ck-google-adk-python

# Google ADK Python Skill Expert guide for Google's Agent Development Kit (ADK) Python — open-source, code-first toolkit for building, evaluating, and deploying AI agents. Optimized for Gemini, model-agnostic by design. ## When to Activate - Build single or multi-agent systems with tool integration - Implement A2A protocol for remote agent communication - Integrate MCP servers as agent tools - Use workflow agents (sequential, parallel, loop) for pipelines - Manage sessions, state, memory, and artifacts - Add callbacks, plugins, or observability hooks - Deploy to Cloud Run, Vertex AI Agent Engine, or GKE - Evaluate agents with `adk eval` framework ## Agent Structure Convention (Required) ``` my_agent/ ├── __init__.py # MUST: from . import agent └── agent.py # MUST: root_agent = Agent(...) OR app = App(...) ``` ## Quick Start ```bash pip install google-adk # stable (weekly releases) uv sync --all-extras # dev setup (uv required, Python 3.10+, 3.11+ recommended) ``` ```python from google.adk import Agent root_agent = Agent( name="assistant", model="gemini-2.5-flash", instruction="You are a helpful assistant.", description="General assistant agent.", tools=[get_weather], ) ``` ## App Pattern (Production) ```python from google.adk import Agent from google.adk.apps import App from google.adk.apps.app import EventsCompactionConfig from google.adk.plugins.save_files_as_artifacts_plugin import SaveFilesAsArtifactsPlugin app = App( name="my_app", root_agent=Agent(name="my_agent", model="gemini-2.5-flash", ...), plugins=[SaveFilesAsArtifactsPlugin()], events_compaction_config=EventsCompactionConfig(compaction_interval=2), ) ``` Use `App` when needing plugins, event compaction, or custom lifecycle management. ## CLI Tools | Command | Purpose | |---------|---------| | `adk web <agents_dir>` | Dev UI (recommended for development) | | `adk run <agent_dir>` | Interactive CLI testing | | `adk api_server <agents_dir>` | FastAPI production server | | `adk eval <agent> <evalset.json>` | Run evaluation suite | ## Agent Types | Type | Use Case | |------|----------| | `Agent` / `LlmAgent` | Dynamic routing, tool use, reasoning | | `SequentialAgent` | Fixed-order pipeline | | `ParallelAgent` | Concurrent execution | | `LoopAgent` | Iterative processing | | `RemoteA2aAgent` | Remote agent via A2A protocol | ## Key APIs | Feature | API | |---------|-----| | State | `tool_context.state[key] = value` | | Artifacts | `tool_context.save_artifact(name, part)` | | Callbacks | `before_agent_callback`, `after_model_callback`, etc. | | MCP Tools | `MCPToolset(connection_params=StdioConnectionParams(...))` | | Sub-agents | `Agent(..., sub_agents=[agent1, agent2])` | | Human-in-loop | `LongRunningFunctionTool(func=my_func)` | | Plugins | `App(..., plugins=[MyPlugin()])` | ## Model Support Latest: `gemini-2.5-flash` (default), `gemini-2.5-pro`, `gemini-2.0-flash` (sunsets Mar 2026) Preview: `gemini-3-flash-preview`, `gemini-3-pro-preview` Also: Anthropic Claude, Ollama, LiteLLM, vLLM, Model Garden ## Best Practices 1. **Code-first** — define agents in Python for version control and testing 2. **Agent convention** — always use `root_agent` or `app` variable in `agent.py` 3. **Modular agents** — specialize per domain, compose via `sub_agents` 4. **Workflow selection** — workflow agents for predictable, LlmAgent for dynamic 5. **State** — `ToolContext.state` for ephemeral, `MemoryService` for long-term 6. **Safety** — callbacks for guardrails, tool confirmation for sensitive ops 7. **Evaluate** — test with `adk eval` + evalset JSON before deployment ## References Detailed guides (load as needed): - `references/agent-types-and-architecture.md` — Agent types, workflows, custom agents - `references/tools-and-mcp-integration.md` — Custom tools, MCP, tool filtering - `references/multi-agent-and-a2a-protocol.md` — Sub-agents, A2A, coordinator patterns - `references/sessions-state-memory-artifacts.md` — State, artifacts, sessions, memory - `references/callbacks-plugins-observability.md` — Lifecycle hooks, plugins, tracing - `references/evaluation-testing-cli.md` — adk eval, CLI, evalset format - `references/deployment-cloud-run-vertex-gke.md` — Cloud Run, Vertex AI, GKE ## External Resources - GitHub: https://github.com/google/adk-python - Docs: https://google.github.io/adk-docs/ - Samples: https://github.com/google/adk-python/tree/main/contributing/samples - llms.txt: https://raw.githubusercontent.com/google/adk-python/refs/heads/main/llms.txt