Google Cloud Agent Starter Pack
Scaffold, evaluate, and deploy production-oriented GenAI agents on Google Cloud with ready-made templates and infrastructure.
The workflows use explicit, relatively scoped permissions for documentation deployment, the release path includes environment approval, the README says resources are deployed into the user's own Google Cloud project, and authorship, licensing, and unsupported-product status are clearly attributed. Deductions apply because the supplied files do not expose complete template IAM grants, granular external effects, or end-to-end data flows; sensitive-data evidence is largely limited to injecting tokens through secrets; dependencies use ranges, Actions are not commit-pinned, setup-uv selects latest, and no cloud-resource rollback procedure is shown.
The README, package metadata, workflows, and sample tests establish concrete dependencies, build and release machinery, and basic response assertions. Deductions apply because the README says the project is in critical-fixes-only maintenance mode while also claiming continuous expansion of its agent library; several dependencies have broad minimum constraints, and failure reporting shown here is mostly simple test assertions rather than systematic CLI, deployment, or network diagnostics.
The material clearly addresses both new and existing agents, several agent patterns, multimodal use, and Cloud Run or Agent Engine deployment, while listing concrete Python, Cloud SDK, Terraform, and Make requirements. Deductions apply because capability limits are expressed mainly through the maintenance notice and demonstrative-purpose disclaimer rather than template-specific boundaries; CLI triggers are clear, but misuse guards, prerequisite-state detection, and matching rules for complex existing projects are not demonstrated.
The README organizes installation, templates, features, architecture, requirements, documentation, and support paths well. The full Apache-2.0 license matches package metadata, and semantic versioning plus an automated release flow are present. Deductions apply because migration to agents-cli changes the naming and update path; examples and tests are shallow and no complete FAQ is supplied; limitations lack template-level detail; no standalone changelog is shown; and the help link targets another repository while the project disclaims official support, leaving maintenance responsibility only partially clear.
The templates combine backend, frontend, infrastructure, CI/CD, evaluation, and observability, offering substantial integration value from prototype through cloud deployment, with outputs intended for further customization. Deductions apply because claims such as 60-second setup, production readiness, and migration without rewrites are not quantitatively substantiated by the supplied static files; users still bear cloud-resource, installation, migration, and operating costs, for which no estimate is provided.
Package version, CLI entry point, release process, and basic agent response checks are traceable across the README, pyproject, workflows, and template tests. Deductions apply because no run results or test reports are supplied, the two integration tests are nearly identical and only check for some text, the adk_live unit test is a fixed weather stub, promotional claims are not consistently separated from demonstrated facts, and there is no independent corroboration within the supplied material.
- The project is in maintenance mode and receives critical fixes only; new projects should evaluate the stated migration path to agents-cli.
- Create, enhance, or deployment operations may create resources, permissions, and charges in the user's Google Cloud project; review generated Terraform, IAM, and CI/CD configuration before execution.
- The release workflow uses token secrets, GitHub Actions not pinned to commit digests, and a latest setup-uv version; supply-chain-sensitive environments should pin these inputs and further restrict token privileges.
- The 60-second setup, production-ready status, and no-rewrite migration claims are not fully substantiated by the supplied static evidence and should not be treated as tested guarantees.
What does this agent do, and when should you use it?
Agent Starter Pack is a Python package that generates production-oriented agent projects for Google Cloud. Its templates cover ADK, ADK with A2A, agentic RAG, LangGraph, ADK for Java, and a real-time multimodal ADK agent, with generated projects including a backend, frontend, and deployment infrastructure. The workflow spans prototyping, Vertex AI evaluation, an interactive playground, Terraform deployment, CI/CD, monitoring, and observability, with Cloud Run and Agent Engine as documented deployment targets. Its RAG paths use Vertex AI Search or Vector Search and can include a Terraform- and CI/CD-managed embedding data pipeline. The repository is now in maintenance mode and will receive critical fixes only; new projects are directed to `agents-cli`, and existing users are encouraged to migrate.
