AI
project_id = os.environ.get("GOOGLE_CLOUD_PROJECT")
location = os.environ.get("GOOGLE_CLOUD_LOCATION", "us-central1")
if project_id:
vertexai.init(project=project_id, location=location)
# Load the review-marathon-plan ADK Skill
_skills_dir = pathlib.Path(__file__).parent.parent / "skills"
_review_skill = load_skill_from_dir(_skills_dir / "review-marathon-plan")
_skill_toolset = SkillToolset(skills=[_review_skill])
_tools = [_skill_toolset, PreloadMemoryTool(), *get_tools()]
from google.genai.types import GenerateContentConfig, ThinkingConfig
_agent_kwargs = dict(
name=AGENT_NAME,
model=MODEL,
description=AGENT_DESCRIPTION,
static_instruction=INSTRUCTION,
tools=_tools,
generate_content_config=GenerateContentConfig(
thinking_config=ThinkingConfig(thinking_budget=0),
max_output_tokens=4096,
),
after_agent_callback=auto_save_memories,
)
if OUTPUT_SCHEMA is not None:
_agent_kwargs["output_schema"] = OUTPUT_SCHEMA
root_agent = LlmAgent(**_agent_kwargs)
Ora crea il gestore di memoria di Simulator in src/simulator_agent/services/memory_manager.py:
"""Memory Manager for Simulation Controller Agent."""
import os
from typing import TYPE_CHECKING
from google.adk.memory import VertexAiMemoryBankService
from vertexai._genai.types import (
MemoryBankCustomizationConfig,
MemoryBankCustomizationConfigMemoryTopic as MemoryTopic,
MemoryBankCustomizationConfigMemoryTopicCustomMemoryTopic as CustomMemoryTopic,
)
if TYPE_CHECKING:
from google.adk.agents.callback_context import CallbackContext
APPROVAL_HISTORY = MemoryTopic(
custom_memory_topic=CustomMemoryTopic(
label="approval_history",
description="""Track approval decisions: verdicts, readiness scores, common blockers.""",
)
)
def create_memory_service(project=None, location=None, agent_engine_id=None):
project = project or os.environ.get("GOOGLE_CLOUD_PROJECT")
location = location or os.environ.get("AGENT_ENGINE_LOCATION") or os.environ.get("GOOGLE_CLOUD_LOCATION", "us-central1")
agent_engine_id = agent_engine_id or os.environ.get("AGENT_ENGINE_ID")
if not project:
raise ValueError("GOOGLE_CLOUD_PROJECT required")
if not agent_engine_id:
return None
return VertexAiMemoryBankService(project=project, location=location, agent_engine_id=agent_engine_id)
async def auto_save_memories(callback_context: "CallbackContext") -> None:
agent_engine_id = os.environ.get("AGENT_ENGINE_ID")
if not agent_engine_id:
return
try:
svc = VertexAiMemoryBankService(
project=os.environ.get("GOOGLE_CLOUD_PROJECT"),
location=os.environ.get("AGENT_ENGINE_LOCATION") or os.environ.get("GOOGLE_CLOUD_LOCATION", "us-central1"),
agent_engine_id=agent_engine_id,
)
await svc.add_session_to_memory(callback_context._invocation_context.session)
except Exception as e:
print(f"Warning: Failed to save memories: {e}")
Configura le esportazioni dei pacchetti. Sostituisci src/simulator_agent/agent/__init__.py:
try:
from .agent import root_agent
from .config import AGENT_NAME, MODEL
__all__ = ["root_agent", "AGENT_NAME", "MODEL"]
except Exception:
pass
Sostituisci src/simulator_agent/__init__.py:
try:
from .agent import root_agent
from .agent.config import AGENT_NAME, MODEL
__all__ = ["root_agent", "AGENT_NAME", "MODEL"]
except Exception:
pass
6. Crea l'agente Marathon Planner
Marathon Planner è l'orchestratore principale. Utilizza le skill ADK per la conoscenza del dominio, delega la valutazione della qualità al sub-agente Evaluator e si connette a Simulation Controller tramite A2A.
