google-adk
SKILL.md
Google ADK
Comprehensive reference for Google's Agent Development Kit. Use this skill when:
- Creating or configuring any ADK agent (
LlmAgent,SequentialAgent,LoopAgent,ParallelAgent, custom agents) - Wiring tools to agents (function tools, built-in tools, MCP, OpenAPI, tool authentication)
- Managing sessions, state, and memory
- Building multi-agent systems or agent pipelines
- Integrating the Gemini Live API or building streaming agents
- Deploying agents to Cloud Run, GKE, or Vertex AI Agent Engine
- Evaluating agent behavior or writing agent tests
- Using the A2A (Agent-to-Agent) protocol for inter-agent communication
- Configuring callbacks, grounding, context caching, or observability
How to Use This Skill
- Identify the task from the Quick Reference table below
- Load the concept reference file (prose, architecture, API parameters)
- If you need runnable code, also load the matching language sidecar:
filename-python.md,filename-typescript.md,filename-go.md, orfilename-java.md
Language sidecar files exist for these 10 high-complexity references — load them alongside the parent when you need code examples:
| Parent file | Python sidecar | TS sidecar | Go sidecar | Java sidecar |
|---|---|---|---|---|
agents/llm-agents.md |
agents/llm-agents-python.md |
agents/llm-agents-typescript.md |
agents/llm-agents-go.md |
agents/llm-agents-java.md |
agents/custom-agents.md |
agents/custom-agents-python.md |
agents/custom-agents-typescript.md |
agents/custom-agents-go.md |
agents/custom-agents-java.md |
agents/multi-agent-systems.md |
agents/multi-agent-systems-python.md |
agents/multi-agent-systems-typescript.md |
agents/multi-agent-systems-go.md |
agents/multi-agent-systems-java.md |
tools/function-tools-overview.md |
tools/function-tools-overview-python.md |
tools/function-tools-overview-typescript.md |
tools/function-tools-overview-go.md |
tools/function-tools-overview-java.md |
tools/mcp-tools.md |
tools/mcp-tools-python.md |
tools/mcp-tools-typescript.md |
tools/mcp-tools-go.md |
tools/mcp-tools-java.md |
tools/tool-authentication.md |
tools/tool-authentication-python.md |
tools/tool-authentication-typescript.md* |
tools/tool-authentication-go.md* |
tools/tool-authentication-java.md* |
callbacks/types-of-callbacks.md |
callbacks/types-of-callbacks-python.md |
callbacks/types-of-callbacks-typescript.md |
callbacks/types-of-callbacks-go.md |
callbacks/types-of-callbacks-java.md |
sessions/state.md |
sessions/state-python.md |
sessions/state-typescript.md |
sessions/state-go.md |
sessions/state-java.md |
sessions/memory.md |
sessions/memory-python.md |
sessions/memory-typescript.md* |
sessions/memory-go.md |
sessions/memory-java.md |
streaming/part1-intro-to-streaming.md |
streaming/part1-intro-to-streaming-python.md |
streaming/part1-intro-to-streaming-typescript.md* |
streaming/part1-intro-to-streaming-go.md* |
streaming/part1-intro-to-streaming-java.md* |
* = stub only, source docs have no examples for this language yet
All sidecar files are under reference/ — prepend reference/ to the paths above.
