SpecTree

Yet another library to generate OpenAPI documents and validate requests & responses with Python annotations.

If all you need is a framework-agnostic library that can generate OpenAPI document, check defspec.

Features

Less boilerplate code, only annotations, no need for YAML ✨
Generate API document with Redoc UI, Scalar UI or Swagger UI 😋
Validate query, JSON data, and response data with supported model backends 😉
- pydantic
- msgspec
- others if you implement the model adapter protocol
Current support:
- Flask demo
- Quart demo
- Falcon demo
- Starlette demo

Quick Start

Install with pip:

pip install "spectree[pydantic]"

If you want to install with offline OpenAPI web pages support:

Offline mode doesn't support SwaggerUI OAuth2 redirection.

pip install "spectree[pydantic,offline]"

If you want to use msgspec instead of pydantic:

pip install "spectree[msgspec]"

Upgrading from v2

Version 3 introduces model adapters and makes the model backend an optional dependency. Read the v2 to v3 migration guide before upgrading an existing application.

Examples

Check the examples folder.

Step by Step

Define your data structures for query, json, headers, cookies, and resp with the model backend you configured for SpecTree.
Create a spectree.SpecTree instance with the web framework name you are using, like spec = SpecTree('flask'). SpecTree uses the pydantic adapter by default; pass model_adapter=get_msgspec_model_adapter() to use the msgspec adapter, or pass another model adapter implementation.
Decorate the route with spec.validate, using any of these arguments (default values are given in parentheses):
- query
- json
- headers
- cookies
- resp
- tags (no tags on endpoint)
- security (None - endpoint is not secured)
- deprecated (False - endpoint is not marked as deprecated)
Access the validated data through the function arguments (see the examples below). You can also access the original data through the web framework.
Register Spectree with the web application by calling spec.register(app). Without this call, validation still works, but Spectree does not generate the OpenAPI document.
Open the generated document at /apidoc/redoc, /apidoc/swagger, or /apidoc/scalar.

If a request fails validation, Spectree returns a 422 response containing error details produced by the configured model adapter.

Adapter models and Python dataclasses

Start by defining request and response models with one of the model types supported by the adapter you choose:

pydantic: use pydantic.BaseModel or a standard-library @dataclass.
msgspec: use msgspec.Struct or a standard-library @dataclass.

For requests, either pass the model to spec.validate (json=Profile) or add it as a parameter annotation (json: Profile). Put response models in Response, for example Response(HTTP_200=Message).

Plain Python dataclasses can be used directly as request or response models, or as field types within another model.

from dataclasses import dataclass

from pydantic import BaseModel
from spectree import Response, SpecTree


spec = SpecTree("flask")


class Profile(BaseModel):
    name: str


@dataclass
class Message:
    text: str


@spec.validate(resp=Response(HTTP_200=Message))
def profile(json: Profile):
    return Message(text=f"Hello, {json.name}")

Profile is the pydantic request model selected by the json annotation. Message is the dataclass response model assigned to status 200. The configured adapter validates and serializes both models and includes them in the generated OpenAPI schema.

Falcon response validation

For Falcon response, this library only validates against media as it is the serializable object. Response.text is a string representing response content and will not be validated. For no assigned media situation, resp parameter in spec.validate should be like Response(HTTP_200=None)

Type annotation feature

Type annotation support is enabled by default. Spectree injects validated fields into view function arguments based on their parameter annotations. This works well with linters that take advantage of typing features such as mypy. Set annotations=False on SpecTree to opt out.

How-To

How to add summary and description to endpoints?

Just add docs to the endpoint function. The 1st line is the summary, and the rest is the description for this endpoint.

How to add a description to parameters?

Check your model backend's field metadata and schema documentation for how to attach descriptions.

Any config I can change?

Of course. Check the Configuration source.

You can update the config when init the spectree like:

SpecTree('flask', title='Demo API', version='v1.0', path='doc')

How do I choose between `pydantic` and `msgspec`?

