In this guide, you will learn how to build a frontend for your Agent Development Kit (ADK) Agents using AG-UI Protocol and CopilotKit. ADK will power the AI agents backend, while CopilotKit powers the frontend, and then AG-UI creates a bridge that enables the frontend to communicate with the backend. - Source: dev.to / 11 days ago

We will be using the Google ADK Library in this case. Prior knowledge of the library isn’t strictly necessary, as the examples are straightforward and easy to understand. - Source: dev.to / about 1 month ago

ADK offers flexibility in the way you interact with your agents: CLI, Web UI, API Server and API (Python). The way you define your agent (the core logic within agent.py) is the same regardless of how you choose to interact with it. The difference lies in how you initiate and manage the interaction. For all you find examples in the ADK documentation. - Source: dev.to / 3 months ago

I used a mix of tools to build this project, each handling a different part of the process. Google ADK helps run the AI agents, Couchbase stores past Kubecon talks data and performs the vector search, and Nebius Embedding model for generating embeddings and LLM models (Example: Qwen) generates summaries and talk abstracts. - Source: dev.to / 3 months ago

Google Agent Development Kit is is a flexible and modular framework for developing and deploying AI agents. While optimized for Gemini and the Google ecosystem, ADK is model-agnostic (fancy word for saying that it can use any agent like openAI,etc), 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... - Source: dev.to / 3 months ago

On the other hand, platforms like Azure AI Foundry, AWS Bedrock, or Vertex AI offer more complete and managed solutions. They take care of most of the heavy lifting like scaling, integrations, and evaluation, and they also include a solid security and governance layer. These platforms are very mature and production-ready. Microsoft, for example, already provides a responsible AI framework out of the box. These... - Source: dev.to / 18 days ago

Google's introduction of new tools for building and managing multi-agent ecosystems through Vertex AI is a pivotal move for enterprises. The Agent Development Kit (ADK) is a notable feature, providing an open-source framework that allows users to create AI agents with fewer than 100 lines of code. This framework supports Python and integrates with the AI capabilities of Vertex AI. - Source: dev.to / 6 months ago

For further exploration, visit: Vertex AI Overview | Live API. - Source: dev.to / 6 months ago

We use Vertex AI to simplify our implementation, to test different LLM providers and models, and to compare metrics such as cost, latency, errors, time to first token, etc, across models. - Source: dev.to / 6 months ago

Ironwood is part of Google's AI Hypercomputer architecture, a system optimized for AI workloads. This integrated supercomputing system leverages over a decade of AI expertise. It supports various frameworks such as Vertex AI and Pathways, enabling developers to utilize Ironwood effectively for distributed computing. - Source: dev.to / 6 months ago