Teams adopt docs-as-code workflows to eliminate drift between documentation and implementation, but most stop short of including the client libraries developers actually use. API reference pages auto-generate from OpenAPI specs while SDKs are maintained by hand in separate repositories. When your API evolves, updates cascade through multiple repos and engineers, reintroducing the same manual bottlenecks and version mismatches that motivated you to automate documentation in the first place.
TLDR:
- Traditional docs-as-code workflows automate Markdown files but leave SDKs manually coded.
- Manual SDK maintenance reintroduces version drift and inconsistency across languages.
- Client libraries are part of the developer experience and should follow the same automation principles as docs.
- Fern generates idiomatic client libraries in 9+ languages and interactive API documentation from a single API spec, integrated directly into CI/CD pipelines.
What client libraries are and why they matter
Client libraries are pre-built code packages that wrap API endpoints into language-specific methods. Instead of manually constructing HTTP requests, developers call functions that handle authentication, serialization, and error handling automatically.
A well-designed client library turns a raw API call into idiomatic code that feels natural in each language. Python developers get Pydantic models. TypeScript developers get typed interfaces. Go developers get functional options. Each library follows the conventions developers already know.
This matters because integration friction is one of the biggest barriers to API adoption. When developers have to parse JSON responses, manage authentication tokens, and handle retries manually, they spend hours on boilerplate instead of building features.
Type safety is another significant benefit. Generated clients catch errors at compile time rather than runtime, preventing entire classes of bugs before code ships.
Client libraries are executable documentation. While written docs explain what an API does, client libraries show exactly how to use it in working code.
How docs as code workflows evolved (but left something behind)
The docs-as-code movement started from a simple idea: treat documentation with the same rigor as software. Teams moved docs into Git, reviewed changes through pull requests, and versioned content alongside code. Documentation became part of the development workflow rather than an afterthought.
This shift brought real benefits. Writers and developers collaborate in the same repositories. CI/CD pipelines validate and deploy docs automatically. Teams track changes, roll back mistakes, and maintain multiple versions simultaneously.
But most docs-as-code implementations focused narrowly on textual content. Teams automated API reference pages from OpenAPI specs. They built static site generators that turned Markdown into polished documentation sites. They integrated linters and validation tools into their pipelines.
What they didn't automate were the client libraries themselves — the actual code developers download and integrate, the SDKs that translate API endpoints into language-specific methods. This created a disconnect where documentation was automated but SDKs were still maintained by hand across multiple languages.
The incomplete promise: what typical docs-as-code workflows miss
Most docs-as-code workflows automate the easy parts while leaving the hard parts manual. Teams version their Markdown files in Git and deploy static documentation sites through CI/CD pipelines. But the client libraries developers actually use? Still hand-coded and maintained separately.
This creates predictable problems. Code examples reference SDK methods that changed three versions ago. API endpoints get renamed, but the Python library still uses the old naming convention. TypeScript developers see one error handling pattern in the docs while the actual SDK implements something different.
The drift happens because documentation and SDK generation are treated as separate problems. Teams auto-generate API reference pages from OpenAPI specs but write SDKs by hand. Documentation updates flow through one pipeline; SDK releases follow another.
When an API changes, updates cascade through multiple repositories. Someone updates the spec. Another engineer updates the docs. Three more engineers update three different language SDKs. Each step introduces delay and potential inconsistency.
The result is a partially automated developer experience — documentation and client libraries maintained through entirely separate processes, with no guarantee they stay in sync.
Why manual SDK maintenance contradicts docs as code principles
Manual SDK maintenance reintroduces every problem docs-as-code was designed to solve. SDKs drift from the API spec when changes aren't synchronized. Manual updates across multiple languages create the same bottlenecks that plagued documentation workflows. Version mismatches between language SDKs mirror the inconsistency issues that motivated docs-as-code adoption in the first place.
The contradiction is stark: teams accept automation for documentation while tolerating manual processes for the code developers actually run. They write about version control in documentation while maintaining SDKs through manual edits in separate repositories. They auto-generate API reference pages while hand-coding the client libraries those references describe.
Teams that adopt docs-as-code workflows but exclude SDK generation accept the problems they worked to eliminate — version drift, manual bottlenecks, and inconsistent artifacts across languages.