Quick Reference: Common Tasks
| Task | Load These References |
|---|---|
| Create a basic LLM agent | reference/getting-started/python-quickstart.md or typescript-quickstart.md / go-quickstart.md / java-quickstart.md |
| Understand LlmAgent parameters (name, model, tools, instructions, output_key) | reference/agents/llm-agents.md + language sidecar |
| Build a sequential pipeline of agents | reference/agents/workflow/sequential-agents.md |
| Build a loop / retry pattern | reference/agents/workflow/loop-agents.md |
| Run agents in parallel | reference/agents/workflow/parallel-agents.md |
| Build a multi-agent system with routing | reference/agents/multi-agent-systems.md + language sidecar |
| Create a custom agent (subclass BaseAgent) | reference/agents/custom-agents.md + language sidecar |
| Write a function tool with ADK patterns | reference/tools/function-tools-overview.md + language sidecar |
| Use LongRunningFunctionTool or AgentTool | reference/tools/function-tools-overview.md + language sidecar |
| Connect an MCP server as tools | reference/tools/mcp-tools.md + reference/tools/mcp-tools-python.md |
| Expose an OpenAPI spec as tools | reference/tools/openapi-tools.md |
| Add OAuth/API key auth to tools | reference/tools/tool-authentication.md + reference/tools/tool-authentication-python.md |
| Add human-in-the-loop confirmations | reference/tools/action-confirmations.md |
| See all built-in & third-party integrations | reference/tools/tools-and-integrations.md |
| Read/write session state, use state prefixes | reference/sessions/state.md + language sidecar |
| Use output_key to pass data between agents | reference/sessions/state.md + language sidecar |
| Use persistent sessions (DatabaseSessionService) | reference/sessions/sessions.md |
| Add long-term memory (MemoryService) | reference/sessions/memory.md + language sidecar |
| Add callbacks (before/after model/tool/agent) | reference/callbacks/types-of-callbacks.md + language sidecar |
| Callback patterns and best practices | reference/callbacks/callback-patterns.md |
| Configure models (Gemini, Claude, Vertex, Ollama, LiteLLM) | reference/models/models-overview.md |
| Use Claude (Anthropic) as the model | reference/models/claude.md |
| Use Vertex AI hosted models | reference/models/vertex-ai.md |
| Use Ollama / local models | reference/models/ollama.md |
| Build a streaming / realtime agent | reference/streaming/streaming-overview.md + reference/streaming/part1-intro-to-streaming.md + reference/streaming/part1-intro-to-streaming-python.md |
| Stream audio, images, or video | reference/streaming/part5-audio-images-video.md |
| Use Google Search grounding | reference/grounding/google-search-grounding.md |
| Use Vertex AI Search grounding | reference/grounding/vertex-ai-search-grounding.md |
| Work with artifacts (files, blobs) | reference/components/artifacts.md |
| Run the agent (CLI / web UI / API server) | reference/runtime/runtime-overview.md |
| Deploy to Cloud Run | reference/deployment/cloud-run.md |
| Deploy to Vertex AI Agent Engine | reference/deployment/agent-engine.md |
| Evaluate agent quality / write eval tests | reference/evaluation/evaluation-overview.md |
| Expose an agent via A2A protocol | reference/a2a/quickstart-exposing-python.md |
| Consume a remote agent via A2A | reference/a2a/quickstart-consuming-python.md |
| Enable tracing / logging / observability | reference/observability/observability-overview.md |
| Use context caching to reduce cost | reference/context/context-caching.md |
| Compress long context windows | reference/context/context-compression.md |
| CLI commands reference | reference/reference/cli-reference.md |
1. Getting Started
Start here when beginning a new ADK project or when you need installation, quickstart, or conceptual overview.
| Reference | When to Consult |
|---|---|
reference/home.md |
ADK high-level overview: what it is, key features, multi-language support |
reference/getting-started/technical-overview.md |
Architecture: agents, tools, sessions, events, runners — how they fit together |
reference/getting-started/advanced-setup.md |
Installation, virtual envs, CLI setup, API key config, optional dependencies |
reference/getting-started/python-quickstart.md |
First Python agent in 5 minutes |
reference/getting-started/typescript-quickstart.md |
First TypeScript agent in 5 minutes |
reference/getting-started/go-quickstart.md |
First Go agent in 5 minutes |
reference/getting-started/java-quickstart.md |
First Java agent in 5 minutes |
reference/getting-started/quickstart-multi-tool-agent.md |
Building an agent with multiple tools: weather + time + translation |
reference/getting-started/tutorial-agent-team.md |
Multi-agent tutorial: orchestrator + specialized sub-agents |
reference/getting-started/quickstart-streaming-python.md |
Getting started with streaming agents in Python |
reference/getting-started/quickstart-streaming-java.md |
Getting started with streaming agents in Java |
reference/getting-started/coding-with-ai.md |
Using AI assistants (Claude, Gemini, Cursor) to build ADK projects |
2. Agents
Core agent types and configuration. Almost every ADK task starts here.
| Reference | When to Consult |
|---|---|
reference/agents/agents-overview.md |
Overview of all agent types and when to use each |
reference/agents/llm-agents.md |
LlmAgent concepts: name, model, description, instructions, tools, output_key, planner, code_executor — load language sidecar for code |
reference/agents/workflow-agents.md |
Workflow agents overview: Sequential, Loop, Parallel — when and why |
reference/agents/workflow/sequential-agents.md |
SequentialAgent: chaining agents in order, passing context between steps |
reference/agents/workflow/loop-agents.md |
LoopAgent: iteration, stopping conditions, max_iterations |
reference/agents/workflow/parallel-agents.md |
ParallelAgent: concurrent execution, independent subtasks, fan-out patterns |
reference/agents/custom-agents.md |
BaseAgent subclassing: _run_async_impl, custom orchestration logic — load language sidecar for the StoryFlowAgent pattern |
reference/agents/multi-agent-systems.md |
Multi-agent coordination: sub_agents hierarchy, AgentTool, LLM transfer, shared state — load language sidecar for composition patterns |
reference/agents/agent-config.md |
Agent configuration file format (agentconfig.json) |
3. Models
Configuring which LLM backs your agent.
