- Google Developers Blog

2025年11月13日 / AI

Introducing Metrax: performant, efficient, and robust model evaluation metrics in JAX

Metrax is a high-performance JAX-based metrics library developed by Google. It standardizes model evaluation by offering robust, efficient metrics for classification, NLP, and vision, eliminating manual re-implementation after migrating from TensorFlow. Key strengths include parallel computation of "at K" metrics (e.g., PrecisionAtK) for multiple K values and strong integration with the JAX AI Stack, leveraging JAX's performance features. It is open-source on GitHub.

2025年11月13日 / AI

Making the terminal beautiful one pixel at a time

Google has launched the redesigned **Android AI Sample Catalog**, a dedicated, open-source application to inspire and educate Android developers on building AI-powered apps. It showcases examples using both on-device (Gemini Nano via ML Kit GenAI API) and Cloud models (via Firebase AI Logic SDK), including image generation with Imagen, on-device summarization, and a "Chat with Nano Banana" chatbot. The code is easy to copy and paste to help developers quickly start their own projects.

Gemini CLI - UI Improvements (hero image)

2025年11月7日 / AI

Announcing the Agent Development Kit for Go: Build Powerful AI Agents with Your Favorite Languages

The Agent Development Kit (ADK), an open-source, code-first toolkit for building powerful and sophisticated AI agents, now supports Go. ADK moves LLM orchestration and agent behavior directly into your code, giving you robust debugging, versioning, and deployment freedom. ADK for Go is idiomatic and performant, leveraging Go's strengths, and includes support for over 30+ databases and the Agent-to-Agent (A2A) protocol for collaborative multi-agent systems. Start building today!

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

2025年11月7日 / AI

Announcing User Simulation in ADK Evaluation

The new **User Simulation** feature in the Agent Development Kit (ADK) replaces rigid, brittle manual test scripts with dynamic, LLM-powered conversation generation. Developers define a high-level `conversation_plan`, and the simulator handles the multi-turn interaction to achieve the goal. This dramatically reduces test creation time, builds more resilient tests, and creates a reliable regression suite for AI agents.

2025年11月4日 / AI

Agent Garden - Samples for learning, discovering and building

Agent Garden is now available to all users to simplify AI agent creation and deployment using the Agent Development Kit (ADK). It provides curated agent samples, one-click deployment via Agent Starter Pack, and customization through Firebase Studio. It helps developers with complex business challenges and multi-agent workflows, with Renault Group cited as an early success story.

Train a GPT2 model with JAX on TPU for free

2025年10月30日 / AI

Beyond Request-Response: Architecting Real-time Bidirectional Streaming Multi-agent System

The blog post argues the request-response model fails for advanced multi-agent AI. It advocates for a real-time bidirectional streaming architecture, implemented by the Agent Development Kit (ADK). This streaming model enables true concurrency, natural interruptibility, and unified multimodal processing. ADK's core features are real-time I/O management, stateful sessions for agent handoffs, and streaming-native tools.