Multi-Agent A2A with the Agent Development Kit(ADK), Amazon EKS, and Gemini CLI

Leveraging the Google Agent Development Kit (ADK) and the underlying Gemini LLM to build Multi-Agent...

title: Multi-Agent A2A with the Agent Development Kit(ADK), Amazon EKS, and Gemini CLI published: true series: ADK

Leveraging the Google Agent Development Kit (ADK) and the underlying Gemini LLM to build Multi-Agent Applications with A2A protocol support using the Python programming language deployed to AWS Elastic Kubernates Service (EKS).

Aren’t There a Billion Python ADK Demos?

Yes there are.

Python has traditionally been the main coding language for ML and AI tools. The goal of this article is to provide a multi-agent test bed for building, debugging, and deploying multi-agent applications.

Where is the Beef?

So what is different about this lab compared to all the others out there?

This is one of the first deep dives into a Multi-Agent application leveraging the advanced tooling of Gemini CLI. The starting point for the demo was an existing Codelab- which was updated and re-engineered with Gemini CLI.

The original Codelab- is here:

Building a Multi-Agent System | Google Codelabs

Python Version Management

One of the downsides of the wide deployment of Python has been managing the language versions across platforms and maintaining a supported version.

The pyenv tool enables deploying consistent versions of Python:

GitHub - pyenv/pyenv: Simple Python version management

As of writing — the mainstream python version is 3.13. To validate your current Python:

python --version
Python 3.13.13

Amazon EKS

Amazon Elastic Kubernetes Service (EKS) is a fully managed service from Amazon Web Services (AWS) that makes it easy to run Kubernetes on AWS without needing to install, operate, or maintain your own Kubernetes control plane. It automates cluster management, security, and scaling, supporting applications on both Amazon EC2 and AWS Fargate.

More information is available here:

What is Amazon EKS?

Gemini CLI

If not pre-installed you can download the Gemini CLI to interact with the source files and provide real-time assistance:

npm install -g @google/gemini-cli

Testing the Gemini CLI Environment

Once you have all the tools and the correct Node.js version in place- you can test the startup of Gemini CLI. You will need to authenticate with a Key or your Google Account:

▝▜▄ Gemini CLI v0.33.1
    ▝▜▄
   ▗▟▀ Logged in with Google /auth
  ▝▀ Gemini Code Assist Standard /upgrade no sandbox (see /docs) /model Auto (Gemini 3) | 239.8 MB

Node Version Management

Gemini CLI needs a consistent, up to date version of Node. The nvm command can be used to get a standard Node environment:

GitHub - nvm-sh/nvm: Node Version Manager - POSIX-compliant bash script to manage multiple active node.js versions

Agent Development Kit

The Google Agent Development Kit (ADK) is an open-source, Python-based framework designed to streamline the creation, deployment, and orchestration of sophisticated, multi-agent AI systems. It treats agent development like software engineering, offering modularity, state management, and built-in tools (like Google Search) to build autonomous agents.

The ADK can be installed from here:

Agent Development Kit (ADK)

Agent Skills

Gemini CLI can be customized to work with ADK agents. Both an Agent Development MCP server, and specific Agent skills are available.

More details are here:

Agent Development Kit (ADK)

To get the Agent Skills in Gemini CLI:

> /skills list
Available Agent Skills:

and the ADK documentation:

> /mcp list
Configured MCP servers:
🟢 adk-docs-mcp (from adk-docs-ext) - Ready (2 tools)
  Tools:
  - mcp_adk-docs-mcp_fetch_docs
  - mcp_adk-docs-mcp_list_doc_sources

Where do I start?

The strategy for starting multi agent development is a incremental step by step approach.

First, the basic development environment is setup with the required system variables, and a working Gemini CLI configuration.

Then, ADK Multi-Agent is built, debugged, and tested locally. Finally — the entire solution is deployed to Google Cloud Run.

Setup the Basic Environment

At this point you should have a working Python environment and a working Gemini CLI installation. All of the relevant code examples and documentation is available in GitHub.

