### Frontend Setup and Execution (Manual)

Source: https://github.com/google/adk-samples/blob/main/python/agents/retail-ai-location-strategy/app/frontend/README.md

Manually set up the frontend by navigating to the app/frontend directory, installing Node.js dependencies, creating the environment file, and starting the development server.

```bash
# In a new terminal, from retail-ai-location-strategy directory
cd app/frontend

# Install dependencies
npm install

# Create environment file
cp .env.local.example .env.local

# Start the frontend
npm run dev
# App runs at http://localhost:3000
```

--------------------------------

### Install dependencies and start servers

Source: https://github.com/google/adk-samples/blob/main/python/agents/deep-search/README.md

Installs project dependencies and starts the development servers. Assumes you are in the project directory.

```bash
cd my-deep-search-agent && make install && make dev
```

--------------------------------

### Backend Setup and Execution (Manual)

Source: https://github.com/google/adk-samples/blob/main/python/agents/retail-ai-location-strategy/app/frontend/README.md

Manually set up the backend by navigating to the app/frontend/backend directory, installing Python dependencies, configuring environment variables with API keys, and starting the FastAPI server.

```bash
# From the retail-ai-location-strategy directory
cd app/frontend/backend

# Install dependencies
pip install -r requirements.txt

# Make sure app/.env has your API keys:
# GOOGLE_API_KEY=your_key
# MAPS_API_KEY=your_maps_key

# Start the backend
python main.py
# Server runs at http://localhost:8000
```

--------------------------------

### Install and Run Backend

Source: https://github.com/google/adk-samples/blob/main/python/agents/ambient-expense-agent/README.md

Installs dependencies and starts the backend development server.

```bash
make install && make dev
```

--------------------------------

### Install and Run Frontend

Source: https://github.com/google/adk-samples/blob/main/python/agents/ambient-expense-agent/README.md

Installs dependencies and starts the frontend development server for the approval UI.

```bash
make install-frontend && make dev-frontend
```

--------------------------------

### Manual Setup for AG-UI Frontend

Source: https://github.com/google/adk-samples/blob/main/python/agents/retail-ai-location-strategy/README.md

Alternative manual setup instructions for the AG-UI frontend. This involves starting the backend and frontend servers in separate terminals. Ensure you follow the specified directory changes and installation steps.

```bash
# Terminal 1: Start the backend
cd app/frontend/backend
uv pip install -r requirements.txt
uv run main.py
# Runs at http://localhost:8000

# Terminal 2: Start the frontend
cd app/frontend
npm install
cp .env.local.example .env.local
npm run dev
# Runs at http://localhost:3000
```

--------------------------------

### Setup Environment for Local Development

Source: https://github.com/google/adk-samples/blob/main/go/agents/navallist/README.md

Copy the example environment configuration file. Ensure you add your Google Gemini API Key to the .env file.

```bash
cp .env.example .env
```

--------------------------------

### Agent Project Setup and Deployment

Source: https://github.com/google/adk-samples/blob/main/python/agents/small-business-loan-agent/README.md

Navigates into the created agent project directory and runs installation, testing, and backend setup commands using the provided Makefile.

```bash
cd target/my-loan-agent && make install && make test && make backend
```

--------------------------------

### Install Dependencies and Run Agent Locally

Source: https://github.com/google/adk-samples/blob/main/python/agents/retail-ai-location-strategy/README.md

Navigate to your newly created project directory, install all necessary dependencies, and start the agent in development mode. The agent will be accessible at http://localhost:8501.

```bash
cd my-retail-agent && make install && make dev
```

--------------------------------

### Project Setup with Make Commands

Source: https://github.com/google/adk-samples/blob/main/python/agents/small-business-loan-agent/README.md

Use make commands to enable necessary APIs, create a Firestore database, set up a GCS bucket, and grant permissions. Ensure you have the 'make' utility installed.

```bash
make enable-services
make create-firestore
make setup-gcs
make grant-permissions
```

--------------------------------

### MCP Toolbox Server Output Example

Source: https://github.com/google/adk-samples/blob/main/python/agents/software-bug-assistant/README.md

This is an example of the expected output when the MCP Toolbox server starts successfully, indicating the number of initialized sources, auth services, tools, and toolsets.

```bash
2025-05-30T02:06:57.479344419Z INFO "Initialized 1 sources." 
2025-05-30T02:06:57.479696869Z INFO "Initialized 0 authServices." 
2025-05-30T02:06:57.479973769Z INFO "Initialized 9 tools." 
2025-05-30T02:06:57.480054519Z INFO "Initialized 2 toolsets." 
2025-05-30T02:06:57.480739499Z INFO "Server ready to serve!" 
```

