fastforge-cli 0.0.2

Production-grade FastAPI project generator for Python backends

🔨 FastForge

Production-grade FastAPI project generator for Python backends.

FastForge generates a ready-to-run, production-grade FastAPI project with SOLID architecture, structured logging, Docker containerization, and async CRUD — all in under 30 seconds.

Use Cases

FastForge is for teams and developers who need to ship production Python APIs fast:

Use Case	What FastForge Generates
Microservice backend	REST API with async CRUD, health checks, structured logging, Docker — ready to deploy
Event-driven service	Kafka/RabbitMQ/NATS producer + consumer, structured events, async processing
Data API layer	PostgreSQL/MySQL/MongoDB with async ORM, repository pattern, Pydantic schemas
Internal tooling API	Fast scaffolding with security headers middleware, CORS, structured error handling
Hackathon / MVP	Working API with Docker in 30 seconds — skip the boilerplate, start building
Team standardization	Enforce consistent architecture, logging, testing, and deployment patterns across projects

Example: Build an Order Service

$ pip install fastforge-cli
$ fastforge

? Project name: order-service
? Model name: order
? Log output: Stdout + File
? Log agent: Vector
? Log target: Elasticsearch
? Include debug compose? Yes
? Generate project? Yes

✔ Project created: ./order-service

$ cd order-service
$ docker compose -f docker-compose.debug.yml up --build
# API running at http://localhost:8000/docs — no venv needed

Standards & Architecture

Every generated project follows these standards:

SOLID Principles

Principle	Implementation
Single Responsibility	Each layer has one job: routes handle HTTP, services hold business logic, repositories manage data
Open/Closed	Add new models by creating new files — no modification to existing code needed
Liskov Substitution	Repository interfaces allow swapping implementations (in-memory → PostgreSQL)
Interface Segregation	Small, focused interfaces — Repository protocol with only the methods each consumer needs
Dependency Inversion	FastAPI's Depends() wires everything; services depend on abstractions, not implementations

12-Factor App Compliance

Factor	Implementation
Config	pydantic-settings — all config via environment variables
Dependencies	pyproject.toml with pinned versions, no system-level deps
Backing services	Database, cache, streaming attached via URL config — swap by changing env vars
Port binding	Self-contained HTTP server via uvicorn
Concurrency	Async I/O with asyncio — scales via process managers (gunicorn, uvicorn workers)
Logs	Structured JSON to stdout/file — treat logs as event streams
Dev/prod parity	Same Docker image for dev and prod, identical dependencies
Disposability	Graceful shutdown via lifespan events, health checks for orchestrators

Code Quality

• Linting: ruff (replaces flake8 + isort + black)
• Testing: pytest + pytest-asyncio, async test client
• Type safety: Pydantic models for all request/response schemas
• Pre-commit hooks: Automated ruff + pytest on every commit
• Security: Non-root Docker user, security headers middleware, CORS configuration

Quick Start

pip install fastforge-cli
fastforge

That's it. Answer a few prompts and you get a running application.

What You Get (Basic Mode)

Feature	Implementation
SOLID Architecture	Repository pattern + Service layer + Dependency Injection
Async CRUD API	FastAPI with full Create/Read/Update/Delete
Structured Logging	structlog with JSON output, request IDs, duration tracking
Docker	Slim Dockerfile, docker-compose, health checks
Debug Docker	docker-compose.debug.yml at project root — no venv needed, auto-reload, debugpy on port 5678
Security	Security headers middleware, CORS, non-root container
Testing	pytest + pytest-asyncio, 80%+ coverage out of the box
Code Quality	ruff linting, pre-commit hooks

CLI Flow

$ fastforge

  ╔═╗╔═╗╔═╗╔╦╗  ╔═╗╔═╗╦═╗╔═╗╔═╗
  ╠╣ ╠═╣╚═╗ ║   ╠╣ ║ ║╠╦╝║ ╦║╣
  ╚  ╩ ╩╚═╝ ╩   ╚  ╚═╝╩╚═╚═╝╚═╝
  Production-grade FastAPI Generator

┌─ Choose your path ───────────────────────────────────┐
│ Basic mode  → SOLID app, JSON logging, Docker, async │
│ Advanced    → + Database, Cache, Streaming, Secrets  │
└──────────────────────────────────────────────────────┘

? Project name: order-service
? Model name: order
? Log output: Stdout + File
? Log agent: Vector
? Log target: Elasticsearch
? Include debug compose? Yes
? Generate project? Yes

✔ Project created: ./order-service

Basic Mode (default)

Just 4-5 questions → production-ready app with structured logging, Docker, and CRUD.

