When building SDKs, it's easy to focus on functionality: mapping API endpoints, data models, and authentication flows. But one area that's often overlooked until things go wrong is fault tolerance. In the real world, APIs timeout, networks drop packets, and servers throw 500s. SDKs that don't anticipate these issues can break apps, frustrate developers, and generate support tickets.

In this post, we'll explore what fault tolerance means in the context of SDKs, some of the common mistakes developers make, and practical strategies for building more resilient SDKs.

What Fault Tolerance Means in SDKs

Fault tolerance is a system's ability to continue operating under partial failure. In SDKs, that means being able to:

• Retry failed requests when appropriate
• Surface clear and actionable error messages
• Avoid crashing the host application
• Provide fallback behavior where possible

Think of it as the difference between a helpful SDK and one that throws an obscure error and leaves developers guessing.

Common Pitfalls in SDK Design

Even experienced SDK developers can fall into traps that lead to brittle, unhelpful tools. The pitfalls below often stem from assuming ideal network conditions or underestimating how SDKs are used in diverse, unpredictable environments. Here are some of the most common pitfalls:

No Retry Logic

Failing to implement retries means every transient error becomes a hard failure. This is especially problematic in mobile or high-latency environments. A brief outage or network hiccup shouldn't require manual intervention to recover.

Some failures are transient — a momentary spike in latency, a dropped packet. Without retries, the SDK just fails immediately:

public async Task<string> GetDataAsync()
{
	using var httpClient = new HttpClient();
	var response = await httpClient.GetAsync("https://api.example.com/data");
	return await response.Content.ReadAsStringAsync();
}

Silent Failures or Generic Errors

When an SDK fails silently or throws a non-descriptive exception, it creates confusion and slows down debugging. Developers deserve error messages that provide insight into what went wrong and how they can recover.

SDKs that swallow errors or return vague messages like "Something went wrong" don't help developers debug:

throw new Exception("Something went wrong");

Error messages are very easy to improve:

throw new ForbiddenException("Access denied. You do not have permission to access this resource.", 403);

Tightly Coupled API Responses

APIs evolve — new fields are added, deprecated ones are removed, and structures can shift. SDKs that rely on rigid assumptions about these structures often break when any part of the API changes, even in backward-compatible ways. For example, a tightly-coupled SDK might deserialize a JSON object into a strict class model without accommodating optional or unexpected fields, leading to runtime exceptions.

Instead, SDKs should adopt defensive coding practices: use flexible deserialization techniques, validate required fields explicitly, and allow optional data to be ignored gracefully. This makes the SDK more resilient to minor server-side updates, reduces support overhead, and minimizes the need for urgent patches.

You'll want to make sure to validate and gracefully fallback when unexpected fields appear or go missing, and ensure parsing logic doesn't assume every field will always be present.

Not Accounting for Offline or Flaky Network Conditions

Many SDKs are written assuming the network is always available. In reality, users may be on unstable Wi-Fi or commuting in and out of coverage zones. SDKs that don't anticipate offline scenarios can crash or hang without feedback, which is frustrating in user-facing apps.

SDKs that assume a stable network can fail badly when a user is offline or experiencing intermittent connectivity. Make sure to fail gracefully and have business logic to fall back on when an error occurs:

try
{
    using var httpClient = new HttpClient();
    var response = await httpClient.GetAsync("https://api.example.com/user");
    response.EnsureSuccessStatusCode();
}
catch (HttpRequestException ex) when (!System.Net.NetworkInformation.NetworkInterface.GetIsNetworkAvailable())
{
    Console.WriteLine("No network connection. Try again later.");
    // fallback logic here
}
catch (Exception ex)
{
    Console.WriteLine($"Request failed: {ex.Message}");
}

Best Practices for Fault-Tolerant SDKs

Designing for resilience isn't just about avoiding failure — it's about recovering from it. A well-architected SDK communicates clearly, degrades gracefully, and offers developers the tools they need to manage failure effectively.

Retries with Exponential Backoff

Automatically retry transient errors (like 502 or 503). Use exponential backoff to avoid spamming the server:

var policy = Policy
  .Handle<HttpRequestException>()
  .OrResult<HttpResponseMessage>(r => (int)r.StatusCode >= 500)
  .WaitAndRetryAsync(3, retryAttempt => TimeSpan.FromSeconds(Math.Pow(2, retryAttempt)));

var response = await policy.ExecuteAsync(() => httpClient.GetAsync("https://api.example.com/data"));

Graceful Degradation

Not all features are critical. SDKs should identify which calls are essential (like authentication or payments) and which are optional (like analytics or UI hints). Failing fast on non-critical calls prevents them from impacting the core experience.