--------------------------------

### Install agent-starter-pack CLI with uvx

Source: https://github.com/google/adk-samples/blob/main/python/agents/short-movie-agents/GEMINI.md

Recommended method for zero-install and automation. Run directly without prior installation.

```bash
uvx agent-starter-pack create ...
```

--------------------------------

### Clone Repository and Navigate

Source: https://github.com/google/adk-samples/blob/main/python/agents/order-processing/README.md

Clone the ADK samples repository and navigate to the order-processing agent directory. This is the starting point for agent setup.

```bash
git clone https://github.com/google/adk-samples.git
cd python/agents/order-processing
```

--------------------------------

### Automated CI/CD Setup

Source: https://github.com/google/adk-samples/blob/main/python/agents/short-movie-agents/GEMINI.md

Initiate the automated CI/CD setup process from the project root. This command guides you through or can be automated with flags.

```bash
uvx agent-starter-pack setup-cicd
```

--------------------------------

### Install Dependencies with uv

Source: https://github.com/google/adk-samples/blob/main/python/agents/invoice-processing/README.md

Navigate to the agent directory and install dependencies using uv.

```bash
cd agents/invoice-processing

uv sync
```

--------------------------------

### Install agent-starter-pack CLI with pip

Source: https://github.com/google/adk-samples/blob/main/python/agents/short-movie-agents/GEMINI.md

Install the CLI within a virtual environment using pip.

```bash
pip install agent-starter-pack
```

--------------------------------

### Install Dependencies and Authenticate

Source: https://github.com/google/adk-samples/blob/main/python/agents/brand-aligned-presentations/README.md

Install project dependencies using uv and authenticate with Google Cloud.

```bash
uv sync --dev
gcloud auth application-default login
```

--------------------------------

### Start Local Database and Migrations

Source: https://github.com/google/adk-samples/blob/main/go/agents/navallist/README.md

Use the Makefile to start the local PostgreSQL database and apply necessary migrations.

```bash
make db-start
```

--------------------------------

### Install Dependencies

Source: https://github.com/google/adk-samples/blob/main/python/agents/retail-ai-location-strategy/DEVELOPER_GUIDE.md

Ensure all project dependencies are installed by running `uv sync` or `make install` to resolve import errors. Also, verify that all `__init__.py` files export the required modules.

```bash
uv sync
# or
make install
```

--------------------------------

### Clone Repository and Install Dependencies

Source: https://github.com/google/adk-samples/blob/main/python/agents/parallel_task_decomposition_execution/README.md

Clones the repository, navigates to the agent directory, and installs project dependencies using uv.

```bash
git clone https://github.com/google/adk-samples.git
cd adk-samples/python/agents/parallel_task_decomposition_execution
curl -LsSf https://astral.sh/uv/install.sh | sh
uv sync --dev
```

--------------------------------

### Initial Project Setup and Configuration

Source: https://github.com/google/adk-samples/blob/main/python/agents/small-business-loan-agent/README.md

Set up your GCP environment by logging in, setting the project ID, and configuring gcloud. This is a prerequisite for running the agent.

```bash
gcloud auth application-default login
export PROJECT_ID=your-project-id
gcloud config set project $PROJECT_ID
```

--------------------------------

### Disable Keyring for Poetry Installation (Linux)

Source: https://github.com/google/adk-samples/blob/main/python/agents/order-processing/README.md

If encountering keyring errors during Poetry installation on Linux, disable keyring support. This is a one-time setup.

```bash
poetry config keyring.enabled false
```

--------------------------------

### Get Gallery Images Output Example

Source: https://github.com/google/adk-samples/blob/main/python/agents/genmedia-for-commerce/genmedia4commerce/workflows/other/README.md

Example JSON output from the /get_gallery_images endpoint, listing product folders and their associated images with URLs and names.

```json
{
  "products": [
    {
      "folder_name": "product_001",
      "images": [
        {"url": "/other/images/products/product_001/front.jpg", "name": "front.jpg"}
      ]
    }
  ]
}
```

--------------------------------

### Setup BigQuery Environment

Source: https://github.com/google/adk-samples/blob/main/python/agents/hierarchical-workflow-automation/README.md