Definisci lo schema e la configurazione del pianificatore
Crea src/planner_agent/agent/schemas.py:
"""Schemas for the Marathon Planner Agent."""
from pydantic import BaseModel, Field
class MarathonPlan(BaseModel):
"""Structured marathon plan output (used in Phase 2+)."""
city: str = Field(description="City where the marathon takes place")
date: str = Field(description="Event date in YYYY-MM-DD format")
theme: str = Field(description="Marathon theme: scenic, fast, charity, etc.")
participants: int = Field(ge=0, description="Expected number of participants")
route_summary: str = Field(description="High-level route description")
route_waypoints: list[str] = Field(default_factory=list, description="Ordered waypoints")
distance_miles: float = Field(ge=0, description="Total route distance in miles")
logistics_summary: str = Field(description="Key logistics overview")
budget_summary: str = Field(description="Budget overview")
key_risks: list[str] = Field(default_factory=list, description="Top risks and mitigations")
Crea src/planner_agent/agent/config.py:
"""Configuration for the Marathon Planner Agent."""
import os
AGENT_NAME = "planner_agent"
AGENT_DESCRIPTION = (
"Marathon Planner Agent — Lead Architect. "
"Designs comprehensive city marathon plans with built-in expertise in route design, "
"traffic management, community impact, and economics. Evaluates plans via the "
"Evaluator Agent (A2A) and submits for approval to the Simulation Controller (A2A)."
)
MODEL = os.getenv("PLANNER_MODEL", "gemini-3-flash-preview")
# Phase 1: No structured output — agent returns free-form text
OUTPUT_SCHEMA = None
Scrivi le istruzioni del pianificatore
Crea src/planner_agent/agent/planner-instruction.md:
# Role
Marathon Planner Agent (city marathon event architect).
Goal: Design comprehensive marathon plan based on user constraints.
# ADK Skills (LOAD ONCE BEFORE PLANNING)
1. `route-planning`: Generate route using `plan_marathon_route`.
2. `plan-evaluation`: Analyze demographics, capacity, revenue, safety.
# Core Requirements
- Safety: Emergency corridors, traffic cover.
- Community: Local business, noise, inclusivity.
- Logistics: Start/finish capacity, restrooms, roads.
- Finances: Maximize revenue/sponsorships.
- Experience: Scenic, runner comfort.
# User Prerequisites
Clarify if missing: City, Date/Season, Theme, Scale (participants), Budget, Special constraints.
# Deliverables
1. Route Design: GeoJSON via `plan_marathon_route` tool.
2. Traffic: Closures, detours, mitigation.
3. Community: Engagement, cheer zones, noise.
4. Economics: Revenue, costs, sponsors.
5. Logistics: Porta-potties, capacity, timing.
6. Timeline: Setup to teardown, waves.
7. Risks: Weather, crowd, emergency.
# A2A Collaboration
1. **Evaluator (`evaluator_agent`)**:
- Send plan for 7-criteria scoring.
- SINGLE PASS ONLY. Do not call twice to verify successful fixes.
2. **Simulation Controller (`simulator_agent`)**:
- Call once after evaluation completes (REGARDLESS OF SCORE).
- Accept result, DO NOT call again.
# Workflow
1. Gather reqs.
2. Load skills (ONCE).
3. Call `plan_marathon_route`.
4. Complete design.
5. Send to Evaluator.
6. Send to Simulator.
7. Present final.
# Rules & Format
- Personality: Pragmatic, detail-oriented.
Crea src/planner_agent/agent/prompts.py:
"""System instructions for the Marathon Planner Agent."""
import pathlib
_PROMPT_DIR = pathlib.Path(__file__).parent
INSTRUCTION = (_PROMPT_DIR / "planner-instruction.md").read_text()
Crea il modulo di autenticazione
Per le chiamate A2A ad Agent Engine, gli agenti hanno bisogno dell'autenticazione Google Cloud. Crea src/planner_agent/agent/auth.py:
"""Authentication utilities for Agent Engine A2A connections.
Provides Google Cloud auth for httpx requests to Agent Engine agents.
"""
import httpx
from google.auth import default
from google.auth.credentials import Credentials
from google.auth.transport.requests import Request as AuthRequest
class GoogleAuthRefresh(httpx.Auth):
"""Google Cloud Auth with lazy credential refresh.
Credentials are initialized lazily on first request to avoid
serialization issues with ADK agents.
"""
def __init__(self, scopes: list[str] | None = None) -> None:
self.scopes = scopes or ["https://www.googleapis.com/auth/cloud-platform"]
self.credentials: Credentials | None = None
self.auth_request: AuthRequest | None = None
def auth_flow(self, request: httpx.Request):
if self.credentials is None:
self.credentials, _ = default(scopes=self.scopes)
self.auth_request = AuthRequest()
if not self.credentials.valid:
self.credentials.refresh(self.auth_request)
request.headers["Authorization"] = f"Bearer {self.credentials.token}"
yield request
Aggiungi le skill ADK
Crea src/planner_agent/skills/route-planning/SKILL.md:
---
name: route-planning
description:
Generates high-fidelity marathon routes using road network data (Dijkstra's algorithm)
and outputting GeoJSON for visualization.
