GraphQL provides a client-friendly API specification perfect for application development, enabling clients to request precisely the data they require. Its approach to querying and traversing data relationships can feel very natural, conceptually similar to how one might think about graph patterns in languages like Cypher. While our Python API offers deep programmatic control (and our GraphQL maintains 1-to-1 parity with it, easing the learning curve), GraphQL shines as the interface layer for front-ends and services. It simplifies data fetching and elegantly abstracts complex underlying graph queries or algorithms.

Absolutely! This is a key strength. You can write and easily surface your custom Graph algorithms and other custom server-side logic as GraphQL queries or mutations, making them directly consumable by your applications.

You can point the Pometry GraphQL server to a directory containing multiple graph instances. The server will intelligently make these graphs available through the API, often reflecting the folder structure, simplifying management and providing a unified endpoint for diverse datasets.

The library supports strong authentication and authorization mechanisms, and critically, integrates with enterprise Single Sign-On (SSO) solutions like SAML and LDAP.

Your GraphQL schema can be designed to expose the temporal dimensions of your data. You can query for data at specific points in time, within time ranges, as a rolling window, or retrieve information about how relationships and attributes have evolved, leveraging the underlying power of the Pometry temporal graph database.

This refers to the ability to extend the GraphQL server's functionality by adding your own specific processing logic or connecting to other services as part of the GraphQL resolution process, giving you fine-grained control over your API behaviour.