azure-functions-scaffold 0.4.0

Scaffolding CLI for Azure Functions Python v2 projects

Azure Functions Scaffold

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Scaffolding CLI for production-ready Azure Functions Python v2 projects.

Why Use It

Starting a new Azure Functions project means setting up boilerplate: host.json, function_app.py, directory structure, tooling config, and tests. azure-functions-scaffold generates a production-ready project layout in one command, so you can focus on business logic from the start.

flowchart LR
    Dev(["Developer"])
    CLI["afs new my-api"]
    T["Templates"]
    P["Generated Project"]
    VAL["azure-functions-validation"]

    Dev --> CLI
    CLI --> T
    T --> P
    CLI -- "--with-validation" --> VAL
    VAL --> P

Scope

• Azure Functions Python v2 programming model
• Decorator-based func.FunctionApp() applications
• CLI-driven project generation and expansion
• Templates for HTTP, Timer, Queue, Blob, and Service Bus triggers

This tool generates project scaffolds. It does not provide runtime libraries.

What this package does not do

This package does not own:

• Runtime behavior — generated projects wire up optional packages (--with-openapi, --with-validation, --with-doctor), but runtime logic belongs to those packages
• API documentation — use azure-functions-openapi for API documentation and spec generation
• Request validation — use azure-functions-validation for request/response validation and serialization

Features

• azure-functions-scaffold new command for project generation
• Five built-in templates: HTTP, Timer, Queue, Blob, Service Bus
• azure-functions-scaffold add command for expanding existing projects
• Optional integrations: --with-openapi, --with-validation, --with-doctor
• Preset tooling levels: --preset minimal|standard|strict
• Interactive guided setup via --interactive
• Short alias: afs works as a drop-in for azure-functions-scaffold

Installation

pip install azure-functions-scaffold

Quick Start

Use this 4-step flow to create and run a local HTTP function:

1. Install the CLI.
2. Generate a new project.
3. Install project dependencies.
4. Start the local Functions runtime.

azure-functions-scaffold new my-api
cd my-api
pip install -e .
func start

Open http://localhost:7071/api/hello in your browser.

Expected response:

Hello, World!

Project names must start with an alphanumeric character and use only letters, numbers, hyphens, or underscores.

What You Get

The generated layout separates trigger bindings, business logic, shared runtime concerns, and tests so teams can scale endpoints without coupling everything to function_app.py.

my-api/
|- function_app.py          # Azure Functions v2 entrypoint
|- host.json                # Runtime configuration
|- local.settings.json.example
|- pyproject.toml           # Dependencies and tooling config
|- app/
|  |- core/
|  |  `- logging.py         # Structured JSON logging
|  |- functions/
|  |  `- http.py            # HTTP trigger (Blueprint)
|  |- schemas/
|  |  `- request_models.py  # Request/response models
|  `- services/
|     `- hello_service.py   # Business logic
`- tests/
   `- test_http.py          # Pytest tests

Why this layout works:

• Keep trigger-specific code in app/functions.
• Keep reusable business rules in app/services.
• Keep model contracts in app/schemas.
• Keep observability and runtime helpers in app/core.
• Keep integration checks in tests.

Templates

Template	Command	Use Case
http	azure-functions-scaffold new my-api	REST APIs, webhooks
timer	azure-functions-scaffold new my-job --template timer	Scheduled tasks, cron
queue	azure-functions-scaffold new my-worker --template queue	Message processing (Azurite)
blob	azure-functions-scaffold new my-blob --template blob	File processing (Azurite)
servicebus	azure-functions-scaffold new my-bus --template servicebus	Enterprise messaging

Note: afs is short for azure-functions-scaffold. Both work.

Template defaults:

• http: public HTTP endpoint and service module.
• timer: scheduled trigger using NCRONTAB expression settings.
• queue: Storage Queue trigger ready for local Azurite development.
• blob: Blob trigger scaffold for file-ingestion pipelines.
• servicebus: Service Bus trigger scaffold with development placeholders.

Optional Features

• --with-openapi - Swagger UI + OpenAPI spec endpoints
• --with-validation - Pydantic request/response validation
• --with-doctor - Health check diagnostics
• --preset minimal|standard|strict - Tooling level
• --interactive - Guided project setup

Example combinations:

azure-functions-scaffold new my-api --preset strict --with-validation
azure-functions-scaffold new my-api --with-openapi --with-validation
azure-functions-scaffold new my-api --template timer --preset minimal

Expand Your Project

Add functions to an existing scaffolded project:

azure-functions-scaffold add http get-user --project-root ./my-api
azure-functions-scaffold add timer cleanup --project-root ./my-api
azure-functions-scaffold add queue sync-jobs --project-root ./my-api
azure-functions-scaffold add blob ingest-reports --project-root ./my-api
azure-functions-scaffold add servicebus process-events --project-root ./my-api

Preview additions before writing files:

azure-functions-scaffold add servicebus process-events --project-root ./my-api --dry-run

Common expansion flow:

1. Add a trigger with azure-functions-scaffold add <trigger> <name>.
2. Implement business logic under app/services.
3. Update contracts in app/schemas if needed.
4. Add or update tests in tests.

Deploy

func azure functionapp publish <APP_NAME>

Before publishing:

• Set required app settings for production connections.
• Review host.json and function auth levels.
• Run your project checks (pytest, lint, and formatting).
• Verify startup locally with func start.

Documentation

• Full docs: yeongseon.github.io/azure-functions-scaffold
• Getting Started: docs/guide/getting-started.md
• CLI Reference: docs/reference/cli.md
• Project Structure: docs/guide/project-structure.md
• Templates: docs/guide/templates.md
• Troubleshooting: docs/guide/troubleshooting.md

Development

Use Makefile commands as the canonical entry points:

make install
make check-all
make docs
make build

Ecosystem

This package is part of the Azure Functions Python DX Toolkit.

Design principle: azure-functions-scaffold owns project generation and template expansion. It does not provide runtime libraries — runtime behavior belongs to azure-functions-openapi (API documentation and spec generation), azure-functions-validation (request/response validation), and azure-functions-langgraph (LangGraph runtime exposure).

Package	Role
azure-functions-scaffold	Project scaffolding CLI
azure-functions-validation	Request/response validation and serialization
azure-functions-openapi	OpenAPI spec generation and Swagger UI
azure-functions-langgraph	LangGraph deployment adapter for Azure Functions
azure-functions-logging	Structured logging and observability
azure-functions-doctor	Pre-deploy diagnostic CLI
azure-functions-durable-graph	Manifest-first graph runtime with Durable Functions (planned)
azure-functions-python-cookbook	Recipes and examples

For AI Coding Assistants

This package includes llms.txt and llms-full.txt files in the repository root designed specifically for LLM-assisted development:

• llms.txt — Concise overview of the CLI, commands, templates, and quick start
• llms-full.txt — Complete CLI reference with all options, patterns, and workflows

Use these files to provide context to AI coding assistants when working with Azure Functions scaffolding.

Reference:

• Repository: https://github.com/yeongseon/azure-functions-scaffold
• Issue: #40

Disclaimer

This project is an independent community project and is not affiliated with, endorsed by, or maintained by Microsoft.

Azure and Azure Functions are trademarks of Microsoft Corporation.

License

MIT