---
# Route Planning Skill
**Goal:** Design a mathematically perfect 42.195 km marathon route using real road network data.
## Capabilities
- **Automated Route Generation**: Uses a built-in road network and Dijkstra's algorithm to calculate a certified 42.195 km route between specified landmarks.
- **GeoJSON Output**: Returns a standards-compliant GeoJSON FeatureCollection.
## Resources
### Tools (Python)
- `tools.py`: Contains the `plan_marathon_route`, `add_water_stations`, and `add_medical_tents` implementations.
### References
- `references/marathon_planning_guide.md`: Marathon standards, road width, traffic severity, landmarks.
Crea src/planner_agent/skills/plan-evaluation/SKILL.md:
---
name: plan-evaluation
description:
Evaluates the plan against financial, community, and logistical goals.
---
# Plan Evaluation Skill
**Goal:** Provide verification data for the event including capacity, budget, demographics, and nuisance checking.
## Resources
### References
- `references/evaluation_criteria.md`: Detailed scoring rubrics.
Collega gli strumenti del pianificatore
Crea src/planner_agent/agent/tools.py : qui gli agenti si connettono:
"""Tools for the Marathon Planner Agent.
Contains:
- A2A infrastructure (SerializableRemoteA2aAgent, URL helpers)
- Remote A2A agent creators for Evaluator and Simulation Controller
- get_tools() — assembles all tools including SkillToolset
"""
import logging
import os
import httpx
from google.adk.agents.remote_a2a_agent import RemoteA2aAgent
from google.adk.tools.agent_tool import AgentTool
from google.adk.tools.function_tool import FunctionTool
from a2a.client.client import ClientConfig as A2AClientConfig
from a2a.client.client_factory import ClientFactory as A2AClientFactory
from a2a.types import TransportProtocol as A2ATransport
from .auth import GoogleAuthRefresh
from ..evaluator.agent import root_agent as evaluator_agent
logger = logging.getLogger(__name__)
AGENT_TIMEOUT_SECONDS = 120
# ============================================================================
# A2A INFRASTRUCTURE
# ============================================================================
def _get_agent_a2a_endpoint(resource_name: str, default_port: int = 8080) -> str:
"""Construct A2A card endpoint URL from resource name or local address."""
if resource_name.startswith("local"):
port = resource_name.split(":")[1] if ":" in resource_name else default_port
return f"http://127.0.0.1:{port}/.well-known/agent-card.json"
parts = resource_name.split("/")
try:
location = parts[parts.index("locations") + 1]
return f"https://{location}-aiplatform.googleapis.com/v1beta1/{resource_name}/a2a/v1/card"
except (ValueError, IndexError):
return resource_name
def _get_agent_a2a_url(resource_name: str) -> str | None:
"""Construct the regional A2A message URL from Agent Engine resource name."""
if resource_name.startswith("local"):
return None
parts = resource_name.split("/")
location = parts[parts.index("locations") + 1]
return f"https://{location}-aiplatform.googleapis.com/v1beta1/{resource_name}/a2a"
class SerializableRemoteA2aAgent(RemoteA2aAgent):
"""RemoteA2aAgent with authentication and Agent Engine URL fix.
Handles two Agent Engine issues:
1. Creates httpx client lazily with Google Cloud auth
2. Fixes agent card URL (Agent Engine returns global URL that 404s)
"""
def __init__(self, *, a2a_url: str | None = None, **kwargs):
super().__init__(**kwargs)
self._a2a_url_override = a2a_url
async def _ensure_httpx_client(self) -> httpx.AsyncClient:
if self._httpx_client is None:
self._httpx_client = httpx.AsyncClient(
timeout=httpx.Timeout(timeout=AGENT_TIMEOUT_SECONDS),
headers={"Content-Type": "application/json"},
auth=GoogleAuthRefresh(),
)
self._httpx_client_needs_cleanup = True
if self._a2a_client_factory is None:
self._a2a_client_factory = A2AClientFactory(
config=A2AClientConfig(
httpx_client=self._httpx_client,
streaming=False, polling=False,
supported_transports=[A2ATransport.http_json, A2ATransport.jsonrpc],
)
)
return self._httpx_client
async def _resolve_agent_card_from_url(self, url: str):
card = await super()._resolve_agent_card_from_url(url)
if self._a2a_url_override:
logger.info(f"Overriding agent card URL: {card.url} → {self._a2a_url_override}")
card.url = self._a2a_url_override
return card
def create_evaluator_tool() -> AgentTool:
"""Create Evaluator Agent tool — local sub-agent, no A2A needed."""
