Agent Templates¶
agents-cli creates projects from agent templates. Each template provides a working agent with the right dependencies, tools, and project structure for its use case.
Available Templates¶
| Template | Description | Use Case |
|---|---|---|
adk |
ReAct agent using ADK | General-purpose conversational agent with tool use |
RAG is not a template — it's a clone-and-study recipe. See RAG below.
adk¶
The default template. Creates a ReAct agent using the Agent Development Kit with a sample tool. Start here if you are new to ADK or building a general-purpose agent.
Every Python ADK agent serves the Agent-to-Agent (A2A) protocol out of the box — the A2A routes (agent card + JSON-RPC) are mounted automatically. Use this when your agent needs to interoperate with agents built on other frameworks (LangGraph, CrewAI, etc.) or when building a distributed multi-agent system; no separate template or hand-written A2A code is required.
RAG (Retrieval-Augmented Generation)¶
RAG is not a template — it's a clone-and-study recipe. Scaffold a base adk project, then study and adapt one of the RAG samples in google/adk-samples, copying its retriever and infra/terraform/ into your project:
rag-vector-search— Vertex AI Vector Search 2.0 with a custom ingestion pipeline (embeddings, similarity search).rag-agent-search— Agent Platform Search (Discovery Engine) with a fully-managed GCS Data Connector — drop files in a bucket, no ingestion code to write.
The workflow skill's references/samples.md lists both with their key files, and each sample's AGENTS.md is the study-and-adapt guide. Provisioning and ingestion run from the sample's own Makefile (make setup-infra, make data-ingestion).