A spec-driven API is an approach to building services where a formal specification, or contract, is created before any code is written. This specification, often written in a standard format like OpenAPI or GraphQL, acts as a single source of truth that describes the API’s endpoints, data structures, authentication methods, and expected behavior. It’s like having a detailed blueprint for a house before laying the foundation—everyone from the architects to the electricians knows exactly what to build and how the pieces fit together.

This contract-first approach stands in contrast to a code-first approach, where the API is built first and the documentation is generated from the code afterward. By defining the contract upfront, teams can design, review, and validate the API’s structure before committing to implementation, leading to more consistent, predictable, and user-friendly services.

The process begins with defining the API’s functionality in a machine-readable specification file. This is a collaborative effort involving backend and frontend developers, product managers, and even technical writers.

1. Design and Define: The team drafts the API specification. For a RESTful API, this is typically an openapi.yaml or openapi.json file. For a GraphQL API, it’s a .graphql schema file. This document details every aspect of the API.
2. Review and Iterate: Stakeholders review the specification to ensure it meets business requirements and follows design best practices. Feedback is incorporated, and the spec is refined until it’s approved.
3. Generate and Build: Once the spec is finalized, teams can use it to generate code and other resources automatically.
   • Boilerplate Code: Server stubs and client-side SDKs can be generated in various languages, saving developers from writing repetitive setup code.
   • Mock Servers: A mock server can be spun up based on the spec, allowing frontend teams to start building and testing against a simulated API without waiting for the backend to be complete.
   • Tests: Automated contract tests can be generated to validate that the final implementation adheres to the specification.
4. Implement and Test: With the boilerplate in place, backend developers focus on implementing the business logic for each endpoint. Throughout the process, the implementation is tested against the contract to catch any deviations early.

This parallel workflow, where frontend, backend, and testing can all proceed simultaneously based on a shared contract, is one of the biggest advantages of spec-driven development.

AI assistants and code generation tools thrive on structure and clarity. A well-defined API specification provides the precise, unambiguous context that AI models need to perform complex tasks effectively. Without a spec, an AI has to infer the API’s behavior from code or documentation, which can lead to errors, inconsistencies, and hallucinations.

For AI-powered development, a spec acts as a detailed prompt, enabling assistants to:

• Generate Accurate SDKs: An AI can read an OpenAPI spec and write a fully functional client SDK in any language, complete with method calls, data models, and error handling.
• Write Reliable Tests: By understanding the expected inputs and outputs for each endpoint, an AI can generate comprehensive test suites, including unit, integration, and end-to-end tests.
• Create Powerful Integrations: When connecting different services, an AI can use their respective specs to understand how they should interact, automating the creation of integration logic and data mapping.
• Draft Clear Documentation: The spec provides all the necessary details for an AI to generate user-friendly documentation, tutorials, and usage examples.

By providing a structured contract, you remove the guesswork for both humans and AI, making the entire development lifecycle faster and more reliable.

A common challenge in spec-driven development is “spec drift,” where the implementation deviates from the specification over time. This breaks the contract and undermines the benefits of the approach. Here are some tips to avoid it:

• Automate Everything: Use CI/CD pipelines to automatically run contract tests every time code is changed. If the implementation doesn’t match the spec, the build should fail.
• Use Code Generation Wisely: Generate server stubs and client SDKs directly from the spec. This ensures that the foundational code is always aligned with the contract. When the spec changes, regenerate the code.
• Keep the Spec as the Source of Truth: All changes to the API, no matter how small, must start with an update to the specification. Treat the spec file as a core artifact of your codebase and manage it with version control.
• Provide Rich Descriptions: Don’t just define the endpoints and schemas. Use the description and summary fields in your OpenAPI spec to add human-readable context. This helps both developers and AI assistants understand the why behind the API’s design.

By embedding the specification into your automated workflows, you can ensure that your code, tests, and documentation always remain in sync.

Kinde embraces a spec-driven philosophy by providing OpenAPI specifications for its powerful APIs. This allows you to integrate Kinde’s authentication, user management, and other features into your application with confidence and speed.

For example, Kinde offers a Management API for handling administrative tasks like managing users, roles, and permissions from your backend. It also provides a Frontend API for client-side operations. Both come with downloadable OpenAPI specs that you can use to:

• Generate a custom SDK for your specific programming language or framework.
• Use an AI assistant to write scripts that automate user management tasks.
• Set up automated tests to ensure your integration with Kinde is robust and reliable.

By providing these specifications, Kinde makes it easier to build secure, scalable applications while leveraging the power of modern, AI-assisted development workflows.

• Kinde Management API
• Kinde Account API