Google Rolls Out Agent Development Kits for Python and Java.pdf

Google Rolls Out Agent Development Kits for Python
and Java
The development of sophisticated AI agents represents a significant step in the evolution of
artificial intelligence. These agents are designed to perform tasks, automate processes, and
interact with digital environments. Google has introduced Agent Development Kits (ADKs)
for Python and Java, aiming to provide developers with comprehensive tools for building
these AI agents. The release of these ADKs is part of Google's broader strategy to facilitate
the creation of intelligent agents that can act as collaborative partners.
The Python ADK has reached version 1.0.0, indicating its stability and readiness for
production environments. Concurrently, the Java ADK has been introduced with version
0.1.0, extending the agent development capabilities to the Java ecosystem. These kits are
designed to simplify the creation, deployment, and orchestration of AI agents.
Context of Release: Milestones in AI Agent Development
The release of Google's ADKs occurs at a time when the development of AI agents is
accelerating. Empowering developers with robust platforms that offer flexibility and
comprehensive capabilities is a key goal. The Python ADK v1.0.0 signifies a
production-ready toolkit. The introduction of the Java ADK v0.1.0 opens agent development
to the large community of Java developers. These releases represent a concerted effort to
provide a stronger foundation for the next generation of AI-powered solutions.
Core Features of the Agent Development Kit
The Agent Development Kit (ADK) from Google is presented as a flexible and modular
framework. Its primary purpose is to aid developers in the creation and deployment of AI
agents. While optimized for use with Google's Gemini models and the broader Google
ecosystem, the ADK is designed to be model-agnostic and deployment-agnostic. This

design promotes compatibility with other frameworks, making it a versatile tool for various
agent development scenarios.
Python ADK v1.0.0: Stability and Readiness for Production
Environments
Google has announced the v1.0.0 stable release of the Python Agent Development Kit. This
version is deemed production-ready, offering a reliable platform for building and deploying
agents in live settings. Developers can utilize this version with confidence for their
operational agent needs. For access to the toolkit, refer to the Python ADK v1.0.0.
Java ADK v0.1.0: Initial Release and Expansion into the Java Ecosystem
Expanding its support, Google has launched the initial release of the Java ADK, version
0.1.0. This initiative brings the ADK's capabilities to Java developers. It allows them to
leverage the framework for their specific agent development projects. To begin using this
version, explore the Java ADK v0.1.0.
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Model-Agnostic and Deployment-Agnostic Design

A core principle of the ADK is its model-agnostic nature. This means developers are not
restricted to a specific AI model and can integrate models that best suit their project
requirements. Similarly, the deployment-agnostic aspect allows agents built with ADK to be
deployed across various environments. This includes local machines, cloud platforms like
Vertex AI Agent Engine, or custom infrastructures using tools like Cloud Run or Docker.
Integration Capabilities with Gemini and Other Frameworks
The ADK is optimized for Google's Gemini models, providing streamlined integration for
developers working within this ecosystem. Beyond Gemini, its design allows for compatibility
with other frameworks. This ensures that developers can incorporate ADK into their existing
workflows and toolchains without being locked into a single technology stack. The ADK
supports integration of third-party libraries such as LangChain and CrewAI.
Modular Framework for Building, Orchestrating, and Deploying Agents
ADK is structured as a modular framework. This modularity facilitates a structured approach
to building agents, where different components can be developed and managed
independently. It supports the orchestration of these components, allowing developers to
define how agents interact and collaborate to perform complex tasks. The framework also
provides tools and methodologies for deploying these agents effectively.
Development Approach
The Agent Development Kit emphasizes a code-first methodology. This approach provides
developers with fine-grained control over agent behavior and logic. It treats agent
development similar to traditional software development, promoting practices like versioning
and testability.
Code-First Methodology for Defining Agent Behavior and Logic
ADK allows developers to define agent logic, tools, and orchestration directly in Python or
Java code. This offers maximum flexibility in customizing agent functionalities. By writing
explicit code, developers can precisely dictate how an agent processes inputs, makes
decisions, and executes actions. This approach makes it easier to debug and maintain agent
systems as complexity grows.
Flexibility in Tool Integration Using Pre-built Tools, Custom Functions,
and OpenAPI Specifications
The ADK provides a rich tool ecosystem. Developers can equip agents with a wide range of
capabilities by integrating various types of tools.
● Pre-built tools: These are readily available tools, such as Google Search or Code
Execution, that can be quickly incorporated into agents.
● Custom functions: Developers can create their own functions in Python or Java to
extend agent capabilities with bespoke logic.