Enable BigQuery integration and optionally create sample data for testing.

```bash
# Enable BigQuery Integration in .env file:
USE_BIGQUERY=true

# Optional: Create sample data for testing
python bigquery_utils/create_bigquery_environment.py
```

--------------------------------

### Clone Repository and Navigate

Source: https://github.com/google/adk-samples/blob/main/python/agents/brand-aligned-presentations/README.md

Clone the agent repository and navigate into the project directory to begin setup.

```bash
git clone <your-repository-url>
cd <your-repository-directory>
```

--------------------------------

### Navigate to Client Directory

Source: https://github.com/google/adk-samples/blob/main/python/agents/realtime-conversational-agent/README.md

Change the current directory to the client folder to begin setup.

```bash
cd client
```

--------------------------------

### Clone the adk-samples Repository

Source: https://github.com/google/adk-samples/blob/main/python/agents/genmedia-for-commerce/README.md

Clone the repository to get the GenMedia for Commerce Agent code. Ensure you have Git installed.

```bash
git clone https://github.com/google/adk-samples.git
cd adk-samples/python/agents/genmedia-for-commerce
```

--------------------------------

### Run MCP Toolkit Server

Source: https://github.com/google/adk-samples/blob/main/python/agents/data-science/README.md

Start the MCP Toolkit server using the specified configuration file for AlloyDB local setup.

```bash
./toolbox --tools-file "toolbox-alloydb-local.yaml"
```

--------------------------------

### Navigate to the sample directory

Source: https://github.com/google/adk-samples/blob/main/python/agents/gemma-food-tour-guide/README.md

Change directory into the specific agent sample.

```bash
cd adk-samples/python/agents/gemma-food-tour-guide
```

--------------------------------

### Create Project from Sample

Source: https://github.com/google/adk-samples/blob/main/python/agents/marketing-agency/README.md

Creates a new project from the marketing-agency sample using the Google Agents CLI.

```bash
agents-cli create my-marketing-agency -a adk@marketing-agency
cd my-marketing-agency
```

--------------------------------

### Setup Locust Virtual Environment

Source: https://github.com/google/adk-samples/blob/main/python/agents/economic-research-agent/tests/load_test/README.md

Create and activate a virtual environment for Locust and install the specified version. This isolates Locust dependencies.

```bash
python3 -m venv locust_env && source locust_env/bin/activate && pip install locust==2.31.1
```

--------------------------------

### Clone the agent repository

Source: https://github.com/google/adk-samples/blob/main/python/agents/incident-management/README.md

Clones the ADK samples repository and navigates into the incident management agent directory. This is the starting point for agent setup.

```bash
git clone https://github.com/google/adk-samples.git
cd python/agents/incident-management
```

--------------------------------

### Configure Environment Variables

Source: https://github.com/google/adk-samples/blob/main/python/agents/gemma-food-tour-guide/README.md

Set up your API keys by renaming the example environment file and filling in your credentials.

```bash
# Rename .env.example to .env and fill in your keys:
# - MAPS_API_KEY: Your Google Maps API Key
# - GEMINI_API_KEY: Your API Key from AI Studio
```

--------------------------------

### Navigate to Server Directory

Source: https://github.com/google/adk-samples/blob/main/python/agents/realtime-conversational-agent/README.md

Change the current directory to the server folder to begin setup.

```bash
cd server
```

--------------------------------

### Clone ADK Python Samples Repository

Source: https://github.com/google/adk-samples/blob/main/python/README.md

Clone the repository to get started with the ADK Python samples. Navigate into the cloned directory to proceed.

```bash
git clone https://github.com/google/adk-samples.git
cd adk-samples/python
```

--------------------------------

### Setup Vector Search Datastore

Source: https://github.com/google/adk-samples/blob/main/python/agents/multiformat-hybrid-rag/data_ingestion_pipeline/README.md

Provision the Vector Search 2.0 Collection, GCS bucket, and service account. Requires terraform to be installed and configured.

```bash
make setup-datastore
```

--------------------------------

### Clone ADK Samples Repository

Source: https://github.com/google/adk-samples/blob/main/typescript/README.md

Clone the ADK samples repository to get started with the TypeScript samples. Navigate into the cloned directory to begin exploring.

```bash
git clone https://github.com/google/adk-samples.git
cd adk-samples/typescript
```

--------------------------------

### Install and Run AG-UI Frontend with Makefile

Source: https://github.com/google/adk-samples/blob/main/python/agents/retail-ai-location-strategy/app/frontend/README.md

