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adk-docs

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Multi-Agent Platforms & Frameworks
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Basic Information

Agent Development Kit (ADK) is an open-source, code-first toolkit for developing, evaluating, and deploying AI agents. It provides a flexible, modular framework intended to make agent development feel like software development by enabling developers to define agent logic, tools, and orchestration directly in Python and Java. ADK is optimized for the Google ecosystem and Gemini while remaining model-agnostic and deployment-agnostic, allowing integration with other frameworks. The project supports composing multiple specialized agents into scalable architectures, and it targets both simple task automation and complex multi-step workflows. The repository includes documentation, packaging for Python and Java, and guidance for containerized deployment and integration with Google hosting and agent runtime services.

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App URL
https://github.com/google/adk-docs
Github Repository
https://github.com/google/adk-docs/blob/main/README.md

App Details

Features
ADK offers a rich tool ecosystem including pre-built tools, custom functions, and support for OpenAPI specifications to extend agent capabilities. It emphasizes code-first development with native Python and Java APIs for defining agents, tools, and orchestration to support testability and version control. The framework supports modular multi-agent systems for composing specialized agents into flexible hierarchies. Built-in tracing and monitoring capabilities provide observability for debugging and optimization with integration options for external providers. Deployment features include containerization patterns and compatibility with Cloud Run and Vertex AI Agent Engine. The project is documented and packaged for easy installation via standard package managers.
Use Cases
ADK helps developers build and operate agentic applications by providing reusable components, language bindings, and deployment patterns that reduce boilerplate and accelerate development. By enabling code-first definitions in Python and Java, it improves maintainability, testing, and versioning of agent logic. Modular multi-agent composition allows teams to scale responsibilities across specialized agents and orchestrate complex workflows. Observability features enable tracing and monitoring to diagnose and optimize behavior in production. Deployment-agnostic design and guidance for containerization make it straightforward to run agents on cloud runtimes and Google services. The documentation and package artifacts streamline onboarding and integration into existing development workflows.

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