Google Unveils Agent Development Kit (ADK) to Simplify Multi-Agent AI Development

One of the highlights revealed during the Google Cloud NEXT 2025 was the Agent Development Kit (ADK), a new open-source framework designed to streamline the creation and deployment of intelligent, autonomous AI agents and advanced multi-agent systems easier. As the AI landscape evolves beyond single-purpose models, building coordinated teams of agents presents new challenges, which ADK aims to solve by providing a full-stack, end-to-end development solution.

The framework is the same one that powers agents within Google's own products, including Agentspace and the Google Customer Engagement Suite (CES). By open-sourcing ADK, Google intends to empower developers with powerful and flexible tools for building in the rapidly changing agentic AI space. ADK is built with flexibility in mind, supporting different models and deployment environments, and designed to make agent development feel similar to traditional software development.

Core Pillars Guiding Development

ADK provides capabilities across the entire agent development lifecycle. Its core principles emphasize flexibility, modularity, and precise control over agent behavior and orchestration. Key pillars include:

• Multi-Agent by Design: Facilitates building modular and scalable applications by composing specialized agents hierarchically, enabling complex coordination and delegation.
• Rich Model Ecosystem: Allows developers to choose the best model for their needs, integrating with Google's Gemini models, Vertex AI Model Garden, and through LiteLLM, a wide selection of models from providers like Anthropic, Meta, and Mistral AI.
• Rich Tool Ecosystem: Agents can be equipped with diverse capabilities using pre-built tools (like Search or Code Execution), custom Function Tools, integrating 3rd-party libraries (like LangChain or LlamaIndex), or even using other agents as tools via AgentTool. ADK also supports Google Cloud tools, MCP tools, and those defined by OpenAPI specifications.
• Built-in Streaming: Enables human-like conversations with agents through bidirectional audio and video streaming capabilities, moving beyond text-only interactions into rich, multimodal dialogue with just a few lines of code.
• Flexible Orchestration: Workflows can be defined using Workflow Agents such as Sequential, Parallel, and Loop agents for predictable pipelines, or by leveraging LLM-driven dynamic routing via LlmAgent transfer for more adaptive behavior.
• Integrated Developer Experience: Offers tools for local development, testing, and debugging, including a powerful CLI, a visual Web UI to inspect execution step-by-step, and an API Server.
• Built-in Evaluation: Provides systematic assessment of agent performance by evaluating both final response quality and the step-by-step execution against predefined test cases.
• Easy Deployment: Agents can be containerized and deployed anywhere, with seamless integration options for Vertex AI Agent Engine, a fully managed, scalable, and enterprise-grade runtime on Google Cloud.

Additional core concepts include Sessions & Memory for managing conversational context and agent state, Artifacts for handling files and binary data tied to a session, and Callbacks for customizing behavior at specific execution points. The Runtime manages execution, tracked via Events, with Context providing relevant information.

A notable architectural component is the Agent2Agent (A2A) Protocol, an open protocol designed to facilitate communication and collaboration between agents across different platforms and frameworks, promoting a more interconnected ecosystem.

Getting Started and Building with ADK

Building a basic agent with ADK is designed for Pythonic simplicity, potentially requiring fewer than 100 lines of code. Developers define an agent's logic, tools, and information processing. ADK provides the structure for managing state, orchestrating tool calls, and interacting with underlying LLMs.

The framework primarily supports the Python programming language, with future plans for additional language support. Installation is straightforward using pip. The documentation includes quickstart guides and examples, such as a simple "question_answer_agent" using a Google Search tool.

For a more integrated experience, ADK includes a developer Web UI that can be launched locally via a CLI command (adk web), providing a user-friendly interface for running, testing, and debugging agents. Agents can also be run and tested via the CLI (adk run) or accessed through an api_server.

ADK truly excels in building multi-agent applications. This involves creating teams of specialized agents that can delegate tasks based on conversation context, utilizing hierarchical structures and intelligent routing. An illustrative example shows a WeatherAgent delegating simple greetings and farewells to specialized GreetingAgent and FarewellAgent instances, while handling weather queries itself using a get_weather tool. Delegation relies heavily on clear, distinct descriptions of each agent's capability, which the LLM uses to route tasks effectively.

Evaluation and Deployment Path

Ensuring agents behave reliably is crucial before deployment. ADK offers built-in evaluation tools that allow developers to systematically assess performance by testing execution paths and response quality against predefined datasets. These checks can be run programmatically or via the ADK eval command-line tool or Web UI.

Once satisfied with performance, ADK provides a clear path to production. Agents can be containerized and deployed on various platforms, including Cloud Run and Google Kubernetes Engine (GKE), or using Docker. For a fully managed, scalable, and enterprise-grade runtime, ADK offers seamless integration with Vertex AI Agent Engine on Google Cloud.

Optimized for Google Cloud, but Versatile

While offering flexibility across various tools and models, ADK is optimized for seamless integration within the Google Cloud ecosystem, particularly with Gemini models and Vertex AI. This integration allows developers to leverage advanced Gemini capabilities and provides a native pathway to deploy on Vertex AI.

