VibePrompting for AI Agents

Transform your Google ADK agents with intelligent prompting and dynamic conversation flows.

Features

• AI Agent Enhancement: Upload your Google ADK agent and enhance it with intelligent prompting capabilities
• Dynamic Conversations: Create branching conversation flows and dynamic agent responses
• Real-time Processing: Process and modify your agents in real-time with instant feedback
• File Upload: Upload ZIP files containing Google ADK agent folders for processing

Getting Started

Prerequisites

• Python 3.8+
• Node.js 18+
• Google ADK (Agent Development Kit)

Installation

1. Clone the repository:

git clone <repository-url>
cd BerkeleyAI

2. Install backend dependencies:

pip install -r requirements.txt

3. Install frontend dependencies:

cd frontend
npm install

Running the Application

1. Start the Flask backend (from the project root):

python app.py

2. Start the Next.js frontend (from the frontend folder):

cd frontend
npm run dev

3. Open your browser and navigate to http://localhost:3000

Usage

1. Landing Page: Visit the root URL to see the VibePrompting landing page
2. Upload Agent: Upload a ZIP file containing your Google ADK agent folder
3. Process Agent: Use the train interface to enhance your agent with intelligent prompting
4. Navigate: Use the navigation to switch between the landing page and the main application

File Upload

The application accepts ZIP files containing Google ADK agent folders. The upload process:

1. Validates the file is a ZIP archive
2. Extracts the contents to a unique directory
3. Identifies agent configuration files
4. Stores the upload information for processing

API Endpoints

• POST /upload_agent - Upload Google ADK agent ZIP file
• POST /process_json/general/<session_id>/<node_id> - Process JSON
• GET /history/<session_id>/<node_id> - Retrieve processing history
• POST /retrigger_adk_web - Restart ADK web server

Project Structure

BerkeleyAI/
├── app.py                      # Flask backend server
├── prompt.py                   # Prompt processing logic
├── requirements.txt            # Python dependencies
├── uploads/                    # Uploaded agent files
├── history/                    # Processing history (per session)
├── hotels_com_api_agent/       # Example agent implementation
│   ├── __init__.py
│   ├── agent.py
│   ├── agent.json
│   └── tools/
│       ├── get_hotel_details_endpoint.py
│       ├── search_hotel_destination_endpoint.py
│       └── search_hotels_endpoint.py
├── frontend/                   # Next.js frontend
│   ├── app/                    # App router pages and layout
│   │   ├── layout.tsx
│   │   ├── globals.css
│   │   ├── page.tsx
│   │   ├── landing.tsx
│   │   ├── History.tsx
│   │   ├── AgentEditor.tsx
│   │   ├── ChatInput.tsx
│   │   ├── Logs.tsx
│   │   └── train/
│   ├── components/             # UI components
│   │   └── ui/
│   │       ├── button.tsx
│   │       ├── input.tsx
│   │       ├── scroll-area.tsx
│   │       └── select.tsx
│   ├── lib/                    # Utilities and types
│   │   ├── agent.json
│   │   ├── types.ts
│   │   └── utils.ts
│   ├── public/                 # Static assets (if any)
│   ├── package.json            # Frontend dependencies
│   ├── tsconfig.json           # TypeScript config
│   └── ... (other config files)
├── __init__.py
├── adk                         # (ADK integration stub)
└── .venv/                      # (optional, for Python virtual environment)

Technologies Used

• Backend: Flask, Python
• Frontend: Next.js 15, React 19, TypeScript
• UI: Tailwind CSS, Radix UI
• Agent Framework: Google ADK

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For more details, see the frontend/README.md for frontend-specific development and customization notes.