The next step is to clone the GitHub repository to your local environment:

cd ~
git clone https://github.com/xbill9/gemini-cli-aws
cd multi-eks

Then run init2.sh from the cloned directory.

The script will attempt to determine your shell environment and set the correct variables:

source init2.sh

If your session times out or you need to re-authenticate- you can run the set_env.sh script to reset your environment variables:

source set_env.sh

Variables like PROJECT_ID need to be setup for use in the various build scripts- so the set_env script can be used to reset the environment if you time-out.

Finally install the packages and dependencies:

make install

Verify The ADK Installation

To verify the setup, run the ADK CLI locally with the researcher agent:

xbill@penguin:~/gemini-cli-aws/multi-eks/agents$ adk run researcher
/home/xbill/.local/lib/python3.13/site-packages/google/adk/features/_feature_decorator.py:72: UserWarning: [EXPERIMENTAL] feature FeatureName.PLUGGABLE_AUTH is enabled.
  check_feature_enabled()
Log setup complete: /tmp/agents_log/agent.20260412_164250.log
To access latest log: tail -F /tmp/agents_log/agent.latest.log
{"asctime": "2026-04-12 16:42:50,986", "name": "root", "levelname": "INFO", "message": "Logging initialized for researcher", "filename": "logging_config.py", "lineno": 54, "service": "researcher", "log_level": "INFO"}
{"asctime": "2026-04-12 16:42:50,987", "name": "researcher.agent", "levelname": "INFO", "message": "Initialized researcher agent with model: gemini-2.5-flash", "filename": "agent.py", "lineno": 85}
{"asctime": "2026-04-12 16:42:50,988", "name": "google_adk.google.adk.cli.utils.envs", "levelname": "INFO", "message": "Loaded .env file for researcher at /home/xbill/gemini-cli-aws/multi-eks/.env", "filename": "envs.py", "lineno": 83}
{"asctime": "2026-04-12 16:42:50,988", "name": "google_adk.google.adk.cli.utils.local_storage", "levelname": "INFO", "message": "Using per-agent session storage rooted at /home/xbill/gemini-cli-aws/multi-eks/agents", "filename": "local_storage.py", "lineno": 84}
{"asctime": "2026-04-12 16:42:50,988", "name": "google_adk.google.adk.cli.utils.local_storage", "levelname": "INFO", "message": "Using file artifact service at /home/xbill/gemini-cli-aws/multi-eks/agents/researcher/.adk/artifacts", "filename": "local_storage.py", "lineno": 110}
{"asctime": "2026-04-12 16:42:50,988", "name": "google_adk.google.adk.cli.utils.service_factory", "levelname": "INFO", "message": "Using in-memory memory service", "filename": "service_factory.py", "lineno": 266}
{"asctime": "2026-04-12 16:42:50,993", "name": "google_adk.google.adk.cli.utils.local_storage", "levelname": "INFO", "message": "Creating local session service at /home/xbill/gemini-cli-aws/multi-eks/agents/researcher/.adk/session.db", "filename": "local_storage.py", "lineno": 60}
Running agent researcher, type exit to exit.
[user]: 

Test The ADK Web Interface

This tests the ADK agent interactions with a browser:

