1. परिचय
इस कोडलैब में, एक मल्टी-एजेंट सिस्टम बनाया गया है. इसमें तीन खास एआई एजेंट मिलकर, शहर में होने वाली मैराथन की योजना बनाते हैं. मैराथन प्लानर एजेंट, प्लान को डिज़ाइन करता है. आकलन करने वाला एजेंट, Vertex AI Evaluation का इस्तेमाल करके प्लान की क्वालिटी का आकलन करता है. साथ ही, सिमुलेशन कंट्रोलर एजेंट, प्लान के तैयार होने की पुष्टि करता है. ये सभी एजेंट, एजेंट-टू-एजेंट (A2A) प्रोटोकॉल के ज़रिए एक-दूसरे से कम्यूनिकेट करते हैं.
आपको क्या करना होगा
- Vertex AI पर Gemini मॉडल का इस्तेमाल करके, Agent Development Kit (ADK) की मदद से एआई एजेंट बनाना
- एजेंट के भरोसेमंद जवाबों के लिए, Pydantic स्कीमा के साथ स्ट्रक्चर्ड आउटपुट तय करना
MetricPromptBuilder(एलएलएम-एज़-जज) का इस्तेमाल करके, Vertex AI Evaluation की कस्टम मेट्रिक बनानाAgentToolकी मदद से, एजेंट को उप-एजेंट के तौर पर वायर करें और A2A प्रोटोकॉल के ज़रिए रिमोट एजेंट कनेक्ट करें- प्रोसीजरल नॉलेज के लिए ADK की स्किल और क्रॉस-सेशन लर्निंग के लिए मेमोरी बैंक का इस्तेमाल करें
- A2A सर्वर की मदद से, एजेंट को स्थानीय तौर पर सेवा देना और मल्टी-एजेंट कोलैबोरेशन की जांच करना
आपको किन चीज़ों की ज़रूरत होगी
- कोई वेब ब्राउज़र, जैसे कि Chrome
- बिलिंग की सुविधा वाला Google Cloud प्रोजेक्ट
यह कोडलैब, Python और एआई के कॉन्सेप्ट के बारे में जानकारी रखने वाले डेवलपर के लिए है.
पूरा होने में लगने वाला अनुमानित समय: 90 मिनट.
इस कोडलैब में बनाए गए संसाधनों की लागत 5 डॉलर से कम होनी चाहिए.
2. शुरू करने से पहले
Google Cloud प्रोजेक्ट बनाना
- Google Cloud Console में जाकर, Google Cloud प्रोजेक्ट चुनें या बनाएं.
- पक्का करें कि आपके Cloud प्रोजेक्ट के लिए बिलिंग चालू हो.
Cloud Shell शुरू करना
- Google Cloud कंसोल में सबसे ऊपर मौजूद, Cloud Shell चालू करें पर क्लिक करें.
- पुष्टि करें:
gcloud auth list
- अपने प्रोजेक्ट की पुष्टि करें:
gcloud config get project
- अगर ज़रूरी हो, तो इसे सेट करें:
export PROJECT_ID=<YOUR_PROJECT_ID>
gcloud config set project $PROJECT_ID
एपीआई चालू करें
gcloud services enable aiplatform.googleapis.com
Python पैकेज मैनेजर इंस्टॉल करना
Python पैकेज मैनेजर uv को इंस्टॉल करें:
curl -LsSf https://astral.sh/uv/install.sh | sh
source $HOME/.local/bin/env
3. प्रोजेक्ट सेट अप करना
इस चरण में, प्रोजेक्ट का स्ट्रक्चर बनाया जाता है, डिपेंडेंसी इंस्टॉल की जाती हैं, और शेयर की गई उन सेवाओं को कॉन्फ़िगर किया जाता है जिनका इस्तेमाल सभी एजेंट करते हैं.