| Reference | When to Consult |
|---|---|
reference/models/models-overview.md |
All supported models, how to specify model strings, model selection guidance |
reference/models/gemini.md |
Google Gemini models (gemini-2.5-flash, gemini-2.0-flash, etc.), API keys, model IDs |
reference/models/claude.md |
Anthropic Claude models via API, supported model IDs, Python/TS/Go/Java setup |
reference/models/vertex-ai.md |
Models hosted on Vertex AI (Gemini, Claude, Llama, etc.) via Vertex endpoint |
reference/models/apigee.md |
Apigee AI Gateway for enterprise model routing and governance |
reference/models/ollama.md |
Ollama for local model inference: setup, model pull, connecting to ADK |
reference/models/litellm.md |
LiteLLM proxy: 100+ providers (OpenAI, Azure, Mistral, etc.) through one interface |
reference/models/vllm.md |
vLLM for high-throughput local inference |
reference/models/litert-lm.md |
LiteRT-LM for on-device inference on Android/embedded |
4. Tools
Giving agents capabilities to act.
| Reference | When to Consult |
|---|---|
reference/tools/tools-and-integrations.md |
All built-in tools (Google Search, code exec, etc.) and third-party integrations (LangChain, CrewAI, Vertex) |
reference/tools/function-tools-overview.md |
Writing custom function tools: type hints, docstrings, return types, async, long-running — load language sidecar for LongRunningFunctionTool and AgentTool patterns |
reference/tools/tool-performance.md |
Parallel tool calls, caching tool results, performance optimization |
reference/tools/action-confirmations.md |
Requiring human approval before tool execution |
reference/tools/mcp-tools.md |
Connecting MCP servers as ADK tools: MCPToolset, stdio and SSE transports — load Python sidecar for full wiring patterns |
reference/tools/openapi-tools.md |
Generating tools from an OpenAPI 3.0 spec: RestApiTool, auth schemes |
reference/tools/tool-authentication.md |
Tool auth concepts: API keys, OAuth 2.0, service accounts — load Python sidecar for complete OAuth flow implementation |
reference/tools/tool-limitations.md |
Tool call limits, unsupported patterns, known constraints |
reference/tools/skills-for-agents.md |
ADK Skills: reusable packaged tool sets for inter-agent sharing |
5. Sessions, State & Memory
Persistence and context management across turns and sessions.
| Reference | When to Consult |
|---|---|
reference/sessions/sessions-overview.md |
Sessions architecture: what a session is, lifecycle, service types |
reference/sessions/sessions.md |
Session CRUD, InMemorySessionService vs DatabaseSessionService, AppConfig |
reference/sessions/rewind-sessions.md |
Rewinding a session to a prior turn |
reference/sessions/migrate-sessions.md |
Migrating session data between service backends |
reference/sessions/state.md |
State concepts: context.state, output_key, state scopes (session/user/app/temp), {key} templating — load language sidecar for code patterns |
reference/sessions/memory.md |
Long-term memory concepts: MemoryService, search, adding memories — load language sidecar for implementation patterns |
6. Context
Managing the LLM's context window efficiently.
| Reference | When to Consult |
|---|---|
reference/context/context-overview.md |
What context is in ADK: system prompt, session history, tool results |
reference/context/context-caching.md |
Caching repeated context (system prompt, documents) to reduce cost/latency |
reference/context/context-compression.md |
Compressing/summarizing long session history to fit within context limits |
7. Callbacks
Intercepting and modifying agent behavior at runtime.
| Reference | When to Consult |
|---|---|
reference/callbacks/callbacks-overview.md |
What callbacks are, registration, invocation order |
reference/callbacks/types-of-callbacks.md |
All 6 callback types: before/after model, before/after tool, before/after agent — load language sidecar for complete runnable examples |
reference/callbacks/callback-patterns.md |
Design patterns: logging, guardrails, caching, modifying requests/responses |
8. Components
Core ADK building blocks beyond agents and tools.