● OpenAPI specifications: ADK supports the use of OpenAPI specifications, enabling
agents to interact with external APIs and services in a standardized way.
This flexibility allows agents to interact with diverse systems and data sources.
Hierarchical Design for Multi-Agent Systems Enabling Scalability and
Specialization
The ADK supports a hierarchical design for building multi-agent systems. This architecture
allows for the creation of modular and scalable applications where multiple specialized
agents can be composed. Parent agents can coordinate and delegate tasks to child agents,
each designed for specific functions. This hierarchical structure enables complex
coordination and facilitates the development of sophisticated applications where different
agents collaborate to achieve overarching goals.
Tool Ecosystem
The Agent Development Kit offers a comprehensive tool ecosystem designed to augment
agent capabilities. This ecosystem allows developers to integrate a variety of functionalities,
enhancing the tasks agents can perform. The flexibility of tool integration is a central aspect
of the ADK's design.
Overview of Pre-built Tools and Third-Party Integrations
ADK provides several pre-built tools that developers can immediately use. Examples include
tools for web searching (like Google Search) and code execution capabilities. Beyond these,
the ADK architecture is open to third-party integrations. Developers can incorporate external
libraries and tools, such as LangChain or CrewAI, into their ADK agents. This extensibility
allows for leveraging a wider range of existing functionalities within the agent framework.
Customization of Agent Capabilities Using External Libraries and APIs
Developers have the freedom to customize agent capabilities extensively. This is achieved
by creating custom functions tailored to specific needs. Agents can also be equipped to
interact with external APIs using standards like OpenAPI specifications. This means an
agent can fetch data from, or trigger actions in, virtually any external service that exposes an
API. The ability to integrate diverse external libraries further expands the scope of what ADK
agents can achieve.
Examples of Tools Available for Both Python and Java Implementations
For both Python and Java versions of ADK, the core concept of tool integration remains
consistent.
● Google Search Tool: Available for direct integration, enabling agents to perform web
searches and utilize the retrieved data in their reasoning process.

● Custom Function Tools: Developers can write bespoke functions in either Python or
Java and register them as tools for their agents. This allows for highly specialized
agent behaviors.
● OpenAPI Tools: Agents in both languages can be configured to use tools based on
OpenAPI specifications, allowing interaction with a multitude of web services.
The specific pre-built tool availability and third-party library compatibility may evolve,
with documentation providing the most current list.
Agent Orchestration and Workflow Design
A key strength of the Agent Development Kit lies in its capabilities for agent orchestration
and workflow design. Developers can define how agents, or sequences of operations within
an agent, are structured to accomplish tasks. This can range from simple linear processes to
complex, adaptive behaviors.
Workflow Agents: Sequential, Parallel, and Loop Configurations
ADK allows the definition of workflows using specific types of "workflow agents". These
agents are designed to manage and execute a series of steps in a controlled manner.
● Sequential Agents: Execute tasks or other agents in a predefined linear order. One
step completes before the next one begins.
● Parallel Agents: Allow multiple tasks or agents to execute concurrently. This can
speed up processes where operations are independent.
● Loop Agents: Enable the repetition of certain tasks or agent invocations until a
specific condition is met or for a set number of iterations.
These configurations provide predictable pipelines for agent operations.
Use of LLM-Driven Dynamic Routing for Adaptive Behavior
For more adaptive and flexible behavior, ADK supports LLM-driven dynamic routing. This is
often achieved through mechanisms like LlmAgent transfer. In this approach, a Large
Language Model (LLM) determines the next step or the appropriate sub-agent to handle a
part of the task based on the current context and input. This allows agents to react to
unforeseen situations or choose the best path forward in a complex decision tree, rather
than following a rigid, predefined workflow.
Modular Composition of Agents for Complex Task Execution
ADK promotes a modular approach where complex tasks can be broken down and handled
by composing multiple specialized agents. This is central to its multi-agent architecture
philosophy. Smaller, focused agents, each proficient in a particular domain or function, can
be combined into a larger system. This modular composition allows for easier development,
testing, and maintenance of individual components, while enabling the overall system to
tackle sophisticated and multi-faceted problems through coordinated effort.
Deployment Capabilities