Use the provided Makefile for a streamlined installation and execution of both the backend and frontend components. Ensure you are in the retail-ai-location-strategy directory.

```bash
# From retail-ai-location-strategy directory
make ag-ui-install  # First time only
make ag-ui          # Starts both backend (8000) and frontend (3000)
```

--------------------------------

### Example Agent Interaction

Source: https://github.com/google/adk-samples/blob/main/python/agents/fomc-research/README.md

Demonstrates a typical user interaction with the FOMC Research agent, starting with a greeting and then providing a specific FOMC meeting date for analysis.

```bash
$ adk run .
Log setup complete: /tmp/agents_log/agent.20250405_140937.log
To access latest log: tail -F /tmp/agents_log/agent.latest.log
Running agent root_agent, type exit to exit.
user: Hello. What can you do for me?
[root_agent]: I can help you analyze past Fed Open Market Committee (FOMC) meetings and provide you with a thorough analysis report. To start, please provide the date of the meeting you would like to analyze. If you have already provided it, please confirm the date. I need the date in ISO format (YYYY-MM-DD).

user: 2025-01-29
[analysis_agent]: Here is a summary and analysis of the January 29, 2025 FOMC meeting, based on the available information:
...
```

--------------------------------

### Clone Repository and Navigate

Source: https://github.com/google/adk-samples/blob/main/python/agents/cyber-guardian-agent/README.md

Clone the adk-samples repository and navigate to the cyber-guardian-agent directory to begin setup.

```bash
git clone https://github.com/google/adk-samples.git
cd adk-samples/python/agents/cyber-guardian-agent
```

--------------------------------

### Interact with Deployed Agent

Source: https://github.com/google/adk-samples/blob/main/python/agents/llm-auditor/README.md

Programmatically interact with a deployed agent using Python. This example shows how to get an agent engine, create a session, and stream queries.

```python
from vertexai import agent_engines

agent_engine_id = "AGENT_ENGINE_ID"
user_input = "Double check this: Earth is further away from the Sun than Mars."

agent_engine = agent_engines.get(agent_engine_id)
session = agent_engine.create_session(user_id="new_user")
for event in agent_engine.stream_query(
    user_id=session["user_id"], session_id=session["id"], message=user_input
):
    for part in event["content"]["parts"]:
        print(part["text"])
```

--------------------------------

### Enabling Comet Opik Tracing (Conceptual)

Source: https://github.com/google/adk-samples/blob/main/python/agents/short-movie-agents/GEMINI.md

This is a conceptual example of how to enable tracing with Comet Opik. Ensure you have installed the necessary package. Call enable_opik_tracing() at application startup.

```python
# Example using Comet Opik integration (conceptual)
# pip install comet_opik_adk
# from comet_opik_adk import enable_opik_tracing
# enable_opik_tracing() # Call at app startup
# Then run your ADK app, traces appear in Comet workspace.
```

--------------------------------

### Setup and Configuration

Source: https://github.com/google/adk-samples/blob/main/python/agents/multiformat-hybrid-rag/pipeline_walkthrough.ipynb

Initializes the project environment, sets the current working directory, and prints configuration details for the RAG pipeline, including model names and chunking parameters.

```python
import os
import sys
from pathlib import Path

PROJECT_ROOT = Path.cwd()
sys.path.insert(0, str(PROJECT_ROOT))
os.chdir(PROJECT_ROOT)

from src.utils.config import config, genai_client

print(f"Project:          {config.project_id}")
print(f"Region:           {config.region}")
print(f"Extraction model: {config.markdown_converter_gemini_model}")
print(f"Relevance model:  {config.relevance_gemini_model}")
print(f"Enrichment model: {config.contextual_chunking_gemini_model}")
print(f"Chunk size:       {config.chunk_size}")
print(f"Chunk overlap:    {config.chunk_overlap}")
```

--------------------------------

### Run RAG Corpus Preparation Script (Default)

Source: https://github.com/google/adk-samples/blob/main/python/agents/RAG/README.md

Execute the preparation script to set up a RAG corpus and upload the default Alphabet 10-K PDF. This is the simplest way to get started.

```bash
uv run python rag/shared_libraries/prepare_corpus_and_data.py
```

--------------------------------

### Clone Repo and Set Up Environment

Source: https://github.com/google/adk-samples/blob/main/python/agents/agent-skills-tutorial/README.md