| Reference | When to Consult |
|---|---|
reference/components/artifacts.md |
Artifacts: storing/retrieving files and binary blobs within a session |
reference/components/events.md |
Event system: event types, reading event history, custom events |
reference/components/apps.md |
App layer: multi-session app management, AppConfig |
reference/components/plugins.md |
ADK plugins: extending the framework, plugin registration |
reference/components/mcp.md |
MCP server built into ADK: exposing your agent as an MCP server |
9. Streaming
Real-time bidirectional communication with agents.
| Reference | When to Consult |
|---|---|
reference/streaming/streaming-overview.md |
Streaming overview: Gemini Live API integration, use cases, architecture |
reference/streaming/part1-intro-to-streaming.md |
Streaming setup concepts: LiveRequestQueue, run_live() — load Python sidecar for full FastAPI WebSocket example |
reference/streaming/part2-sending-messages.md |
Sending text and audio messages to a streaming agent |
reference/streaming/part3-event-handling.md |
Handling streaming events: text chunks, audio, function calls, turn completion |
reference/streaming/part4-run-configuration.md |
RunConfig for streaming: voice, response modalities, speech config |
reference/streaming/part5-audio-images-video.md |
Multimodal streaming: audio input/output, images, video frames |
reference/streaming/streaming-tools.md |
Using tools in streaming mode: streaming-compatible tool patterns |
reference/streaming/streaming-configuration.md |
Full streaming configuration reference |
10. A2A Protocol (Agent-to-Agent)
Connecting ADK agents across services and organizations.
| Reference | When to Consult |
|---|---|
reference/a2a/a2a-introduction.md |
A2A protocol overview: agent cards, task lifecycle, discovery |
reference/a2a/quickstart-exposing-python.md |
Expose a Python ADK agent as an A2A server |
reference/a2a/quickstart-consuming-python.md |
Consume a remote A2A agent from a Python ADK agent |
reference/a2a/quickstart-exposing-go.md |
Expose a Go ADK agent as an A2A server |
reference/a2a/quickstart-consuming-go.md |
Consume a remote A2A agent from a Go ADK agent |
11. Grounding
Connecting agents to real-world data sources.
| Reference | When to Consult |
|---|---|
reference/grounding/google-search-grounding.md |
Google Search grounding: enable web search in responses, citation handling |
reference/grounding/vertex-ai-search-grounding.md |
Vertex AI Search grounding: enterprise search over your own documents |
12. Runtime & CLI
Running, serving, and inspecting agents.
| Reference | When to Consult |
|---|---|
reference/runtime/runtime-overview.md |
Runner, InvocationContext, execution lifecycle overview |
reference/runtime/web-interface.md |
adk web dev UI: testing agents interactively |
reference/runtime/command-line.md |
adk run and other CLI commands for running agents |
reference/runtime/api-server.md |
adk api_server: serving agents as a REST/WebSocket API |
reference/runtime/resume-agents.md |
Resuming long-running or paused agents |
reference/runtime/runtime-config.md |
RunConfig: max turns, streaming mode, response modalities |
reference/runtime/event-loop.md |
Event loop internals: how ADK processes events between runner and agent |
reference/reference/cli-reference.md |
Full CLI reference: all commands and flags |
13. Deployment
Taking agents to production.
| Reference | When to Consult |
|---|---|
reference/deployment/deployment-overview.md |
Deployment options comparison: Agent Engine vs Cloud Run vs GKE |
reference/deployment/agent-engine.md |
Vertex AI Agent Engine: managed serverless deployment |
reference/deployment/agent-engine-standard-deployment.md |
Standard Agent Engine deployment: packaging, deploying, versioning |
reference/deployment/cloud-run.md |
Deploying ADK agents to Cloud Run: Dockerfile, service config, scaling |
reference/deployment/gke.md |
Deploying ADK agents to GKE: Helm charts, autoscaling, GPU support |
14. Observability & Evaluation
Understanding and measuring agent behavior.
| Reference | When to Consult |
|---|---|
reference/observability/observability-overview.md |
Tracing with OpenTelemetry/Cloud Trace, metrics, Vertex AI evaluation integration |
reference/observability/logging.md |
Logging configuration: log levels, structured logging, Cloud Logging |
reference/evaluation/evaluation-overview.md |
Agent evaluation framework: test cases, criteria, running evals |
reference/evaluation/evaluation-criteria.md |
Evaluation criteria types: trajectory match, response match, custom metrics |
reference/evaluation/user-simulation.md |
Simulating multi-turn user conversations for automated evaluation |
15. Safety
| Reference | When to Consult |
|---|---|
reference/safety.md |
Safety guidelines: input/output filtering, responsible AI practices |
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