liblab vs Speakeasy:
Enterprise SDK Generation
Platform Comparison (2025)
See how liblab's enterprise-grade SDK generation platform stacks up against Speakeasy with our detailed feature comparison, security compliance, and proven success metrics.
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Auto-generated code snippets and enhanced OpenAPI specifications
TL;DR how do liblab and SpeakEasy differ?
Choosing the right SDK generation tool can make a huge difference in making your API services available for your clients. liblab and Speakeasy both generate SDKs from OpenAPI specs, but they are not the same.
When deciding between liblab and Speakeasy, it often comes down to what you value most in an SDK generation tool. If you want a cleaner, easier setup with minimal configuration and strong modularity, liblab is often the preferred choice. Its use of a centralized configuration file keeps your OpenAPI spec clean while producing simpler, more usable code.
With liblab, you get:
- ✅ Cleaner, more user-friendly code out of the box
- ✅ Simple, clean OpenAPI specs with no need for modifying your OpenAPI spec
- ✅ Faster integration with CI/CD pipelines, including GitHub Actions, GitLab, Jenkins, and BitBucket
With Speakeasy, you get:
- ❌ More complex configuration based on modifying your OpenAPI spec
- ❌ The generated SDK code that is visibly auto-generated and difficult to read
- ❌ Limited CI/CD integrations
This article compares liblab and Speakeasy from a developer-first perspective, examining performance, extensibility, security, documentation, and integration with modern software workflows.
SDK Generation
liblab uses a centralized configuration file (liblab.config.json), kept separate from the OpenAPI spec, to define how the SDK should be generated. This:
- Avoids maintaining modifications to your OpenAPI spec.
- Separates concerns between your OpenAPI specification and SDK generation.
{
"sdkName": "test-sdk",
"apiVersion": "1.0.0",
"apiName": "test-api",
"specFilePath": "./openapi.yaml",
"languages": [
"python"
],
"auth": [
"bearer"
],
"customizations": {
"includeOptionalSnippetParameters": true,
"authentication": {
"access": {
"prefix": "Bearer"
}
},
"devContainer": true,
"generateEnv": true,
"inferServiceNames": false,
"injectedModels": [],
"license": {
"type": "MIT"
},
"responseHeaders": false,
"retry": {
"enabled": true,
"maxAttempts": 3,
"retryDelay": 150
}
},
"languageOptions": {
"python": {
"alwaysInitializeOptionals": false,
"pypiPackageName": "",
"githubRepoName": "",
"ignoreFiles": [],
"sdkVersion": "1.0.0",
"liblabVersion": "2"
},
},
"publishing": {
"githubOrg": ""
}
}
Speakeasy relies on making and maintaining modifications to your OpenAPI spec which can introduce unnecessary complexity. Instead of keeping the API definition clean, users must add Speakeasy-specific changes (x-speakeasy flags) directly into the OpenAPI spec, making it less clean and harder to manage. This often requires additional scripts to apply changes dynamically, adding extra fragility to the workflow.
For teams that prefer a clean, maintainable API spec, this approach can be problematic—especially when working with manually written OpenAPI specs. Additionally, if these modifications are stored as separate JSON/YAML files, they essentially function like a config file, but without the simplicity and structure of a centralized configuration like liblab's.
Spec Support
liblab and Speakeasy differ in the specs they can support with liblab offering significantly more native coverage:
| Spec Coverage | liblab | Speakeasy |
|---|---|---|
| OpenAPI Versions | OpenAPI 2.0 (Swagger), 3.0, and 3.1 | OpenAPI 3.0.x, 3.1.x |
| Postman Collections | Yes, supported | Not supported |
liblab supports OpenAPI 2.0 (Swagger) and Postman Collections, while Speakeasy does not. This makes liblab the better choice for teams working with legacy APIs or Postman-based workflows.
Both tools can generate SDKs from OpenAPI 3.0 and 3.1 and handle complex specifications like discriminated unions and SSE. However, liblab's approach is easier to manage since it maintains a clean API definition through a clear separation of concerns, avoiding unnecessary modifications to the OpenAPI spec.