Advanced Mode

Enable with "Enable advanced configuration?" → unlocks:

• Database — PostgreSQL, MySQL, SQLite (SQLAlchemy async), MongoDB (Motor)
• Cache — Redis, Memcached, In-memory (cachetools)
• Streaming — Kafka, RabbitMQ, Redis Pub/Sub, NATS (producer + consumer)
• Secrets — HashiCorp Vault, AWS Secrets Manager, Azure Key Vault, GCP Secret Manager
• Logging — Vector or Fluent Bit sidecar → Elasticsearch, OpenSearch, Kafka, Loki, or HTTP endpoint
• Quality Gate — SonarQube, SonarCloud, Qodana, CodeClimate

Generated Project Structure

your-service/
├── app/
│   ├── main.py                    # App factory + lifespan
│   ├── config.py                  # pydantic-settings
│   ├── dependencies.py            # DI wiring
│   ├── api/
│   │   ├── routes/
│   │   │   ├── health.py          # Health + readiness
│   │   │   └── orders.py          # CRUD routes
│   │   └── models/
│   │       └── order.py           # Pydantic schemas
│   ├── services/
│   │   └── order_service.py       # Business logic
│   ├── repositories/
│   │   └── order_repository.py    # Data access (interface + impl)
│   └── middleware/
│       ├── security_headers.py
│       └── logging_middleware.py
├── tests/
│   └── test_api.py
├── docker-compose.debug.yml       # Dev: debugpy + auto-reload (no venv needed)
├── docker/
│   ├── docker-compose.yml         # Production stack
│   └── vector/ or fluentbit/      # Log agent config (if selected)
├── Dockerfile
├── pyproject.toml
└── .pre-commit-config.yaml

Run Immediately

cd your-service

# Option A: Docker (recommended — no venv needed)
docker compose -f docker-compose.debug.yml up --build   # dev + auto-reload
docker compose -f docker/docker-compose.yml up --build   # production stack

# Option B: Local
pip install -e ".[dev]"
uvicorn app.main:app --reload
pytest

Log Agent Targets

When you select a log agent (Vector or Fluent Bit), FastForge asks where to send logs:

Target	Description
Elasticsearch	Full-text search and analytics — ELK stack
OpenSearch	AWS-managed alternative to Elasticsearch
Kafka	Log streaming to a Kafka topic for downstream consumers
Loki	Grafana's log aggregation system — lightweight, label-based
HTTP	Generic HTTP endpoint — works with any log ingestion API

Extend Your Project

# Add features
fastforge add model         # CRUD model (route, service, repo, tests)
fastforge add postgres      # PostgreSQL database support
fastforge add observability # OpenTelemetry tracing + Prometheus metrics

# Deploy
fastforge deploy local      # Build and run with docker compose
fastforge deploy compose    # Production Docker Compose manifest
fastforge deploy swarm      # Docker Swarm stack
fastforge deploy k8s        # Kubernetes manifests
fastforge deploy helm       # Helm chart
fastforge deploy marathon   # Marathon app definition

# Security
fastforge secure setup      # Gitleaks + Trivy configs
fastforge secure scan       # Trivy image scan
fastforge secure sbom       # CycloneDX SBOM
fastforge secure license    # License compliance check
fastforge secure audit      # Dependency vulnerability audit

# CI/CD
fastforge ci github         # GitHub Actions pipeline
fastforge ci gitlab         # GitLab CI pipeline
fastforge ci bitbucket      # Bitbucket Pipelines
fastforge ci jenkins        # Jenkinsfile

# Operations
fastforge infra             # Standalone infrastructure stack
fastforge doctor            # Project health check (8 checks)

Requirements

• Python 3.10+
• pip

License

MIT