Google ADK, how much do you know?
Explore how Google ADK revolutionizes AI development and unlocks a new era of multi-agent systems.
Core content:
1. The innovative background and core value of Google ADK
2. The technical features and business applications of the ADK framework
3. The definition, function and collaboration of agents
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What is Google ADK?
So, technically speaking, what exactly are agents in the ADK?
In the ADK framework, an agent is defined as an autonomous and self-contained execution unit that aims to achieve a specific goal. These agents are like intelligent "task executors" with multiple capabilities, including:
1. Execute tasks: Independently complete predefined goals, such as answering questions or generating reports, demonstrating efficient automation capabilities.
2. Interact with users: Communicate seamlessly with human users through natural language dialogue or interface operations, simulating a real conversation experience.
3. Use external tools: call APIs, databases, or third-party services to expand functional boundaries and solve complex problems.
4. Collaborate with other agents: Through multi-agent orchestration, jointly complete complex business processes, such as legal case analysis or multi-language translation tasks.
The flexibility and collaboration of these agents make ADK a core component for building intelligent workflows, especially in scenarios that require high automation and collaboration. Imagine them as an "efficient team", where each member performs their duties and can work closely together to complete "intelligent missions" from simple to complex!
What are the core features of Google ADK?
Agent Development Kit (ADK) has become a highly anticipated highlight of Google Cloud NEXT 2025 with its powerful functions and flexibility, enabling developers to build intelligent AI agent systems. The following are the core features of ADK, combined with its technical advantages and application scenarios, to present a comprehensive and professional overview for beginners, technical developers and enterprise architects to gain in-depth understanding. For details, please refer to:
1. Modular architecture
Generally, ADK adopts a modular design concept, allowing developers to easily build complex multi-agent systems by integrating multiple specialized small components. This architecture not only simplifies the development process, but also provides a high degree of flexibility, allowing the system to dynamically expand according to specific needs. For example, we can start with a single-task agent and gradually expand to a multi-level collaborative network, just like "Lego blocks" to piece together a powerful intelligent ecosystem.
2. Code-first development efficiency
ADK is code-driven, and developers can use Python to precisely define agent behavior, giving them full programmatic control. This approach eliminates the tediousness of traditional configuration, allowing everyone to flexibly adjust logic, optimize performance, and iterate quickly through scripts, which can be called a "programmer's creative canvas."
3. Rich tool ecosystem
ADK has a rich set of pre-built tools, covering data processing, API calls and other functions, while supporting developers to create customized capabilities. Whether it is calling external services or developing exclusive plug-ins, this ecosystem provides solid support for agent function expansion, like a "master key in the toolbox."
4. Flexible deployment options
ADK provides a variety of deployment options. We can choose to develop and debug in the local environment, use personal devices for rapid prototyping, or deploy it to the production environment of Google Cloud to enjoy enterprise-level high availability and scalability. This flexibility allows developers to "try dishes in their own kitchens and serve them in five-star restaurants" as they please.
5. Multi-model support
In addition, ADK also supports the integration of multiple large language models, including Google's Gemini series, OpenAI's GPT series, and other compatible LLMs (such as Anthropic Claude). This feature enables developers to choose the optimal model based on task requirements, just like a "bartender" selecting the "best wine" based on taste.
A brief analysis of Google ADK architecture design
In ADK, the agent is the core entity that processes input and generates output, and can be called the "soul" of the intelligent system. Its basic architecture consists of the following key components, which together weave the life cycle of the agent:
1. Agent Core: As the command center, it is responsible for coordinating the entire life cycle of the agent, including task allocation, state management, and process control, similar to the "conductor of an orchestra."
2. Input/Output Handlers: Manage communication channels with users or other systems to ensure efficient transmission of information, like a "communication bridge".
3. Memory module: stores conversation history and context information, supports the continuity of multiple rounds of interactions, and is like the memory bank of an “intelligent brain”.
4. Tools: Provides functions or integration capabilities, allowing agents to perform specific operations, such as calling external services, similar to a "multi-function toolbox".
5. Models: Driven by large language models (LLMs), they enable the agent's reasoning and generation capabilities, like a "language engine".
In addition, ADK also supports hierarchical agent composition, allowing an "Agent of Agents" model, where a coordinating agent can assign tasks to specialized sub-agents to form a complex collaborative network, just like "teamwork" to complete large projects!
Model Ensemble:
ADK provides integrated support for multiple language models, giving developers the ability to flexibly choose. The specific models include the following:
Gemini: A model recommended by Google that is deeply integrated with ADK and suitable for enterprise applications.
OpenAI: Supports GPT-3.5 and GPT-4 series, covering a wide range of tasks.
