Please read UPGRADE-v1.0.md to learn how to upgrade to Graphene 1.0
.
Graphene
Graphene is a Python library for building GraphQL schemas/types fast and easily.
- Easy to use: Graphene helps you use GraphQL in Python without effort.
- Relay: Graphene has builtin support for Relay.
- Data agnostic: Graphene supports any kind of data source: SQL (Django, SQLAlchemy), NoSQL, custom Python objects, etc. We believe that by providing a complete API you could plug Graphene anywhere your data lives and make your data available through GraphQL.
Integrations
Graphene has multiple integrations with different frameworks:
integration | Package |
---|---|
Django | graphene-django |
SQLAlchemy | graphene-sqlalchemy |
Google App Engine | graphene-gae |
Peewee | In progress (Tracking Issue) |
Also, Graphene is fully compatible with the GraphQL spec, working seamlessly with all GraphQL clients, such as Relay, Apollo and gql.
Installation
For instaling graphene, just run this command in your shell
pip install "graphene>=1.0"
1.0 Upgrade Guide
Please read UPGRADE-v1.0.md to learn how to upgrade.
Examples
Here is one example for you to get started:
class Query(graphene.ObjectType):
hello = graphene.String(description='A typical hello world')
def resolve_hello(self, args, context, info):
return 'World'
schema = graphene.Schema(query=Query)
Then Querying graphene.Schema
is as simple as:
query = '''
query SayHello {
hello
}
'''
result = schema.execute(query)
If you want to learn even more, you can also check the following examples:
- Basic Schema: Starwars example
- Relay Schema: Starwars Relay example
Contributing
After cloning this repo, ensure dependencies are installed by running:
pip install -e ".[test]"
After developing, the full test suite can be evaluated by running:
py.test graphene --cov=graphene --benchmark-skip # Use -v -s for verbose mode
You can also run the benchmarks with:
py.test graphene --benchmark-only
Documentation
The documentation is generated using the excellent Sphinx and a custom theme.
The documentation dependencies are installed by running:
cd docs
pip install -r requirements.txt
Then to produce a HTML version of the documentation:
make html