The Agent Development Kit is engineered with flexible deployment options, allowing
developers to run their agents in diverse environments. This ranges from local development
setups to scalable cloud infrastructures. The goal is to enable easy containerization and
deployment tailored to specific operational needs.
Containerization Support and Deployment Flexibility
ADK supports containerization technologies, most notably Docker. This means that agents
developed with ADK can be packaged into containers, along with all their dependencies.
Containerization ensures consistency across different deployment environments and
simplifies the deployment process. This flexibility allows agents to be deployed on any
platform that supports Docker containers, whether on-premises or in various cloud
environments.
Integration with Google Vertex AI Agent Engine for Scaling
For developers seeking to scale their agents, ADK offers integration with Google Vertex AI
Agent Engine. Vertex AI Agent Engine is designed to help developers deploy, manage, and
scale agents in production environments. This platform provides robust infrastructure and
tools necessary for handling a large number of agent interactions and complex workloads.
By leveraging Agent Engine, developers can move from prototype to production with greater
ease.
Compatibility with Platforms like Cloud Run and Docker
Beyond Vertex AI Agent Engine, agents built with ADK are compatible with other deployment
platforms. Google Cloud Run is one such platform, suitable for deploying containerized
applications, including ADK agents. As ADK supports Docker, any environment that can run
Docker containers can host ADK agents. This broad compatibility ensures developers can
choose the deployment solution that best fits their existing infrastructure and scaling
requirements.
Evaluation and Debugging
The Agent Development Kit includes built-in mechanisms to assist developers in testing,
evaluating, and debugging their agents. These tools are designed to provide insights into
agent performance and behavior, facilitating iterative improvement.
Built-in Mechanisms for Agent Testing and Performance Evaluation
ADK provides facilities for systematically assessing agent performance. This involves
evaluating both the quality of the final response generated by an agent and the step-by-step
execution trajectory it followed. Developers can define test cases against which agent
behavior is measured. The Python ADK, for example, includes command-line tools like adk
eval for running evaluations based on defined evaluation sets. This allows for quantitative
assessment of how well an agent performs on specific tasks.

Debugging Tools for Analyzing Execution Trajectories
Understanding how an agent arrives at a decision or output is crucial for development and
troubleshooting. ADK offers tools for analyzing execution trajectories. When integrated with
platforms like Vertex AI Agent Engine, developers can use features within the Agent Engine
UI to trace and debug actions. This allows for a detailed look into the intermediate steps, tool
invocations, and model interactions that occur during an agent's operation.
Metrics for Assessing Final Outputs and Intermediate Steps
The evaluation process focuses on metrics that cover various aspects of agent performance.
● Final Output Quality: Metrics can be defined to assess the accuracy, relevance, and
completeness of the agent's final response to a query or task.
● Intermediate Step Analysis: Metrics can also apply to the steps taken by the agent
to reach the final output. This could include the choice of tools, the parameters
passed to tools, and the sequence of actions.
● Resource Usage: When deployed, especially via Agent Engine, metrics such as
request counts and CPU usage can be monitored.
These metrics provide developers with a comprehensive view of agent behavior and
efficiency.
Agent-to-Agent Communication
For AI agents to collaborate effectively, particularly in multi-agent systems, they require
standardized and secure methods of communication. Google is advancing the Agent2Agent
(A2A) protocol to facilitate such interactions. The ADK integrates with A2A to enable these
capabilities.
Integration of the A2A Protocol for Secure and Efficient Inter-Agent
Communication
The A2A protocol is designed to enable sophisticated and reliable interactions between
different agents. Version 0.2 of the A2A protocol specification introduces enhancements for
more lightweight and secure communication. ADK leverages this protocol, allowing agents
built with the toolkit to communicate with other A2A-compatible agents, whether they are
also ADK-based or from different platforms supporting A2A. This promotes an ecosystem
where specialized agents can discover and interact with each other to solve complex
problems.
Stateless Interactions and Standardized Authentication Mechanisms
Recent updates to the A2A protocol (version 0.2) include key improvements:
● Support for Stateless Interactions: This simplifies development for scenarios
where persistent session management between communicating agents is not