प्रोजेक्ट डायरेक्ट्री बनाना
mkdir -p marathon-agents && cd marathon-agents
प्रोजेक्ट कॉन्फ़िगरेशन बनाना
pyproject.toml बनाएं:
[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"]
डिपेंडेंसी इंस्टॉल करना
uv sync
एनवायरमेंट वैरिएबल कॉन्फ़िगर करना
.env फ़ाइल बनाने के लिए:
cat > .env << 'EOF'
GOOGLE_CLOUD_PROJECT=<YOUR_PROJECT_ID>
GOOGLE_CLOUD_LOCATION=us-central1
GOOGLE_GENAI_USE_VERTEXAI=true
EOF
सोर्स डायरेक्ट्री ट्री बनाना
यह प्रोजेक्ट, ADK के मल्टी-एजेंट कन्वेंशन का पालन करता है. हर एजेंट, src/ के तहत एक सेल्फ-कंटेन्ड पैकेज होता है:
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}
Python पैकेज शुरू करना
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
शेयर किया गया कॉन्फ़िगरेशन बनाना
src/config.py बनाएं — सभी एजेंट के लिए शेयर किया गया GCP कॉन्फ़िगरेशन:
"""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
Memory Manager बनाना
हर एजेंट, क्रॉस-सेशन लर्निंग के लिए मेमोरी बैंक का इस्तेमाल करता है. src/planner_agent/services/memory_manager.py बनाएं:
"""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 कॉलबैक, एजेंट के हर जवाब के बाद चलता है. साथ ही, एजेंट इंजन पर डिप्लॉय किए जाने पर, बातचीत को मेमोरी बैंक में सेव करता है. लोकल डेवलपमेंट के दौरान, अगर AGENT_ENGINE_ID सेट नहीं है, तो यह फ़ंक्शन स्किप हो जाता है.
सेशन मैनेजर बनाना
Create src/planner_agent/services/session_manager.py — यह टीटीएल कैशिंग की मदद से, सेशन के लाइफ़साइकल को मैनेज करता है:
"""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
अब इन सेवा फ़ाइलों को अन्य एजेंट के लिए कॉपी करें. ये एजेंट के हिसाब से मेमोरी के विषयों के साथ-साथ, एक ही तरह के स्ट्रक्चर को फ़ॉलो करते हैं:
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
प्रोजेक्ट के स्ट्रक्चर की पुष्टि करना
find src -type f -name "*.py" | sort
आपको तीनों एजेंट पैकेज में, शेयर की गई कॉन्फ़िगरेशन फ़ाइल, मेमोरी मैनेजर, सेशन मैनेजर, और __init__.py फ़ाइलें दिखनी चाहिए.
4. आकलन करने वाला एजेंट बनाना
समीक्षक एजेंट, सिस्टम की क्वालिटी का आकलन करता है. यह Vertex AI Evaluation का इस्तेमाल करके, सात कसौटियों के आधार पर मैराथन प्लान को स्कोर करता है. इसमें, एलएलएम को जज के तौर पर इस्तेमाल करने वाली कस्टम मेट्रिक का इस्तेमाल किया जाता है. यहां आपको पता चलेगा कि Gemini के फ़ैसले लेने की क्षमता को स्ट्रक्चर्ड तरीके से आकलन करने के लिए बनाए गए रूब्रिक के साथ इस्तेमाल करने से क्या फ़ायदे मिलते हैं.
आकलन के स्कीमा तय करना
src/planner_agent/evaluator/schemas.py बनाएं — Pydantic मॉडल, स्ट्रक्चर्ड आउटपुट फ़ॉर्मैट तय करते हैं:
"""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",
)
एजेंट पर output_schema=EvaluationResult का इस्तेमाल करने से, यह पक्का होता है कि हर जवाब इस स्ट्रक्चर से मेल खाता है. इसके लिए, पार्सिंग की ज़रूरत नहीं होती.
