Deploy AG2 Agent to Google Cloud Platform - AG2

Deploy to GCP

This guide walks you through deploying an AG2 conversational agent to Google Cloud Platform using Cloud Run. Your agent will be exposed as a REST API using FastAPI and can scale automatically based on traffic.

Prerequisites

Before you begin, ensure you have:

Google Cloud Platform Account with billing enabled
Google Cloud SDK (gcloud CLI) installed
Python 3.13+ installed
Gemini API Key from Google AI Studio
Docker

Step 1: Set Up Your Local Environment

Install Dependencies

First, create a virtual environment and install the required packages:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

Configure Environment Variables

Create a .env file in your project root:

GEMINI_API_KEY=your_gemini_api_key_here

Important: Never commit your .env file to version control.

Step 2: Test Locally

Before deploying, test your agent locally to ensure everything works:

# Run the FastAPI server
python agent.py

The server will start on http://localhost:8080. Test it with:

# Health check
curl http://localhost:8080/

# Chat endpoint
curl -X POST http://localhost:8080/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "A joke about NYC.", "max_turn": 1}'

Step 3: Set Up Google Cloud Project

Create a New Project

You can create a project via the console or using the gcloud CLI:

gcloud projects create YOUR_PROJECT_ID --name="AG2 Agent Project"
gcloud config set project YOUR_PROJECT_ID

Enable Required APIs

Enable the necessary Google Cloud APIs:

gcloud services enable \
  cloudbuild.googleapis.com \
  run.googleapis.com \
  artifactregistry.googleapis.com

Authenticate

Set up authentication for your local machine:

gcloud auth login
gcloud auth application-default login
gcloud auth configure-docker

Create Artifact Registry Repository

Create a Docker repository to store your container images:

gcloud artifacts repositories create ag2-agent-repo2 \
  --repository-format=docker \
  --location=us-central1 \
  --description="Docker repository for AG2 agent"

Step 4: Configure Deployment Files

Update deploy.sh

Edit deploy.sh and update the project-specific variables:

PROJECT_ID="your-actual-project-id"
REGION="us-central1"
IMAGE_NAME="ag2-agent"
REPOSITORY="ag2-agent-repo2"  # Match your Artifact Registry repo name

Update cloudbuild.yaml (Optional)

If using Cloud Build, update the substitutions:

substitutions:
  _REGION: 'us-central1'
  _REPOSITORY: 'ag2-agent-repo2'
  _IMAGE_NAME: 'ag2-agent'

Step 5: Deploy to Cloud Run

Quick Deployment

The simplest way to deploy is using the provided script:

# Set your Gemini API key
export GEMINI_API_KEY=your_gemini_api_key_here

# Make script executable and run
chmod +x deploy.sh
./deploy.sh

The script will: 1. Build your Docker image 2. Push it to Artifact Registry 3. Deploy to Cloud Run 4. Display your service URL

Manual Deployment

If you prefer manual control, deploy step by step:

PROJECT_ID="your-project-id"
REGION="us-central1"
REPOSITORY="ag2-agent-repo2"
IMAGE_NAME="ag2-agent"

# Build and push image
gcloud builds submit --tag ${REGION}-docker.pkg.dev/${PROJECT_ID}/${REPOSITORY}/${IMAGE_NAME}:latest

# Deploy to Cloud Run
gcloud run deploy ${IMAGE_NAME} \
  --image ${REGION}-docker.pkg.dev/${PROJECT_ID}/${REPOSITORY}/${IMAGE_NAME}:latest \
  --platform managed \
  --region ${REGION} \
  --allow-unauthenticated \
  --set-env-vars GEMINI_API_KEY=${GEMINI_API_KEY} \
  --memory 2Gi \
  --cpu 2 \
  --timeout 3600 \
  --port 8080

Step 6: Verify Deployment

Get Your Service URL

gcloud run services describe ag2-agent --region=us-central1 --format="value(status.url)"

Test the Deployed Service

Test your deployed agent:

# Health check
curl https://your-service-url.run.app/

# Chat endpoint
curl -X POST https://your-service-url.run.app/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "A joke about NYC.", "max_turn": 1}'

Example Output

When you call the chat endpoint, you should receive a response like:

{
  "response": "Why did the New Yorker go to therapy? Because they had too many issues!"
}

Complete Deployment Example

Here are all the files you need for a complete deployment:

agent.py

from autogen import ConversableAgent, LLMConfig
import os
from dotenv import load_dotenv
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import uvicorn

load_dotenv()

app = FastAPI()

Gemini_API_KEY = os.getenv("GEMINI_API_KEY")

# Configure LLM
llm_config = LLMConfig(
    config_list={
        "model": "gemini-2.5-flash",
        "api_type": "google",
        "api_key": Gemini_API_KEY,
    }
)

