Feat(Expansion Pack): Part 1 - Google Cloud Setup
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antmikinka
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# 1. Create new Google Cloud Project
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gcloud projects create {{PROJECT_ID}} --name="{{COMPANY_NAME}} AI Agent System"
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# 2. Set default project
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gcloud config set project {{PROJECT_ID}}
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# 3. Enable required APIs
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gcloud services enable aiplatform.googleapis.com
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gcloud services enable storage.googleapis.com
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gcloud services enable cloudfunctions.googleapis.com
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gcloud services enable run.googleapis.com
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gcloud services enable firestore.googleapis.com
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gcloud services enable secretmanager.googleapis.com
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# 1. Create new Google Cloud Project
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gcloud projects create {{PROJECT_ID}} --name="{{COMPANY_NAME}} AI Agent System"
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# 2. Set default project
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gcloud config set project {{PROJECT_ID}}
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# 3. Enable required APIs
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gcloud services enable aiplatform.googleapis.com
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gcloud services enable storage.googleapis.com
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gcloud services enable cloudfunctions.googleapis.com
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gcloud services enable run.googleapis.com
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gcloud services enable firestore.googleapis.com
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gcloud services enable secretmanager.googleapis.com
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{{company_name}}-ai-agents/
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├── agents/
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│ ├── __init__.py
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│ ├── {{team_1}}/
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│ │ ├── __init__.py
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│ │ ├── {{agent_1}}.py
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│ │ └── {{agent_2}}.py
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│ └── {{team_2}}/
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├── tasks/
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│ ├── __init__.py
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│ ├── {{task_category_1}}/
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│ └── {{task_category_2}}/
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├── templates/
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│ ├── {{document_type_1}}/
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│ └── {{document_type_2}}/
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├── checklists/
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├── data/
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├── workflows/
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├── config/
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│ ├── settings.py
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│ └── agent_config.yaml
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├── main.py
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└── deployment/
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├── Dockerfile
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└── cloudbuild.yaml
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import os
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from pydantic import BaseSettings
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class Settings(BaseSettings):
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# Google Cloud Configuration
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project_id: str = "{{PROJECT_ID}}"
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location: str = "{{LOCATION}}" # e.g., "us-central1"
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# Company Information
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company_name: str = "{{COMPANY_NAME}}"
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industry: str = "{{INDUSTRY}}"
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business_type: str = "{{BUSINESS_TYPE}}"
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# Agent Configuration
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default_model: str = "gemini-1.5-pro"
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max_iterations: int = 10
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timeout_seconds: int = 300
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# Storage Configuration
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bucket_name: str = "{{COMPANY_NAME}}-ai-agents-storage"
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database_name: str = "{{COMPANY_NAME}}-ai-agents-db"
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# API Configuration
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session_service_type: str = "vertex" # or "in_memory" for development
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artifact_service_type: str = "gcs" # or "in_memory" for development
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memory_service_type: str = "vertex" # or "in_memory" for development
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# Security
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service_account_path: str = "./{{COMPANY_NAME}}-ai-agents-key.json"
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class Config:
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env_file = ".env"
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settings = Settings()
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import asyncio
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from google.adk.agents import LlmAgent
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from google.adk.runners import Runner
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from google.adk.sessions import VertexAiSessionService
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from google.adk.artifacts import GcsArtifactService
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from google.adk.memory import VertexAiRagMemoryService
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from google.adk.models import Gemini
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from config.settings import settings
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from agents.{{primary_team}}.{{main_orchestrator}} import {{MainOrchestratorClass}}
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class {{CompanyName}}AISystem:
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def __init__(self):
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self.settings = settings
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self.runner = None
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self.main_orchestrator = None
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async def initialize(self):
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"""Initialize the AI agent system"""
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# Create main orchestrator
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self.main_orchestrator = {{MainOrchestratorClass}}()
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# Initialize services
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session_service = VertexAiSessionService(
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project=self.settings.project_id,
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location=self.settings.location
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)
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artifact_service = GcsArtifactService(
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bucket_name=self.settings.bucket_name
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)
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memory_service = VertexAiRagMemoryService(
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rag_corpus=f"projects/{self.settings.project_id}/locations/{self.settings.location}/ragCorpora/{{COMPANY_NAME}}-knowledge"
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)
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# Create runner
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self.runner = Runner(
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app_name=f"{self.settings.company_name}-AI-System",
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agent=self.main_orchestrator,
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session_service=session_service,
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artifact_service=artifact_service,
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memory_service=memory_service
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)
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print(f"✅ {self.settings.company_name} AI Agent System initialized successfully!")
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async def run_agent_interaction(self, user_id: str, session_id: str, message: str):
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"""Run agent interaction"""
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if not self.runner:
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await self.initialize()
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async for event in self.runner.run_async(
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user_id=user_id,
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session_id=session_id,
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new_message=message
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):
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yield event
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# Application factory
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async def create_app():
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ai_system = {{CompanyName}}AISystem()
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await ai_system.initialize()
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return ai_system
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if __name__ == "__main__":
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# Development server
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import uvicorn
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uvicorn.run("main:app", host="0.0.0.0", port=8000, reload=True)
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steps:
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# Build the container image
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- name: 'gcr.io/cloud-builders/docker'
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args: ['build', '-t', 'gcr.io/{{PROJECT_ID}}/{{COMPANY_NAME}}-ai-agents:$COMMIT_SHA', '.']
