Google ADK + Docker Model Runner Agents

AI agents built using Google ADK and Docker Model Runner.

Available Agents

Agent	Description	Key Features
Sequential Agent	Code development pipeline	Write → Review → Refactor
Parallel Agent	Market intelligence analysis	Concurrent competitive/trend/sentiment analysis
Loop Agent	Iterative recipe development	Recipe creation with dietician feedback loops
Human-in-Loop	Travel planning with feedback	Human decision points in AI workflows
Google Search	Web research and synthesis	Live search with comprehensive reports
Find Job	Job market analysis	Career guidance and opportunity analysis

🏗️ Architecture

System Overview

┌─────────────────────────────────────┐
│        Application Layer            │ ← Your Agent Logic
├─────────────────────────────────────┤
│      🤖 Google ADK Framework       │ ← Agent Orchestration
│   • Multi-agent workflows          │
│   • State management               │
│   • Tool integration               │
├─────────────────────────────────────┤
│    📡 Centralized Configuration    │ ← Environment Detection
│   • Auto endpoint detection        │
│   • Container networking           │
│   • Model configuration            │
├─────────────────────────────────────┤
│      🔌 LiteLLM Abstraction        │ ← Model API Layer
├─────────────────────────────────────┤
│    🚢 Docker Model Runner          │ ← Local Inference
│   • llama.cpp engine               │
│   • OpenAI-compatible API          │
├─────────────────────────────────────┤
│      🧠 AI Model (Llama 3.2)       │ ← The Actual Model
└─────────────────────────────────────┘

Quick Start

Prerequisites

• Docker Desktop 4.40+ with Model Runner enabled
• Python 3.9+ (for local development)
• Google API Key (optional, for Google Search agents)

1. Pull and Run a Model

# Enable Docker Model Runner
docker desktop enable model-runner --tcp 12434

# Pull a model
docker model pull ai/llama3.2:1B-Q8_0

# Verify Model Runner is working
curl http://localhost:12434/engines/llama.cpp/v1/models

2. Clone and Setup

git clone https://github.com/dockersamples/google-adk-docker-model-runner.git
cd google-adk-docker-model-runner

# Copy environment template
cp agents/.env.example agents/.env

# Edit .env file with your configuration
nano agents/.env

Sample .env file

DOCKER_MODEL_RUNNER=http://localhost:12434/engines/llama.cpp/v1
MODEL_NAME=ai/llama3.2:1B-Q8_0
OPENAI_API_KEY=anything
GOOGLE_API_KEY=XXX
GOOGLE_GENAI_USE_VERTEXAI=FALSE
GOOGLE_CLOUD_LOCATION=us-central1
GOOGLE_CLOUD_PROJECT=XXX
LOG_LEVEL=DEBUG
DEV_MODE=true
LITELLM_DROP_PARAMS=true
LITELLM_TIMEOUT=180
LITELLM_REQUEST_TIMEOUT=120
LITELLM_MAX_RETRIES=5
LITELLM_LOG=DEBUG
OPENAI_API_TYPE=openai
OPENAI_API_BASE=http://localhost:12434/engines/llama.cpp/v1
OPENAI_MODEL_NAME=ai/llama3.2:1B-Q8_0
LITELLM_CONNECTION_TIMEOUT=60
LITELLM_READ_TIMEOUT=180
LITELLM_FALLBACK_MODEL=ai/llama3.2:1B-Q8_0
HTTPX_TIMEOUT=180

3. Run Locally

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Run specific agents
cd agents && adk web

Now open - http://localhost:8000

4. Run with Docker

# Build the image
docker build -t docker-adk-agents:v1 .

# Run with local .env variables
docker run -p 8000:8000 --env-file agents/.env docker-adk-agents:v1

Now open - http://localhost:8000

Sample prompts

Agent	Prompts
Sequential Agent	Write a HTML code with title and description for front main website page
Parallel Agent	Customer sentiment and feedback trends on Docker Model Runner and Docker AI
Loop Agent	Suggest some healthy recipe with paneer
Human-in-Loop	Plan a trip to dubai
Google Search	Share details about docker model runner features release
Find Job	Share job related to python