136 lines
4.8 KiB
Markdown
136 lines
4.8 KiB
Markdown
---
|
|
name: ck:google-adk-python
|
|
description: "Build AI agents with Google ADK Python. Multi-agent systems, A2A protocol, MCP tools, workflow agents, state/memory, callbacks/plugins, Vertex AI deployment, evaluation."
|
|
license: Apache-2.0
|
|
argument-hint: "[agent or feature]"
|
|
metadata:
|
|
author: claudekit
|
|
version: "2.0.0"
|
|
---
|
|
|
|
# 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
|