आकलन करने के निर्देश लिखना
src/planner_agent/evaluator/instruction.md बनाएं:
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.
src/planner_agent/evaluator/prompts.py बनाएं:
"""Prompts for the Evaluator Agent."""
import pathlib
_PROMPT_DIR = pathlib.Path(__file__).parent
INSTRUCTION = (_PROMPT_DIR / "instruction.md").read_text()
Vertex AI की मदद से, आकलन करने वाले टूल बनाना
यह Evaluator का मुख्य हिस्सा है. इसमें MetricPromptBuilder का इस्तेमाल करके कस्टम मेट्रिक बनाई जाती हैं. src/planner_agent/evaluator/tools.py बनाएं:
"""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})")
यहां मुख्य पैटर्न हाइब्रिड तरीके से आकलन करना है: यह टूल, सबसे पहले LLM-as-Judge मेट्रिक के साथ Vertex AI Eval का इस्तेमाल करता है. अगर एपीआई उपलब्ध नहीं है, तो यह अनुमानित जांच पर वापस आ जाता है.
Evaluator एजेंट को वायर करना
src/planner_agent/evaluator/agent.py बनाएं:
"""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
अब src/planner_agent/evaluator/services/memory_manager.py पर, Evaluator के मेमोरी मैनेजर को बनाएं. यह आकलन के इतिहास और स्कोरिंग के रुझानों को ट्रैक करता है:
"""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}")
आखिर में, जांच करने वाले के पैकेज एक्सपोर्ट सेट अप करें. src/planner_agent/evaluator/__init__.py बदलें:
try:
from .agent import root_agent, AGENT_NAME, MODEL
__all__ = ["root_agent", "AGENT_NAME", "MODEL"]
except Exception:
__all__ = ["root_agent"]
5. सिमुलेशन कंट्रोलर एजेंट बनाना
सिमुलेशन कंट्रोलर, आखिरी गेटकीपर होता है. यह ज़रूरी शर्तों की तुरंत जांच करता है, ताकि यह पुष्टि की जा सके कि किसी प्लान के पास सिम्युलेशन चलाने के लिए ज़रूरी डेटा है. हालांकि, यह क्वालिटी का फिर से आकलन नहीं करता.
मंज़ूरी का स्कीमा तय करना
src/simulator_agent/agent/schemas.py बनाएं:
"""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")
तैयारी की जांच करने वाला टूल बनाना
src/simulator_agent/agent/tools.py बनाएं — यह एक तय की गई चेकलिस्ट होती है. इसके लिए, एलएलएम की ज़रूरत नहीं होती:
"""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]
ADK स्किल बनाना
ADK Skills, प्रोसीजरल नॉलेज उपलब्ध कराती हैं. एजेंट, इस नॉलेज को रनटाइम में लोड करता है. src/simulator_agent/skills/review-marathon-plan/SKILL.md बनाएं:
---
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.
सिमुलेशन कंट्रोलर एजेंट को वायर करना
src/simulator_agent/agent/config.py बनाएं:
"""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
src/simulator_agent/agent/prompts.py बनाएं:
"""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."""
src/simulator_agent/agent/agent.py बनाएं:
"""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)
अब src/simulator_agent/services/memory_manager.py पर सिम्युलेटर का मेमोरी मैनेजर बनाएं:
"""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}")
पैकेज एक्सपोर्ट करने की सुविधा सेट अप करें. src/simulator_agent/agent/__init__.py बदलें:
try:
from .agent import root_agent
from .config import AGENT_NAME, MODEL
__all__ = ["root_agent", "AGENT_NAME", "MODEL"]
except Exception:
pass
src/simulator_agent/__init__.py बदलें:
try:
from .agent import root_agent
from .agent.config import AGENT_NAME, MODEL
__all__ = ["root_agent", "AGENT_NAME", "MODEL"]
except Exception:
pass
6. मैराथन प्लानर एजेंट बनाना
मैराथन प्लानर, मुख्य ऑर्केस्ट्रेटर होता है. यह डोमेन की जानकारी के लिए, ADK की स्किल का इस्तेमाल करता है. साथ ही, क्वालिटी का आकलन करने का काम, Evaluator सब-एजेंट को सौंपता है. इसके अलावा, यह A2A के ज़रिए Simulation Controller से कनेक्ट होता है.