# Initialize agent
assistant = ConversableAgent(
    "assistant",
    system_message="You are a helpful assistant",
    llm_config=llm_config,
    human_input_mode="TERMINATE"
)

class MessageRequest(BaseModel):
    message: str
    max_turn: int = 1

@app.get("/")
def health_check():
    return {"status": "healthy"}

@app.post("/chat")
def chat(request: MessageRequest):
    try:
        response = assistant.run(
            messages=request.message,
            max_turn=request.max_turn
        )
        result = response.process()
        return {"response": str(result)}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

if __name__ == "__main__":
    port = int(os.getenv("PORT", 8080))
    uvicorn.run(app, host="0.0.0.0", port=port)

requirements.txt

ag2[openai]>=0.9.9,<0.10.0
ag2[gemini]
python-dotenv>=1.0.0
fastapi>=0.104.0
uvicorn[standard]>=0.24.0
pydantic>=2.0.0

Dockerfile

FROM python:3.13-slim

WORKDIR /app

# Install system dependencies
RUN apt-get update && apt-get install -y \
    gcc \
    && rm -rf /var/lib/apt/lists/*

# Copy requirements and install Python dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy application code
COPY agent.py .

# Create coding directory for code execution
RUN mkdir -p coding

# Expose port
EXPOSE 8080

# Run the application
CMD ["python", "agent.py"]

cloudbuild.yaml

steps:
  # Build the container image
  - name: 'gcr.io/cloud-builders/docker'
    args: ['build', '-t', '${_REGION}-docker.pkg.dev/$PROJECT_ID/${_REPOSITORY}/${_IMAGE_NAME}:$COMMIT_SHA', '.']

# Push the container image
  - name: 'gcr.io/cloud-builders/docker'
    args: ['push', '${_REGION}-docker.pkg.dev/$PROJECT_ID/${_REPOSITORY}/${_IMAGE_NAME}:$COMMIT_SHA']

# Tag as latest
  - name: 'gcr.io/cloud-builders/docker'
    args: ['tag', '${_REGION}-docker.pkg.dev/$PROJECT_ID/${_REPOSITORY}/${_IMAGE_NAME}:$COMMIT_SHA', '${_REGION}-docker.pkg.dev/$PROJECT_ID/${_REPOSITORY}/${_IMAGE_NAME}:latest']

- name: 'gcr.io/cloud-builders/docker'
    args: ['push', '${_REGION}-docker.pkg.dev/$PROJECT_ID/${_REPOSITORY}/${_IMAGE_NAME}:latest']

substitutions:
  _REGION: 'us-central1'
  _REPOSITORY: 'ag2-agent-repo2'
  _IMAGE_NAME: 'ag2-agent'

images:
  - '${_REGION}-docker.pkg.dev/$PROJECT_ID/${_REPOSITORY}/${_IMAGE_NAME}:$COMMIT_SHA'
  - '${_REGION}-docker.pkg.dev/$PROJECT_ID/${_REPOSITORY}/${_IMAGE_NAME}:latest'

deploy.sh

#!/bin/bash

# Set your GCP project ID
PROJECT_ID="your-project-id"
REGION="us-central1"
IMAGE_NAME="ag2-agent"
REPOSITORY="ag2-agent-repo2"  # Artifact Registry repo name

# Set the project
gcloud config set project ${PROJECT_ID}

# Build and push the image to Artifact Registry
echo "Building Docker image..."
gcloud builds submit --tag ${REGION}-docker.pkg.dev/${PROJECT_ID}/${REPOSITORY}/${IMAGE_NAME}:latest

# Deploy to Cloud Run (Recommended for web APIs)
echo "Deploying to Cloud Run..."
gcloud run deploy ${IMAGE_NAME} \
  --image ${REGION}-docker.pkg.dev/${PROJECT_ID}/${REPOSITORY}/${IMAGE_NAME}:latest \
  --platform managed \
  --region ${REGION} \
  --allow-unauthenticated \
  --set-env-vars GEMINI_API_KEY=${GEMINI_API_KEY} \
  --memory 2Gi \
  --cpu 2 \
  --timeout 3600 \
  --port 8080

echo "Deployment complete!"
echo "Get your service URL:"
gcloud run services describe ${IMAGE_NAME} --region=${REGION} --format="value(status.url)"

Troubleshooting

Container Failed to Start

If you see an error about the container not listening on port 8080, ensure your agent.py uses the PORT environment variable:

if __name__ == "__main__":
    port = int(os.getenv("PORT", 8080))
    uvicorn.run(app, host="0.0.0.0", port=port)

Authentication Errors

If you encounter authentication issues:

gcloud auth login
gcloud auth application-default login
gcloud auth configure-docker

View Logs

Check your deployment logs:

gcloud run services logs read ag2-agent --region=us-central1