1. 简介
在此 Codelab 中，您将构建一个多智能体系统，其中三个专业 AI 智能体将协作规划城市马拉松。马拉松规划师 智能体负责编排设计，评估器 智能体使用 Vertex AI Evaluation 对方案质量进行评分，模拟控制器 智能体负责验证准备情况，所有智能体都通过智能体到智能体 (A2A) 协议进行通信。
您将执行的操作
- 使用 Vertex AI 上的 Gemini 模型，通过智能体开发套件 (ADK) 创建 AI 智能体
- 使用 Pydantic 架构定义结构化输出 ，以确保智能体响应的可靠性
- 使用
MetricPromptBuilder（LLM 即法官）构建自定义 Vertex AI Evaluation 指标 - 使用
AgentTool将智能体连接为子智能体 ，并通过 A2A 协议 连接远程智能体 - 使用 ADK 技能 获取程序知识，并使用记忆库 进行跨会话学习
- 使用 A2A 服务器 在本地提供智能体，并测试多智能体协作
所需条件
- 网络浏览器，例如 Chrome
- 启用了结算功能的 Google Cloud 项目
此 Codelab 适用于熟悉 Python 和 AI 概念的中级开发者。
预计完成时间：90 分钟。
在此 Codelab 中创建的资源费用应低于 5 美元。
2. 准备工作
创建 Google Cloud 项目
- 在 Google Cloud 控制台中，选择或创建 Google Cloud 项目。
- 确保您的 Cloud 项目已启用结算功能。
启动 Cloud Shell
- 点击 Google Cloud 控制台顶部的激活 Cloud Shell 。
- 验证身份验证：
gcloud auth list
- 确认您的项目：
gcloud config get project
- 如果需要，请进行设置：
export PROJECT_ID=<YOUR_PROJECT_ID>
gcloud config set project $PROJECT_ID
启用 API
gcloud services enable aiplatform.googleapis.com
安装 Python 软件包管理器
安装 uv（快速 Python 软件包管理器）：
curl -LsSf https://astral.sh/uv/install.sh | sh
source $HOME/.local/bin/env
3. 设置项目
在此步骤中，您将创建项目结构、安装依赖项，并配置所有智能体使用的共享服务。
创建项目目录
mkdir -p marathon-agents && cd marathon-agents
创建项目配置
创建 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"]
安装依赖项
uv sync
配置环境变量
创建 .env 文件：
cat > .env << 'EOF'
GOOGLE_CLOUD_PROJECT=<YOUR_PROJECT_ID>
GOOGLE_CLOUD_LOCATION=us-central1
GOOGLE_GENAI_USE_VERTEXAI=true
EOF
创建源目录树
该项目遵循 ADK 的多智能体惯例，即每个智能体都是 src/ 下的自包含软件包：
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}
初始化 Python 软件包
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
创建共享配置
创建 src/config.py，这是所有智能体的共享 GCP 配置：
"""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
创建内存管理器
每个智能体都使用记忆库 进行跨会话学习。创建 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}")
auto_save_memories 回调会在每个智能体响应后运行，并在部署到 Agent Engine 时将对话保留到记忆库中。在本地开发期间，如果未设置 AGENT_ENGINE_ID，则会正常跳过。
创建会话管理器
创建 src/planner_agent/services/session_manager.py，用于管理具有 TTL 缓存的会话生命周期：
"""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
现在，为其他代理复制这些服务文件，它们遵循相同的基架模式，并具有特定于代理的记忆主题：
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
验证项目结构
find src -type f -name "*.py" | sort
您应该会在所有三个智能体软件包中看到共享配置、内存管理器、会话管理器和 __init__.py 文件。
4. 创建评估器智能体
评估器智能体是系统的质量评判者。它使用 Vertex AI Evaluation 和自定义 LLM 即法官指标，根据 7 个标准对马拉松方案进行评分。您可以在这里看到将 Gemini 的判断与结构化评估评分标准相结合的强大功能。
定义评估架构
创建 src/planner_agent/evaluator/schemas.py，Pydantic 模型定义了结构化输出格式：
"""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",
)
在智能体上使用 output_schema=EvaluationResult 可保证每个响应都与此结构匹配，无需解析。
编写评估说明
创建 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.
创建 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()
使用 Vertex AI 构建评估工具
这是评估器的核心，即使用 MetricPromptBuilder 的自定义指标。创建 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})")
此处的关键模式是混合评估：该工具首先尝试使用 LLM 即法官指标进行 Vertex AI Eval，如果 API 不可用，则回退到启发式检查。
连接评估器智能体
创建 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
现在，在 src/planner_agent/evaluator/services/memory_manager.py 中创建评估器的内存管理器，它会跟踪评估历史记录和评分趋势：
"""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}")
最后，设置评估器软件包导出。替换 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. 创建模拟控制器智能体
模拟控制器是最终的把关者，它会执行快速前提条件检查，以确认方案有足够的数据来运行模拟，而无需重新评估质量。
定义审批架构
创建 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")
创建准备情况检查工具
创建 src/simulator_agent/agent/tools.py，这是一个确定性清单，无需 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]
创建 ADK 技能
ADK 技能提供智能体在运行时加载的程序知识 。创建 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.