return AgentTool(agent=evaluator_agent)
def create_simulator_agent() -> RemoteA2aAgent:
"""Create remote connection to Simulation Controller Agent via A2A."""
resource_name = os.environ.get("SIMULATOR_AGENT_RESOURCE_NAME")
if not resource_name:
raise ValueError("SIMULATOR_AGENT_RESOURCE_NAME environment variable must be set")
endpoint = _get_agent_a2a_endpoint(resource_name, default_port=8089)
logger.info(f"Creating Simulation Controller Agent connection: {endpoint}")
return SerializableRemoteA2aAgent(
name="simulator_agent",
description=(
"Simulation Controller Agent for marathon plans. "
"Reviews plans for simulation readiness, assessing route feasibility, "
"logistics completeness, and safety clearance."
),
agent_card=endpoint,
a2a_url=_get_agent_a2a_url(resource_name),
)
def create_simulation_controller_tool() -> AgentTool:
return AgentTool(agent=create_simulator_agent())
# ============================================================================
# TOOL EXPORT
# ============================================================================
def get_tools() -> list:
"""Return the tools for the Marathon Planner Agent."""
import pathlib
import importlib.util
from google.adk.skills import load_skill_from_dir
from google.adk.tools.preload_memory_tool import PreloadMemoryTool
from google.adk.tools.skill_toolset import SkillToolset
# Load ADK Skills
skills_dir = pathlib.Path(__file__).parent.parent / "skills"
skills = [
load_skill_from_dir(skills_dir / name)
for name in sorted(skills_dir.iterdir())
if name.is_dir() and not name.name.startswith("_")
]
skill_toolset = SkillToolset(skills=skills)
# Load route-planning tools dynamically (handles hyphenated directory names)
def load_tool_from_skill(skill_name, tool_name):
skill_tool_path = skills_dir / skill_name / "tools.py"
if not skill_tool_path.exists():
return None
try:
spec = importlib.util.spec_from_file_location(f"{skill_name}.tools", skill_tool_path)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
return getattr(module, tool_name, None)
except Exception as e:
logger.error(f"Error loading tool {tool_name} from {skill_name}: {e}")
return None
plan_marathon_route_func = load_tool_from_skill("route-planning", "plan_marathon_route")
tools = [skill_toolset, PreloadMemoryTool()]
if plan_marathon_route_func:
tools.append(FunctionTool(func=plan_marathon_route_func))
# Add local evaluator sub-agent
tools.append(create_evaluator_tool())
logger.info("Added local Evaluator tool")
# Add remote Simulation Controller via A2A (if configured)
if os.environ.get("SIMULATOR_AGENT_RESOURCE_NAME"):
tools.append(create_simulation_controller_tool())
logger.info("Added A2A Simulation Controller tool")
return tools
La funzione get_tools() mostra tre diversi pattern di strumenti:
- SkillToolset : carica le conoscenze procedurali dalle skill ADK
- AgentTool : esegue il wrapping di Evaluator come sub-agente locale
- SerializableRemoteA2aAgent : connette Simulator tramite il protocollo A2A
Collega l'agente del pianificatore
Crea src/planner_agent/agent/agent.py:
"""Marathon Planner Agent — LlmAgent wiring.
Wires config + prompts + tools + skills into the LlmAgent.