Developer Experience
A well-designed SDK enhances the developer experience by ensuring seamless integration, customization, and usability. liblab prioritizes an OOP approach with clean, modular, and idiomatic SDKs that look like they were written by you. In contrast, Speakeasy produces code that has many hallmarks of autogeneration including namespace pollution, repeated code, and a lack of modularity.
| Criteria | liblab | Speakeasy |
|---|---|---|
| Code Quality | ✅ Clean, modular, OOP-based structure | ❌ Monolithic, autogenerated structure with namespace pollution |
| Readability & Maintainability | ✅ Feels like hand-written code, easy to understand | ❌ Autogenerated style with duplicated code, harder to follow |
| Error Handling & Scalability | ✅ Well-structured, easy to debug and extend | ❌ Complex, harder to debug, lacks modularity |
| Documentation Quality | ✅ Clear, well-structured, practical quick-start guides | ❌ Can be overwhelming, contains broken links and formatting issues |
Code Quality
liblab SDKs follow OOP best practices, keeping the namespace clean and intuitive for users. Users will generally be unaware that they're using a liblab generated SDK at all.
from .utils.validator import Validator
from .utils.base_service import BaseService
from ..net.transport.serializer import Serializer
from ..models.utils.cast_models import cast_models
from ..models.get_book_by_id_ok_response import GetBookByIdOkResponseGuard
from ..models.get_all_books_ok_response import GetAllBooksOkResponseGuard
from ..models.add_book_created_response import AddBookCreatedResponseGuard
from ..models import (
AddBookCreatedResponse,
AddBookRequest,
GetAllBooksOkResponse,
GetBookByIdOkResponse,
UpdateBookCoverByIdRequest,
))
In contrast Speakeasy generates single monolithic files with Speakeasy branding that pollutes the generated library's namespace. In some languages this can lead to users being confused by and accidentally importing Speakeasy-specific modules.
from .basesdk import BaseSDK
from speakeasy_book import models, utils
from speakeasy_book._hooks import HookContext
from speakeasy_book.types import BaseModel, OptionalNullable, UNSET
from speakeasy_book.utils import get_security_from_env
from typing import List, Mapping, Optional, Union, cast
Here's a complete example of the quality of code that liblab and Speakeasy will produce. As you can see below liblab takes a modular approach that looks and feels like idiomatic, hand-written code while Speakeasy's contains many hallmarks of autogenerated "spaghetti code" where code is duplicated, lacks modularity, and doesn't follow Object-oriented principles. Users making advanced use of these libraries and reading their code will have a much easier time comprehending the liblab-generated code; enhancing developer velocity. Full Comparison
python
from typing import List
from .utils.validator import Validator
from .utils.base_service import BaseService
from ..net.transport.serializer import Serializer
from ..models.utils.cast_models import cast_models
from ..models.get_book_by_id_ok_response import GetBookByIdOkResponseGuard
from ..models.get_all_books_ok_response import GetAllBooksOkResponseGuard
from ..models.add_book_created_response import AddBookCreatedResponseGuard
from ..models import (
AddBookCreatedResponse,
AddBookRequest,
GetAllBooksOkResponse,
GetBookByIdOkResponse,
UpdateBookCoverByIdRequest,
)
class BooksService(BaseService):
@cast_models
def get_all_books(self) -> List[GetAllBooksOkResponse]:
"""Returns a list of books
...
:raises RequestError: Raised when a request fails, with optional HTTP status code and details.
...
:return: The parsed response data.
:rtype: List[GetAllBooksOkResponse]
"""
serialized_request = (
Serializer(f"{self.base_url}/books", self.get_default_headers())
.serialize()
.set_method("GET")
)
response, _, _ = self.send_request(serialized_request)
return [GetAllBooksOkResponseGuard.return_one_of(item) for item in response]
@cast_models
def add_book(self, request_body: AddBookRequest) -> AddBookCreatedResponse:
"""Adds a new book to the bookstore
:param request_body: The request body.
:type request_body: AddBookRequest
...
:raises RequestError: Raised when a request fails, with optional HTTP status code and details.
...
:return: The parsed response data.
