AI-Powered Negotiation Practice App with Real-Time Coaching & Session Reports
Built with the tools and technologies:
Counterpart is an AI negotiation practice app that lets you rehearse high-stakes conversations — salary negotiations, rent renewals, freelance pricing, vendor disputes, or a scenario you write yourself — against an in-character AI counterpart with its own incentives, personality, and walk-away point.
Counterpart tracks the tactics used against you, coaches your moves in real time, and grades your performance in an end-of-session report. It's a single-user practice tool with no accounts or roles, built around one core loop: pick a scenario, negotiate live, get coached, review a report.
- 🎭 In-Character AI Counterpart — negotiates with its own persona, personality, and walk-away point
- 📝 Custom Scenarios — write and save your own negotiation scenarios (up to 3 at a time)
- 🧭 Live Tactic Tracking — recognizes and surfaces the tactics being used against you as the conversation unfolds
- 🪄 Real-Time Coaching — get move-by-move coaching in a live sidebar during the negotiation
- 📊 End-of-Session Report — deterministic concession/tactic counts combined with LLM-generated takeaways
- 🔊 Voice Output — AI replies read aloud via the browser's built-in Web Speech API
- ⚡ In-Memory Sessions — negotiations run live in memory for a fast, stateless practice loop
Counterpart is built around a single core loop: pick a scenario, negotiate live, get coached, review a report. Session state (active conversations, live coaching, in-progress negotiations) is held in memory on the backend and is lost on restart — only saved custom scenarios persist, via SQLite.
graph TD
User([User]) -->|Selects Scenario| App[React Frontend]
App -->|Custom Scenario| DB[(SQLite - Custom Scenarios)]
App -->|POST negotiation move| Server[FastAPI Backend]
Server -->|Sends context + history| Groq[Groq API - llama-3.3-70b-versatile]
Groq -->|In-Character Reply| Server
Groq -->|Tactic + Move Classification| Server
Server -->|Live Sidebar Update| App
App -->|Optional| Speech[Web Speech API - speechSynthesis]
User -->|Ends Session| Server
Server -->|Deterministic Concession/Tactic Counts + LLM Takeaways| Report[Scored End-of-Session Report]
Report -->|Rendered To| User
The only persistent storage is custom user-created scenarios. There is no schema for users, negotiation sessions, or reports — those are runtime/in-memory only.
erDiagram
CUSTOM_SCENARIOS {
INTEGER id PK
TEXT persona_name
TEXT persona_description
TEXT personality
TEXT walk_away_point
TEXT opening_move
TIMESTAMP createdAt
}
CounterPart-AI/
├── backend/
│ ├── app/
│ │ ├── __init__.py # Package initialization marker
│ │ ├── config.py # Settings, environment configuration, & database path setup
│ │ ├── custom_scenarios.py # Database access models and routes for user-created scenarios
│ │ ├── main.py # Core application routing, Groq LLM API integrations, and CORS config
│ │ └── scenarios.py # Pre-configured default negotiation scenarios (salary, rent, etc.)
│ ├── counterpart.db # SQLite database for storing custom scenarios (auto-created on first save)
│ └── requirements.txt # Python dependencies list (FastAPI, uvicorn, groq, sqlalchemy, pydantic)
├── frontend/
│ ├── public/
│ │ ├── counterpart-mark.svg # App brand mark icon logo
│ │ └── favicon.ico # Browser tab favicon
│ ├── src/
│ │ ├── App.jsx # Main UI component (Scenario picker, active negotiation, coaching sidebar, scoring reports)
│ │ ├── index.css # Main stylesheet declaring Tailwind CSS directives
│ │ └── main.jsx # React application entry point (mounts App to index.html)
│ ├── .env.example # Template for frontend-specific environment variables
│ ├── index.html # Main index template containing the application container mount point
│ ├── package.json # Node.js project configuration, metadata, and dependencies
│ ├── package-lock.json # Node.js dependency lock file
│ ├── postcss.config.js # Configuration for Tailwind's PostCSS parser
│ ├── tailwind.config.js # Custom Tailwind CSS configuration for layouts and color themes
│ └── vite.config.js # Vite configuration including backend proxy settings
├── .env.example
├── .gitignore
├── DEPLOYMENT.md
├── LICENSE
└── README.mdMake sure Python (with
venvsupport) and Node.js are installed on your system.
# Clone the repo
git clone https://github.com/Muhammad-Ahmed-Rayyan/CounterPart-AI.git
cd CounterPart-AI/backend
# Create and activate a virtual environment
python -m venv .venv
.venv\Scripts\activate # Windowssource .venv/bin/activate # macOS/Linux# Install dependencies
pip install -r requirements.txt
# Start the server
uvicorn app.main:app --reloadA local SQLite database file is created automatically the first time you save a custom scenario — no additional setup required. The backend runs on http://127.0.0.1:8000.
# In a separate terminal, from the project rootcd frontend
# Install dependencies
npm install
# Start the development server
npm run devOpen http://localhost:5173 in your browser.
Copy the example environment file and fill in your details:
cp .env.example .envGROQ_API_KEY="YOUR-GROQ-API-KEY"GROQ_MODEL="llama-3.3-70b-versatile"VITE_API_URL="http://127.0.0.1:8000"You can obtain your key from the Groq Console — a free tier is available.
Codex was used as the coding agent across every phase of this project: scaffolding, the persona/negotiation engine, tactic-recognition and coaching classifier, end-of-session report, database-backed custom scenarios, voice output, and UI polish.
⭐ Found this project useful? Drop a star on GitHub!