Agent Development Kit
Agent Development Kit (ADK) is a flexible and modular framework for developing and deploying AI agents. While optimized for Gemini and the Google ecosystem, ADK is model-agnostic, deployment-agnostic, and is built for compatibility with other frameworks. ADK was designed to make agent development feel more like software development, to make it easier for developers to create, deploy, and orchestrate agentic architectures that range from simple tasks to complex workflows.
ADK Python Security Advisory: LiteLLM supply chain compromise
Unauthorized code was identified in LiteLLM versions 1.82.7 and 1.82.8 on
PyPI on March 24, 2026. If you use ADK Python with the eval or
extensions extras, update to the latest version of ADK Python immediately.
If you installed or upgraded LiteLLM during this period, rotate all secrets
and credentials. For details and required actions, refer to the ADK
security advisory and
LiteLLM's Security Update: Suspected Supply Chain
Incident.
News: ADK Python 2.0 Alpha with graph-based workflows!
ADK 2.0 Alpha release provides a huge orchestration upgrade for agents with support for graph-based workflows. Download the Alpha release and try out ADK 2.0.
News: ADK Python Skills released!
ADK Python development Agent Skills allow you to code ADK agents quickly and more effectively with AI-powered development environments. For more details, check out the Coding with AI, ADK Skills docs.
Get started:
Start with Python Start with TypeScript Start with Go Start with Java
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Watch "Introducing Agent Development Kit"!
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Flexible Orchestration
Define workflows using workflow agents (
Sequential,Parallel,Loop) for predictable pipelines, or leverage LLM-driven dynamic routing (LlmAgenttransfer) for adaptive behavior. -
Multi-Agent Architecture
Build modular and scalable applications by composing multiple specialized agents in a hierarchy. Enable complex coordination and delegation.
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Rich Tool Ecosystem
Equip agents with diverse capabilities: use pre-built tools (Search, Code Exec), create custom functions, integrate 3rd-party libraries, or even use other agents as tools.
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Deployment Ready
Containerize and deploy your agents anywhere – run locally, scale with Vertex AI Agent Engine, or integrate into custom infrastructure using Cloud Run or Docker.
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Built-in Evaluation
Systematically assess agent performance by evaluating both the final response quality and the step-by-step execution trajectory against predefined test cases.
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Building Safe and Secure Agents
Learn how to building powerful and trustworthy agents by implementing security and safety patterns and best practices into your agent's design.