Agent Development Kit is a Python framework for building, evaluating, deploying, and coordinating AI agents as software applications. Developers use it to compose workflows, delegate tasks between specialized agents, manage state, and connect agents to tools while choosing supported models and deployment environments. The catalogue add-ons provide workflows for using the toolkit.
Borrowing it
Nothing to install: this file belongs to google/adk-python. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/google/adk-python/main/AGENTS.mdgit clone --depth 1 https://github.com/google/adk-pythonWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/google/adk-python/agents-md)<a href="https://agentmods.dev/instructions/google/adk-python/agents-md"><img src="https://agentmods.dev/badge/instructions/google/adk-python/agents-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/google/adk-python/agents-md"><img src="https://agentmods.dev/badge/instructions/google/adk-python/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00324 | $0.00324 |
| Opus 5.5 | $0.00130 | $0.00130 |
| Sonnet 5.5 | $0.00065 | $0.00065 |
| Haiku 4.5 | $0.00032 | $0.00032 |
Grade A, and why
adk-python AGENTS.md 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 1mo 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.
What it actually says
Project Overview
The Agent Development Kit (ADK) is an open-source, code-first Python toolkit for building, evaluating, and deploying sophisticated AI agents.
Key Components
- Agent: Blueprint defining identity, instructions, and tools.
- Runner: Stateless execution engine that orchestrates agent execution.
- Tool: Functions/capabilities agents can call.
- Session: Conversation state management.
- Memory: Long-term recall across sessions.
- Workflow (ADK 2.0): Graph-based orchestration of complex, multi-step agent interactions.
- BaseNode (ADK 2.0): Contract for all nodes, supporting output streaming and human-in-the-loop steps.
- Context (ADK 2.0): Holds execution state and telemetry context mapped 1:1 to nodes.
For details on how the Runner works and the invocation lifecycle, please refer to the adk-architecture skill and the referenced documentation therein.
ADK Knowledge, Architecture, and Style
Skills related to ADK development are in .agents/skills/.
Project Architecture
For detailed architecture patterns, component descriptions, and core interfaces, please refer to the adk-architecture skill at .agents/skills/adk-architecture/SKILL.md.
Development Setup
The project uses uv for package management and Python 3.11+. Please refer to
the adk-setup skill at .agents/skills/adk-setup/SKILL.md for detailed
instructions.
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.
- 1mo ago Changed · +2 lines · +2 tokens per session 7b7b52e14702
- 2mo ago First seen · 29 lines · 322 tokens per session scan A 98933cf6f62b
adk-python AGENTS.md is an instructions file published in the GitHub repository google/adk-python (22,117 stars, last pushed yesterday), licensed Apache-2.0. It adds 324 tokens to every session, about $0.0013 per session on Opus 5.5. 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.
adk-python AGENTS.md compared with instructions from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| adk-python AGENTS.mdgoogle/adk-python | Instructions file | 22k | yesterday | Apache-2.0 | A | 324 | Codex, OpenCode |
| vscode buildNext.instructions.mdmicrosoft/vscode | Instructions file | 195k | today | MIT | A | 9,719 | GitHub Copilot |
| next.js AGENTS.mdvercel/next.js | Instructions file | 143k | today | MIT | A | 7,461 | Codex, OpenCode |
| vscode oss-third-party-notices.instructions.mdmicrosoft/vscode | Instructions file | 195k | today | MIT | A | 5,516 | GitHub Copilot |
| langchain AGENTS.mdlangchain-ai/langchain | Instructions file | 149k | today | MIT | A | 4,680 | Codex, OpenCode |
| vscode copilot-instructions.mdmicrosoft/vscode | Instructions file | 195k | today | MIT | A | 3,093 | GitHub Copilot |
| deepseek-harness AGENTS.mddeepseek-ai/deepseek-harness | Instructions file | 274k | 2d ago | MIT | A | 4,313 | Codex, OpenCode |