:rtype: AddBookCreatedResponse
"""
Validator(AddBookRequest).validate(request_body)
serialized_request = (
Serializer(f"{self.base_url}/books", self.get_default_headers())
.serialize()
.set_method("POST")
.set_body(request_body)
)
response, _, _ = self.send_request(serialized_request)
return AddBookCreatedResponseGuard.return_one_of(response)
@cast_models
def get_book_by_id(self, book_id: int) -> GetBookByIdOkResponse:
"""Returns a single book
:param book_id: ID of the book to return
:type book_id: int
...
:raises RequestError: Raised when a request fails, with optional HTTP status code and details.
...
:return: The parsed response data.
:rtype: GetBookByIdOkResponse
"""
Validator(int).validate(book_id)
serialized_request = (
Serializer(f"{self.base_url}/books/{{bookId}}", self.get_default_headers())
.add_path("bookId", book_id)
.serialize()
.set_method("GET")
)
response, _, _ = self.send_request(serialized_request)
return GetBookByIdOkResponseGuard.return_one_of(response)
@cast_models
def update_book_cover_by_id(
self, request_body: UpdateBookCoverByIdRequest, book_id: int
) -> None:
"""Updates a single book cover
:param request_body: The request body.
:type request_body: UpdateBookCoverByIdRequest
:param book_id: ID of the book to update
:type book_id: int
...
:raises RequestError: Raised when a request fails, with optional HTTP status code and details.
...
"""
Validator(UpdateBookCoverByIdRequest).validate(request_body)
Validator(int).validate(book_id)
serialized_request = (
Serializer(
f"{self.base_url}/books/{{bookId}}/cover", self.get_default_headers()
)
.add_path("bookId", book_id)
.serialize()
.set_method("PUT")
.set_body(request_body, "multipart/form-data")
)
self.send_request(serialized_request)
Documentation
A well-structured SDK is only useful if developers can understand how to use it. liblab's documentation prioritizes clarity and ease of use, while Speakeasy's documentation can be overwhelming and sometimes lacks organization.
The documentation for liblab is often praised for its clarity, featuring:
- A focus on practical usage with quick start guides and clear API references.
- Clean structure – Documentation is well-organized, making it easy for developers to find the information they need.
- Minimal clutter – Avoids unnecessary complexity, ensuring a smooth onboarding experience.
Comparing Speakeasy during testing, we found that the generated documentation contained multiple issues which can negatively impact user's experience. These included broken links pointing to non-existent files and improperly formatted content.
Comparison of Generated Code Snippets
While both liblab and Speakeasy generate Python and TypeScript SDKs from OpenAPI specs, their approaches to code structure and usage differ. Below are sample snippets from each tool's generated documentation, showcasing how they organize and implement API calls in Python and Typescript SDKs.
python
from exampleapi import Exampleapi
from exampleapi.models import OAuth2ClientConfiguration
sdk = Exampleapi(
access_token="YOUR_ACCESS_TOKEN"
)
request_body = OAuth2ClientConfiguration(
redirect_uris=[
"redirect_uris"
],
client_name="client_name"
)
result = sdk.oauth2.create_client(request_body=request_body)
print(result)
typescript
import { Exampleapi, OAuth2ClientConfiguration } from 'exampleapi';
(async () => {
const exampleapi = new Exampleapi({
token: 'YOUR_TOKEN',
});
const oAuth2ClientConfiguration: OAuth2ClientConfiguration = {
redirectUris: ['redirect_uris'],
clientName: 'client_name',
};
const { data } = await exampleapi.oauth2.createClient(oAuth2ClientConfiguration);
console.log(data);
})();
liblab constructs the request body explicitly before API calls, ensuring better type-checking, clarity, and explicit field definitions. This approach improves readability, particularly for complex requests, and automatically includes all request fields, reducing errors and minimizing the need to check external documentation. Additionally, it enhances IDE support with autocomplete and type safety, making development more efficient.
