Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/googlecloudplatform/agent-starter-pack/overviewgit clone --depth 1 https://github.com/GoogleCloudPlatform/agent-starter-packWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00000 | $0.01670 |
| Opus 5 | $0.00000 | $0.00835 |
| Sonnet 5 | $0.00000 | $0.00334 |
| Haiku 4.5 | $0.00000 | $0.00167 |
Grade A, and why
overview scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Templates
The Agent Starter Pack follows a "bring your own agent" approach. It provides several production-ready agent templates designed to accelerate your development while offering the flexibility to use your preferred agent framework or pattern.
Available Templates
| Agent Name | Description | Use Case |
|---|---|---|
adk |
A base ReAct agent implemented using Google's Agent Development Kit | General purpose conversational agent |
adk_go |
A base ReAct agent implemented using Google's Agent Development Kit for Go | Go-based conversational agent |
adk_ts |
A base ReAct agent implemented using Google's Agent Development Kit for TypeScript | TypeScript/Node.js-based conversational agent |
adk_java |
A base ReAct agent implemented using Google's Agent Development Kit for Java | Java-based conversational agent |
adk_a2a |
An ADK agent with Agent2Agent (A2A) Protocol support | Distributed agent communication and interoperability across frameworks |
agentic_rag |
A RAG agent for document retrieval and Q&A | Document search and question answering |
langgraph |
A base ReAct agent implemented using LangChain's LangGraph | Graph based conversational agent |
adk_live |
A real-time multimodal RAG agent | Audio/video/text chat with knowledge base |
Choosing the Right Template
When selecting a template, consider these factors:
- Primary Goal: Are you building a conversational bot, a Q&A system over documents, a task-automation network, or something else?
- Programming Language: Do you prefer Python, Go, TypeScript, or Java? Most templates are Python-based, but
adk_go,adk_ts, andadk_javaprovide Go, TypeScript, and Java alternatives. - Core Pattern/Framework: Do you have a preference for Google's ADK, LangChain/LangGraph, or implementing a pattern like RAG directly? The Starter Pack supports various approaches.
- Reasoning Complexity: Does your agent need complex planning and tool use (like ReAct), or is it more focused on retrieval and synthesis (like basic RAG)?
- Collaboration Needs: Do you need multiple specialized agents working together?
- Modality: Does your agent need to process or respond with audio, video, or just text?
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 115 lines · 0 tokens per session scan A 171c248f8509
overview is an agent published in the GitHub repository GoogleCloudPlatform/agent-starter-pack (6,547 stars, last pushed 1mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 1,670 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
memory
LangGraph supports two types of memory essential for building conversational agents.
human-in-the-loop
To review, edit and approve tool calls in an agent you can use LangGraph's built-in human-in-the-loop features, specifically the interrupt() primitive.
streaming
Streaming is key to building responsive applications. There are a few types of data you’ll want to stream.
dev
TDD Developer agent - implements features using test-driven development and clean code principles.
test-doc
Use this agent when the user needs markdown files created in the test-files/ directory. This includes generating test data files, sample documentation, mock content, or any markdown-formatted files for testing purposes.\n\nExamples:\n\n \nContext: User needs sample markdown files for testing a documentation…
ui
You can use a prebuilt chat UI for interacting with any LangGraph agent through the Agent Chat UI. Using the deployed version is the quickest way to get started, and allows you to interact with both local and deployed graphs.