May 2025 Summaries

In the world of software development, APIs (Application Programming Interfaces) and SDKs (Software Development Kits) are fundamental building blocks that enable developers to integrate external services and functionality into their applications. Understanding the distinct roles and when to use each is crucial for making informed architectural decisions. An API provides a set of rules and protocols that allows different systems to communicate with each other, defining endpoints, request/response formats, authentication mechanisms, and available operations. In contrast, an SDK is a collection of tools, libraries, documentation, and code samples that helps developers build applications for a specific platform or service, abstracting away the monotony of managing direct API calls. The key differences between APIs and SDKs lie in their scope, implementation, learning curve, setup, debugging, updates, size, and dependencies. When to use each depends on factors such as project requirements, timeline, team expertise, integration complexity, customization needs, performance requirements, maintenance considerations, platform constraints, and more. A hybrid approach often yields the best results, with SDKs used for complex operations where they add value, and direct API calls considered for simple, performance-critical paths. Ultimately, making a deliberate, informed decision is key to choosing between APIs and SDKs, rather than defaulting to one approach for all scenarios.

AI assistants like ChatGPT, Claude, and Cursor are rapidly reshaping how developers interact with software. Anthropic's new Model Context Protocol (MCP) is an open standard that solves the problem of exposing APIs to Large Language Models (LLMs) in a portable and predictable way. MCP is a lightweight, machine-readable specification that bridges the gap between traditional APIs and AI agents. It translates your API into a format that AI tools can understand and use, opening up the ability for anyone to use natural language to interact with your API. By standardizing this layer, MCP turns your API into an AI-native interface. Using MCP servers provides seamless AI integration, consistent, spec-driven behavior, secure and controlled access, and portable deployment. Developers can generate a ready-to-use MCP server from their OpenAPI spec using liblab in just a few minutes, making APIs usable by AI with no extra boilerplate.

Choosing the right programming language for a Software Development Kit (SDK) is crucial for driving adoption and providing a positive experience for developers. The most critical factor in selecting languages is understanding who the target audience is and the specific ecosystems they operate within. While trends can provide context, it's essential to prioritize building SDKs in languages that fit seamlessly into their existing workflows. Understanding broader language usage can help identify other languages that could expand the SDK's potential user base. The top languages for SDKs in 2025 include Python, JavaScript/TypeScript, Java/Kotlin, C#, and Go, each with their key industries and characteristics. Developing high-quality, idiomatic SDKs requires significant effort and specialized expertise, but tools like liblab can address this challenge by creating SDKs directly from OpenAPI specs and supporting multiple programming languages. Prioritizing building SDKs in the languages and platforms your developers use is essential for facilitating integration and adoption, ultimately serving as the main connection between an API and developers.

Fault tolerance is a crucial aspect of building robust software development kits (SDKs) that can handle transient errors, network issues, and server failures. A well-designed SDK should anticipate these issues, provide clear error messages, retry failed requests when necessary, surface actionable error messages, avoid crashing the host application, and offer fallback behavior where possible. Common pitfalls in SDK design include relying on ideal network conditions, underestimating environmental unpredictability, failing to implement retries, throwing generic or silent failures, tightly coupling API responses, neglecting offline or flaky network conditions, and not accounting for developer-friendly errors. To build more resilient SDKs, developers should adopt best practices such as implementing retries with exponential backoff, graceful degradation, timeouts, cancellation, circuit breakers, and providing clear error messages. By doing so, they can ensure their SDKs handle failure gracefully, improve reliability and user experience, and reduce support requests and development cycles for consumers.

Debugging SDKs can be challenging due to the multiple layers of potential complexity, including the app using the SDK, the SDK itself, OpenAPI specs, documentation, API gateways, and backend APIs. Additionally, some API calls may be constrained by rate limits, cost, access restrictions, or required call sequences. To make debugging easier, tools like Proxyman can help inspect and manipulate HTTP(S) traffic, providing a clean interface for developers to see exactly what their SDK is sending and receiving. A real-world example demonstrates how Proxyman can be used to debug an SDK by intercepting requests, modifying responses, and simulating API calls with edge-case data or scenarios that are difficult to trigger naturally. By using tools like Proxyman, developers can build, test, and iterate on SDKs more efficiently, ensuring they are reliable, correct, and easy for developers to consume.