azure-functions-openapi 0.17.0

OpenAPI (Swagger) integration for Azure Functions Python v2 programming model

Azure Functions OpenAPI

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OpenAPI (Swagger) documentation and Swagger UI for the Azure Functions Python v2 programming model.

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Part of the Azure Functions Python DX Toolkit → Bring FastAPI-like developer experience to Azure Functions

Why this exists

Azure Functions Python v2 has no built-in API documentation story:

• No auto-generated docs — you maintain OpenAPI specs by hand or not at all
• No Swagger UI — no browser-based API explorer for testing endpoints
• Hard to test — consumers rely on tribal knowledge or external tools to discover your API
• Spec drift — hand-written docs diverge from actual handler behavior over time

What it does

• @openapi decorator — attach operation metadata directly to your handler
• Auto-generated spec — /openapi.json and /openapi.yaml endpoints from decorated handlers
• Swagger UI — built-in /docs endpoint with security defaults
• CLI tooling — generate specs at build time for CI validation

FastAPI comparison

Feature	FastAPI	azure-functions-openapi
API docs generation	Built-in from type hints	@openapi decorator on handlers
Swagger UI	/docs auto-served	render_swagger_ui() endpoint
OpenAPI spec	Auto-generated /openapi.json	get_openapi_json() endpoint
CLI spec export	N/A	azure-functions-openapi generate
Pydantic integration	Native	request_model= / response_model=

Scope

• Azure Functions Python v2 programming model
• Decorator-based func.FunctionApp() applications
• HTTP-triggered functions documented with @openapi
• Pydantic schema generation (requires Pydantic v2)

This package does not support the legacy function.json-based v1 programming model.

What this package does not do

This package does not own:

• Runtime exposure or graph deployment — use azure-functions-langgraph
• Request/response validation or serialization — use azure-functions-validation
• Project scaffolding — use azure-functions-scaffold

Features

• @openapi decorator for operation metadata
• /openapi.json, /openapi.yaml, and /docs endpoints
• Query, path, header, body, and response schema support
• Swagger UI helper with security defaults
• CLI tooling for spec generation (JSON and YAML output)

CLI Quick Start

Generate an OpenAPI spec from your decorated function app:

# Install
pip install azure-functions-openapi

# Generate spec from a function app module (registers @openapi routes)
azure-functions-openapi generate --app function_app --title "My API" --format json

# Write to file with pretty-printing
azure-functions-openapi generate --app function_app --title "My API" --pretty --output openapi.json

# YAML output
azure-functions-openapi generate --app function_app --format yaml --output openapi.yaml

Pass module:variable when the FunctionApp instance has a non-default name:

azure-functions-openapi generate --app function_app:my_app --title "My API"

See the CLI Guide for all options and CI integration examples.

Installation

pip install azure-functions-openapi

Your Function App dependencies should include:

azure-functions
azure-functions-openapi

Quick Start

import json

import azure.functions as func

from azure_functions_openapi.decorator import openapi
from azure_functions_openapi.openapi import get_openapi_json, get_openapi_yaml
from azure_functions_openapi.swagger_ui import render_swagger_ui


app = func.FunctionApp()


@app.function_name(name="http_trigger")
@app.route(route="http_trigger", auth_level=func.AuthLevel.ANONYMOUS, methods=["POST"])
@openapi(
    summary="Greet user",
    route="/api/http_trigger",
    method="post",
    request_body={
        "type": "object",
        "properties": {"name": {"type": "string"}},
        "required": ["name"],
    },
    response={
        200: {
            "description": "Successful greeting",
            "content": {
                "application/json": {
                    "schema": {
                        "type": "object",
                        "properties": {"message": {"type": "string"}},
                    }
                }
            },
        }
    },
    tags=["Example"],
)
def http_trigger(req: func.HttpRequest) -> func.HttpResponse:
    data = req.get_json()
    name = data.get("name", "world")
    return func.HttpResponse(
        json.dumps({"message": f"Hello, {name}!"}),
        mimetype="application/json",
    )


@app.function_name(name="openapi_json")
@app.route(route="openapi.json", auth_level=func.AuthLevel.ANONYMOUS, methods=["GET"])
def openapi_json(req: func.HttpRequest) -> func.HttpResponse:
    return func.HttpResponse(
        get_openapi_json(
            title="Sample API",
            description="OpenAPI document for the Sample API.",
        ),
        mimetype="application/json",
    )


@app.function_name(name="openapi_yaml")
@app.route(route="openapi.yaml", auth_level=func.AuthLevel.ANONYMOUS, methods=["GET"])
def openapi_yaml(req: func.HttpRequest) -> func.HttpResponse:
    return func.HttpResponse(
        get_openapi_yaml(
            title="Sample API",
            description="OpenAPI document for the Sample API.",
        ),
        mimetype="application/x-yaml",
    )


@app.function_name(name="swagger_ui")
@app.route(route="docs", auth_level=func.AuthLevel.ANONYMOUS, methods=["GET"])
def swagger_ui(req: func.HttpRequest) -> func.HttpResponse:
    return render_swagger_ui()

Run locally with Azure Functions Core Tools:

func start

Verify locally and on Azure

After deploying (see docs/deployment.md), the same request produces the same response in both environments.

Local

curl -s http://localhost:7071/api/http_trigger \
  -H "Content-Type: application/json" \
  -d '{"name": "World"}'

{"message": "Hello, World!"}

Azure

curl -s "https://<your-app>.azurewebsites.net/api/http_trigger" \
  -H "Content-Type: application/json" \
  -d '{"name": "World"}'

{"message": "Hello, World!"}

The /api/openapi.json, /api/openapi.yaml, and /api/docs endpoints are also available in both environments.

Verified against a temporary Azure Functions deployment in koreacentral (Python 3.12, Consumption plan). Response captured and URL anonymized.

Demo

The representative hello example shows the full outcome of adopting this library:

• You annotate an Azure Functions v2 HTTP handler with @openapi.
• The package generates a real OpenAPI document for that route.
• The same route is rendered in Swagger UI for browser-based inspection.

Generated Spec Result

The generated OpenAPI file is captured as a static preview from the same example run, so the README shows the actual document produced by the representative function.

Swagger UI Result

The web preview below is generated from the same representative example and captured automatically from the rendered Swagger UI page produced by that example flow.

When to use

• You have HTTP-triggered Azure Functions and need API documentation
• You want Swagger UI for browser-based API testing
• You need OpenAPI specs for client code generation or CI validation
• You want to keep docs in sync with handler code automatically

Documentation

• Full docs: yeongseon.github.io/azure-functions-openapi
• Smoke-tested examples: examples/
• Installation Guide
• Usage Guide
• API Reference
• CLI Guide

Ecosystem

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

Design principle: azure-functions-openapi owns API documentation and spec generation. azure-functions-validation owns request/response validation and serialization. azure-functions-langgraph owns LangGraph runtime exposure.

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

For AI Coding Assistants

This repository includes llms.txt and llms-full.txt in the root directory. These files provide comprehensive package and API information optimized for LLM context windows.

• llms.txt — Quick reference with core API, installation, and quick-start example
• llms-full.txt — Complete reference with full signatures, patterns, design principles, and ecosystem context

Use these files to get better context when working with this package in AI-assisted coding environments.

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