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This repository is a curated, community-maintained catalog for building AI agents with Google’s Agent Development Kit (ADK). It collects 90+ production-ready agents, templates, starters, featured implementations, hackathon winners and official ADK examples to help developers discover high-quality starting points and real-world architectures. It groups projects by domain such as research, education, customer service, finance, and e-commerce and highlights production-ready projects that include deployment code and infrastructure. The list combines curated resources with hands-on showcase agents located under a featured folder, and it aggregates learning materials, tutorials, and best-practice writeups to accelerate the path from prototype to deployed multi-agent systems. The repository also documents essential ADK commands and recommended deployment targets used by examples.

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Features
Curated collection of templates and battle-tested, production-ready ADK implementations covering single-purpose and multi-agent systems. Emphasizes code-first Python development with ADK and a rich tool ecosystem including pre-built tools, OpenAPI specs, RAG examples, MCP and A2A patterns. Highlights multi-agent orchestration patterns and domain-specific solutions with deployment examples for Cloud Run, Vertex AI Agent Engine and containerized environments. Includes featured projects, hackathon winners, community excellence examples, and learning resources such as crash courses, codelabs, walkthroughs and official samples. Provides essential ADK commands and recommended workflows for creating, running, testing and deploying agents. Maintains contribution guidelines and quality standards for production-readiness.
Use Cases
The repository shortens discovery and development time by surfacing high-quality ADK templates and real-world examples so developers do not start from scratch. It addresses the production-readiness barrier with end-to-end examples that include integration, error handling and deployment patterns. Progressive tutorials and official samples bridge the gap between simple demos and scalable multi-agent systems. Domain-specific showcases and hackathon winners illustrate practical architectures for research, customer support, trading, education and more. The collection also centralizes learning resources, testing frameworks and best practices, making it easier to clone, extend and contribute production-grade agents while following ADK best practices and deployment recommendations.

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