After playing around with few Python GraphQL libraries for a few weeks, I realized that a good GQL python lib should: - be less invasive, work on top of the existing stack (FastAPI/starlette), reuse as much code as possible(Pydantic) - generate GQL schema from python code, ideally from built-in types and Pydantic types - supports Subscriptions out of the box

Currently, I’m happy with Ariadne in a code-first approach. This post tracks the journey with issues we found and the workarounds/solutions.


Both and fastapi point to, so we get started with it. 

as you may or may not know, GraphQL has a concept called “Schema”,  Graphene took “a code-first approach”, which is cool:  

 Instead of writing GraphQL Schema Definition Language (SDL), we write Python code to describe the data provided by your server.

## Hello world works weel, but it’s too verbose

import graphene

class Query(graphene.ObjectType):
  hello = graphene.String(name=graphene.String(default_value="World"))

  def resolve_hello(self, info, name):
    return 'Hello ' + name

schema = graphene.Schema(query=Query)
result = schema.execute('{ hello }')
print(['hello']) # "Hello World"

Looks simple yet still complex.  there’re too many graphenes, why I need to learn another typing system?  which can be done by the framework,  what about this one? 

# hello = graphene.String(name=graphene.String(default_value="World"))
hello: str = "World" 

Reuse Pydantic types with graphene-pydantic

Since we’re using Pydantic,  which has all the typing details, why not simply use Pydantic?! is exactly what we need! but even with graphene-pydantic an adaptor layer is required between Pydantic and Graphene, e.g.: 

class PersonInput(PydanticInputObjectType):
    class Meta:
        model = PersonModel
        # exclude specified fields
        exclude_fields = ("id",)

class CreatePerson(graphene.Mutation):
    class Arguments:
        person = PersonInput()
    # more code trimmed

Still very verbose, but much better than the original one. 

## Subscriptions is not well supported yet

The document is super confusing:

To implement websocket-based support for GraphQL subscriptions, you’ll need to do the following: Install and configure django-channels. Install and configure* a third-party module for adding subscription support over websockets. A few options include: graphql-python/graphql-ws datavance/django-channels-graphql-ws jaydenwindle/graphene-subscriptions Ensure that your application (or at least your GraphQL endpoint) is being served via an ASGI protocol server like daphne (built in to django-channels), uvicorn, or hypercorn.

  • Note: By default, the GraphiQL interface that comes with graphene-django assumes that you are handling subscriptions at the same path as any other operation (i.e., you configured both and to handle GraphQL operations at the same path, like /graphql).

what? why Django gets mentioned? I’m not interested and I’m lost!

Maybe it’s time to move on.


This is from the Graphene’s “Getting started”:

 Compare Graphene’s code-first approach to building a GraphQL API with schema-first approaches like Apollo Server (JavaScript) or Ariadne (Python). Instead of writing GraphQL Schema Definition Language (SDL), we write Python code to describe the data provided by your server.

Yeah, schema-first is not cool, but Ariadne’s documetation looks much better than Graphene.

Subscriptions, it just works

After the experience with Graphene, the first feature I check was subscriptions:’s simple and it just works!  the documentation is clean and no django mentioned at all!

import asyncio
from ariadne import SubscriptionType, make_executable_schema
from ariadne.asgi import GraphQL

type_def = """
    type Query {
        _unused: Boolean

    type Subscription {
        counter: Int!

subscription = SubscriptionType()

async def counter_generator(obj, info):
    for i in range(5):
        await asyncio.sleep(1)
        yield i

def counter_resolver(count, info):
    return count + 1

schema = make_executable_schema(type_def, subscription)
app = GraphQL(schema, debug=True)

Schema first? it doesn’t have to be

What if I change the counter_generator to return str? I need to update the Schema. if I forgot that, I’m lying to my users.  I hate it. 

In the above example, type_def is kind fo the duplication of the method counter_generator (if we add return type) like:

async def counter_generator(obj, info) -> int

the Schema looks reasonably easy to generate, why cannot we generate a schema from python code? especially with Pydantic? if we define a method with proper tying, we cloud to generate the Schema easily:

class HelloMessage(BaseModel):
    body: str
    from_user: UUID

query = QueryType()

def resolve_hello(_, info) -> HelloMessage:
    request = info.context['request']
    user_agent = request.headers.get('user-agent', 'guest')
    return HelloMessage(
        body='Hello, %s!' % user_agent,

# Generate type_defs from Pydantic types in the query definition.
type_defs = generate_gql_schema_str([query])

schema = make_executable_schema(
    type_defs, query, snake_case_fallback_resolvers,
app = GraphQL(schema, debug=True)

the details could be found here:

With a small schema generation utility,  we managed to run Ariadne in a code-first approach

  • Code is much simpler than the original version of both Ariadne and Graphene 
  • Resuing Pydantic typing 
  • the GQL query definition method is very simple, take input, forward to the core application, return the output.