adk-core

name	ADK Core
description	This skill should be used when the user asks about "creating a new ADK project", "initializing ADK", "setting up Google ADK", "adk create command", "ADK project structure", "YAML agent configuration", "creating an agent", "LlmAgent", "BaseAgent", "custom agent", "agent with different model", "Claude with ADK", "OpenAI with ADK", "LiteLLM", "multi-model agent", or needs guidance on bootstrapping an ADK development environment, authentication setup, choosing between Python code and YAML-based agent definitions, agent configuration, model selection, system instructions, or extending the base agent class for non-LLM logic.
version	2.0.0

ADK Core

Comprehensive guide for initializing Google Agent Development Kit (ADK) projects and creating agents. Covers project setup, authentication, agent configuration, and model selection.

When to Use

Project Setup:

• Starting a new ADK project from scratch
• Setting up authentication (API key or Vertex AI)
• Understanding ADK project structure
• Choosing between Python and YAML configuration
• Running agents locally for development

Agent Creation:

• Creating a new ADK agent from scratch
• Configuring agent parameters (model, name, instruction, description)
• Using non-Gemini models (Claude, OpenAI via LiteLLM)
• Building custom agents without LLM reasoning

When NOT to Use

• Adding tools to agents → Use @adk-tools instead
• Multi-agent orchestration → Use @adk-multi-agent instead
• Callbacks and state management → Use @adk-behavior instead
• Deploying to production → Use @adk-deployment instead
• Memory and knowledge management → Use @adk-memory instead

Key Concepts

Project Initialization

Installation: pip install google-adk in Python 3.10+ virtual environment. Verify with adk --version.

Project Creation: adk create <name> scaffolds a new agent project with agent.py, __init__.py, and .env.

Authentication Options: Google AI Studio (GOOGLE_API_KEY) for prototyping, Vertex AI (GOOGLE_CLOUD_PROJECT) for production.

Running Agents: adk run for CLI, adk web for development UI, adk api_server for HTTP API.

YAML Configuration: Declarative agent definition without Python code. Quick prototyping for simple agents.

Project Structure: agent.py exports root_agent. ADK CLI discovers and runs the exported agent.

Agent Types

LlmAgent is the standard agent type for AI reasoning, conversation, and tool use. Requires model and name parameters. Use instruction for system prompts and description for routing in multi-agent systems.

BaseAgent is for custom non-LLM logic. Extend it and implement run_async() to yield responses. Use when you need deterministic behavior or external API orchestration.

Model Selection: Default to gemini-3-flash-preview. Use LiteLLM prefix for other providers (e.g., anthropic/claude-sonnet-4).

References

Detailed guides with code examples:

Project Setup:

• references/init.md - Complete initialization workflow
• references/create-project.md - Python project scaffolding
• references/yaml-config.md - YAML-based configuration

Agent Creation:

• references/llm-agent.md - Complete LlmAgent configuration and parameters
• references/custom-agent.md - BaseAgent extension patterns
• references/multi-model.md - LiteLLM setup and model switching