"""
import os
import vertexai
from google.adk.agents import LlmAgent
from .config import AGENT_NAME, AGENT_DESCRIPTION, MODEL, OUTPUT_SCHEMA
from .prompts import INSTRUCTION
from .tools import get_tools
from ..services.memory_manager import auto_save_memories
# Initialize Vertex AI
project_id = os.environ.get("GOOGLE_CLOUD_PROJECT")
location = os.environ.get("GOOGLE_CLOUD_LOCATION", "us-central1")
if project_id:
vertexai.init(project=project_id, location=location)
from google.genai.types import GenerateContentConfig, ThinkingConfig
agent_kwargs = dict(
name=AGENT_NAME,
model=MODEL,
description=AGENT_DESCRIPTION,
static_instruction=INSTRUCTION,
tools=get_tools(),
generate_content_config=GenerateContentConfig(
thinking_config=ThinkingConfig(thinking_budget=2048),
),
after_agent_callback=auto_save_memories,
)
if OUTPUT_SCHEMA is not None:
agent_kwargs["output_schema"] = OUTPUT_SCHEMA
root_agent = LlmAgent(**agent_kwargs)
Configura le esportazioni dei pacchetti. Sostituisci src/planner_agent/agent/__init__.py:
from .agent import root_agent
from .config import AGENT_NAME, MODEL, AGENT_DESCRIPTION, OUTPUT_SCHEMA
__all__ = ["root_agent", "AGENT_NAME", "MODEL", "AGENT_DESCRIPTION", "OUTPUT_SCHEMA"]
Sostituisci src/planner_agent/__init__.py:
try:
from .agent import root_agent
from .agent.config import AGENT_NAME, MODEL
__all__ = ["root_agent", "AGENT_NAME", "MODEL"]
except Exception:
__all__ = ["root_agent"]
7. Esegui il deployment e testa il sistema multi-agente
Ora metti insieme tutto: crea server A2A per ogni agente, quindi testa la modalità Solo (Planner + Evaluator) e la modalità Full Team (tutti e tre gli agenti).
Crea la scheda dell'agente A2A del pianificatore
Crea src/planner_agent/runtime/agent_card.py:
"""A2A Agent Card for Marathon Planner Agent."""
from a2a.types import AgentCapabilities, AgentCard, AgentSkill
from vertexai.preview.reasoning_engines.templates.a2a import create_agent_card
def create_marathon_planner_card() -> AgentCard:
skill = AgentSkill(
id="plan_marathon",
name="Plan City Marathon",
description=(
"Design a comprehensive city marathon plan by coordinating with "
"specialist agents. Evaluates plan quality and returns an actionable plan."
),
tags=["marathon", "planning", "orchestration", "multi-agent"],
examples=[
"Plan a scenic marathon through Las Vegas for 30,000 runners",
"Design a charity marathon in Austin for October 2026",
],
)
card = create_agent_card(
agent_name="planner_agent",
description=(
"Marathon Planner Agent — Lead Orchestrator that designs city marathon "
"plans. Coordinates specialist agents via A2A. Evaluates plans with "
"Vertex AI Eval. Powered by Gemini 3 Flash Preview."
),
skills=[skill],
)
if card.capabilities is None:
card.capabilities = AgentCapabilities(streaming=True)
else:
card.capabilities.streaming = True
return card
Crea l'esecutore A2A del pianificatore
Crea src/planner_agent/runtime/agent_executor.py:
"""A2A Agent Executor for Marathon Planner Agent."""
import json
import os
import vertexai
from a2a.server.agent_execution import AgentExecutor, RequestContext
from a2a.server.events import EventQueue
from a2a.server.tasks import TaskUpdater
from a2a.types import TaskState, TextPart, UnsupportedOperationError
from a2a.utils import new_agent_text_message
from a2a.utils.errors import ServerError
from google.adk import Runner
from google.genai import types
from ..services.memory_manager import create_memory_service
from ..services.session_manager import SessionManager, create_session_service
class MarathonPlannerExecutor(AgentExecutor):
"""Execute requests via A2A protocol."""
def __init__(self):
self.agent = None
self.runner = None
self.session_manager: SessionManager | None = None
def _init_agent(self) -> None:
if self.agent is None:
try:
from planner_agent.agent import root_agent
except ImportError:
from ..agent import root_agent
project_id = os.environ.get("GOOGLE_CLOUD_PROJECT")
location = os.environ.get("AGENT_ENGINE_LOCATION") or os.environ.get("GOOGLE_CLOUD_LOCATION", "us-central1")
vertexai.init(project=project_id, location=location)
self.agent = root_agent
if self.runner is None:
project_id = os.environ.get("GOOGLE_CLOUD_PROJECT")
location = os.environ.get("AGENT_ENGINE_LOCATION") or os.environ.get("GOOGLE_CLOUD_LOCATION", "us-central1")
session_service = create_session_service(project=project_id, location=location)
memory_service = create_memory_service(project=project_id, location=location)
from google.adk.apps import App
from google.adk.agents.context_cache_config import ContextCacheConfig
app = App(
name=self.agent.name,
root_agent=self.agent,
context_cache_config=ContextCacheConfig(
cache_intervals=10, ttl_seconds=3600, min_tokens=4096,
)
)
self.runner = Runner(
app=app,
session_service=session_service,
memory_service=memory_service,
)
if self.session_manager is None:
self.session_manager = SessionManager(
session_service=self.runner.session_service,
)
async def execute(self, context: RequestContext, event_queue: EventQueue) -> None:
if self.agent is None:
self._init_agent()
user_id = (
context.message.metadata.get("user_id")
if context.message and context.message.metadata
else "marathon_user"
)
updater = TaskUpdater(event_queue, context.task_id, context.context_id)
if not hasattr(context, "current_task") or not context.current_task:
await updater.submit()
await updater.start_work()
request_data = context.get_user_input()
if not request_data:
await updater.update_status(TaskState.failed, message=new_agent_text_message("No request data"), final=True)
return
try:
from google.adk.runners import RunConfig
from google.adk.agents.run_config import ToolThreadPoolConfig
from google.genai.types import ContextWindowCompressionConfig
await updater.update_status(TaskState.working, message=new_agent_text_message("Planning marathon..."))
