ADK Agents with Amazon Bedrock

2026-05-29 · Source: Artificial Intelligence in Plain English - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Cloud Computing & IT Infrastructure · Depth: Intermediate, medium

Summary

This article provides a step-by-step deployment guide for configuring a Google Agent Development Kit (ADK) agent, version 1.32.0, to utilize Amazon Bedrock models. It details the setup of a development environment, including installing the Gemini CLI (v0.39.1), managing Node.js versions with nvm, and configuring the AWS CLI (v2.34.41). The process involves cloning a GitHub repository, running init.sh and set_env.sh scripts, and saving AWS credentials. LiteLLM, an open-source AI gateway (v1.83.14), is central to proxying ADK requests to Bedrock, specifically demonstrating interaction with the amazon.nova-micro-v1:0 model. The guide validates the ADK installation by running a local "hello" agent and testing its web interface at http://0.0.0.0:8000. A Gemini CLI code review of the agent's Python implementation offers insights into model configuration, agent identity, and extensibility.

Key takeaway

For AI Engineers building multi-agent systems, this guide demonstrates a robust method for integrating Google ADK agents with Amazon Bedrock models. You should utilize LiteLLM as an AI gateway to simplify API management across various LLMs, ensuring consistent access and features like cost tracking. Consider adding detailed agent descriptions and defining tools to enhance discoverability and functionality within your ADK deployments. This approach streamlines complex multi-cloud LLM integrations.

Key insights

LiteLLM unifies access to diverse LLMs, simplifying multi-agent system integration with platforms like Amazon Bedrock.

Principles

• Agent development benefits from modularity and state management.
• Unified LLM interfaces streamline multi-provider deployments.
• Incremental development validates complex agent systems.

Method

The article outlines a method for deploying ADK agents with Bedrock: set up the environment, install Python libraries, use LiteLLM to proxy ADK to Bedrock, then test via CLI and web interface.

In practice

• Utilize LiteLLM for unified LLM API access.
• Configure os.getenv for model environment variables.
• Add agent descriptions for multi-agent discovery.

Topics

• ADK Agents
• Amazon Bedrock
• LiteLLM
• Multi-agent Systems
• LLM Integration
• Gemini CLI

Code references

• BerriAI/litellm
• nvm-sh/nvm
• modelcontextprotocol/python-sdk
• xbill9/gemini-cli-aws

Best for: AI Engineer, Machine Learning Engineer, MLOps Engineer

Related on AIssential

• BerriAI / litellm — LiteLLM unifies access to 100+ LLMs and agents via a single API, simplifying multi-provider AI integration.
• AWS Bedrock Agents: The Beginner’s Guide — AWS Bedrock Agents provide a managed platform for building and deploying AI agents with integrated orchestration and security.
• Datadog Integrates Google Agent Development Kit into LLM Observability Tools — Datadog's ADK integration provides deep observability for AI agents, addressing their non-deterministic nature in production.
• Meet LiteLLM Agent Platform: A Kubernetes-Based, Self-Hosted Infrastructure Layer for Isolated Agent Sandboxes and Persistent Session Management in Production — The LiteLLM Agent Platform offers self-hosted, Kubernetes-based infrastructure for isolated, persistent AI agent sandboxes.
• A Visual Guide to LLM Agents — LLM Agents augment basic LLMs with memory, tools, and planning for autonomous, complex task execution.
• Building Production-Ready AI Agents with Agent Development Kit — ADK simplifies building scalable, production-ready multi-agent AI systems by abstracting orchestration and integrating cloud services.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence in Plain English - Medium.