Previously, writing AI contracts: setting up environment, writing tests, configuring frontend, handling deployment—no business logic written yet, time’s up.

Now: GenLayer Project Boilerplate, clone, run.

Complete football betting example, web access + LLM integration ready-made.

Direct mode testing at millisecond-level speed, no Studio needed.

Integration tests, contract linter, CI pipeline all set up.

Frontend Next.js 15 + TypeScript + TanStack Query, ready out of the box.

https://github.com/genlayerlabs/genlayer-project-boilerplate…

genlayerlabs/genlayer-project-boilerplate

Source: https://github.com/genlayerlabs/genlayer-project-boilerplate

Sample GenLayer project

License: MIT (https://opensource.org/license/mit/) Discord (https://discord.gg/8Jm4v89VAu) Telegram (https://t.me/genlayer) Twitter (https://x.com/GenLayer) GitHub star chart (https://star-history.com/#yeagerai/genlayer-js)

About

This project includes the boilerplate code for a GenLayer use case implementation, specifically a football bets game.

What’s included

• An example intelligent contract (Football Bets) with web access and LLM integration
• Direct mode tests — fast, in-memory unit tests with web/LLM mocking (~ms per test)
• Integration tests — full end-to-end tests against GenLayer Studio
• Contract linting — static analysis to catch common contract issues before deployment
• CI pipeline — GitHub Actions workflow for linting and direct tests
• A production-ready Next.js 15 frontend with TypeScript, TanStack Query, and Radix UI
• Configuration file template and deployment scripts

Requirements

• Python >= 3.12
• GenLayer CLI (https://github.com/genlayerlabs/genlayer-cli) globally installed: npm install -g genlayer
• GenLayer Studio (for integration tests and deployment): Install from Docs (https://docs.genlayer.com/developers/intelligent-contracts/tooling-setup#using-the-genlayer-studio) or use the hosted GenLayer Studio (https://studio.genlayer.com/)

Project Structure

contracts/ # Python intelligent contracts tests/ direct/ # Fast in-memory tests (no Studio required) test_create_bet.py # Bet creation logic test_resolve_bet.py # Bet resolution with web/LLM mocks test_views.py # Read-only view methods integration/ # Full tests against GenLayer Studio test_football_bets.py fixtures.py # Expected state fixtures frontend/ # Next.js 15 app (TypeScript, TanStack Query, Radix UI) deploy/ # TypeScript deployment scripts gltest.config.yaml # Test runner network configuration pyproject.toml # Python/pytest configuration .github/workflows/ # CI pipeline

Quick Start

1. Set up Python environment

``shell python3 -m venv .venv source .venv/bin/activate pip install -r requirements.txt

### 2. Lint your contracts

Run the GenVM linter to catch issues before deployment:

``shell
genvm-lint check contracts/football_bets.py

The linter catches:

• Forbidden imports and non-deterministic calls
• Invalid storage types (must use TreeMap, DynArray, u256, etc.)
• Missing decorators and return type annotations
• Non-deterministic operations outside equivalence principle blocks
• And 20+ other rules (https://github.com/genlayerlabs/genvm-linter)

3. Run direct mode tests

Direct mode tests run contracts in-memory without needing GenLayer Studio. They use mocks for web requests and LLM calls, giving you fast feedback (~milliseconds per test):

``shell pytest tests/direct/ -v

Direct mode features used in these tests:
- `direct_deploy("contracts/file.py")` — deploy contract in memory
- `direct_vm.sender = address` — set transaction sender
- `direct_vm.mock_web(pattern, response)` — mock HTTP/render calls
- `direct_vm.mock_llm(pattern, response)` — mock LLM responses
- `direct_vm.expect_revert("message")` — assert expected failures
- `direct_vm.clear_mocks()` — reset mocks between calls

### 4. Deploy the contract

1. Choose your network: `genlayer network`
2. Deploy: `genlayer deploy` (runs the script in `/deploy/deployScript.ts`)

### 5. Run integration tests

Integration tests deploy the contract to GenLayer Studio and test with real consensus:

``shell
gltest tests/integration/ -v -s

These require GenLayer Studio running (local or hosted).

6. Set up the frontend

1. Copy frontend/.env.example to frontend/.env
2. Add your deployed contract address as NEXT_PUBLIC_CONTRACT_ADDRESS
3. Run:

``shell cd frontend npm install npm run dev

The app will be available at http://localhost:3000/.

## How the Football Bets Contract Works

1. **Creating Bets**: Users bet on a football match by providing the game date, teams, and predicted winner.
2. **Resolving Bets**: After the match, the contract fetches results from BBC Sport, uses an LLM to extract the score, and validates via the equivalence principle.
3. **Points**: Correct predictions earn points. Users can query their points or the leaderboard.

## Testing Strategy

| Test Type | Command | Speed | Requires Studio |
|-----------|---------|-------|-----------------|
| **Lint** | `genvm-lint check contracts/*.py` | ~250ms | No |
| **Direct** | `pytest tests/direct/ -v` | ~ms/test | No |
| **Integration** | `gltest tests/integration/ -v -s` | ~min/test | Yes |

**Recommended workflow:**
1. Lint after every contract change
2. Run direct tests frequently during development
3. Run integration tests before deployment to verify consensus behavior

For AI coding agents (Claude Code, Cursor, etc.), the linter and direct tests provide the fast feedback loop needed for iterative development without requiring a running Studio instance.

## Community
- **Discord (https://discord.gg/8Jm4v89VAu)**: Discussions, support, and announcements
- **Telegram (https://t.me/genlayer)**: Informal chats and quick updates

## Documentation
For detailed information, see our documentation (https://docs.genlayer.com/).

## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.