session_id = await self.session_manager.get_or_create_session(
context_id=context.context_id, app_name=self.runner.app_name, user_id=user_id,
)
content = types.Content(role="user", parts=[types.Part(text=request_data)])
final_event = None
async for event in self.runner.run_async(
session_id=session_id, user_id=user_id, new_message=content,
run_config=RunConfig(
tool_thread_pool_config=ToolThreadPoolConfig(max_workers=4),
max_llm_calls=15,
context_window_compression=ContextWindowCompressionConfig(),
)
):
if event.is_final_response():
final_event = event
if final_event and final_event.content and final_event.content.parts:
text = "".join(p.text for p in final_event.content.parts if hasattr(p, "text") and p.text)
if text:
await updater.add_artifact([TextPart(text=text)], name="marathon_plan")
await updater.complete()
return
await updater.update_status(TaskState.failed, message=new_agent_text_message("Failed to generate plan"), final=True)
except Exception as e:
await updater.update_status(TaskState.failed, message=new_agent_text_message(f"Planning failed: {e}"), final=True)
async def cancel(self, context: RequestContext, event_queue: EventQueue) -> None:
raise ServerError(error=UnsupportedOperationError())
Crea i server A2A locali
Crea src/planner_agent/runtime/local_server.py:
"""Local A2A server for the Marathon Planner Agent.
Usage: uv run python -m src.planner_agent.runtime.local_server
"""
import os
from dotenv import load_dotenv
load_dotenv()
os.environ["GOOGLE_GENAI_USE_VERTEXAI"] = "true"
import asyncio
import uvicorn
from a2a.server.apps import A2AStarletteApplication
from a2a.server.request_handlers import DefaultRequestHandler
from a2a.server.tasks import InMemoryTaskStore
from a2a.types import TransportProtocol
from google.adk import Runner
from google.adk.a2a.executor.a2a_agent_executor import A2aAgentExecutor, A2aAgentExecutorConfig
from google.adk.sessions import InMemorySessionService
AGENT_PORT = 8084
def create_marathon_planner_server():
from ..agent import root_agent
from .agent_card import create_marathon_planner_card
runner = Runner(
app_name=root_agent.name,
agent=root_agent,
session_service=InMemorySessionService(),
)
executor = A2aAgentExecutor(runner=runner, config=A2aAgentExecutorConfig())
handler = DefaultRequestHandler(agent_executor=executor, task_store=InMemoryTaskStore())
card = create_marathon_planner_card()
card.url = f"http://localhost:{AGENT_PORT}"
card.preferred_transport = TransportProtocol.jsonrpc
return A2AStarletteApplication(agent_card=card, http_handler=handler)
async def run_server():
project = os.environ.get("GOOGLE_CLOUD_PROJECT")
if not project:
raise ValueError("GOOGLE_CLOUD_PROJECT must be set. Check .env file.")
print("=" * 60)
print("Starting Marathon Planner Agent Local A2A Server")
print(f"Project: {project}")
print("=" * 60)
app = create_marathon_planner_server()
config = uvicorn.Config(app.build(), host="127.0.0.1", port=AGENT_PORT, log_level="info", loop="none")
print(f"Planner A2A server: http://127.0.0.1:{AGENT_PORT}")
print(f"Agent card: http://127.0.0.1:{AGENT_PORT}/.well-known/agent-card.json")
print("Press Ctrl+C to stop")
print("=" * 60)
server = uvicorn.Server(config)
await server.serve()
if __name__ == "__main__":
asyncio.run(run_server())
Crea src/simulator_agent/runtime/agent_card.py:
"""A2A Agent Card for Simulation Controller Agent."""
