# Error Types and Which to Retry — Claude API / OpenAI API Basics

Source: https://www.geekswithgeeks.com/en/llm-apis/r-errors

> Map status codes to actions.

## Retry the transient, fix the permanent

HTTP status codes tell you what to do. **400** invalid request (a bug in your request, such as a missing `max_tokens` or a malformed message list): do not retry, fix the code. **401/403** authentication or permission problem: do not retry, check the key and access. **404** unknown model or endpoint. **413** request too large. **429** rate limited (too many requests or tokens per minute) and **5xx** server errors or overloaded: **transient**, retry after waiting (honour a `retry-after` header if present). Network timeouts and dropped connections are also retryable. The official SDKs already retry some of these automatically (a small default number of retries with backoff) and raise typed exceptions such as `RateLimitError`, `BadRequestError` and `APIConnectionError`, each carrying the status code and the error body.

## Expect failure, recover gracefully

Networks and rate limits fail; classify errors, retry the right ones with backoff, and degrade gracefully.

![Four tools: classify, retry, limit, fall back.](assets/figures/llm-apis/section-5-map.svg) — Figure 5.1 — Classify, retry, limit and fall back.

## Retries and typed errors, run

I ran this in a Python virtual environment with the anthropic 1.11.0 and openai 3.22.1 SDKs against a small local stand-in server (shown in the testing topic, saved as `mock.py`). The server returns canned replies, so no key, network or real model is involved: it proves how the SDK builds requests and handles replies, not what a real model would say. The stand-in server answers 429 once. With `max_retries=2` the SDK retries by itself and succeeds on the second request. With `max_retries=0` the same 429 raises `RateLimitError` after one request. A missing `max_tokens` (set to 0) returns a 400 that raises `BadRequestError` with the server's message, and the OpenAI SDK raises its own `RateLimitError` for a 429.

```python
import mock, anthropic, openai
srv = mock.start(); base = f"http://127.0.0.1:{srv.server_address[1]}"      # local stand-in server, not a real API

body = dict(model="demo-model", max_tokens=50, messages=[{"role": "user", "content": "Hi"}])

mock.STATE["log"].clear(); mock.STATE["fail_next"] = 1                # server answers 429 once
ok = anthropic.Anthropic(api_key="k", base_url=base, max_retries=2).messages.create(**body)
print("with retries=2 : succeeded after", len(mock.STATE["log"]), "requests ->", ok.content[0].text[:5])

mock.STATE["log"].clear(); mock.STATE["fail_next"] = 1
try:
    anthropic.Anthropic(api_key="k", base_url=base, max_retries=0).messages.create(**body)
except anthropic.RateLimitError as e:
    print("with retries=0 : RateLimitError", e.status_code, "after", len(mock.STATE["log"]), "request")

try:
    anthropic.Anthropic(api_key="k", base_url=base).messages.create(**{**body, "max_tokens": 0})
except anthropic.BadRequestError as e:
    print("bad request    :", e.status_code, "-", e.body["error"]["message"])

mock.STATE["fail_next"] = 1
try:
    openai.OpenAI(api_key="k", base_url=base + "/v1", max_retries=0).chat.completions.create(
        model="demo-model", messages=[{"role": "user", "content": "Hi"}])
except openai.RateLimitError as e:
    print("openai 429     :", type(e).__name__, e.status_code)

```

Output:

```
with retries=2 : succeeded after 2 requests -> Paris
with retries=0 : RateLimitError 429 after 1 request
bad request    : 400 - max_tokens: Field required
openai 429     : RateLimitError 429
```

## Log the request id

Providers return a request id in response headers or the error body. Log it so support can trace a failed call.

**Quiz:** Which error should you NOT retry?

- [x] 400 invalid request
- [ ] 429 rate limited
- [ ] 503 overloaded
- [ ] A dropped connection

*Answer:* 400 invalid request. Repeating a malformed request just fails again; fix the request.