required. Stateless communication can be more efficient for certain types of
inter-agent tasks.
● Standardized Authentication: The protocol has formalized authentication schemes,
drawing parallels with OpenAPI-like authentication schemas. This ensures that
authentication requirements are clearly communicated and understood between
agents, strengthening security and reliability in agent-to-agent interactions. Auth0, for
instance, is working on open-source agents demonstrating secure multi-agent
communication using A2A and Auth0's capabilities.
Use Cases for Collaborative Multi-Agent Systems
The A2A protocol, in conjunction with ADK, unlocks various use cases for collaborative
multi-agent systems.
● Workflow Orchestration: An agent can invoke other specialized agents to perform
sub-tasks as part of a larger workflow. For example, SAP is adding A2A support to its
AI assistant Joule, enabling it to orchestrate agents within the SAP ecosystem and
invoke external A2A agents.
● Data Augmentation and Processing: Box AI Agents, which process unstructured
content, can use A2A to collaborate with external agents for complex, multi-system
processes, acting directly where content resides.
● Cross-Platform Collaboration: Microsoft has announced support for the A2A
protocol in Azure AI Foundry and the ability to invoke A2A agents in Microsoft Copilot
Studio, showcasing workplace productivity use cases. Zoom also announced A2A
support for multi-agent collaboration on its platform.
These examples illustrate the growing industry adoption and potential of A2A for
building interconnected agent ecosystems. Google provides a Python SDK for A2A to
simplify integration for developers.
Development UI for Agent Management
To complement the code-first approach of the ADK, Google provides user interface tools
aimed at simplifying the agent lifecycle, particularly for testing, debugging, and management.
This is evident in the Agent Engine UI and the built-in development UI available with the
Python ADK.
Features of the UI for Testing, Debugging, and Monitoring Agents
The Agent Engine UI, accessible within the Google Cloud console, offers a comprehensive
dashboard for managing deployed agents. Its features include:
● Viewing and Managing Deployed Agents: Users can list their deployed agents and
view their status.
● Session Listing: The UI allows for inspection of active and past agent sessions.
● Tracing and Debugging Actions: Developers can trace the execution flow of agents
and debug their actions, providing insights into the agent's decision-making process.