If a feature isn't critical, let it fail silently and log the error:

try
{
    await httpClient.PostAsync("https://api.example.com/track", new StringContent("{}"));
}
catch (Exception ex)
{
    Console.WriteLine("Telemetry failed but won't block user flow: " + ex.Message);
}

Timeouts and Cancellation

A slow or hanging API call can lock up the user interface or waste server resources. Every network operation should have a timeout, and developers should be able to cancel ongoing requests to free up system resources or respond to user input.

Never rely on the client's patience. Set reasonable timeouts:

var cts = new CancellationTokenSource(TimeSpan.FromSeconds(5));
try
{
    var response = await httpClient.GetAsync("https://api.example.com/slow", cts.Token);
    response.EnsureSuccessStatusCode();
}
catch (TaskCanceledException)
{
    Console.WriteLine("Request timed out.");
}

Circuit Breakers

When a downstream system is failing repeatedly, it's better to stop sending requests than to keep retrying and risk cascading failure. Circuit breakers protect both your system and your users from sustained outages or degraded performance.

Protect your SDK from overwhelming the server (or itself) when failures are persistent:

var circuitBreaker = Policy
  .Handle<HttpRequestException>()
  .CircuitBreakerAsync(2, TimeSpan.FromMinutes(1));

await circuitBreaker.ExecuteAsync(() => httpClient.GetAsync("https://api.example.com"));

Developer-Friendly Errors

Think of your error messages as part of your developer UX. Helpful, structured errors not only make debugging easier but also reduce support requests and speed up development cycles for your SDK consumers.

Throw custom error classes that include status codes, request context, and helpful messages:

public class ApiException : Exception
{
    public int StatusCode { get; }

    public ApiException(string message, int statusCode) : base(message)
    {
        StatusCode = statusCode;
    }
}

---

Real-World Example: Before & After

Before:

using var httpClient = new HttpClient();
var data = await httpClient.GetStringAsync("https://api.example.com/info");

After:

private static readonly IAsyncPolicy<HttpResponseMessage> RetryPolicy = Policy
    .Handle<HttpRequestException>()
    .OrResult<HttpResponseMessage>(r => !r.IsSuccessStatusCode)
    .WaitAndRetryAsync(
        3,
        attempt => TimeSpan.FromSeconds(Math.Pow(2, attempt)),
        (outcome, timespan, attempt, context) =>
        {
            Console.WriteLine($"Retry {attempt}: {outcome.Exception?.Message ?? outcome.Result.StatusCode.ToString()}");
        });

async Task<string> FetchWithResilience(HttpClient client, string url)
{
    try
    {
        var response = await RetryPolicy.ExecuteAsync(() => client.GetAsync(url));

        if (!response.IsSuccessStatusCode)
        {
            throw new ApiException($"Failed with status {response.StatusCode}", (int)response.StatusCode);
        }

        return await response.Content.ReadAsStringAsync();
    }
    catch (Exception ex)
    {
        Console.WriteLine($"Final error after retries: {ex.Message}");
        throw;
    }
}

How LibLab Handles This

At LibLab, we generate SDKs across multiple languages. Fault tolerance is built in, not bolted on. Our SDKs include:

• Configurable retry behavior
• Clear error classes
• Configurable timeout value
• Defensive parsing for backward compatibility

We support a flexible retry mechanism that allows developers to define their own strategies through a configuration file. Developers can customize settings like the number of retry attempts and delay intervals via liblab.config.json, such as:

{
  "retry": {
    "enabled": true,
    "maxAttempts": 3,
    "retryDelay": 150
  }
}

This approach not only handles transient network or server issues gracefully, but also reduces the likelihood of overwhelming downstream systems by adding smart delay and randomness.

Whether you're building a payment integration or syncing data across services, LibLab-generated SDKs give you resilience by default — improving both reliability and user experience.

We believe SDKs should help developers succeed — not force them to dig through stack traces when things go wrong.

Conclusion

A resilient SDK doesn't just make life easier for developers — it builds trust in your platform. By avoiding common pitfalls and applying simple best practices, you can ship SDKs that handle failure gracefully, so developers don't have to.