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# Push the container image to Container Registry
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- name: 'gcr.io/cloud-builders/docker'
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args: ['push', 'gcr.io/{{PROJECT_ID}}/{{COMPANY_NAME}}-ai-agents:$COMMIT_SHA']
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# Deploy container image to Cloud Run
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- name: 'gcr.io/google.com/cloudsdktool/cloud-sdk'
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entrypoint: gcloud
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args:
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- 'run'
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- 'deploy'
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- '{{COMPANY_NAME}}-ai-agents'
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- '--image'
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- 'gcr.io/{{PROJECT_ID}}/{{COMPANY_NAME}}-ai-agents:$COMMIT_SHA'
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- '--region'
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- '{{LOCATION}}'
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- '--platform'
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- 'managed'
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- '--allow-unauthenticated'
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images:
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- 'gcr.io/{{PROJECT_ID}}/{{COMPANY_NAME}}-ai-agents:$COMMIT_SHA'
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# BMad Expansion Pack: Google Cloud Vertex AI Agent System
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[](https://opensource.org/licenses/MIT)
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[](https://www.google.com/search?q=https://github.com/antmikinka/BMAD-METHOD)
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[](https://cloud.google.com/)
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This expansion pack provides a complete, deployable starter kit for building and hosting sophisticated AI agent systems on Google Cloud Platform (GCP). It bridges the gap between the BMad Method's natural language framework and a production-ready cloud environment, leveraging Google Vertex AI, Cloud Run, and the Google Agent Development Kit (ADK).
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## Features
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* **Automated GCP Setup**: `gcloud` scripts to configure your project, service accounts, and required APIs in minutes.
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* **Production-Ready Deployment**: Includes a `Dockerfile` and `cloudbuild.yaml` for easy, repeatable deployments to Google Cloud Run.
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* **Rich Template Library**: A comprehensive set of BMad-compatible templates for Teams, Agents, Tasks, Workflows, Documents, and Checklists.
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* **Pre-configured Agent Roles**: Includes powerful master templates for key agent archetypes like Orchestrators and Specialists.
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* **Highly Customizable**: Easily adapt the entire system with company-specific variables and industry-specific configurations.
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* **Powered by Google ADK**: Built on the official Google Agent Development Kit for robust and native integration with Vertex AI services.
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## Prerequisites
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Before you begin, ensure you have the following installed and configured:
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* A Google Cloud Platform (GCP) Account with an active billing account.
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* The [Google Cloud SDK (`gcloud` CLI)](https://www.google.com/search?q=%5Bhttps://cloud.google.com/sdk/docs/install%5D\(https://cloud.google.com/sdk/docs/install\)) installed and authenticated.
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* [Docker](https://www.docker.com/products/docker-desktop/) installed on your local machine.
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* Python 3.11+
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## Quick Start Guide
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Follow these steps to get your own AI agent system running on Google Cloud.
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### 1\. Configure Setup Variables
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The setup scripts use placeholder variables. Before running them, open the files in the `/scripts` directory and replace the following placeholders with your own values:
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* `{{PROJECT_ID}}`: Your unique Google Cloud project ID.
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* `{{COMPANY_NAME}}`: Your company or project name (used for naming resources).
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* `{{LOCATION}}`: The GCP region you want to deploy to (e.g., `us-central1`).
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### 2\. Run the GCP Setup Scripts
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Execute the setup scripts to prepare your Google Cloud environment.
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```bash
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# Navigate to the scripts directory
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cd scripts/
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# Run the project configuration script
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sh 1-initial-project-config.sh
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# Run the service account setup script
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sh 2-service-account-setup.sh
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```
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These scripts will enable the necessary APIs, create a service account, assign permissions, and download a JSON key file required for authentication.
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### 3\. Install Python Dependencies
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Install the required Python packages for the application.
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```bash
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# From the root of the expansion pack
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pip install -r requirements.txt
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```
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### 4\. Deploy to Cloud Run
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Deploy the entire agent system as a serverless application using Cloud Build.
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```bash
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# From the root of the expansion pack
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gcloud builds submit --config deployment/cloudbuild.yaml .
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```
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This command will build the Docker container, push it to the Google Container Registry, and deploy it to Cloud Run. Your agent system is now live\!
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## How to Use
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Once deployed, the power of this system lies in its natural language templates.
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1. **Define Your Organization**: Go to `/templates/teams` and use the templates to define your agent teams (e.g., Product Development, Operations).
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2. **Customize Your Agents**: In `/templates/agents`, use the `Master-Agent-Template.yaml` to create new agents or customize the existing Orchestrator and Specialist templates. Define their personas, skills, and commands in plain English.
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3. **Build Your Workflows**: In `/templates/workflows`, link agents and tasks together to create complex, automated processes.
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The deployed application reads these YAML and Markdown files to dynamically construct and run your AI workforce. When you update a template, your live agents automatically adopt the new behaviors.
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## What's Included
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This expansion pack has a comprehensive structure to get you started:
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```
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/
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├── deployment/ # Dockerfile and cloudbuild.yaml for deployment
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├── scripts/ # GCP setup scripts (project config, service accounts)
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├── src/ # Python source code (main.py, settings.py)
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├── templates/
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│ ├── agents/ # Master, Orchestrator, Specialist agent templates
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│ ├── teams/ # Team structure templates
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│ ├── tasks/ # Generic and specialized task templates
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│ ├── documents/ # Document and report templates
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│ ├── checklists/ # Quality validation checklists
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│ ├── workflows/ # Workflow definition templates
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│ └── ...and more
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├── config/ # Customization guides and variable files
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└── requirements.txt # Python package dependencies
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```
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## Contributing
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Contributions are welcome\! Please follow the main project's `CONTRIBUTING.md` guidelines. For major changes or new features for this expansion pack, please open an issue or discussion first.
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