Agent Development Kit: Making it easy to build multi-agent applications

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The sector of AI is unexpectedly shifting past single-purpose fashions in opposition to clever, self reliant multi-agent techniques. Construction those multi-agent techniques, alternatively, gifts new demanding situations. For this reason nowadays, we have now introduced Agent Development Kit (ADK) at Google Cloud NEXT 2025, a brand new open-source framework from Google designed to simplify the whole stack end-to-end building of brokers and multi-agent techniques. ADK empowers builders such as you to construct production-ready agentic programs with larger flexibility and exact keep an eye on.

ADK is similar framework powering brokers inside of Google merchandise like Agentspace and the Google Buyer Engagement Suite (CES). By way of open-sourcing ADK, we goal to supply builders with tough, versatile gear to construct within the unexpectedly evolving agent panorama. The ADK is designed to be versatile, use other fashions and construct manufacturing waiting brokers for various deployment environments.

Core Pillars of ADK: Construct, Engage, Review, Deploy

ADK supplies features throughout all the agent building lifecycle:

  • Multi-Agent by means of Design: Construct modular and scalable programs by means of composing a couple of specialised brokers in a hierarchy. Allow complicated coordination and delegation.
  • Wealthy Style Ecosystem: Make a choice the style that works perfect on your wishes. ADK works together with your style of selection – if it is Gemini or your any style out there by means of Vertex AI Style Lawn. The framework additionally provides LiteLLM integration letting you choose between a big selection of fashions from suppliers like Anthropic, Meta, Mistral AI, AI21 Labs, and plenty of extra!
  • Wealthy Instrument Ecosystem: Equip brokers with numerous features: use pre-built gear (Seek, Code Exec), Style Context Protocol (MCP) gear, combine Third-party libraries (LangChain, LlamaIndex), and even use different brokers as gear (LangGraph, CrewAI, and so on).
  • Integrated streaming: Engage together with your brokers in human-like conversations with ADK’s distinctive bidirectional audio and video streaming features. With only some strains of code, you’ll be able to create herbal interactions that modify how you’re employed with brokers – shifting past textual content into wealthy, multimodal discussion.
  • Versatile Orchestration: Outline workflows the usage of workflow brokers (Sequential, Parallel, Loop) for predictable pipelines, or leverage LLM-driven dynamic routing (LlmAgent switch) for adaptive conduct.
  • Built-in Developer Enjoy: Increase, check, and debug in the community with a formidable CLI and a visible Internet UI. Check up on occasions, state, and agent execution step by step.
  • Integrated Analysis: Systematically assess agent efficiency by means of comparing each the general reaction high quality and the step by step execution trajectory towards predefined check instances.
  • Simple Deployment: Containerize and deploy your brokers anyplace.

Getting began together with your first agent

Whilst we inspire you to discover the examples within the docs, the core thought is Pythonic simplicity. You outline your agent’s common sense, the gear it will probably use, and the way it must procedure knowledge. ADK supplies the construction to regulate state, orchestrate software calls, and have interaction with the underlying LLMs. This is an illustrative instance of a fundamental agent.

The code can also be discovered within the quickstart guide.

from google.adk.brokers import LlmAgent 
from google.adk.gear import google_Search

dice_agent = LlmAgent(
    style="gemini-2.0-flash-exp", # Required: Specify the LLM 
    identify="question_answer_agent", # Requdired: Distinctive agent identify
    description="A useful assistant agent that may resolution questions.",
    instruction="""Reply to the question the usage of google seek""",
    gear=[google_search], # Supply an example of the software
)

# you'll be able to run this by means of the usage of adk internet

This easy instance presentations the fundamental construction. ADK actually shines when construction extra complicated programs involving a couple of brokers, subtle software use, and dynamic orchestration, all whilst keeping up keep an eye on.

ADK provides flexibility in the way in which you have interaction together with your brokers: CLI, Internet UI, API Server and API (Python). The best way you outline your agent (the core common sense inside of agent.py) is similar without reference to how you select to have interaction with it. The variation lies in the way you start up and organize the interplay. For all you to find examples within the ADK documentation.

Construction Multi-Agent Packages with ADK

ADK actually shines whilst you transfer past unmarried brokers to construct collaborative multi-agent techniques that leverage gear. Believe making a staff of specialised brokers the place a number one agent can delegate duties in accordance with the dialog. ADK makes this simple thru hierarchical constructions and clever routing.