Install the extra for the backend you want to use, then pass the adapter when creating SpecTree:

from spectree.model_adapter import get_msgspec_model_adapter

SpecTree("falcon", model_adapter=get_msgspec_model_adapter())
SpecTree("flask")  # uses pydantic by default

What is `Response` and how to use it?

To build a response for the endpoint, you need to declare the status code with format HTTP_{code} and corresponding data (optional).

Response(HTTP_200=None, HTTP_403=ForbidModel)
Response('HTTP_200') # equals to Response(HTTP_200=None)
# with custom code description
Response(HTTP_403=(ForbidModel, "custom code description"))

How can I skip the validation?

Add skip_validation=True to the decorator.

Before v1.3.0, this only skip the response validation.

Starts from v1.3.0, this will skip all the validations. As a result, you won't be able to access the validated data from context.

@spec.validate(json=Profile, resp=Response(HTTP_200=Message, HTTP_403=None), skip_validation=True)

What is the callback signature for `before` and `after` hooks?

Both hooks now receive the active model adapter as their last argument:

def before(req, resp, req_validation_error, instance, model_adapter):
    ...


def after(req, resp, resp_validation_error, instance, model_adapter):
    ...

This is useful when you need adapter-specific error details or other model-backend behavior in a custom hook.

How can I use the validation without the OpenAPI document?

The OpenAPI endpoints are added by spec.register(app). If you don't want to add the OpenAPI endpoints, you don't need to register it to the application.

How to secure API endpoints?

For secure API endpoints, it is needed to define the security_schemes argument in the SpecTree constructor. security_schemes argument needs to contain an array of SecurityScheme objects. Then there are two ways to enforce security:

You can enforce security on individual API endpoints by defining the security argument in the spec.validate decorator of relevant function/method (this corresponds to define security section on operation level, under paths, in OpenAPI). security argument is defined as a dictionary, where each key is the name of security used in security_schemes argument of SpecTree constructor and its value is required security scope, as is shown in the following example:

Click to expand the code example:

spec = SpecTree(security_schemes=[
        SecurityScheme(
            name="auth_apiKey",
            data={"type": "apiKey", "name": "Authorization", "in": "header"},
        ),
        SecurityScheme(
            name="auth_oauth2",
            data={
                "type": "oauth2",
                "flows": {
                    "authorizationCode": {
                        "authorizationUrl": "https://example.com/oauth/authorize",
                        "tokenUrl": "https://example.com/oauth/token",
                        "scopes": {
                            "read": "Grants read access",
                            "write": "Grants write access",
                            "admin": "Grants access to admin operations",
                        },
                    },
                },
            },
        ),
        # ...
    ],
    # ...
)


# Not secured API endpoint
@spec.validate(
    resp=Response(HTTP_200=None),
)
def foo():
    ...


# API endpoint secured by API key type or OAuth2 type
@spec.validate(
    resp=Response(HTTP_200=None),
    security={"auth_apiKey": [], "auth_oauth2": ["read", "write"]},  # Local security type
)
def bar():
    ...

You can enforce security on the whole API by defining the security argument in the SpecTree constructor (this corresponds to the define security section on the root level in OpenAPI). It is possible to override global security by defining local security, as well as override to no security on some API endpoint, in the security argument of spec.validate decorator of relevant function/method as was described in the previous point. It is also shown in the following small example:

Click to expand the code example:

spec = SpecTree(security_schemes=[
        SecurityScheme(
            name="auth_apiKey",
            data={"type": "apiKey", "name": "Authorization", "in": "header"},
        ),
        SecurityScheme(
            name="auth_oauth2",
            data={
                "type": "oauth2",
                "flows": {
                    "authorizationCode": {
                        "authorizationUrl": "https://example.com/oauth/authorize",
                        "tokenUrl": "https://example.com/oauth/token",
                        "scopes": {
                            "read": "Grants read access",
                            "write": "Grants write access",
                            "admin": "Grants access to admin operations",
                        },
                    },
                },
            },
        ),
        # ...
    ],
    security={"auth_apiKey": []},  # Global security type
    # ...
)

# Force no security
@spec.validate(
    resp=Response(HTTP_200=None),
    security={}, # Locally overridden security type
)
def foo():
    ...