from a2a.types import AgentCard, AgentSkill
from vertexai.preview.reasoning_engines.templates.a2a import create_agent_card
def create_simulation_controller_card() -> AgentCard:
skill = AgentSkill(
id="review_marathon_plan",
name="Review Marathon Plan",
description="Review a marathon plan for simulation readiness.",
tags=["simulation", "review", "approval", "marathon"],
)
return create_agent_card(
agent_name="simulator_agent",
description="Simulation Controller Agent - Reviews marathon plans for simulation readiness.",
skills=[skill],
)
Crea src/simulator_agent/runtime/agent_executor.py seguendo lo stesso pattern dell'esecutore del pianificatore (aggiorna le importazioni per simulator_agent):
"""A2A Agent Executor for Simulation Controller Agent."""
import os
import vertexai
from a2a.server.agent_execution import AgentExecutor, RequestContext
from a2a.server.events import EventQueue
from a2a.server.tasks import TaskUpdater
from a2a.types import TaskState, TextPart, UnsupportedOperationError
from a2a.utils import new_agent_text_message
from a2a.utils.errors import ServerError
from google.adk import Runner
from google.genai import types
from ..services.memory_manager import create_memory_service
from ..services.session_manager import SessionManager, create_session_service
class SimulationControllerExecutor(AgentExecutor):
def __init__(self):
self.agent = None
self.runner = None
self.session_manager = None
def _init_agent(self):
if self.agent is None:
try:
from simulator_agent.agent import root_agent
except ImportError:
from ..agent.agent import root_agent
project_id = os.environ.get("GOOGLE_CLOUD_PROJECT")
location = os.environ.get("AGENT_ENGINE_LOCATION") or os.environ.get("GOOGLE_CLOUD_LOCATION", "us-central1")
vertexai.init(project=project_id, location=location)
self.agent = root_agent
if self.runner is None:
project_id = os.environ.get("GOOGLE_CLOUD_PROJECT")
location = os.environ.get("AGENT_ENGINE_LOCATION") or os.environ.get("GOOGLE_CLOUD_LOCATION", "us-central1")
session_service = create_session_service(project=project_id, location=location)
memory_service = create_memory_service(project=project_id, location=location)
from google.adk.apps import App
from google.adk.agents.context_cache_config import ContextCacheConfig
app = App(name=self.agent.name, root_agent=self.agent,
context_cache_config=ContextCacheConfig(cache_intervals=10, ttl_seconds=3600, min_tokens=4096))
self.runner = Runner(app=app, session_service=session_service, memory_service=memory_service)
if self.session_manager is None:
self.session_manager = SessionManager(session_service=self.runner.session_service)
async def execute(self, context: RequestContext, event_queue: EventQueue):
if self.agent is None:
self._init_agent()
user_id = context.message.metadata.get("user_id") if context.message and context.message.metadata else "planner_agent"
updater = TaskUpdater(event_queue, context.task_id, context.context_id)
if not hasattr(context, "current_task") or not context.current_task:
await updater.submit()
await updater.start_work()
plan_data = context.get_user_input()
if not plan_data:
await updater.update_status(TaskState.failed, message=new_agent_text_message("No plan data"), final=True)
return
try:
await updater.update_status(TaskState.working, message=new_agent_text_message("Reviewing plan..."))
session_id = await self.session_manager.get_or_create_session(
context_id=context.context_id, app_name=self.runner.app_name, user_id=user_id)
content = types.Content(role="user", parts=[types.Part(text=plan_data)])
final_event = None
async for event in self.runner.run_async(session_id=session_id, user_id=user_id, new_message=content):
if event.is_final_response():
final_event = event
if final_event and final_event.content and final_event.content.parts:
text = "".join(p.text for p in final_event.content.parts if hasattr(p, "text") and p.text)
if text:
await updater.add_artifact([TextPart(text=text)], name="result")
await updater.complete()
return
await updater.update_status(TaskState.failed, message=new_agent_text_message("Failed"), final=True)
except Exception as e:
await updater.update_status(TaskState.failed, message=new_agent_text_message(f"Review failed: {e}"), final=True)
async def cancel(self, context, event_queue):
raise ServerError(error=UnsupportedOperationError())
Crea src/simulator_agent/runtime/local_server.py:
"""Local A2A server for the Simulation Controller Agent.