● Monitoring Agents: The UI provides tools for monitoring agent performance and
behavior in real-time or through historical data.
The Python ADK also ships with a built-in development UI that serves a similar
purpose during the local development phase, aiding in testing, evaluation,
debugging, and showcasing agents.
Centralized View of Agent Performance and Resource Usage
A key benefit of such UIs is the provision of a centralized view of agent operations. Within
the Agent Engine UI, developers can access metrics related to their agents, such as request
volumes and resource consumption (e.g., CPU usage). This centralized monitoring capability
is helpful for understanding how agents are performing in a deployed environment and for
identifying potential bottlenecks or areas for optimization. This streamlined approach aims to
enhance the overall development and management process.
Workflow Visualization and Interaction Tracing
While the provided texts focus more on trace capabilities for debugging actions, the ability to
visualize workflows and trace interactions is a common and valuable feature in agent
management UIs. For instance, the Agent Engine UI allows for deep dives into traces of
agent activity. This can help developers understand the sequence of calls, tool usage, and
data flow within an agent or between multiple agents. Such visualizations are instrumental in
debugging complex agent behaviors and ensuring that orchestrated workflows are executing
as intended.
Technical Installation and Setup
The Agent Development Kits for Python and Java have distinct installation procedures
tailored to their respective ecosystems. Both are designed to be straightforward for
developers familiar with these languages.
Steps for Installing Python ADK Using Pip and Accessing the Stable and
Development Versions
The Python ADK can be installed using pip, the standard package installer for Python.
● Stable Release (Recommended): For the most recent official and stable version,
developers can use the command:
pip install google-adk
This version is updated weekly and is recommended for most users building
production applications.
● Development Version: For access to the latest features and bug fixes that have not
yet made it into an official PyPI release, the ADK can be installed directly from the
main branch on GitHub:
pip install git+https://github.com/google/adk-python.git@main
This development version contains the newest code commits but may include

experimental changes or bugs not present in the stable release. It is primarily
intended for testing upcoming changes or accessing critical fixes.
Instructions for Java ADK Integration with Maven and Gradle
The Java ADK (version 0.1.0 as of its initial release) can be integrated into Java projects
using common build automation tools like Maven and Gradle.
● Maven Integration: To add the Java ADK to a Maven project, developers need to
include a specific dependency in their pom.xml file.
● Gradle Integration: For Gradle projects, the dependency is added to the build.gradle
file within the dependencies block.
These configurations ensure that the Java ADK libraries are downloaded and
included in the project's classpath.
Repository Details and Open-Source Community Contributions
Both the Python and Java ADKs are open-source projects. Their source code, issue
trackers, and further details can typically be found on platforms like GitHub. These
repositories also usually contain information on contributing to the projects, including general
contribution guidelines and specific code contributing guidelines. They are commonly
licensed under permissive open-source licenses like Apache 2.0.
The open-source nature of these ADKs allows the community to report bugs, request
features, and contribute directly to their development. Documentation, samples, and
community discussion forums often provide additional resources for developers.
Conclusion
Google's release of the Agent Development Kits for Python and Java marks a focused effort
to simplify and enhance the creation of AI agents. These toolkits provide developers with a
structured, code-first approach, combined with flexibility in model choice, tool integration,
and deployment options.
Summary of the ADK's Role in Advancing Agent Development
The ADK aims to transition agent development into a discipline more aligned with traditional
software engineering practices. By offering stable versions like Python ADK v1.0.0 and
expanding into new ecosystems with Java ADK v0.1.0, Google provides robust platforms for
building everything from simple task-specific agents to complex, hierarchical multi-agent
systems. Features like flexible orchestration, a rich tool ecosystem, built-in evaluation, and
support for the A2A protocol collectively contribute to a more powerful and controlled
development experience.
Impact on Developers and the Broader AI Ecosystem
For developers, the ADKs offer greater control and precision in defining agent behavior and
logic through code. The model-agnostic and deployment-agnostic nature of the kits means

developers are not locked into specific AI models or infrastructures, fostering broader
applicability. In the wider AI ecosystem, these ADKs, along with initiatives like the A2A
protocol, encourage interoperability and collaboration between agents developed on different
platforms. This can lead to the emergence of more sophisticated and interconnected AI
solutions capable of addressing complex, multi-faceted challenges. The involvement of
various industry partners in adopting A2A signifies a growing momentum towards such
interconnected agent ecosystems.
Opportunities for Adopting ADK in Diverse Workflows and Applications
The capabilities offered by the ADKs open up numerous opportunities for their adoption
across various workflows and applications. Developers can leverage ADK to build
specialized agents for tasks in customer service, data analysis, process automation, content
generation, and more. The ability to create multi-agent systems allows for the development
of applications where different agents handle distinct parts of a complex problem,
coordinating their efforts to achieve a common goal. As the ADKs mature and the supporting
ecosystem of tools and integrations grows, the range of potential applications will continue to
expand.
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