xbill@penguin:~/gemini-cli-aws/multi-eks/agents$ adk web --host 0.0.0.0
/home/xbill/.local/lib/python3.13/site-packages/google/adk/features/_feature_decorator.py:72: UserWarning: [EXPERIMENTAL] feature FeatureName.PLUGGABLE_AUTH is enabled.
  check_feature_enabled()
2026-04-12 16:43:14,152 - INFO - service_factory.py:266 - Using in-memory memory service
2026-04-12 16:43:14,153 - INFO - local_storage.py:84 - Using per-agent session storage rooted at /home/xbill/gemini-cli-aws/multi-eks/agents
2026-04-12 16:43:14,153 - INFO - local_storage.py:110 - Using file artifact service at /home/xbill/gemini-cli-aws/multi-eks/agents/.adk/artifacts
/home/xbill/.local/lib/python3.13/site-packages/google/adk/cli/fast_api.py:198: UserWarning: [EXPERIMENTAL] InMemoryCredentialService: This feature is experimental and may change or be removed in future versions without notice. It may introduce breaking changes at any time.
  credential_service = InMemoryCredentialService()
/home/xbill/.local/lib/python3.13/site-packages/google/adk/auth/credential_service/in_memory_credential_service.py:33: UserWarning: [EXPERIMENTAL] BaseCredentialService: This feature is experimental and may change or be removed in future versions without notice. It may introduce breaking changes at any time.
  super(). __init__ ()
INFO: Started server process [32675]
INFO: Waiting for application startup.

+-----------------------------------------------------------------------------+
| ADK Web Server started |
| |
| For local testing, access at http://0.0.0.0:8000. |
+-----------------------------------------------------------------------------+

INFO: Application startup complete.
INFO: Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)

Then use the web interface — either on the local interface 127.0.0.1 or the catch-all web interface 0.0.0.0 -depending on your environment:

Special note for Google Cloud Shell Deployments- add a CORS allow_origins configuration exemption to allow the ADK agent to run:

adk web --host 0.0.0.0 --allow_origins 'regex:.*'

Multi Agent Design

The multi-agent deployment consists of 5 agents:

• Researcher
• Judge
• Orchestrator
• Content Builder
• Course Builder

For a detailed analysis of the multi-agent architecture- this article provides the background information:

Multi-Agent A2A with the Agent Development Kit(ADK), Cloud Run, and Gemini CLI

Running/Testing/Debugging Locally

The main Makefile has been extended with extensive targets for managing the agents on the local development environment.

The key targets include:

xbill@penguin:~/multi-agent$ make help
Available commands:
  install - Install all dependencies for root, agents, and app
  start - Start all services locally (alias for start-local)
  stop - Stop all local services (alias for stop-local)
  run - Start all services locally (alias for start-local)
  local - Show local service URLs
  start-local - Start all local services in background
  stop-local - Stop all local processes
  test - Run all tests (pytest)
  test-researcher - Test the Researcher agent directly
  test-judge - Test the Judge agent directly
  test-orchestrator - Test the Orchestrator logic
  lint - Run linting checks (ruff)
  deploy - Deploy all services to Cloud Run
  destroy - Delete all Cloud Run services
  clean - Remove caches and logs

First check for local running agents:

xbill@penguin:~/multi-agent$ make status
Checking status of locally running agents and servers...
--- Network Status ---
No services listening on expected ports (8000-8004, 5173).
--- Process Status ---
No matching processes found.

Then all the agents can be started together:

xbill@penguin:~/multi-agent$ make start
Stopping any existing agent and server processes...
Starting all agents in background...
Waiting for sub-agents to start...
All agents started. Logs: researcher.log, judge.log, content_builder.log, orchestrator.log
Starting App Backend in background...
Starting Frontend dev server in background...
All services started. Logs: researcher.log, judge.log, content_builder.log, orchestrator.log, backend.log, frontend.log
Frontend: http://localhost:5173
Backend: http://localhost:8000

make status

--- Local Service URLs ---
Frontend: http://localhost:5173
Backend: http://localhost:8000 (main app)
Agents:
  Researcher: http://localhost:8001
  Judge: http://localhost:8002
  Content Builder: http://localhost:8003
  Orchestrator: http://localhost:8004

Then connect to the front end locally:

And the entire agent system will run in the local environment:

Local Logging / Debugging

Gemini CLI has full access to the local agent logs for debugging and troubleshooting:

> review the local logs for the multi-agent run
✦ I will check run_local.sh and the project structure to determine where logs are stored or how they are generated.