Usage: uv run python -m src.simulator_agent.runtime.local_server
"""
import asyncio
import logging
import os
os.environ["GOOGLE_GENAI_USE_VERTEXAI"] = "true"
import uvicorn
from a2a.server.apps import A2AStarletteApplication
from a2a.server.request_handlers import DefaultRequestHandler
from a2a.server.tasks import InMemoryTaskStore
from dotenv import load_dotenv
load_dotenv()
AGENT_PORT = 8089
async def run_server():
from .agent_card import create_simulation_controller_card
from .agent_executor import SimulationControllerExecutor
project = os.environ.get("GOOGLE_CLOUD_PROJECT")
if not project:
raise ValueError("GOOGLE_CLOUD_PROJECT must be set.")
print("=" * 60)
print("Starting Simulation Controller Agent Local A2A Server")
print("=" * 60)
agent_card = create_simulation_controller_card()
executor = SimulationControllerExecutor()
handler = DefaultRequestHandler(agent_executor=executor, task_store=InMemoryTaskStore())
app = A2AStarletteApplication(agent_card=agent_card, http_handler=handler)
config = uvicorn.Config(app.build(), host="127.0.0.1", port=AGENT_PORT, log_level="info", loop="none")
print(f"Simulator A2A server: http://127.0.0.1:{AGENT_PORT}")
print(f"Agent card: http://127.0.0.1:{AGENT_PORT}/.well-known/agent.json")
print("Press Ctrl+C to stop")
print("=" * 60)
server = uvicorn.Server(config)
await server.serve()
if __name__ == "__main__":
asyncio.run(run_server())
Testa la modalità Solo: Planner + Evaluator
In modalità Solo, Planner utilizza Evaluator come sub-agente locale. Non è necessario A2A.
Avvia il server del pianificatore:
uv run python -m src.planner_agent.runtime.local_server
Dovresti visualizzare un output simile al seguente:
============================================================ Starting Marathon Planner Agent Local A2A Server Project: your-project-id ============================================================ Planner A2A server: http://127.0.0.1:8084 Agent card: http://127.0.0.1:8084/.well-known/agent-card.json Press Ctrl+C to stop
Apri una nuova scheda di Cloud Shell e invia una richiesta di test:
curl -s http://127.0.0.1:8084/.well-known/agent-card.json | python3 -m json.tool
Dovresti visualizzare la scheda dell'agente con il nome planner_agent e la skill plan_marathon.
Testa la modalità Full Team: tutti e tre gli agenti tramite A2A
In modalità Full Team, Simulation Controller viene eseguito come server A2A separato.
Nella seconda scheda di Cloud Shell, avvia il server Simulator:
uv run python -m src.simulator_agent.runtime.local_server
Quindi, nella prima scheda, riavvia Planner con Simulator connesso:
SIMULATOR_AGENT_RESOURCE_NAME=local:8089 uv run python -m src.planner_agent.runtime.local_server
Nei log dovresti vedere:
Added local Evaluator tool Added A2A Simulation Controller tool
Planner è ora connesso a entrambi gli agenti. Quando riceve una richiesta di pianificazione della maratona:
- Progetta il piano utilizzando le skill ADK
- Delega a Evaluator (sub-agente locale tramite
AgentTool) - Invia a Simulation Controller (agente remoto tramite A2A)
- Presenta il piano finale con i punteggi di valutazione e l'approvazione della simulazione
8. Libera spazio
Per evitare addebiti continui sul tuo account Google Cloud, libera spazio dalle risorse create durante questo codelab.
Interrompi tutti i server locali in esecuzione premendo Ctrl+C in ogni scheda di Cloud Shell.
Poiché questo codelab ha utilizzato solo chiamate API Vertex AI (nessuna risorsa cloud persistente come servizi Cloud Run o deployment di Agent Engine), non è necessario eliminare le risorse cloud.
Se hai creato il progetto esclusivamente per questo codelab, puoi eliminare il progetto:
gcloud projects delete $PROJECT_ID
9. Complimenti
Complimenti! Hai creato un sistema di pianificazione della maratona multi-agente in cui tre agenti AI collaborano tramite il protocollo A2A, con la garanzia di qualità fornita da Vertex AI Evaluation.
Che cosa hai imparato
- Come creare agenti AI utilizzando Agent Development Kit (ADK) con
LlmAgent,ThinkingConfige output Pydantic strutturati - Come creare metriche Vertex AI Evaluation personalizzate utilizzando
MetricPromptBuilderper la valutazione LLM-as-Judge e i controlli deterministici - Come collegare gli agenti come sub-agenti utilizzando
AgentToolper la delega locale - Come connettere gli agenti remoti tramite il protocollo A2A utilizzando
RemoteA2aAgenteA2AStarletteApplication - Come utilizzare ADK Skills (
SkillToolset) per la conoscenza procedurale e Memory Bank (PreloadMemoryTool) per l'apprendimento tra le sessioni