Agent Starter Pack
Agent Starter Pack is a Python package that provides production-ready templates for building GenAI agents on Google Cloud with built-in infrastructure, CI/CD, observability, and security features.
https://github.com/GoogleCloudPlatform/agent-starter-pack?ref=producthunt
Product Information
Updated:Dec 15, 2025
What is Agent Starter Pack
Agent Starter Pack is a comprehensive toolkit developed by Google Cloud Platform that helps developers rapidly build and deploy production-ready generative AI agents. It acts as a 'create-react-app' equivalent for AI agents, providing pre-built templates, infrastructure setup, and deployment automation. The package supports various agent patterns including ReAct, RAG (Retrieval Augmented Generation), multi-agent systems, and live multimodal API integration, allowing developers to focus on their agent's core logic while the starter pack handles everything else.
Key Features of Agent Starter Pack
Agent Starter Pack is a Python package that provides production-ready templates for building and deploying Generative AI agents on Google Cloud. It offers a comprehensive solution that handles infrastructure, CI/CD pipelines, observability, and security, allowing developers to focus solely on agent logic. The package includes pre-built agent templates, supports multiple frameworks like ADK and LangGraph, and enables rapid deployment through Cloud Run or Agent Engine.
Pre-built Agent Templates: Provides ready-to-use templates for various agent types including ReAct, RAG, multi-agent, and Live API, allowing quick start with common AI agent patterns
Automated CI/CD Pipeline: One-command setup for complete CI/CD pipeline supporting both Google Cloud Build and GitHub Actions, with automated builds, tests, and deployments
Integrated Observability: Built-in monitoring and observability features using OpenTelemetry, enabling detailed tracing and logging of agent interactions in Google Cloud
RAG Data Pipeline: Production-ready data ingestion pipeline for processing and embedding custom data, supporting both Vertex AI Search and Vector Search for enhanced response relevance
Use Cases of Agent Starter Pack
Document-based Q&A Systems: Build intelligent systems that can process, index, and answer questions from large document repositories using RAG capabilities
Real-time Multimodal Interactions: Create agents capable of handling real-time audio, video, and text interactions using the ADK Live template
Distributed Agent Networks: Develop interconnected agent systems using the A2A protocol for complex task automation and multi-agent collaboration
Enterprise Search Enhancement: Implement advanced search capabilities in enterprise systems using the RAG pipeline with Vertex AI Search integration
Pros
Rapid deployment with production-ready infrastructure
Comprehensive observability and monitoring built-in
Flexible framework support (ADK, LangGraph, CrewAI)
Cons
Limited to Google Cloud Platform environment
Requires technical knowledge of Python and cloud infrastructure
May have higher operational costs due to cloud service dependencies
How to Use Agent Starter Pack
Install Prerequisites: Ensure you have Python 3.10+, Google Cloud SDK, Terraform, and Make installed on your system
Install Agent Starter Pack: Choose one of two installation methods: 1) Using uv: Run 'uvx agent-starter-pack create', or 2) Using pip: Create virtual environment with 'python -m venv .venv && source .venv/bin/activate' then run 'pip install --upgrade agent-starter-pack'
Create New Agent Project: Run 'agent-starter-pack create' and follow the interactive prompts to select your agent template (e.g., adk_base, agentic_rag, langgraph_base) and deployment target (cloud_run or agent_engine)
Configure Agent: Navigate to the generated project directory and customize the agent logic in app/agent.py according to your needs. The template provides the basic structure and infrastructure
Set Up Data Pipeline (Optional): For RAG agents, configure data ingestion pipeline using '--include-data-ingestion' flag to process embeddings for Vertex AI Search or Vector Search
Test Locally: Use the interactive UI playground with hot-reloading to test your agent's functionality before deployment
Set Up CI/CD: Run 'agent-starter-pack setup-cicd' to configure automated deployment pipeline using either Google Cloud Build or GitHub Actions
Deploy to Production: Follow the deployment guide to deploy your agent to Google Cloud using the established CI/CD pipeline. The infrastructure will be provisioned using Terraform
Monitor and Observe: Use the built-in observability tools including Cloud Trace and Cloud Logging to monitor your agent's performance and behavior in production
Enhance Existing Agents (Optional): For existing agents, use 'agent-starter-pack enhance' in the project root folder to add production-ready deployment and infrastructure capabilities
Agent Starter Pack FAQs
Agent Starter Pack is a Python package that provides production-ready templates for GenAI agents on Google Cloud. It handles infrastructure, CI/CD, observability, and security, allowing developers to focus on agent logic.
Agent Starter Pack Video
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