Agent Development Kit (ADK) Samples Overview
The Agent Development Kit (ADK) Samples repository is an open-source collection maintained by Google, providing developers with practical, ready-to-use examples of AI agents built using the ADK framework. Launched as part of Google's efforts to simplify and standardize AI agent development, this repository serves as a valuable resource for builders looking to integrate advanced agentic capabilities into their applications.
What is ADK?
The underlying Agent Development Kit (ADK) is a lightweight, extensible framework developed by Google to streamline the creation, deployment, and management of AI agents. It abstracts away much of the complexity involved in handling multi-step reasoning, tool integration, and state management, allowing developers to focus on high-level logic. ADK supports integration with popular LLMs like Gemini and is designed for scalability across different environments, from cloud-based services to edge devices.
Repository Highlights
This GitHub repository organizes samples by programming language—Python, Go, and Java—each with dedicated subfolders containing agent implementations. Key features include:
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Diverse Use Cases: Samples span everyday applications like customer service bots, financial advisors, and travel concierges, to specialized ones such as medical pre-authorization, software bug assistance, and time-series forecasting. For instance, the 'academic-research' agent demonstrates literature review and summarization, while 'RAG' showcases retrieval-augmented generation for enhanced knowledge retrieval.
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Multi-Language Support:
- Python: The most extensive collection, with over 20 agents including 'blog-writer' for content generation, 'deep-search' for advanced querying, and 'safety-plugins' for ethical AI safeguards.
- Go: Focuses on performance-oriented agents like 'llm-auditor' for monitoring LLM outputs.
- Java: Includes enterprise-ready examples such as 'software-bug-assistant' and 'time-series-forecasting' for data analytics.
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Ease of Setup: Each language folder provides step-by-step instructions, requiring only ADK installation (via pip for Python, go mod for Go, or Maven for Java). Prerequisites emphasize having an ADK setup, with links to official installation guides.
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Advanced Workflows: Beyond basic chatbots, samples illustrate multi-agent systems (e.g., 'short-movie-agents' for collaborative storytelling) and real-time conversational agents, highlighting ADK's strengths in orchestration and plugin integration.
Getting Started and Best Practices
To dive in, clone the repository and follow the README in your chosen language folder. Install ADK first, configure API keys for LLMs, and run the agents via provided scripts. The samples emphasize modularity—agents are composable, allowing developers to mix and match components like tools, memory stores, and reasoning engines.
For deeper learning, refer to the ADK Documentation, which covers core concepts like agent lifecycles, error handling, and optimization techniques. The repository also links to language-specific ADK repos (e.g., ADK Python) for core library details.
Community and Contributions
With over 6,900 stars, the project has garnered significant interest from the AI community. Contributions are encouraged via GitHub issues and pull requests, following guidelines in CONTRIBUTING.md. While not officially supported, it's ideal for prototyping and education.
Limitations and Disclaimers
Samples are for demonstration only, not production use. They rely on ADK's Apache 2.0 license, promoting open collaboration. Note that this isn't part of Google's vulnerability rewards program.
In summary, ADK Samples empowers developers to build sophisticated AI agents efficiently, bridging the gap between conceptual designs and deployable solutions in the rapidly evolving field of agentic AI.
