⚠️ DISCLAIMER: THIS IS NOT AN OFFICIALLY SUPPORTED GOOGLE PRODUCT. THIS PROJECT IS INTENDED FOR DEMONSTRATION PURPOSES ONLY. IT IS NOT INTENDED FOR USE IN A PRODUCTION ENVIRONMENT.

This demo shows how to enable A2A (Agent2Agent) protocol communication between purchasing concierge agent with the remote pizza and burger seller agents using A2A Python SDK. Burger and Pizza seller agents is a independent agent that can be run on different server with different frameworks, in this demo example we, burger agent is built on top of Crew AI and pizza agent is built on top of LangGraph.

Detailed tutorial about this repo: Getting Started with Agent2Agent (A2A) Protocol: A Purchasing Concierge and Remote Seller Agent Interactions with Gemini on Cloud Run

• If you are executing this project from your local IDE, Login to Gcloud using CLI with the following command :

  gcloud auth application-default login

• Enable the following APIs

  gcloud services enable aiplatform.googleapis.com 

• Install uv dependencies and prepare the python env

  curl -LsSf https://astral.sh/uv/install.sh | sh
  uv python install 3.12
  uv sync --frozen

First, we need to run the remote seller agents. We have two remote seller agents, one is burger agent and the other is pizza agent. We need to run them separately. These agents will serve the A2A Server

1. Copy the remote_seller_agents/burger_agent/.env.example to remote_seller_agents/burger_agent/.env.

2. Fill in the required environment variables in the .env file. Substitute GOOGLE_CLOUD_PROJECT with your Google Cloud Project ID.

   GOOGLE_CLOUD_LOCATION=us-central1
   GOOGLE_CLOUD_PROJECT={your-project-id}

3. Run the burger agent.

   cd remote_seller_agents/burger_agent
   uv sync --frozen
   uv run .

4. It will run on http://localhost:10001

1. Copy the remote_seller_agents/pizza_agent/.env.example to remote_seller_agents/pizza_agent/.env.

2. Fill in the required environment variables in the .env file. Substitute GOOGLE_CLOUD_PROJECT with your Google Cloud Project ID.

   GOOGLE_CLOUD_LOCATION=us-central1
   GOOGLE_CLOUD_PROJECT={your-project-id}

3. Run the pizza agent.

   cd remote_seller_agents/pizza_agent
   uv sync --frozen
   uv run .

4. It will run on http://localhost:10000

Finally, we can run our A2A client capabilities owned by purchasing concierge agent.

1. Go back to demo root directory ( where purchasing_concierge directory is located )

2. Copy the purchasing_concierge/.env.example to purchasing_concierge/.env.

3. Fill in the required environment variables in the .env file. Substitute GOOGLE_CLOUD_PROJECT with your Google Cloud Project ID. And fill in the PIZZA_SELLER_AGENT_URL and BURGER_SELLER_AGENT_URL with the URL of the remote seller agents.

   PIZZA_SELLER_AGENT_URL=http://localhost:10000
   BURGER_SELLER_AGENT_URL=http://localhost:10001
   GOOGLE_GENAI_USE_VERTEXAI=TRUE
   GOOGLE_CLOUD_PROJECT={your-project-id}
   GOOGLE_CLOUD_LOCATION=us-central1

4. Run the purchasing concierge agent with the adk web dev UI

   uv sync --frozen
   uv run adk web

Run the following command

gcloud run deploy burger-agent \
    --source remote_seller_agents/burger_agent \
    --port=8080 \
    --allow-unauthenticated \
    --min 1 \
    --region us-central1 \
    --update-env-vars GOOGLE_CLOUD_LOCATION=us-central1 \
    --update-env-vars GOOGLE_CLOUD_PROJECT={your-project-id}

Run the following command

gcloud run deploy pizza-agent \
    --source remote_seller_agents/pizza_agent \
    --port=8080 \
    --allow-unauthenticated \
    --min 1 \
    --region us-central1 \
    --update-env-vars GOOGLE_CLOUD_PROJECT={your-project-id} \
    --update-env-vars GOOGLE_CLOUD_LOCATION=us-central1

1. Create the staging bucket first

   gcloud storage buckets create gs://purchasing-concierge-{your-project-id} --location=us-central1

2. Copy the .env.example to .env.

3. Fill in the required environment variables in the .env file. Substitute GOOGLE_CLOUD_PROJECT with your Google Cloud Project ID.

   GOOGLE_GENAI_USE_VERTEXAI=TRUE
   GOOGLE_CLOUD_PROJECT={your-project-id}
   GOOGLE_CLOUD_LOCATION=us-central1
   STAGING_BUCKET=gs://purchasing-concierge-{your-project-id}
   PIZZA_SELLER_AGENT_URL={your-pizza-agent-url}
   BURGER_SELLER_AGENT_URL={your-burger-agent-url}

4. Deploy the purchasing concierge agent to agent engine

   uv sync --frozen
   uv run deploy_to_agent_engine.py

1. Update the .env file with the AGENT_ENGINE_RESOURCE_NAME which obtained from the previous step.

2. Run the Gradio app

uv sync --frozen
uv run purchasing_concierge_ui.py