Open source model: compatible with a variety of open source LLMs (such as LLaMA 4) to meet customized needs.
from adk.models import GeminiModel# Configure Gemini modelmodel = GeminiModel(api_key = "your_api_key_here" , # replace with your own API keymodel_name = "gemini-1.5-pro" , # Specify the model nametemperature = 0.7 , # Control creativity, 0.7 is the balance pointmax_tokens= 1024 # Limit the maximum number of output tokens)
Tool Ecosystem:
Tools are the "weapons" for agents to perform tasks. ADK provides a wealth of built-in tools and supports custom development. According to the ADK tool documentation, built-in tools include:
WebSearchTool: Search Internet information and obtain the latest data in real time.
CalculatorTool: Perform mathematical calculations and handle numerical tasks.
DatabaseTool: Query and manipulate databases, and support data management.
FileTool: read and write files and process local data.
APITool: Send HTTP requests to external services to extend functionality.
from adk.tools import Tool, ToolParameterclass WeatherTool ( Tool ):name = "WeatherTool" # Tool namedescription = "Get the current weather information for the specified location" # Tool descriptionparameters = [ToolParameter(name = "location" , # parameter namedescription = "City and Country/State" , # Parameter descriptiontype = "string" , # parameter typerequired= True # Is it required?)]async def execute ( self, parameters ):location = parameters.get( "location" ) # Get location parameters# The actual implementation should call the weather API, here is to demonstrate the return of simulated dataweather_data = {"location" : location,"temperature" : "72°F" ,"condition" : "Partly Cloudy" ,"humidity" : "45%" ,"wind" : "8 mph NW"}return weather_data
Operating Environment
The operating environment of ADK determines the operation mode and expansion capability of the agent. The specific environment can be referred to as follows:
Local Environment: Suitable for development and testing, using personal devices for rapid prototyping, similar to the trial stage of "your own kitchen".
Vertex AI Environment: Deployed on Google Cloud, it supports enterprise-level production needs and enjoys high availability and scalability, just like the official dishes served in a "five-star restaurant".
Custom Environments: Provide personalized configurations for special infrastructure needs, like a "custom kitchen" to meet unique recipes.
Each environment manages the agent's lifecycle, resource allocation, and expansion strategy, flexibly adapting to different scenarios.
Professional highlights: Vertex AI integrates Google Kubernetes Engine (GKE), supports 99.9% availability, and custom environments support Docker containerization.
Readability Tip: Like a "multi-scene stage", ADK allows you to practice from "home kitchen", to "cloud chef" serving food, and then to "custom kitchen" innovation!
Google ADK Tool? Type Analysis
As a powerful open source framework, Agent Development Kit (ADK) provides developers with a variety of tool types, giving the agent system a high degree of flexibility and customizability. These tool types not only cover the needs of custom development, but also integrate built-in functions and rich resources of the third-party ecosystem to help developers build intelligent and modular AI solutions.
1. Custom tool type
Generally speaking, ADK supports a variety of custom tool types to meet the unique logic and business process requirements of specific application scenarios, specifically involving:
Function Tools
These tools are tailor-made solutions for specific applications. Developers can design exclusive functions according to business needs and optimize workflow efficiency. They are like "exclusive tailors" who "tailor" according to our needs and create unique smart modules.
Functions/Methods
ADK allows you to register standard synchronous Python functions (def) or class methods as tools, providing complete programmatic control. Whether it is simple calculation logic or complex business processing, these tools are like the basic toolbox of a "universal craftsman", ready to go at any time.
Agents-as-Tools
Developers can embed specialized agents as callable tools into parent agents to achieve modular behavior. This design is similar to "teamwork", allowing each "expert agent" to share tasks and jointly complete complex intelligent workflows.
Long-Running Function Tools
Such tools are designed for asynchronous or time-consuming operations, supporting background execution of tasks such as data processing or API calls, just like "endurance athletes" who maintain stable performance during long-term tasks.
2. Built-in tools: ready-to-use tools provided by the framework
ADK has a series of predefined tools built in to cover common task requirements and provide developers with out-of-the-box solutions, specifically:
WebSearchTool: Search Internet information in real time and obtain the latest data support.
CodeExecutionTool: Execute code snippets to assist in development and debugging.
RAGTool: Enhances knowledge base query based on Retrieval-Augmented Generation technology.
These tools are like “ready-made magic props” that allow developers to quickly build powerful proxy systems without starting from scratch.
3. Third-party tools: seamlessly integrated ecosystem expansion
ADK supports easy integration of third-party tools from popular ecosystems to further enrich its functionality. Developers can seamlessly access tools in well-known frameworks such as LangChain or CrewAI, and take full advantage of the mature functions and community resources of these ecosystems.
For example, LangChain’s memory module or CrewAI’s task allocation tool can be directly embedded into ADK to enhance the collaborative capabilities of agents.
That’s all for today’s analysis. For more in-depth analysis, best practices, and related technology frontiers about Google’s Agent Development Kit (ADK) related technologies, please follow our WeChat public account: Architecture Station to get more exclusive technical insights!
