What Is Google ADK and How Does It Work?

Written by Coursera Staff • Updated on

Learn about Google’s Agent Development Kit, or ADK, and discover what it can do and how it works, including its key features, benefits, and limitations.

[Featured image] Two smiling developers look at a computer monitor in an office setting, working with Google ADK.

Key takeaways

  • Google Agent Development Kit (ADK) is an open-source agent development framework that helps you build, debug, and deploy AI agents.

  • Regarding Google ADK vs LangGraph, both frameworks build AI agents, yet Google ADK uses hierarchical agent structures, whereas LangGraph uses graph-based formations.

  • As an open-source platform, Google ADK is free for you to use and integrate with other systems, both within Google Cloud and with other providers.

Learning more about Google ADK can help you understand the difference between ADK and SDK: an ADK is a specialized toolkit that helps you build an autonomous AI agent, while a software development kit (SDK) is better suited for building general software applications for a specific platform.

If you’re ready to learn more about Google ADK, consider enrolling in Google’s AI Essentials Specialization. In addition to learning how to apply generative AI tools in your industry, you’ll have the opportunity to develop strategies to stay up-to-date on the changing landscape of artificial intelligence. 

What is Google ADK? 

Google Agent Development Kit (ADK) is an open-source agent development framework meant for building, debugging, and deploying AI agents at an enterprise scale. Available in Python, TypeScript, Go, and Java, Google ADK provides you with pre-built and customizable tools within a modular framework. This means that you can develop these agents yourself, whether you need to create a personal assistant, a business workflow, or a more complex, multi-agent system [1]. 

AI agents use large language models (LLMs) like Gemini to understand, process, and formulate information. Acting as the “brain” of the system, the LLM enables the agent to plan and carry out multi-step tasks based on the information it receives, demonstrating the multimodal capacity of generative AI. As AI agents become increasingly capable of performing tasks on behalf of their users, Google ADK streamlines the development process, making it easier for you to create your own AI agent, regardless of your level of expertise [2]. 

Google ADK features 

Google ADK lets you build both single-agent and multi-agent systems, enabling your AI agents to collaborate, solve complex problems, and achieve individual or collective goals more efficiently. Google ADK also lets you extend your agents’ capabilities using built-in or custom tools that connect to APIs, databases, or external services. 

The modular architecture of Google ADK enables you to create reusable agents and workflows that are easy to maintain, improve, and adapt as needed. In addition to its customizable architecture, Google ADK offers built-in evaluation and testing criteria to ensure your AI agent is properly debugged before deployment.

Key features of ADK AI agents

AI agents built with Google ADK have distinct features and capabilities made possible by the multimodal capabilities of foundational generative AI models. The key distinctive features of this framework are reasoning and acting, which, when combined with LLMs, allow AI agents to perform more dynamic tasks than their predecessors. 

AI agents with reasoning capabilities can identify patterns, analyze data, and plan actions based on the context you provide them. Acting capabilities allow the agent to perform tasks based on external information. This decision-making ability enables the AI to interact within its environment and achieve goals through digital or physical actions. When paired with reasoning capabilities, AI agents can make informed decisions and adapt their actions based on context and logic [3].

Learn more: Generative AI vs. Agentic AI: What Is the Difference?

Google ADK tools and integrations

ADK provides you with a comprehensive set of tools and integration possibilities to build and run AI architectures. Integrating tools such as Python functions also lets you build an agent capable of performing tasks beyond text generation. You can integrate built-in tools like Google Search or BigQuery, leverage third-party orchestration frameworks such as LangChain, or design a custom tool for your specific needs.

Is Google ADK free or paid?

Google ADK is an open-source platform, meaning it is free to use, publicly available, and does not require a licensing fee to build AI agents. It offers flexibility in its application and deployment, allowing you to connect with third-party applications or work locally within the Google Cloud.

How does Google ADK work?

Google ADK uses a modular, layered architecture to coordinate AI agents, LLMs, persistent state, and a variety of internal and external tools. 

The runner, the primary entry point for all user activities, is at the center of the Google ADK framework and orchestrates interactions among agents, sessions, and plugins. With its event processor, the runner takes raw model outputs and yields them into a stream of structured events, enabling real-time feedback. Supporting this workflow is a session services layer that handles the context of conversation sessions, as well as the agent’s history and memory across multiple turns.

