The Google Agent Development Kit (ADK) for Python is an open-source, code-first framework designed for building, evaluating, and deploying sophisticated AI agents with emphasis on flexibility and control by applying software development principles. While optimized for Gemini, ADK is model-agnostic, deployment-agnostic, and compatible with other frameworks.

The core methodology of ADK centers on Code-First Development, allowing definition of agent logic, tools, and orchestration directly in Python for enhanced flexibility, testability, and versioning. Alternatively, the "Agent Config" feature facilitates agent creation without explicit code.

Key technical features and methodologies include:

1. Agent Definition and Composition:
   • Single Agent: Agents are defined as instances of Agent (or LlmAgent/BaseAgent), configured with parameters such as name, model (e.g., "gemini-2.5-flash"), instruction, description, and a list of tools. For instance, $root_agent = Agent(name="search_assistant", model="gemini-2.5-flash", instruction="...", tools=[google_search])$ illustrates a basic agent with web search capabilities.
   • Modular Multi-Agent Systems: ADK supports designing scalable applications by composing multiple specialized agents into flexible hierarchies. A coordinator agent (e.g., $coordinator = LlmAgent(...)$) can manage and orchestrate sub_agents (e.g., greeter, task_executor) to guide their collaborative work towards accomplishing complex tasks. This allows for distributed intelligence and specialized responsibilities within an agent system.
2. Rich Tool Ecosystem: Agents gain diverse capabilities through integration with pre-built tools (like google_search), custom functions, OpenAPI specifications, and MCP tools, ensuring tight integration with the Google ecosystem. A "Tool Confirmation" flow introduces a Human-in-the-Loop (HITL) mechanism, requiring explicit confirmation and custom input before tool execution.
3. Deployment Flexibility: Agents can be easily containerized and deployed on platforms like Google Cloud Run or scaled seamlessly using Vertex AI Agent Engine.
4. Evaluation and Debugging: ADK includes a built-in development UI for interactive testing, evaluation, and debugging of agents. Additionally, a command-line utility (adk eval) supports programmatic evaluation against predefined datasets (e.g., adk eval samples_for_testing/hello_world samples_for_testing/hello_world/hello_world_eval_set_001.evalset.json).
5. Agent2Agent (A2A) Protocol Integration: For remote agent-to-agent communication, ADK integrates with the A2A protocol, enabling distributed agent interactions.
6. Recent Enhancements:
   • Custom Service Registration: A service registry allows generic registration of custom service implementations for use in FastAPI servers.
   • Rewind: Provides the ability to revert a session to a state prior to a specific invocation.
   • New CodeExecutor: Introduces AgentEngineSandboxCodeExecutor for executing agent-generated code using the Vertex AI Code Execution Sandbox API.

Installation is primarily via pip install google-adk for stable releases, with a development version available from the GitHub main branch for early access to features and fixes. Contributions are welcomed following detailed guidelines, and the adk-python-community repository serves as a hub for community-contributed tools and integrations.