Beyond model integration, ADK enables agents to connect directly to enterprise systems and data. This includes over 100 pre-built connectors, utilization of workflows built with Application Integration, and access to data in systems like AlloyDB, BigQuery, and NetApp, without requiring data duplication. Agents can also securely tap into existing API investments managed through Apigee. This comprehensive connectivity enhances ADK's power within the Google Cloud environment.

Despite this optimization, ADK is designed to be model and deployment agnostic. Through integration with Vertex AI Model Garden and LiteLLM, it supports models from providers like Anthropic, Meta, Mistral AI, and AI21 Labs. It also allows integration with third-party libraries such as LangChain and CrewAI.

Open Source and Community Driven

ADK is released as an open-source framework, with its primary codebase hosted on GitHub. The repository includes essential files like CHANGELOG.md, CONTRIBUTING.md, LICENSE (Apache-2.0), and README.md, providing information and guidelines for the project and community engagement.

Google actively encourages contributions from the community, welcoming issue reports, feature suggestions, documentation improvements, and code contributions. There is already an active community, with a number of contributors submitting changes. Stable releases are available weekly via pip, while a development version can be installed directly from GitHub for access to the latest changes (though this version may contain experimental features or bugs).

Community Perspectives and Comparisons

Initial community reception of ADK includes significant praise for its excellent CLI and developer-first tools (adk web, adk run, api_server), smooth building and debugging process, and robust support for multiple model providers beyond Google's, thanks to LiteLLM integration. Features like artifact management for stateful agents and the AgentTool concept have also been highlighted as useful additions. Deployment options, including the fully managed Agent Engine and Cloud Run, are seen as providing appropriate levels of control.

However, community feedback also points out areas for potential improvement. Some developers found initial setup steps cumbersome and perceived the different agent types (LlmAgent, Sequential, Parallel, Loop) as potentially over-engineered. The developer experience for evaluation and guardrails is not consistently perceived as intuitive or smooth. Session state management has been identified as a notable weak point, making it challenging to work with effectively. A recurring concern is that the overall developer experience seems optimized for advanced users, potentially posing a higher barrier for beginners.

Compared to other open-source frameworks:

• LangChain/LangGraph: ADK is presented as a higher-level framework potentially easier for simpler workflows, whereas LangGraph offers lower-level, granular control, especially for persistence. ADK has a built-in chat UI and monitoring, while LangChain is a widely adopted standard. ADK integrates with LangChain tools, leveraging its extensive library.
• CrewAI: Both facilitate complex agents. ADK offers smoother integration with Google products, stronger enterprise features like built-in RAG and connectors, and better deployment options like Vertex AI Agent Engine. CrewAI focuses specifically on role-based multi-agent collaboration.
• AutoGen: Microsoft's AutoGen focuses on swarms of specialized agents and multi-agent conversation frameworks. ADK is a more general toolkit and uniquely offers built-in bidirectional audio/video streaming. Some users prefer ADK.
• OpenAI Agents SDK: Both are for multi-agent workflows. ADK supports multiple providers via LiteLLM (including OpenAI models), while OpenAI Agents SDK focuses on native integration with OpenAI endpoints. ADK includes artifact management.
• Semantic Kernel: A model-agnostic SDK for building/orchestrating agents, with a focus on enterprise integration. ADK integrates with Semantic Kernel via MCP and A2A for cross-cloud collaboration. Semantic Kernel supports more languages and has a plugin ecosystem.
• LlamaIndex: Primarily focuses on connecting LLMs to external data for Retrieval-Augmented Generation (RAG). ADK integrates with LlamaIndex as a library to leverage its data indexing/retrieval capabilities.

This comparison highlights that ADK brings unique strengths, particularly its deep integration with the Google Cloud ecosystem and the A2A protocol, while fitting into a broader landscape of tools, each with its own focus and advantages.

Intended Use Cases and Future Potential

Google ADK is designed for a wide range of AI agent applications, from simple automated tasks to complex, collaborative multi-agent systems. Its flexible orchestration makes it suitable for applications requiring both deterministic task execution and intelligent, context-aware routing. The ability to compose specialized agents hierarchically is useful for breaking down intricate tasks. The rich tool ecosystem and deployment flexibility broaden its applicability across diverse scenarios. Built-in evaluation and emphasis on safety and security make it suitable for applications where reliability and trustworthiness are key.

Specific potential applications mentioned include travel planning, retail automation, and internal automation.

The Agent Development Kit provides a robust, flexible, and open-source foundation for building the next generation of AI applications. While community feedback indicates areas for refinement, particularly in developer experience for beginners and session state management, its strengths in modularity, Google Cloud integration, and the innovative A2A protocol position it as a significant tool in the evolving field of agentic AI. Its open-source nature and the encouragement of community contributions suggest a promising path for future development.

For developers interested in exploring ADK, the official documentation and quickstart guide are recommended starting points. Utilizing the developer Web UI for local testing provides valuable hands-on experience. Considering contributions to the GitHub project is also encouraged to engage with the community and shape the framework's evolution. Developers with projects requiring strong Google Cloud integration or leveraging Gemini models should seriously evaluate ADK, while also considering other frameworks based on specific project needs and infrastructure.