# Force another type of security than global one
@spec.validate(
    resp=Response(HTTP_200=None),
    security={"auth_oauth2": ["read"]}, # Locally overridden security type
)
def bar():
    ...


# Use the global security
@spec.validate(
    resp=Response(HTTP_200=None),
)
def foobar():
    ...

How to mark deprecated endpoint?

Use deprecated attribute with value True in spec.validate() decorator. This way, an endpoint will be marked as deprecated and will be marked with a strikethrough in API documentation.

Code example:

@spec.validate(
    deprecated=True,
)
def deprecated_endpoint():
    ...

What should I return when I'm using the library?

No need to change anything. Just return what the framework required.

How to log when the validation failed?

Validation errors are logged with the INFO level. Details are passed into extra. Check the falcon example for details.

How can I write a customized plugin for another backend framework?

Inherit spectree.plugins.base.BasePlugin and implement the functions you need. After that, init like spec = SpecTree(backend=MyCustomizedPlugin).

How to use a customized template page?

SpecTree(page_templates={"page_name": "customized page contains {spec_url} for rendering"})

In the above example, the key "page_name" will be used in the URL to access this page "/apidoc/page_name". The value should be a string that contains {spec_url} which will be used to access the OpenAPI JSON file.

How can I change the response when there is a validation error? Can I record some metrics?

This library provides before and after hooks to do these. Check the documentation or the Flask adapter tests. You can change the handlers for SpecTree or a specific endpoint validation.

How to change the default `ValidationError` status code?

You can change the validation_error_status in SpecTree (global) or a specific endpoint (local). This also takes effect in the OpenAPI documentation.

How can I return my model directly?

Yes, returning an instance produced by your configured model backend will assume the model is valid and bypass Spectree's validation. Spectree will serialize that instance through the active model adapter.

For starlette you should return a SpecTreeStarletteResponse:

from spectree.plugins.starlette_plugin import SpecTreeStarletteResponse

return SpecTreeStarletteResponse(MyModel)

Demo

Try it with http post :8000/api/user name=alice age=18. (if you are using httpie)

Flask

from flask import Flask, jsonify
from pydantic import BaseModel, Field, ConfigDict

from spectree import Response, SpecTree


class Profile(BaseModel):
    name: str
    age: int = Field(..., gt=0, lt=150, description="user age(Human)")

    model_config = ConfigDict(
        json_schema_extra = {
            # provide an example
            "example": {
                "name": "very_important_user",
                "age": 42,
            }
        }
    )


class Message(BaseModel):
    text: str


app = Flask(__name__)
spec = SpecTree("flask")


@app.route("/api/user", methods=["POST"])
@spec.validate(resp=Response(HTTP_200=Message, HTTP_403=None), tags=["api"])
def user_profile(json: Profile):
    """
    verify user profile (summary of this endpoint)

    user's name, user's age, ... (long description)
    """
    print(json)  # or `request.json`
    return jsonify(text="it works")  # or `Message(text='it works')`


if __name__ == "__main__":
    spec.register(app)  # if you don't register in api init step
    app.run(port=8000)

Quart

from pydantic import BaseModel, Field, ConfigDict
from quart import Quart, jsonify

from spectree import Response, SpecTree


class Profile(BaseModel):
    name: str
    age: int = Field(..., gt=0, lt=150, description="user age")

    model_config = ConfigDict(
        json_schema_extra = {
            # provide an example
            "example": {
                "name": "very_important_user",
                "age": 42,
            }
        }
    )


class Message(BaseModel):
    text: str


app = Quart(__name__)
spec = SpecTree("quart")


@app.route("/api/user", methods=["POST"])
@spec.validate(resp=Response(HTTP_200=Message, HTTP_403=None), tags=["api"])
async def user_profile(json: Profile):
    """
    verify user profile (summary of this endpoint)

    user's name, user's age, ... (long description)
    """
    print(json)  # or `request.json`
    return jsonify(text="it works")  # or `Message(text="it works")`


if __name__ == "__main__":
    spec.register(app)
    app.run(port=8000)