This will verify the local operation of the code:

✦ The local logs confirm a successful multi-agent run for the topic "the history of the internet." Here is a summary of the execution flow:

   * Orchestrator (orchestrator.log): Successfully managed the pipeline. It captured the topic, coordinated the Research-Judge loop, and escalated to
     content generation after the first iteration.
   * Researcher (researcher.log): Gathered approximately 9.2KB of research data using the google_search tool. It completed its task with a 200 OK
     response.
   * Judge (judge.log): Evaluated the findings and gave a "pass" status. The judge noted that the research was "comprehensive, accurate, and
     well-structured," making it an "excellent foundation for educational content."
   * Content Builder (content_builder.log): Successfully transformed the validated research into a structured Markdown course module and finished
     execution.
   * Backend & Frontend (backend.log, frontend.log): The FastAPI backend (port 8000) and Vite frontend (port 5173) are running and communicating with
     the orchestrator as expected.

  Notable Observation:
   * A minor warning appeared in researcher.log: Researcher callback: 'response' not found or has no parts. However, this did not impact the
     successful completion of the research or the overall pipeline.

  All services are currently active and logged at the DEBUG level as specified in run_local.sh.

Deploying to Amazon Elastic Kubernates Service(EKS)

The project level Makefile has targets for managing the Agent deployment to serverless endpoints. A utility script checks the deployment status:

make status

You can submit the build for GKE deployment:

make deploy

xbill@penguin:~/gemini-cli-aws/multi-eks$ make deploy
chmod +x eks/*.sh
./eks/deploy-eks.sh
=== AWS EKS Deployment ===
AWS Region: us-east-1
Account ID: 106059658660
EKS Cluster: adk-eks-penguin
==========================
=== AWS EKS Cluster Setup/Check ===
AWS Region: us-east-1
Account ID: 106059658660
EKS Cluster: adk-eks-penguin
===================================
Checking ECR Repository researcher...
ECR Repository researcher already exists.
Checking ECR Repository judge...
ECR Repository judge already exists.
Checking ECR Repository content-builder...
ECR Repository content-builder already exists.
Checking ECR Repository orchestrator...
ECR Repository orchestrator already exists.
Checking ECR Repository course-creator...
ECR Repository course-creator already exists.
Checking if EKS Cluster adk-eks-penguin exists and is ACTIVE...
Updating kubeconfig for EKS Cluster adk-eks-penguin...
Updated context arn:aws:eks:us-east-1:106059658660:cluster/adk-eks-penguin in /home/xbill/.kube/config
EKS/ECR Setup Check Complete!
Logging in to Amazon ECR...

Waiting for deployments to complete...
deployment "researcher" successfully rolled out
deployment "judge" successfully rolled out
deployment "content-builder" successfully rolled out
deployment "orchestrator" successfully rolled out
deployment "course-creator" successfully rolled out
Deployment complete!
Course Creator External URL (may take a moment to appear):
a27c61bc6fb3c425ca13d862e0fe4aed-865627292.us-east-1.elb.amazonaws.com

Once the containers are deployed- you can then get the endpoint:

xbill@penguin:~/gemini-cli-aws/multi-eks$ make endpoint
--- AWS EKS Endpoint ---
a27c61bc6fb3c425ca13d862e0fe4aed-865627292.us-east-1.elb.amazonaws.com

The status can then be checked:

xbill@penguin:~/gemini-cli-aws/multi-eks$ make status
Checking EKS Deployment status...
NAME READY UP-TO-DATE AVAILABLE AGE
content-builder 1/1 1 1 18m
course-creator 1/1 1 1 18m
judge 1/1 1 1 18m
orchestrator 1/1 1 1 18m
researcher 1/1 1 1 18m
NAME READY STATUS RESTARTS AGE
content-builder-8854b857b-rnb8q 1/1 Running 0 18m
course-creator-55b5864d58-79p56 1/1 Running 0 18m
judge-5cd7ff57f6-cscdc 1/1 Running 0 18m
orchestrator-6fcfd7cb8f-bgdm6 1/1 Running 0 18m
researcher-8694d7bcd5-r6cb4 1/1 Running 0 18m
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
content-builder ClusterIP 10.100.206.249 <none> 8000/TCP 18m
course-creator LoadBalancer 10.100.125.244 a27c61bc6fb3c425ca13d862e0fe4aed-865627292.us-east-1.elb.amazonaws.com 80:32483/TCP 18m
judge ClusterIP 10.100.229.35 <none> 8000/TCP 18m
kubernetes ClusterIP 10.100.0.1 <none> 443/TCP 147m
orchestrator ClusterIP 10.100.91.62 <none> 8000/TCP 18m
researcher ClusterIP 10.100.182.137 <none> 8000/TCP 18m
xbill@penguin:~/gemini-cli-aws/multi-eks$ 

