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agent starter pack

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Basic Information

The agent-starter-pack is a Python package and CLI for developers who want to build, evaluate, and deploy production-ready generative AI agents on Google Cloud. It provides a curated library of agent templates and project scaffolds so teams can create full agent projects that include backend, frontend, and deployment infrastructure. The starter pack targets common needs across the agent lifecycle by addressing prototyping, evaluation, CI/CD, observability, data pipelines for retrieval-augmented generation, and production deployment on services like Cloud Run or Google Agent Engine. It includes example agent patterns such as ReAct, RAG, multi-agent systems, a real-time multimodal Live API, and integrations with Google tools like the Agent Development Kit and Vertex AI features. The README documents quick-start commands to create or enhance projects, system requirements such as Python 3.10+, Google Cloud SDK and Terraform, and points to comprehensive documentation and walkthroughs.

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2348
App URL
https://github.com/GoogleCloudPlatform/agent-starter-pack
Github Repository
https://github.com/GoogleCloudPlatform/agent-starter-pack/blob/main/README.md

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App Details

Features
The repository bundles pre-built agent templates (ReAct, RAG, multi-agent, Live API) and a CLI to create or enhance agent projects with a single command. It provides production-oriented tooling including CI/CD automation for Cloud Build and GitHub Actions, deployment scaffolding, and observability/monitoring integrations. Data-ingestion pipelines and Terraform templates support processing embeddings and RAG workflows targeting Vertex AI Search and Vector Search. Additional features include remote templating to share custom starter templates, Gemini CLI context for in-terminal guidance about templates and architecture, ADK and LangGraph sample integrations, and an interactive playground plus Vertex AI evaluation support. The pack also offers examples, documentation, video walkthroughs, and zero-setup options via Firebase Studio or Cloud Shell to simplify onboarding and experimentation.
Use Cases
This starter pack accelerates development by reducing boilerplate and operational overhead associated with building generative AI agents. Teams get runnable project scaffolds that include frontend and backend wiring, infrastructure-as-code, CI/CD pipelines, and monitoring best practices so they can progress from prototype to production faster. Built-in templates and data pipelines make it easier to integrate document retrieval, embeddings, and Vector/Vertex AI search for RAG scenarios. Evaluation tooling and interactive playgrounds support model testing and iteration. The Gemini CLI context and documentation provide in-terminal guidance and examples, while remote templating and enhancement commands let developers reuse and extend templates for existing projects. Overall, it lowers the barrier to deploying robust, observable GenAI agents on Google Cloud.

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