In contrast, Speakeasy passes parameters directly into functions, sacrificing maintainability, and readability for complex APIs. Inline parameter passing can lead to missing optional fields and inconsistent SDK structures, making debugging and modifications harder.
liblab enforces a structured, predictable API call process—instantiating the SDK, defining a request object, and invoking the method. This ensures maintainability, simplifies debugging, and makes API evolution easier to manage. By separating request construction from execution, liblab improves scalability and reduces errors when updating API calls.
| Criteria | liblab | Speakeasy |
|---|---|---|
| Code Structure | ✅ Explicit request body construction | ❌ Direct inline parameter passing |
| Type Safety | ✅ Strong typing throughout generated SDKs | ❌ Weaker type safety in some languages |
| Error Reduction | ✅ Well defined structure reduces misconfiguration | ❌ More prone to configuration errors due to less structure |
While Speakeasy's approach is slightly less code, liblab's structured model offers superior type safety, readability, and long-term maintainability, making it the better choice for professional API integrations.
Integration with CI/CD Pipelines
A smooth CI/CD workflow is essential for automating SDK generation and ensuring up-to-date client libraries. liblab makes this process seamless by offering native integrations with all major CI/CD platforms:
liblab offers a fully automated setup, eliminating the need for additional scripting to integrate with your DevOps stack. It works seamlessly across all platforms. With built-in automation, SDKs stay up to date without manual intervention, ensuring a consistent and reliable workflow.
Speakeasy, in contrast, integrates only with GitHub Actions.
Security Considerations
Security is a critical factor when choosing an SDK generation tool. liblab provides security throughout the company and product. Speakeasy provides lighter authentication options, has a heavy dependence on external dependencies in generated SDKs, and lacks SOC2 compliance.
- Advanced security controls: liblab lets you manage tokens, keys, and custom auth flows from a single config file.
- SOC 2 compliance: liblab is SOC 2 compliant, meaning it meets strict security and data protection standards for handling sensitive information. This is particularly important for companies operating in regulated industries or those working with enterprise clients who require third-party security audits.
- Flexible authentication setup: liblab's flexible configuration makes it easy to implement both standard and complex authentication scenarios. Including authentication keys, bearer tokens, standard OAuth 2.0, specialized OAuth 2.0 flows, multi-tenant authentication, and granular access control.
On the other hand, Speakeasy provides limited security measures:
- Heavy external dependencies: Speakeasy-generated libraries rely heavily on third-party dependencies, which increases their attack surface.
- Basic authentication support: Speakeasy provides standard authentication patterns with an emphasis on token lifecycle management but does not have the same granularity as liblab's approach.
- No SOC 2 compliance: Speakeasy does not meet SOC 2 security standards, which may be a dealbreaker for organizations requiring certified security measures.
| Criteria | liblab | Speakeasy |
|---|---|---|
| Authentication Flexibility | ✅ Flexible token, key, and auth management | ❌ Limited authentication granularity |
| SOC 2 Compliance | ✅ SOC 2 compliant | ❌ Not SOC 2 compliant |
| External Dependencies | ✅ Minimal reliance on third-party libraries | ❌ Heavy use of third-party dependencies which can increase security vulnerabilities |
liblab's flexible configuration and SOC2 compliance can be advantageous for both typical and complex security scenarios.
Testing Generated SDKs
Testing an SDK soon after generating it can save developers a lot of time. The liblab's and Speakeasy approach differs regarding how to provide a testing environment for developers.
liblab automatically provides example scripts with each generated SDK. This means you can run a single command to see if the SDK works with either a local or remote API. All necessary environment variables and configurations are pre-configured so you can quickly confirm that the generated code matches your expectations. Because of this, testing with liblab often feels faster and more convenient.
Additionally, liblab provides a sandbox environment for all languages using Dev Containers. By using devcontainers, liblab ensures instant reproducibility. Thus, any developer who clones the repo will get the exact same working environment without manual setup.