The service will be visible in the AWS console:

And the entire system can be tested:

 > make e2e-test-eks
✦ I will execute the e2e-test-eks command, which will attempt to fetch the EKS endpoint and run the end-to-end test.

╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ ✓ Shell make e2e-test-eks [current working directory /home/xbill/gemini-cli-aws/multi-eks] │
│ │
│ ... first 6 lines hidden (Ctrl+O to show) ... │
│ {"type": "progress", "text": "\ud83d\udd0d Researcher is gathering information..."} │
│ {"type": "progress", "text": "\u2696\ufe0f Judge is evaluating findings..."} │
│ {"type": "progress", "text": "\u2696\ufe0f Judge is evaluating findings..."} │
│ {"type": "progress", "text": "\u270d\ufe0f Building the final course content..."} │
│ {"type": "progress", "text": "\u270d\ufe0f Content Builder is writing the course..."} │
│ {"type": "result", "text": "# The History of the Internet\n\n## Module 1: The Genesis of Connectivity (1960s-1970s)\n\n### 1.1 Early Concepts │
│ and Visionaries\n\n* **J.C.R. Licklider's \"Galactic Network\"** \n * In 1962, J.C.R. Licklider envisioned a globally interconnected │
│ set of computers for universal data and program access.\n * Licklider, from MIT and ARPA, described a concept strikingly similar to │
│ today's internet.\n* **Packet Switching** \n * **Paul Baran** (RAND Corporation, early 1960s) and **Donald Davies** (UK's National │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

✦ The e2e-test-eks command completed successfully, confirming that the EKS deployment is fully operational and reachable. The test successfully
  triggered the research and content generation pipeline, resulting in a detailed Markdown course on the history of the internet.

Running the Web Interface

Start a connection to the Cloud Run deployed app:

http://a27c61bc6fb3c425ca13d862e0fe4aed-865627292.us-east-1.elb.amazonaws.com

Then connect to the app :

Then use online course generator:

Final Gemini CLI Code Review

As a final step — Gemini CLI was used for a full code review of the project:

✦ This multi-agent project is a well-engineered application of ADK 2.5 and the A2A protocol. The separation of specialized agents (Researcher,
  Judge, Content Builder) coordinated by a central Orchestrator demonstrates a mature microservice-oriented design.

Key Strengths
   * Coordinated Orchestration: The use of SequentialAgent and LoopAgent creates a robust, iterative research-judge cycle.
   * Resilient State Flow: The strategy of passing data through event content and "recovering" it via before_agent_callback heuristics is a clever
     way to handle state across distributed, independent session stores.
   * Polished Streaming: The web app's SSE implementation, specifically the greedy overlap deduplication (merge_strings) and system message
     cleanup, ensures a high-quality user experience despite the inherent noise in multi-agent LLM streams.
   * Cloud-Native Readiness: Using Identity Tokens for authenticated service-to-service communication and middleware for dynamic A2A URL rewriting
     makes the system ready for production deployment on Cloud Run.

Summary

The Agent Development Kit (ADK) was used to build a multi-agent system with A2A support using the Gemini Flash LLM Model. This application was tested locally with Gemini CLI and then deployed to AWS EKS. Finally, Gemini CLI was used for a complete project code review.