The methodology's benefits stop at the documentation site instead of extending to the client libraries developers depend on.
Client libraries as executable documentation
Documentation explains how an API works. Client libraries execute that explanation. Both artifacts describe the same endpoints, parameters, and response types — just in different forms. One targets human readers. The other targets compilers and runtime environments.
This parallel structure means SDKs function as executable documentation. Type definitions encode the same schema information that appears in API reference pages. Method signatures mirror the parameter descriptions in written guides. Error handling in generated code reflects the same failure modes documented in error response tables.
When documentation and client libraries are generated from the same API specification, they become two representations of a single source of truth. Changes to the spec produce updated reference pages and updated SDK code simultaneously.
This reframing matters because it changes how teams think about SDK maintenance. SDKs are generated artifacts that should follow the same automation principles as documentation. Treating them differently introduces the drift and inconsistency that docs-as-code workflows were designed to eliminate.
Human-readable docs show what's possible. Machine-executable SDKs enforce what's correct. Both should flow from the same definition.
The single source of truth: API specs that generate both docs and SDKs
API specifications provide the foundation for generating both documentation and client libraries from a single definition. They describe endpoints, parameters, request bodies, and response schemas in a structured format that documentation generators and SDK builders can both consume.
The workflow mirrors standard docs-as-code practices. Teams version their API spec files in Git alongside application code. When the spec changes, CI/CD pipelines regenerate both documentation sites and client libraries automatically. Updates to endpoint paths, parameter types, or response structures flow into both artifacts simultaneously.
This removes the manual translation step where someone interprets API changes and updates docs and SDKs separately. The spec becomes the authoritative definition that both human readers and code generators consume directly.
The synchronization is structural. Documentation pages and SDK methods are built from identical type definitions and endpoint descriptions. When both artifacts derive from the same source file in the same build process, drift has nowhere to enter.
Extending Fern's docs-as-code workflow to include client libraries
Fern extends docs-as-code principles to cover the complete developer experience. The platform generates interactive API documentation and idiomatic client libraries in nine languages from a single API definition. OpenAPI specs or Fern Definition files serve as the source of truth for both artifacts.
The workflow matches standard docs-as-code practices. Teams store their API specs in Git alongside application code. When specs change, CI/CD pipelines regenerate documentation sites and client libraries automatically. The same pull request review process applies to both docs and SDK updates, and the same versioning strategy controls both artifact releases.
This delivers what typical docs-as-code workflows miss. API reference pages, code examples, and the SDKs developers actually download all stay synchronized because they're built from identical source files in the same pipeline.
Client libraries become versioned, automated artifacts that follow the same principles as documentation — no separate repositories for each language SDK, no manual publishing to package registries, no drift between what the docs promise and what the code delivers.
Human-readable guides and machine-executable libraries receive the same treatment.
Final thoughts on closing the automation gap
When you automate documentation but not client libraries, you're solving half the problem while accepting all the consequences of the other half — SDK drift, manual bottlenecks, version mismatches across languages. Your developers download code that contradicts the docs you worked to keep synchronized. The methodology that eliminated documentation drift should extend to the artifacts developers actually run. Fern generates both from your API spec, keeping everything in sync automatically.
FAQ
What's the difference between docs as code and including SDKs in the workflow?
Docs-as-code automates documentation through version control and CI/CD pipelines, but typically excludes client libraries. Including SDKs means generating both documentation and client libraries from the same API specification, so they're versioned, built, and deployed together.
How do manually maintained SDKs contradict docs as code principles?
Manual SDK maintenance reintroduces the same problems docs-as-code solved: drift from source specifications, bottlenecks from manual updates across languages, and version inconsistencies. Automating documentation while hand-coding SDKs means accepting the issues the methodology was designed to eliminate.
Can client libraries really be considered executable documentation?
Client libraries encode the same information as written documentation — endpoints, parameters, types, and error responses — in executable form. Type definitions, method signatures, and error handling in generated SDKs mirror the API specification that documentation references describe, making them two representations of the same source of truth.
What happens when documentation and SDKs are generated separately?
Code examples reference SDK methods that changed versions ago, API endpoint renames don't propagate to library naming conventions, and error handling patterns diverge between docs and actual SDK implementations. Updates cascade through separate repositories on different timelines, introducing delay and inconsistency at every step.