Clone the repository, navigate to the directory, and set up a Python virtual environment. Install project dependencies using pip.

```bash
git clone https://github.com/GoogleCloudPlatform/adk-samples.git
cd adk-samples/python/agents/agent-skills-tutorial

python3 -m venv .venv && source .venv/bin/activate
pip install -e .
```

--------------------------------

### Start MCP Inspector

Source: https://github.com/google/adk-samples/blob/main/java/agents/software-bug-assistant/README.md

Install and run the web-based MCP Inspector using npx to verify the MCP Toolbox server is running. Connect to the server via SSE and list available tools.

```bash
npx @modelcontextprotocol/inspector
```

--------------------------------

### Run the Client

Source: https://github.com/google/adk-samples/blob/main/python/agents/realtime-conversational-agent/README.md

Start the Next.js development server. The client will be accessible at http://localhost:3000.

```bash
npm run dev
```

--------------------------------

### Clone Repository and Navigate

Source: https://github.com/google/adk-samples/blob/main/python/agents/airflow_version_upgrade_agent/README.md

Clone the repository and navigate into the project directory to begin local development.

```bash
git clone <your-repo-url>
cd <your-repo-folder>
```

--------------------------------

### Run the agent locally with ADK playground

Source: https://github.com/google/adk-samples/blob/main/python/agents/high-volume-document-analyzer/README.md

Installs development dependencies and starts the agent locally using 'uv run adk web'. This command launches the ADK interactive playground on port 8080.

```bash
uv sync --dev
uv run adk web . --port 8080 --reload_agents
```

--------------------------------

### Configure and Source MCP Toolkit Environment Variables

Source: https://github.com/google/adk-samples/blob/main/python/agents/data-science/README.md

Copy the example environment file, populate it with your project's details, and then source it to apply the variables.

```bash
. ./toolbox_env.sh
```

--------------------------------

### Install and Sync Dependencies

Source: https://github.com/google/adk-samples/blob/main/python/agents/llm-auditor/README.md

Installs the project dependencies using uv. Ensure you have Python 3.10+ and uv installed.

```bash
git clone https://github.com/google/adk-samples.git
cd adk-samples/python/agents/llm-auditor
uv sync
```

--------------------------------

### Install Poetry

Source: https://github.com/google/adk-samples/blob/main/python/agents/camel/README.md

Installs Poetry, a dependency management and packaging tool for Python. Ensure Python 3.12+ is installed.

```bash
pip install poetry
```

--------------------------------

### Install Dependencies with uv

Source: https://github.com/google/adk-samples/blob/main/python/agents/supply-chain/README.md

Install project dependencies using the 'uv' package manager. Verify 'uv' installation first.

```bash
# Verify if uv is installed correctly
uv --version

# Sync dependencies.
uv sync
```

--------------------------------

### Install Agent Dependencies

Source: https://github.com/google/adk-samples/blob/main/python/agents/google-trends-agent/README.md

Installs the agent package and its dependencies using uv. Ensure you have uv installed and are in the agent's directory.

```bash
cd adk-samples/python/agents/google-trends-agent
uv sync --dev
```

--------------------------------

### Run Programmatic Example

Source: https://github.com/google/adk-samples/blob/main/python/agents/travel-concierge/README.md

Execute the test client code to observe agent interactions and responses.

```bash
python tests/programmatic_example.py 
```

--------------------------------

### Install uv package manager

Source: https://github.com/google/adk-samples/blob/main/python/agents/RAG/README.md

Installs the uv package manager using a curl script. Ensure you have Python 3.10+ installed.

```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
```

--------------------------------

### Clone Repository for Manual Setup

Source: https://github.com/google/adk-samples/blob/main/python/agents/retail-ai-location-strategy/README.md

Clone the ADK samples repository to begin the manual setup process for the retail AI location strategy agent. This step is necessary to access the project files.

```bash
git clone https://github.com/google/adk-samples.git
cd adk-samples/python/agents/retail-ai-location-strategy
```

--------------------------------

### Install BigQuery ADK Support

Source: https://github.com/google/adk-samples/blob/main/python/agents/hierarchical-workflow-automation/cookie_scheduler_agent/bigquery_utils/BIGQUERY_SETUP.md

Installs the Google ADK with BigQuery support using pip. Verifies the installation by importing the BigQuery Toolset.

```bash
# Install ADK with BigQuery support
pip install google-adk[bigquery]

# Verify installation
python -c "from google.adk.tools.bigquery import BigQueryToolset; print('ADK BigQuery available')"
```