The execution logic layer handles the running of the AI agents, which are the core building blocks and primary decision-making components of Google ADK. LLM agents act as the “brain” to understand natural language input and decide on a course of action, while workflow agents direct the execution of tasks. Depending on their hierarchical role, agents either act as parents or sub-agents to ensure tasks are appropriately delegated and carried out. Designing an agent to accomplish a specific task requires tools, which give the agent capabilities beyond conversational output and enable it to execute external actions.

What are common use cases for Google ADK?

You can use Google ADK to build a wide range of AI-powered applications, either for business or individual use. While generative AI chatbots are useful for answering questions and assisting with individual tasks, AI agents can help you complete multi-step processes and continue working autonomously on your behalf. 

For personal use, you can use Google ADK to build AI agents that simplify decision-making. For example, you could create a museum or concert recommendation agent that suggests activities based on your interests, or a shopping assistant that compares prices and features on items and helps you make informed purchase decisions.

Businesses can also use Google ADK to develop AI agents tailored to their industry and customer needs. For example, hotels might create virtual concierge agents that assist guests with bookings, travel recommendations, or local attractions. In other industries, AI agents can detect fraud, deliver personalized advice, enhance customer service, and automate workflows, improving operational efficiency and the customer’s experience. 

What are the benefits and limitations of Google ADK?

When deciding to create AI agents with Google ADK, it is important to be aware of both the benefits and limitations of this modular framework. Understanding both can help you anticipate issues and use Google ADK more effectively.

Benefits

Google ADK offers several key benefits to optimize your creation of agentic applications, such as:

  • Multi-agent collaboration: Compose specialized agents into hierarchical structures that complete tasks more efficiently.

  • Flexible tool orchestration: Built-in and custom tools provide adaptive routing capabilities

  • Local debugging: Unlike most SDKs, ADK lets you coordinate models and debug locally before deployment.

Limitations

Typically, you can equip your AI agent with multiple tools to extend its capabilities. However, some tools are mutually exclusive and cannot work together within the same agent. For example, Code Execution and Google Search with the Gemini API must operate independently, except in specific use cases.

It is also important to understand that AI agents cannot fully understand cause-and-effect relationships or hypothetical scenarios. Rather than relying on them to explain why something is happening or predict outcomes based on causal reasoning, use them to detect statistical correlations and complete defined tasks [4].

What is the difference between ADK and SDK?

ADK is an agent or accessory development kit, while SDK is a software development kit. An ADK is a specialized toolkit you use to build intelligent AI agents, while an SDK is more practical for building software applications for a specific platform. SDKs provide a collection of tools like APIs and sample code to build applications, while ADKs provide frameworks and interactive tools to help you create multi-agent systems [5].

Google ADK and LangGraph are popular frameworks for developing AI agents, but they are designed with different priorities. Google ADK emphasizes enterprise-ready development with built-in support for multi-agent systems, tool integration, and scalable deployment, particularly within the Google Cloud. LangGraph, on the other hand, focuses more on creating graph-based workflows that give you control over your agent’s behavior and orchestration. 

Google ADK is a great choice if you are already accustomed to Google Cloud, prefer a simplified agent creation process, and need an AI agent with built-in debugging UIs. If you desire more control and need to design complex AI agents and workflows with precision, LangGraph may be the optimal choice. LangGraph requires more human interaction, whereas Google ADK lets you design an AI agent that can run autonomously [6].

Getting started with Google ADK

To get started with Google ADK, first determine what kind of AI agent you want to build and the tasks you need it to perform. Next, install the ADK framework, choose a language model such as Gemini, and configure the tools your agent needs to complete its goals. As you develop your agent, you can test and debug its behavior locally before deploying it.

Google ADK is just one area of AI-powered software development. If you want to continue learning about this industry, you may consider earning a degree in software engineering, computer science, or artificial intelligence. With continued dedication, you can pursue careers in software engineering, application development, and systems administration.

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Article sources

1. 

Google Cloud. “Agent Development Kit, https://docs.cloud.google.com/gemini-enterprise-agent-platform/build/adk/.” Accessed July 16, 2026. 

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