Speakeasy offers sandbox environments for Go, TypeScript, and Python, allowing developers to test API calls. However, it does not include built-in test scripts in the generated SDK. Instead, testing requires additional setup and may not be as immediate, especially if you need to run tests locally before deployment.
liblab generates clean, concise, and Pythonic code, making it easy to read and maintain. In contrast, Speakeasy's SDKs tend to be verbose and hard to read — they look autogenerated. liblab's simplicity makes it the better choice for developers who prefer minimal configuration and straightforward implementation. The table below highlights the key differences:
| Criteria | liblab | Speakeasy |
|---|---|---|
| Readability & Simplicity | ✅ Cleaner code | ❌ More verbose |
| Error Handling & Retries | ✅ Support retries and error handling | ✅ Support retries and error handling |
| Type Safety & Validation | ✅ Standard, built-in implementation | ❌ Depends on third party libraries |
| Developer Experience | ✅ Easier to adopt | ❌ Larger learning curve |
liblab is the best choice if you want a straightforward SDK with minimal setup. The simpler, more readable code also makes it easier for developers to debug and understand. Speakeasy delivers comparable features with added complexity.
SDK Directory Structure & Architecture
When generating SDKs, how you arrange folders and files can greatly impact code readability and the overall developer experience. The examples below come from a fictional “bookstore” API, so you can see how each tool might structure its output in practice.
liblab creates a modern and modular project structure :
test_sdk/
├── hooks/
├── models/
│ ├── utils/
├── net/
│ ├── environment/
│ ├── headers/
│ ├── request_chain/
│ │ ├── handlers/
│ ├── transport/
├── services/
│ ├── async_/
│ │ ├── utils/
│ ├── utils/
├── sdk.py
├── sdk_async.py
Here, folders like net/ handle networking tasks, models/ store data classes, and separate directories exist for synchronous vs. asynchronous services. This clear split can simplify large projects.
Speakeasy has a flatter layout:
speakeasy_book/
├── _hooks/
├── models/
├── types/
├── utils/
├── books.py
├── orders.py
├── basesdk.py
├── sdk.py
├── sdkconfiguration.py
├── httpclient.py
├── _version.py
In this structure, core features like networking (httpclient.py) and configuration (sdkconfiguration.py) are kept at the top level, which can make navigation less modular.
The following table summarizes the differences
| Category | liblab | Speakeasy |
|---|---|---|
| Directory Structure | More modular (net/, services/, models/) | Flatter layout, less modular |
| Networking & Requests | Modular request chain (request_chain/handlers/) allows flexibility | Centralized in httpclient.py, simpler but less customizable |
| Models & Type Safety | Built-in models and validation | External dependencies for validation |
| SDK Services Structure | Separate sync/async directories for clearer organization | Combines both in a single module, potentially confusing to users |
| Hooks & Extensibility | Hooks for managing requests, responses, and errors | Event-driven hooks |
Versioning & Maintenance
API changes are inevitable, and both tools handle versioning differently:
- liblab maintains an SDK history in its dashboard, enabling you to:
- Track and revert to previous builds.
- Auto-update client libraries as your API evolves.
- Manage multiple versions in parallel if needed.
- Speakeasy does not include built-in version tracking, relying instead on your existing Git or CI workflows. This can work fine, but it requires more manual effort when dealing with multiple versions or frequent API updates.
Summary
liblab stands out as the clear winner in SDK generation tools, offering an exceptionally clean, developer-friendly experience with its thoughtfully designed configuration system and highly modular architecture. Its streamlined approach produces elegant, maintainable code that significantly reduces development time and potential errors. liblab's sophisticated version tracking system and intuitive dashboard make it a superior choice for modern development teams.
Speakeasy, while functional, falls short in several critical areas. Its overly complex implementation and reliance on external dependencies can lead to bloated codebases and potential security vulnerabilities. The verbose code output and namespace pollution create unnecessary complications for developers. Its lack of built-in version tracking and flat directory structure make long-term maintenance more challenging and time-consuming.
Teams choosing liblab benefit from its superior code architecture, robust type safety implementation, and exceptional developer experience. The tool's emphasis on clean code practices and intuitive design makes it the ideal choice for organizations that value efficiency, maintainability, and code quality. liblab's comprehensive feature set and attention to developer needs make it the standout option in the SDK generation space.
Proven Enterprise Success Metrics
Increase in developer adoption within 6 months of implementing liblab SDKs
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