The agent-starter-pack create command reads a bundled or remote template and writes a project containing agent code, a backend, a frontend, tests, and deployment infrastructure. Available templates include the Google Agent Development Kit-based adk, the Agent2Agent Protocol-enabled adk_a2a, agentic_rag with Vertex AI Search or Vector Search, a LangGraph ReAct agent, adk_java, and the Gemini-powered adk_live agent for audio, video, and text chat. Generated projects can be exercised through an interactive playground and Vertex AI evaluation, provisioned with Terraform, connected to Google Cloud Build or GitHub Actions, and deployed to Cloud Run or Agent Engine. Running agent-starter-pack enhance from an existing agent's root directory adds the production deployment and infrastructure layer. The package also supports templates stored in Git repositories and includes GEMINI.md context for Gemini CLI guidance.
- A Google Cloud engineering team needs an agent project with Terraform, CI/CD, monitoring, and observability without assembling the delivery stack from scratch.
- A developer wants to prototype a ReAct agent with Google ADK or LangGraph from a working template.
- A retrieval team needs a RAG agent backed by Vertex AI Search or Vector Search, including a pipeline that processes embeddings.
- A distributed-agent team wants to test ADK communication and interoperability through the Agent2Agent Protocol.
- A multimodal product team needs a Gemini-powered real-time RAG agent supporting audio, video, and text conversations.
- A team with an existing agent wants to add Google Cloud deployment infrastructure through
enhance, while accepting the repository's maintenance-only status.
What are this agent's strengths and limitations?
- The template catalog covers specific patterns—ReAct, RAG, A2A, and real-time multimodal agents—with ADK, LangGraph, and Java options.
- Generated projects extend beyond agent logic to include a backend, frontend, Terraform, CI/CD, evaluation, monitoring, and observability.
- It documents two deployment targets, Cloud Run and Agent Engine, plus CI/CD through either Google Cloud Build or GitHub Actions.
- The RAG tooling supports both Vertex AI Search and Vector Search, with an embedding data pipeline managed through Terraform and CI/CD.
- The repository is in maintenance mode: only critical fixes remain, with no new features, templates, or deployment targets planned.
- Its documented deployment, evaluation, retrieval, and model integrations are centered on Google Cloud, Vertex AI, and Gemini, so moving to another provider requires integration work.
- The development and deployment toolchain requires Python 3.10+, Google Cloud SDK, Terraform, and Make.
- Users deploy resources into their own Google Cloud projects and remain responsible for permissions, operations, service terms, and resource costs.
- The repository is demonstrative and is explicitly not an officially supported Google product.
How do you install or deploy this agent?
Prerequisites are Python 3.10+, Google Cloud SDK, Terraform for deployment, Make for development tasks, and a Google Cloud project that will own the deployed resources. The recommended first invocation is:
uvx agent-starter-pack createWithout uv, use:
python -m venv .venv && source .venv/bin/activate
pip install --upgrade agent-starter-pack
agent-starter-pack createThis writes a customizable agent project to the local filesystem. Google Cloud authentication is required when the project calls Cloud APIs or deploys resources, but the supplied material does not specify an exact login command or IAM role set.
How do you use this agent?
For a new project, run uvx agent-starter-pack create in a shell and select and customize a template such as adk, adk_a2a, agentic_rag, langgraph, adk_java, or adk_live. Use the generated project's interactive playground for experimentation and Vertex AI evaluation for assessment, then deploy through the included Terraform and CI/CD setup to Cloud Run or Agent Engine. To add deployment infrastructure to an existing agent, run uvx agent-starter-pack enhance from that project's root directory. Because Agent Starter Pack is in maintenance mode, the documented route for a new project is now uvx google-agents-cli setup. The migration guidance states that existing agent code, tests, Terraform, and CI/CD carry over without rewrites.
How does this agent compare with similar options?
Compared with its successor, agents-cli, Agent Starter Pack uses a Makefile-oriented workflow and is limited to maintenance fixes. agents-cli provides one CLI for commands such as run, deploy, eval run, eval compare, playground, and lint; it also adds coding-agent skills, end-to-end scaffold-to-observe lifecycle tooling, and first-class support for Google Cloud's Agent Platform. The repository explicitly recommends agents-cli for new projects and migration for existing ones.
FAQ
Should I start a new project with Agent Starter Pack?
agents-cli.Where are generated resources deployed?
What credentials and permissions are required?
Does it support non-Google models or other clouds?
Can it add infrastructure to an agent I already have?
uvx agent-starter-pack enhance from the existing project's root directory, while also considering migration to agents-cli because this package is maintenance-only.