Let’s stroll thru an illustrative instance – a WeatherAgent that handles climate queries however delegates greetings to a specialised GreetingAgent.


1. Outline a Instrument: Brokers use gear to accomplish movements. Right here, our WeatherAgent wishes a device to fetch climate information. We outline a Python serve as; ADK makes use of its docstring to know when and the right way to use it.

def get_weather(town: str) -> Dict:

    # Easiest Apply: Log software execution for more uncomplicated debugging
    print(f"--- Instrument: get_weather referred to as for town: {town} ---")
    city_normalized = town.decrease().substitute(" ", "") # Elementary enter normalization

    # Mock climate information for simplicity (matching Step 1 construction)
    mock_weather_db = {
        "newyork": {"standing": "good fortune", "file": "The elements in New York is sunny with a temperature of 25°C."},
        "london": {"standing": "good fortune", "file": "It is cloudy in London with a temperature of 15°C."},
        "tokyo": {"standing": "good fortune", "file": "Tokyo is experiencing gentle rain and a temperature of 18°C."},
        "chicago": {"standing": "good fortune", "file": "The elements in Chicago is sunny with a temperature of 25°C."},
        "toronto": {"standing": "good fortune", "file": "It is in part cloudy in Toronto with a temperature of 30°C."},
        "chennai": {"standing": "good fortune", "file": "It is wet in Chennai with a temperature of 15°C."},
 }

    # Easiest Apply: Deal with possible mistakes gracefully inside the software
    if city_normalized in mock_weather_db:
        go back mock_weather_db[city_normalized]
    else:
        go back {"standing": "error", "error_message": f"Sorry, I do not have climate knowledge for '{town}'."}

2. Outline the Brokers and Their Dating: We use LlmAgent to create our brokers. Pay shut consideration to the instruction and outline fields – the LLM is based closely on those for working out roles and making delegation choices the usage of auto delegations for sub brokers.

greeting_agent = Agent(
         style=LiteLlm(style="anthropic/claude-3-sonnet-20240229"),
            identify="greeting_agent",
            instruction="You're the Greeting Agent. Your ONLY process is to supply a pleasant greeting to the consumer. " "Don't have interaction in some other dialog or duties.",
            # An important for delegation: Transparent description of capacity
            description="Handles easy greetings and hellos",
            
 )

farewell_agent = Agent(
          style=LiteLlm(style="anthropic/claude-3-sonnet-20240229"),
            identify="farewell_agent",
            instruction="You're the Farewell Agent. Your ONLY process is to supply a well mannered good-bye message. "
                        "Don't carry out some other movements.",
            # An important for delegation: Transparent description of capacity
            description="Handles easy farewells and goodbyes",
            
 )

root_agent = Agent(
        identify="weather_agent_v2", 
        style="gemini-2.0-flash-exp",
        description="You're the primary Climate Agent, coordinating a staff. - Your primary process: Supply climate the usage of the `get_weather` software. Deal with its 'standing' reaction ('file' or 'error_message'). - Delegation Laws: - If the consumer provides a easy greeting (like 'Hello', 'Hi'), delegate to `greeting_agent`. - If the consumer provides a easy farewell (like 'Bye', 'See you'), delegate to `farewell_agent`. - Deal with climate requests your self the usage of `get_weather`. - For different queries, state obviously if you can't take care of them.",
        gear=[get_weather], # Root agent nonetheless wishes the elements software
        sub_agents=[greeting_agent, farewell_agent]
)

How Delegation Works:

  • The default agent conduct is to permit delegation.
  • When processing a consumer message, the LLM considers the question, the present agent’s description, and the description fields of similar brokers (father or mother / sub brokers explained within the hierarchy).
  • If the LLM determines any other agent is a greater are compatible in accordance with its description (e.g., consumer says “Hello”, matching the GreetingAgent description, it initiates a switch.

Transparent, distinct descriptions are necessary! The LLM makes use of them to course duties successfully.

On this setup, if a consumer begins with “Hello”, the WeatherAgent (if it is the root agent processing the enter) can acknowledge it isn’t a climate question, see the GreetingAgent is acceptable by means of its description, and robotically switch keep an eye on. If the consumer asks “What is the climate in Chicago?”, the WeatherAgent handles it without delay the usage of its get_weather software.

