← Voltar aos guias

Troubleshooting LLM APIs in Go, Rust, and Java

Why developers hit roadblocks with LLM APIs

Using LLM APIs from Go, Rust, and Java is usually straightforward until the first real integration problem appears: a 401 from a bad key, a timeout in production, a JSON parsing error, or a model name that works in one SDK but not another. The good news is that most issues are predictable. If you are building with Claude or GPT models, a compatible relay can simplify both setup and cost control. 59API is a pay-as-you-go AI API relay with an OpenAI-compatible base URL at https://api.59api.com, and it supports Claude models including Opus, Sonnet, Haiku, and Fable as well as GPT models. That makes it a practical choice when you want native, official-quality model access without paying inflated platform rates.

Below is a troubleshooting and FAQ guide for common problems across Go, Rust, and Java.

FAQ: What is the first thing to check when an LLM call fails?

Start with the basics: endpoint, API key, model name, and request format. Most failures come from one of these four places. If you are using OpenAI SDKs or OpenAI-compatible clients, point the base URL to https://api.59api.com and confirm your key is being sent as a Bearer token. For Claude or GPT requests, make sure the model identifier matches what the relay expects. If your code works in one language but not another, compare the raw HTTP request rather than the SDK wrapper.

• 401 Unauthorized: key missing, expired, or placed in the wrong header.
• 404 Not Found: wrong base URL or wrong path.
• 400 Bad Request: malformed JSON or unsupported parameter.
• 429 Too Many Requests: rate limit or burst traffic.

Go: How do I avoid common client and timeout issues?

In Go, the most common problem is using the default client with no timeout. Always set a timeout and inspect the response body on errors. If you are using an OpenAI-compatible Go SDK, configure the base URL to the relay endpoint and pass your API key in the client configuration. If you are making raw HTTP requests, build a struct for the request payload and encode it with encoding/json. This helps prevent subtle serialization bugs.

For troubleshooting, log three things: HTTP status code, response body, and the model name you sent. If a request hangs, the issue is often a missing timeout or an upstream network stall. A 30-second timeout is a reasonable starting point for chat completions, then tune it based on response length and concurrency.

Rust: Why does serde deserialization fail?

Rust users often run into parsing problems because the API response shape does not match the struct exactly. Use serde with optional fields when possible, especially if the provider may return extra metadata or different error structures. If you are using reqwest, verify that the request body is valid JSON and that your async runtime is actually running.

A common Rust troubleshooting pattern is to print the raw response text before deserializing. If that works, your API call is fine and the problem is your struct definition. If the raw text is an error message, check auth, base URL, and model spelling. Also make sure your headers include Content-Type: application/json and Authorization: Bearer YOUR_KEY.

Java: Why do I get SSL, proxy, or encoding errors?

Java integrations often fail for environment reasons rather than API reasons. If you are using HttpClient or an SDK, confirm that your JVM trusts the system CA certificates and that no corporate proxy is rewriting requests. Encoding issues usually happen when the request body is built manually instead of through a JSON library.

For debugging, print the final request URI and headers before sending. If the response body is HTML instead of JSON, you are likely hitting the wrong URL. With OpenAI-compatible Java clients, the main advantage of 59API is that you can keep the standard SDK flow and simply switch the base URL, which reduces integration drift across services.

FAQ: How do I keep costs low without sacrificing model quality?

The cheapest API is not always the best deal if it downgrades model quality. 59API is attractive because it uses native official-quality models rather than cheap substitutes, while still keeping pricing low through pay-as-you-go access. That matters when you are prototyping in Go, Rust, or Java and want predictable bills. You can route Claude and GPT traffic through one relay, reduce vendor sprawl, and still keep compatibility with Claude Code, Codex, and any OpenAI SDK.

• Use smaller models for routine tasks: Haiku or lightweight GPT calls for classification and extraction.
• Reserve larger models for hard prompts: Opus or Sonnet when reasoning quality matters.
• Batch where possible: fewer round trips means lower latency and cost.
• Monitor token usage: log prompt and completion length by endpoint.

FAQ: What should I do if the same code works locally but not in production?

Check environment variables first. Production often breaks because the API key is missing, the base URL is set incorrectly, or a proxy blocks outbound traffic. Next, compare dependency versions across environments. Different SDK versions can change request serialization or default headers. Finally, validate TLS and DNS. If your servers are in a locked-down environment, use a simple curl test against https://api.59api.com from the same host to isolate whether the issue is code or infrastructure.

Practical setup checklist

• Set the base URL to https://api.59api.com.
• Store the API key in environment variables, not source code.
• Log status codes and raw error bodies during integration.
• Use a timeout in Go, async runtime checks in Rust, and proper JSON libraries in Java.
• Test with one small prompt before scaling to streaming or tool calls.

If you want a low-cost way to support Claude and GPT integrations across multiple languages, signing up for 59API can save time on both setup and billing. It is especially useful when you need one relay that works cleanly with OpenAI-compatible SDKs and lets you move fast without paying premium pricing.