प्लानर स्कीमा और कॉन्फ़िगरेशन तय करना
src/planner_agent/agent/schemas.py बनाएं:
"""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")
src/planner_agent/agent/config.py बनाएं:
"""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
प्लानर के लिए निर्देश लिखना
src/planner_agent/agent/planner-instruction.md बनाएं:
# 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.
src/planner_agent/agent/prompts.py बनाएं:
"""System instructions for the Marathon Planner Agent."""
import pathlib
_PROMPT_DIR = pathlib.Path(__file__).parent
INSTRUCTION = (_PROMPT_DIR / "planner-instruction.md").read_text()
पुष्टि करने वाला मॉड्यूल बनाना
Agent Engine को A2A कॉल करने के लिए, एजेंट को Google Cloud की पुष्टि करनी होगी. src/planner_agent/agent/auth.py बनाएं:
"""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
ADK स्किल जोड़ना
src/planner_agent/skills/route-planning/SKILL.md बनाएं:
---
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.
src/planner_agent/skills/plan-evaluation/SKILL.md बनाएं:
---
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.
प्लानर टूल को वायर करना
बनाएं src/planner_agent/agent/tools.py — यहीं से एजेंट कनेक्ट होते हैं:
"""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() फ़ंक्शन में, टूल के तीन अलग-अलग पैटर्न दिखाए गए हैं:
- SkillToolset — ADK Skills से प्रोसेस की गई जानकारी लोड करता है
- AgentTool — यह Evaluator को लोकल सब-एजेंट के तौर पर रैप करता है
- SerializableRemoteA2aAgent — A2A प्रोटोकॉल के ज़रिए सिम्युलेटर को कनेक्ट करता है
प्लानर एजेंट को वायर करना
src/planner_agent/agent/agent.py बनाएं:
"""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)
पैकेज एक्सपोर्ट करने की सुविधा सेट अप करें. src/planner_agent/agent/__init__.py बदलें:
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"]
src/planner_agent/__init__.py बदलें:
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. मल्टी-एजेंट सिस्टम को डिप्लॉय और टेस्ट करना
अब सभी चीज़ों को एक साथ लाएं: हर एजेंट के लिए A2A सर्वर बनाएं. इसके बाद, सोलो मोड (प्लानर + इवैल्यूएटर) और फ़ुल टीम मोड (तीनों एजेंट) को टेस्ट करें.
प्लानर का A2A एजेंट कार्ड बनाना
src/planner_agent/runtime/agent_card.py बनाएं:
"""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
प्लानर का A2A एक्ज़ीक्यूटर बनाएं
src/planner_agent/runtime/agent_executor.py बनाएं:
"""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())
लोकल A2A सर्वर बनाना
src/planner_agent/runtime/local_server.py बनाएं:
"""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())
src/simulator_agent/runtime/agent_card.py बनाएं:
"""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],
)
प्लानर के एक्ज़ीक्यूटर के पैटर्न के मुताबिक src/simulator_agent/runtime/agent_executor.py बनाएं (simulator_agent के लिए इंपोर्ट अपडेट करें):
"""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())
src/simulator_agent/runtime/local_server.py बनाएं:
"""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())
टेस्ट सोलो मोड — प्लानर + इवैल्यूएटर
सोलो मोड में, प्लानर, इवैल्यूएटर का इस्तेमाल लोकल सब-एजेंट के तौर पर करता है. A2A की ज़रूरत नहीं है.
प्लानर सर्वर शुरू करें:
uv run python -m src.planner_agent.runtime.local_server
आपको इससे मिलता-जुलता आउटपुट दिखेगा:
============================================================ 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
Cloud Shell का नया टैब खोलें और टेस्ट का अनुरोध भेजें:
curl -s http://127.0.0.1:8084/.well-known/agent-card.json | python3 -m json.tool
आपको एजेंट कार्ड में planner_agent का नाम और plan_marathon की स्किल दिखनी चाहिए.