Falcon

from wsgiref import simple_server

import falcon
from pydantic import BaseModel, Field

from spectree import Response, SpecTree


class Profile(BaseModel):
    name: str
    age: int = Field(..., gt=0, lt=150, description="user age(Human)")


class Message(BaseModel):
    text: str


spec = SpecTree("falcon")


class UserProfile:
    @spec.validate(resp=Response(HTTP_200=Message, HTTP_403=None), tags=["api"])
    def on_post(self, req, resp, json: Profile):
        """
        verify user profile (summary of this endpoint)

        user's name, user's age, ... (long description)
        """
        print(json)  # or `req.media`
        resp.media = {"text": "it works"}  # or `resp.media = Message(text='it works')`


if __name__ == "__main__":
    app = falcon.App()
    app.add_route("/api/user", UserProfile())
    spec.register(app)

    httpd = simple_server.make_server("localhost", 8000, app)
    httpd.serve_forever()

Starlette

import uvicorn
from pydantic import BaseModel, Field
from starlette.applications import Starlette
from starlette.responses import JSONResponse
from starlette.routing import Mount, Route

from spectree import Response, SpecTree

# from spectree.plugins.starlette_plugin import SpecTreeStarletteResponse


class Profile(BaseModel):
    name: str
    age: int = Field(..., gt=0, lt=150, description="user age(Human)")


class Message(BaseModel):
    text: str


spec = SpecTree("starlette")


@spec.validate(resp=Response(HTTP_200=Message, HTTP_403=None), tags=["api"])
async def user_profile(request, json: Profile):
    """
    verify user profile (summary of this endpoint)

    user's name, user's age, ... (long description)
    """
    print(json)  # or await request.json()
    return JSONResponse(
        {"text": "it works"}
    )  # or `return SpecTreeStarletteResponse(Message(text='it works'))`


if __name__ == "__main__":
    app = Starlette(
        routes=[
            Mount(
                "/api",
                routes=[
                    Route("/user", user_profile, methods=["POST"]),
                ],
            )
        ]
    )
    spec.register(app)

    uvicorn.run(app)

FAQ

ValidationError: missing field for headers

The HTTP headers' keys in Flask are capitalized, in Falcon are upper cases, in Starlette are lower cases. You can use pydantic.model_validator(mode="before") to change all the keys into lower cases or upper cases.

ValidationError: value is not a valid list for the query

Since there is no standard for HTTP queries with multiple values, it's hard to find a way to handle this for different web frameworks.

Name		Name	Last commit message	Last commit date
.github		.github
docs		docs
examples		examples
spectree		spectree
tests		tests
.gitignore		.gitignore
.pre-commit-config.yaml		.pre-commit-config.yaml
.readthedocs.yaml		.readthedocs.yaml
CHANGELOG.md		CHANGELOG.md
LICENSE		LICENSE
Makefile		Makefile
README.md		README.md
build.envd		build.envd
cliff.toml		cliff.toml
pyproject.toml		pyproject.toml
uv.lock		uv.lock

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SpecTree

Features

Quick Start

Upgrading from v2

Examples

Step by Step

Adapter models and Python dataclasses

Falcon response validation

Type annotation feature

How-To

How to add summary and description to endpoints?

How to add a description to parameters?

Any config I can change?

How do I choose between pydantic and msgspec?

What is Response and how to use it?

How can I skip the validation?

What is the callback signature for before and after hooks?

How can I use the validation without the OpenAPI document?

How to secure API endpoints?

How to mark deprecated endpoint?

What should I return when I'm using the library?

How to log when the validation failed?

How can I write a customized plugin for another backend framework?

How to use a customized template page?

How can I change the response when there is a validation error? Can I record some metrics?

How to change the default ValidationError status code?

How can I return my model directly?

Demo

Flask

Quart

Falcon

Starlette

FAQ

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How do I choose between `pydantic` and `msgspec`?

What is `Response` and how to use it?

What is the callback signature for `before` and `after` hooks?

How to change the default `ValidationError` status code?