Find API patterns and examples for coding agents with ADK, the Agent Development Kit. Covers tools, state, callbacks, multi-agent coordination, and graph workflows, with Python and Go references plus guidance on studying existing implementations.
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This skill should be used when the user wants to "write agent code", "build an agent with ADK", "add a tool", "create a callback", "define an agent", "use state management" — in a project that needs ADK (Agent Development Kit) API patterns and code examples. It provides a quick reference for agent types, tool definitions, orchestration patterns, callbacks, state management, the graph Workflow API, and reference recipes to study. Do NOT use for scaffolding (use google-agents-cli-scaffold) or deployment (use google-agents-cli-deploy).
安装前可以查看交给 AI 代理的指令内容。
Activate /google-agents-cli-workflow first for required development phases and scaffolding steps.
Python — read the topic index in references/samples.md before answering "how do I build X".
Worked implementations exist for: sandboxed/per-user code execution, agent-loadable SKILL.md
skills, cross-session memory, approval gates before risky actions, tool guardrails, per-user
credentials, and scheduled/event-driven runs.
Go — read the upstream examples/.
clone the repo and read the one matching the capability you need before implementing it.
Do NOT write agent code until a project is scaffolded.
agents-cli info (proceed if config exists).agents-cli scaffold create <name> — add --agent adk_go for Go.agents-cli scaffold enhance ..See references/adk-python.md and references/adk-go.md for Python and Go code examples, respectively.
Use the cheatsheets for common patterns. For deep knowledge, fetch the docs index or inspect the installed package.
| Reference | Language | When to read |
|---|---|---|
references/samples.md | Python | Topic-indexed catalog of ADK reference recipes. Read in workflow Phase 1 — before scaffolding and before writing code — maps a capability to the recipe that implements it. |
references/adk-python.md | Python | Core ADK API: Agent, tools, callbacks, plugins, state, artifacts, multi-agent systems, SequentialAgent / ParallelAgent / LoopAgent, custom BaseAgent, ManagedAgent (server-hosted first-party agents), A2A protocol, A2UI. Default for most agents. |
references/adk-python-workflows.md | Python | Graph-based Workflow API (ADK Python 2.0): nodes, edges, fan-out/fan-in, HITL, parallel processing. Use when you need explicit graph topology. |
references/adk-python-live.md | Python | Live and voice agents: Runner.run_live, LiveRequestQueue, model and regional availability, voice/speech_config, VAD, session limits, /run_live serving. Use for real-time voice or video agents. |
references/adk-go.md | Go | Core ADK Go API: llmagent, tools, callbacks, plugins, state, artifacts, multi-agent systems, sequential/parallel/loop agents, custom agents, the runner, serving over HTTP, A2A protocol, and ambient triggers. Default for most agents. |
references/adk-go-workflows.md | Go | Graph-based Workflow API: nodes, edges, fan-out/fan-in, HITL, parallel processing. Use when you need explicit graph topology. |
examples/ | Go | Runnable upstream programs — the closest thing to a recipe catalog for Go. |
curl https://adk.dev/llms.txt | Python | Docs index (every page title + URL). Fetch it, then WebFetch the specific page for anything beyond the cheatsheets. |
| Installed ADK package | Python | Exact signatures and symbols — inspect the source (see "Inspecting ADK Source Code" in references/adk-python.md). |
/google-agents-cli-workflow — Development workflow, coding guidelines, and operational rules/google-agents-cli-scaffold — Project creation and enhancement with agents-cli scaffold create / scaffold enhance/google-agents-cli-eval — Evaluation methodology, dataset schema, and the eval-fix loop/google-agents-cli-deploy — Deployment targets, CI/CD pipelines, and production workflows还没有评价。欢迎分享你的使用感受。
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Guide development of a LangChain or LangGraph project created with agents-cli. It explains tool additions, local runs, evaluation, and serving while identifying the required graph structure and commands that differ from ADK projects.
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Plan and carry out agent deployments on Google Cloud using agents-cli. The guide covers deployment targets, permissions, secrets, automated build and deployment pipelines, verification, and recovery procedures for agents built with supported frameworks.
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Evaluate AI agents by preparing test cases, scoring responses and tool use, investigating failures, and comparing results after changes. Supports built-in checks and custom scoring rules, including evaluations performed by a language model.
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Set up monitoring for deployed agents to inspect execution paths, latency, and model interactions. It explains Cloud Trace, prompt and response logging, BigQuery analytics, and external monitoring integrations, with checks for missing data and unwanted content capture.
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Register a deployed agent with Gemini, choose the appropriate registration method, and troubleshoot setup problems. Also covers managing agents and registering MCP servers, which expose tools to agents, in Google Cloud Agent Registry.
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Create starter projects for AI agents, add deployment configuration and automated build and release pipelines to existing projects, or update generated project files. Supports Python and Go ADK templates and a prototype-first approach.
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