पूरी टीम के मोड की जांच करें — A2A के ज़रिए तीनों एजेंट
फ़ुल टीम मोड में, सिम्युलेशन कंट्रोलर एक अलग A2A सर्वर के तौर पर काम करता है.
दूसरे Cloud Shell टैब में, सिम्युलेटर सर्वर शुरू करें:
uv run python -m src.simulator_agent.runtime.local_server
इसके बाद, पहले टैब में सिम्युलेटर कनेक्ट करके, प्लानर को फिर से शुरू करें:
SIMULATOR_AGENT_RESOURCE_NAME=local:8089 uv run python -m src.planner_agent.runtime.local_server
लॉग में, आपको यह जानकारी दिखनी चाहिए:
Added local Evaluator tool Added A2A Simulation Controller tool
अब प्लानर, दोनों एजेंट से कनेक्ट हो गया है. मैराथन की प्लानिंग का अनुरोध मिलने पर, यह:
- ADK की सुविधाओं का इस्तेमाल करके प्लान बनाना
- आकलन करने वाले व्यक्ति को सौंपें (
AgentToolके ज़रिए स्थानीय सब-एजेंट) - सिमुलेशन कंट्रोलर को भेजें (A2A के ज़रिए रिमोट एजेंट)
- समीक्षा के स्कोर और सिम्युलेशन की मंज़ूरी के साथ फ़ाइनल प्लान सबमिट करना
8. व्यवस्थित करें
अपने Google Cloud खाते पर लगने वाले शुल्क से बचने के लिए, इस कोडलैब के दौरान बनाए गए संसाधनों को हटा दें.
चल रहे सभी लोकल सर्वर को बंद करें. इसके लिए, हर Cloud Shell टैब में Ctrl+C दबाएं.
इस कोडलैब में सिर्फ़ Vertex AI API कॉल का इस्तेमाल किया गया है. इसमें Cloud Run सेवाओं या Agent Engine डिप्लॉयमेंट जैसे क्लाउड संसाधनों का इस्तेमाल नहीं किया गया है. इसलिए, किसी भी क्लाउड संसाधन को मिटाने की ज़रूरत नहीं है.
अगर आपने यह प्रोजेक्ट सिर्फ़ इस कोडलैब के लिए बनाया है, तो प्रोजेक्ट मिटाया जा सकता है:
gcloud projects delete $PROJECT_ID
9. बधाई हो
बधाई हो! आपने मैराथन की प्लानिंग करने वाला एक मल्टी-एजेंट सिस्टम बनाया है. इसमें तीन एआई एजेंट, A2A प्रोटोकॉल के ज़रिए साथ मिलकर काम करते हैं. साथ ही, Vertex AI Evaluation की मदद से क्वालिटी अश्योरेंस किया जाता है.
आपको क्या सीखने को मिला
LlmAgent,ThinkingConfig, और स्ट्रक्चर्ड Pydantic आउटपुट के साथ Agent Development Kit (ADK) का इस्तेमाल करके, एआई एजेंट बनाने का तरीका- LLM-as-Judge स्कोरिंग और डिटरमिनिस्टिक जांच के लिए,
MetricPromptBuilderका इस्तेमाल करके कस्टम Vertex AI Evaluation मेट्रिक बनाने का तरीका - स्थानीय डेलिगेशन के लिए,
AgentToolका इस्तेमाल करके एजेंट को सब-एजेंट के तौर पर कैसे जोड़ा जाता है RemoteA2aAgentऔरA2AStarletteApplicationका इस्तेमाल करके, A2A प्रोटोकॉल के ज़रिए रिमोट एजेंट को कनेक्ट करने का तरीका- प्रोसीजरल नॉलेज के लिए ADK Skills (
SkillToolset) और क्रॉस-सेशन लर्निंग के लिए Memory Bank (PreloadMemoryTool) का इस्तेमाल कैसे करें