连接模拟控制器智能体
创建 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
创建 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."""
创建 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)
现在，在 src/simulator_agent/services/memory_manager.py 中创建模拟器的内存管理器：
"""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}")
设置软件包导出。替换 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
替换 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. 构建马拉松规划师智能体
马拉松规划师是主要编排者。它使用 ADK 技能获取领域知识，将质量评估委托给评估器分代理，并通过 A2A 连接到模拟控制器。
定义规划师架构和配置
创建 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")
创建 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
编写规划师说明
创建 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.
创建 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()
创建身份验证模块
对于对 Agent Engine 的 A2A 调用，智能体需要 Google Cloud 身份验证。创建 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
添加 ADK 技能
创建 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.
创建 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.
连接规划师工具
创建 src/planner_agent/agent/tools.py，智能体在此处连接：
"""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
get_tools() 函数显示了三种不同的工具模式：
- SkillToolset ：从 ADK 技能加载程序知识
- AgentTool ：将评估器封装为本地分代理
- SerializableRemoteA2aAgent ：通过 A2A protocol 连接模拟器
连接规划师智能体
创建 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)
设置软件包导出。替换 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"]
替换 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. 部署和测试多智能体系统
现在，您将所有内容整合在一起：为每个智能体创建 A2A 服务器，然后测试单人模式（规划师 + 评估器）和完整团队模式（所有三个智能体）。
创建规划师的 A2A 智能体卡片
创建 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
创建规划师的 A2A 执行器
创建 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())
创建本地 A2A 服务器
创建 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())
创建 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],
)
按照与规划师执行器相同的模式创建 src/simulator_agent/runtime/agent_executor.py（更新 simulator_agent 的导入）：
"""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())
创建 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())
测试单人模式：规划师 + 评估器
在单人模式下，规划师将评估器用作本地分代理。无需 A2A。
启动规划师服务器：
uv run python -m src.planner_agent.runtime.local_server
您看到的输出结果应该类似于以下内容：
============================================================ 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
打开新的 Cloud Shell 标签页并发送测试请求：
curl -s http://127.0.0.1:8084/.well-known/agent-card.json | python3 -m json.tool
您应该会看到智能体卡片，其中包含 planner_agent 名称和 plan_marathon 技能。
测试完整团队模式：通过 A2A 连接所有三个智能体
在完整团队模式下，模拟控制器作为单独的 A2A 服务器运行。
在第二个 Cloud Shell 标签页中，启动模拟器服务器：
uv run python -m src.simulator_agent.runtime.local_server
然后在第一个标签页中，重启规划师并连接模拟器：
SIMULATOR_AGENT_RESOURCE_NAME=local:8089 uv run python -m src.planner_agent.runtime.local_server
在日志中，您应该会看到：
Added local Evaluator tool Added A2A Simulation Controller tool
规划师现在已连接到这两个智能体。收到马拉松规划请求后，它将：
- 使用 ADK 技能设计方案
- 委托给Evaluator （通过
AgentTool连接的本地分代理） - 发送给模拟控制器 （通过 A2A 连接的远程代理）
- 呈现包含评估分数和模拟审批的最终方案
8. 清理
为避免系统持续向您的 Google Cloud 账号收取费用，请清理在此 Codelab 期间创建的资源。
在每个 Cloud Shell 标签页中按 Ctrl+C 停止所有正在运行的本地服务器。
由于此 Codelab 仅使用 Vertex AI API 调用（没有 Cloud Run 服务或 Agent Engine 部署等永久性云资源），因此无需删除任何云资源。
如果您仅为此 Codelab 创建了项目，则可以删除该项目：
gcloud projects delete $PROJECT_ID
9. 恭喜
恭喜！您已构建一个多智能体马拉松规划系统，其中三个 AI 智能体通过 A2A protocol 进行协作，并由 Vertex AI Evaluation 提供质量保证。
您学到的内容
- 如何使用 智能体开发套件 (ADK) 通过
LlmAgent、ThinkingConfig和结构化 Pydantic 输出创建 AI 智能体 - 如何使用
MetricPromptBuilder构建自定义 Vertex AI Evaluation 指标，以进行 LLM 即法官评分和确定性检查 - 如何使用
AgentTool将智能体连接为子智能体 ，以进行本地委托 - 如何使用
RemoteA2aAgent和A2AStarletteApplication通过 A2A 协议 连接远程智能体 - 如何使用 ADK 技能 (
SkillToolset) 获取程序知识，并使用记忆库 (PreloadMemoryTool) 进行跨会话学习