Agent Starter Pack

Agent Starter Pack ships a set of GenAI agent templates you can generate and run immediately. The lineup covers a base ReAct agent template built on Google's Agent Development Kit, a RAG agent template for document retrieval and Q&A, a multi-agent setup, an Agent2Agent variant for distributed agent communication, and a real-time multimodal agent powered by Gemini that handles audio, video, and text. You don't start from a blank file. You start from a working pattern that matches most common agent designs, then shape it to your use case. Need something custom? Remote templates let you pull a starter pack from any Git repository.

The core command, uvx agent-starter-pack create, scaffolds an entire project in one step: a backend, a frontend, tests, and Terraform infrastructure land in a single folder. That's the point of the toolkit. It collapses the setup phase, which is usually a week of glue work, into a command you run before coffee gets cold. No manual wiring. If you already have an agent, the upgrade command adds the same production scaffolding to your existing project instead of forcing a fresh start.

Agent Starter Pack sets up a full agent deployment pipeline for every environment with a single command. It supports both Google Cloud Build and GitHub Actions, so you wire releases into the tooling your team already uses. The result is a repeatable build-and-deploy path rather than a set of manual steps someone has to remember. For teams that treat agent code as production code, this is the feature that keeps releases boring and predictable.

Generated projects deploy to Google Cloud's Cloud Run or Vertex AI Agent Engine. Cloud Run suits agents that fit a container and want to scale on request, while Agent Engine handles agent workloads Google optimized for that platform. Because the deployment config is generated with the project, you don't hand-write the container and service definitions. You pick a target and the scaffold already speaks its language.

The templates come with monitoring, observability, and security defaults already in place. That means logs, traces, and access controls are part of the project from the first commit, not something bolted on after an incident. For anyone who has shipped an agent and then scrambled to figure out why it failed in production, having observability present from the start is the quiet difference between a demo and a service.

For retrieval-heavy agents, the toolkit includes a data pipeline that processes embeddings for RAG and wires it into the agent system through Terraform and CI/CD. It supports both Vertex AI Search and Vector Search, so you can connect the retrieval backend that fits your data. Setting up document ingestion and embedding pipelines by hand is tedious and easy to get wrong. This handles it as part of the generated project.

Generated projects include Vertex AI evaluation and an interactive playground. The playground lets you test prompts and agent behavior locally before you deploy, and the evaluation tooling measures model output against your criteria. Why does that matter for agents? Because agent behavior is harder to eyeball than a plain chatbot reply. You get a way to compare changes in quality, not just a way to see that the code runs.