Modern APIs have evolved far beyond simple REST endpoints. Today's APIs feature hundreds of operations, polymorphic responses, recursive schemas, and complex authentication flows that make SDK development significantly more challenging than it was a decade ago.
Most teams approach SDK development by running their OpenAPI spec through a basic generator, expecting usable code. This approach fails when faced with real-world API complexity, producing SDKs with broken types, confusing method names, and poor developer ergonomics that ultimately hurt adoption.
Why modern APIs complicate SDK development
Modern SDK development is complicated because APIs themselves are no longer simple collections of endpoints. They now feature hundreds of operations, deeply nested resources, polymorphic responses where a field can be one of several types, and even recursive schemas for things like organizational charts.
A raw OpenAPI specification can describe the shape of these complex APIs. However, it often lacks the semantic information needed to generate a truly usable, idiomatic SDK without an additional layer of thoughtful configuration. Understanding how to create OpenAPI specs is the first step, but generating quality SDKs requires more nuance.
When an API benefits from an SDK
An API benefits from a dedicated SDK when the goal is to accelerate developer adoption and reduce integration friction. While API documentation is essential, an SDK provides a superior developer experience by offering pre-built, language-specific tools that handle the necessary boilerplate.
A quality SDK moves developers past the tedious setup of HTTP clients, authentication, and retry logic. Instead, they get the immediate benefits of IDE autocompletion, strong type-safety to catch errors at compile time, and helpers for complex operations like pagination. This allows them to focus on using your API's core logic, not on reinventing the wheel for basic connectivity.
Pitfalls of direct OpenAPI-to-SDK conversion
Relying on direct, unmodified OpenAPI-to-SDK generation often leads to a poor developer experience. These tools can get the basics right but stumble when faced with the nuances of modern, complex APIs.
Schema translation errors
A common failure point is the translation of complex JSON schemas into strongly-typed languages. Many generators struggle with advanced schema constructs, leading to unusable or incorrect types.
Unions and Polymorphism: OpenAPI's
anyOfandoneOfare powerful for describing polymorphic responses. Naive generators often produce confusing, untyped objects that force developers into manual type casting and runtime checks.Circular References: Schemas that reference themselves, common in data models with nested relationships like a user with a manager who is also a user, can cause generators to enter infinite loops or produce code that will not compile.
Invalid Enums: Generators may fail to handle non-string enums or produce names that conflict with language keywords, requiring manual intervention to fix. Making Java enums forwards compatible is particularly challenging when dealing with evolving APIs.
Endpoint overload
A large API surface with hundreds of endpoints presents another challenge. Without careful organization, the resulting SDK can become a flat, unwieldy list of methods that is difficult to navigate.
Naming Collisions: Different endpoints like
GET /users/{id}andGET /organizations/{id}might both be naively namedretrieve, causing conflicts within the SDK.Resource Grouping: Endpoints related to a single resource, like
users, might be scattered throughout the SDK instead of being grouped logically under aclient.usersnamespace.Pagination Variants: An API might use different pagination schemes, such as cursor-based or offset-based, for different endpoints. Most basic generators cannot handle this nuance automatically.
Language limitations
Finally, what works in one programming language does not always translate well to another. A one-size-fits-all generation approach ignores the conventions and idioms that developers in each language expect, resulting in an SDK that feels foreign and clumsy.
For example, a generator might produce snake_case method names in Java, where camelCase is standard, or fail to use Python's context managers for resource handling. These small but significant details betray the automated nature of the SDK and erode developer trust.
Build SDKs from complex OpenAPI specs
Building a high-quality SDK from a complex spec requires a more sophisticated approach than direct conversion. The Stainless SDK generator achieves this by layering semantic organization on top of the raw spec to guide the generation process, ensuring the final code is both robust and idiomatic.
Map resources
The first step is to define the logical structure of your SDK. Instead of a flat list of functions, you should group related endpoints into resources, which often translate to classes in object-oriented languages.
This is achieved by creating a configuration that maps API endpoints to logical resources and subresources. For example, you can specify that POST /users and GET /users/{id} belong to a users resource, resulting in an intuitive client.users.create() and client.users.retrieve() interface.
Generate types
Next, you must ensure that the JSON schemas in your OpenAPI spec are translated into clean, reusable, and strongly-typed models in the SDK. This prevents developers from working with raw, untyped dictionaries or objects.
A powerful generator uses a configuration to map specific schemas to named models, such as mapping #/components/schemas/UserObject to a User type. It can also handle shared models used across different resources and apply custom casings to ensure type names follow language conventions. This process also defines the classes needed for handling various pagination schemes consistently.
Inject custom logic
No code generator can anticipate every need. A modern SDK development process must include an open customization loop, allowing you to add your own bespoke logic on top of the generated code.
This means the generated code lives in a standard repository where you can make edits, such as adding custom helper methods or detailed examples in the README. Crucially, custom code that persists through regenerated code prevents your manual changes from being overwritten when the SDK is regenerated from an updated spec.
Automate generation and releases
The ultimate goal is to make SDK maintenance an automated, hands-off process. Once you have a solid configuration, you can integrate SDK generation directly into your development workflow.
Every time you update your OpenAPI spec, a CI/CD pipeline should automatically trigger a new build. You can edit configs and OpenAPI specs with branches to safely test changes before they reach production. This process runs diagnostics, checks for issues, and if successful, opens a pull request with the updated SDK code. This pull request includes a generated changelog based on semantic commit messages, making it easy to review what’s changed before merging. This ensures your SDKs are always perfectly in sync with your API.
Keep multi-language sdks synchronized
For companies serving a diverse developer community, maintaining SDKs in multiple languages is a significant challenge. An automated system is the only scalable way to ensure consistency and quality across all of them.
Detect spec changes
The process starts with a centralized system that monitors your OpenAPI spec for changes. Whether you commit the spec to a Git repository or host it at a stable URL, the system should detect updates automatically. This detection can be triggered by a Git push or a periodic poll, kicking off the generation process for all target languages simultaneously.
Ship coordinated releases
Once new SDK versions are generated, the system should handle the entire release process. This involves creating release pull requests in the production repositories for each language. After review and merge, it automatically versions, tags, and publishes the new packages to their respective registries like npm, PyPI, and Maven Central.
Manage breaking changes
A mature SDK pipeline also helps manage the lifecycle of your API. It can be configured to handle breaking changes gracefully by automatically incrementing major version numbers based on your spec updates. Furthermore, it can bake in robust API client features like support for idempotency keys and Retry-After headers, ensuring that all your SDKs provide a consistent and reliable experience.
Frequently asked questions about complex SDK development
How do I handle breaking schema changes without breaking users?
You handle breaking changes by releasing a new major version of your SDK. A good automation pipeline will detect breaking changes in your spec and bump the version accordingly, signaling the change to users.
Can I generate a subset SDK for a large API?
Yes, you can configure the generator to include or exclude specific resources or endpoints. This is useful for creating a more focused SDK or for hiding beta features from the public release.
What if my spec includes both rest and GraphQL styles?
You can handle mixed-protocol APIs by structuring your configuration to treat them as distinct resources. This allows you to generate separate namespaces within the SDK, for example client.rest.users() and client.graphql.users().
How do I expose my API to AI agents safely?
You can expose your API to agents when you generate an MCP server from an OpenAPI spec. This process creates tools that LLMs can use and should be paired with a remote deployment that uses OAuth for secure, user-specific authentication.
Which languages should I prioritise for my first release?
Prioritize languages based on your target developer audience and where you see the most demand. A multi-language generation pipeline makes it easy to add new languages later without redoing foundational work.
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