--------------------------------

### Configure Environment Variables for Deployment

Source: https://github.com/google/adk-samples/blob/main/python/agents/airflow_version_upgrade_agent/README.md

Copy the example environment file and configure your project-specific variables for deployment. Ensure these variables are sourced before proceeding with deployment.

```bash
cp .env.example .env
# Open .env and fill in your details (PROJECT_ID, GOOGLE_CLOUD_LOCATION, SERVICE_NAME, etc.)
# Make sure to source your env variables before proceeding, or let your deployment pipeline load them.
source .env
```

--------------------------------

### Install dependencies with pip

Source: https://github.com/google/adk-samples/blob/main/python/agents/bidi-demo/README.md

Installs project dependencies using pip and creates a virtual environment. Activate the environment before running pip install.

```bash
python3 -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
pip install -e .
```

--------------------------------

### Create a virtual environment

Source: https://github.com/google/adk-samples/blob/main/python/agents/gemma-food-tour-guide/README.md

Set up a Python virtual environment for the project.

```bash
python -m venv .venv
```

--------------------------------

### Clone Repository and Navigate

Source: https://github.com/google/adk-samples/blob/main/python/agents/youtube-analyst/README.md

Clone the repository and navigate to the agent's directory. This is the first step for local development.

```bash
git clone https://github.com/google/adk-samples.git
cd adk-samples/python/agents/youtube-analyst
```

--------------------------------

### Clone Repository and Install Dependencies

Source: https://github.com/google/adk-samples/blob/main/python/agents/medical-pre-authorization/README.md

Clones the ADK samples repository and installs project dependencies using uv. Ensure uv is installed first.

```bash
# Clone this repository.
git clone https://github.com/google/adk-samples.git
cd adk-samples/python/agents/medical-pre-authorization

# Verify if uv is installed correctly
uv --version

# Sync dependencies.
uv sync
```

--------------------------------

### Install Dependencies with uv

Source: https://github.com/google/adk-samples/blob/main/python/agents/economic-research-agent/README.md

Installs and synchronizes project dependencies using the `uv` package manager. Ensure `uv` is installed and configured for development dependencies.

```bash
# Create and synchronize the virtual environment
uv sync --dev
```

--------------------------------

### Automated agent project creation example

Source: https://github.com/google/adk-samples/blob/main/python/agents/short-movie-agents/GEMINI.md

Example of creating an agent project with specified template, deployment target, region, and auto-approval for automation.

```bash
uvx agent-starter-pack create my-automated-agent \
  -a adk_base \
  -d cloud_run \
  --region europe-west4 \
  --auto-approve
```

--------------------------------

### Configure API Key

Source: https://github.com/google/adk-samples/blob/main/python/agents/agent-skills-tutorial/README.md

Copy the example environment file and edit it to include your Google API key.

```bash
cp .env.example app/.env
# Edit app/.env with your GOOGLE_API_KEY
```

--------------------------------

### Install Dependencies

Source: https://github.com/google/adk-samples/blob/main/python/agents/gemma-food-tour-guide/README.md

Install all required Python packages for the agent.

```bash
pip install -r requirements.txt
```

--------------------------------

### Install Python Dependencies

Source: https://github.com/google/adk-samples/blob/main/python/agents/realtime-conversational-agent/README.md

Install the necessary Python packages for the server.

```bash
uv pip install .
```

--------------------------------

### Install and Run AG-UI Frontend

Source: https://github.com/google/adk-samples/blob/main/python/agents/retail-ai-location-strategy/README.md

Use these commands to install frontend dependencies and run both the backend and frontend servers for the AG-UI interface. The backend runs on http://localhost:8000 and the frontend on http://localhost:3000.

```bash
# First time: Install frontend dependencies
make ag-ui-install

# Run both backend and frontend servers
make ag-ui
```

--------------------------------

### Install Dependencies

Source: https://github.com/google/adk-samples/blob/main/python/agents/blog-writer/tests/README.md

Install required dependencies for running the blogger-agent tests.

```bash
uv pip install -r requirements.txt
```

--------------------------------

### Install Project Dependencies and Configure

Source: https://github.com/google/adk-samples/blob/main/python/agents/RAG/README.md

Run this command after creating the project. It prompts to build the RAG Corpus and configure the .env file.

```bash
make install
```