This situation demonstrates how ADK’s hierarchical construction and description-driven delegation mean you can construct arranged, maintainable, and complex multi-agent programs.

Finishing the Lifecycle: Analysis and Deployment

Construction clever brokers like our climate agent is foundational, however bringing them reliably to customers comes to an important subsequent steps: rigorous analysis and seamless deployment. Prior to going are living, making sure your agent behaves predictably and accurately is paramount. ADK’s built-in analysis gear are designed exactly for this, letting you systematically check execution paths and reaction high quality towards predefined datasets, like analysis.check.json or check.json. You’ll be able to run those tests programmatically inside of your check suites the usage of AgentEvaluator.assessment(). You’ll be able to additionally use analysis without delay by means of the ADK eval command-line software or by means of the internet UI.

As soon as you might be happy with efficiency, ADK provides a transparent and streamlined trail to manufacturing during the way to deploy to any container runtime or the usage of its integration with Vertex AI Agent Engine. This permits you to leverage a completely controlled, scalable, and enterprise-grade runtime, finishing the advance lifecycle and empowering you to transport from subtle prototypes to powerful, production-ready agentic programs.

Opting for the framework for you: ADK or Genkit?

As you discover the probabilities of construction multi-agent techniques with ADK, you may well be questioning the way it suits into the wider panorama of GenAI building gear from Google. Whilst a number of SDKs and frameworks are to be had, such because the Genkit framework, it is useful to know ADK’s relative focal point. Here is a fast comparability:


Agent Construction Package:

  • Optimized for complicated brokers and multi-agent techniques, it supplies higher-level abstractions for agent building with integrated integration for LiteLLM and Vertex AI Style Lawn supporting a number of fashions.
  • Makes a speciality of defining agent behaviors and interactions.
  • Helps bidirectional streaming.


Genkit:

  • Supplies elementary construction blocks for construction a big number of AI powered stories.
  • Comprises developer tooling for iterating, checking out and debugging your AI similar interactions.
  • Enhance all kinds of enormous language fashions from Google AI, Vertex AI, and from Third events thru neighborhood plugins.


Opting for the Proper Instrument

In the end, your best option will depend on your challenge’s explicit targets. If you’re construction intricate, collaborative agent techniques inside of a well-defined framework, ADK provides a formidable answer. For lots of different GenAI initiatives requiring flexibility and large style toughen, Genkit is a wonderful selection.

ADK works anyplace, however optimized for Google Cloud

Whilst the Agent Construction Package (ADK) provides flexibility to paintings with quite a lot of gear, it’s optimized for seamless integration inside the Google Cloud ecosystem, in particular with Gemini fashions and Vertex AI. This adapted design lets in builders to totally leverage the complex features of Gemini, reminiscent of the improved reasoning and gear use present in Gemini 2.5 Professional Experimental, and gives a right away, local pathway to deploy those brokers onto Vertex AI’s fully-managed, enterprise-grade runtime for scalability.

Crucially, this deep integration extends for your broader venture panorama; ADK allows brokers to attach without delay to techniques and knowledge thru over 100 pre-built connectors, make the most of workflows constructed with Utility Integration, and get entry to information saved in techniques like AlloyDB, BigQuery, and NetApp with out requiring information duplication.

Moreover, brokers constructed with ADK can securely faucet into your company’s present API investments controlled thru Apigee, additional improving their features by means of leveraging established interfaces.

This complete connectivity throughout complex AI fashions, scalable deployment, numerous information resources, and present APIs makes ADK exceptionally tough when used inside the Google Cloud atmosphere.

Construct the following technology of Brokers with ADK

The Agent Construction Package (ADK) supplies a formidable, versatile, and open-source basis for construction the following technology of AI programs. It tackles the core demanding situations of multi-agent building by means of providing:

  • Exact keep an eye on over agent conduct and orchestration.
  • A wealthy ecosystem for gear and integrations.
  • An built-in developer revel in for construction and debugging.
  • A powerful analysis framework very important for dependable brokers.
  • A transparent trail to deployment, together with controlled choices.

We are extremely excited to look what you construct with ADK!

Discover the Code: Official ADK Documentation.



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