BerriAI/litellm · error · Exception

{most_recent_exception_str}. All fallback attempts failed. E

Error message

{most_recent_exception_str}. All fallback attempts failed. Enable verbose logging with `litellm.set_verbose=True` for details.

What it means

Raised by litellm's fallback machinery after EVERY fallback attempt (and the primary attempt) raised an exception. The message contains the most recent exception string plus guidance to enable verbose logging; nothing succeeded, so the caller receives a bare Exception, not a typed litellm error.

Source

Thrown at litellm/litellm_core_utils/fallback_utils.py:79

            response = await litellm.acompletion(
                **completion_kwargs,
                model=model,
                litellm_logging_obj=litellm_logging_obj,
            )

            if response is not None:
                return add_fallback_headers_to_response(
                    response=response,
                    attempted_fallbacks=attempted_fallbacks,
                )

        except Exception as e:
            verbose_logger.exception("Fallback attempt failed for model %s: %s", model, e)
            most_recent_exception_str = str(e)
            continue

    raise Exception(
        f"{most_recent_exception_str}. All fallback attempts failed. Enable verbose logging with `litellm.set_verbose=True` for details."
    )


def completion_with_fallbacks(**kwargs):
    return run_async_function(async_function=async_completion_with_fallbacks, **kwargs)

View on GitHub (pinned to 6c2dcb801b)

Solutions

1. Set litellm.set_verbose = True (or check logs) and rerun to see each individual failure and its cause.
2. Fix the most_recent_exception_str cause first — often one root issue (quota, invalid param) fails all models.
3. Verify every model in the fallback chain exists and is correctly configured (keys, api_base).
4. Add cooldowns/retries so transient failures don't burn through all fallbacks instantly.

Example fix

# before
litellm.completion(model='gpt-4o', fallbacks=['gpt-4o-mini'], messages=msgs)

# after
import litellm
litellm.set_verbose = True
try:
    litellm.completion(model='gpt-4o', fallbacks=['gpt-4o-mini', 'claude-3-5-sonnet'], messages=msgs)
except Exception as e:
    logger.error('all fallbacks failed, last error: %s', e)
    raise

import litellm

def chain_valid(models: list[str]) -> bool:
    return bool(models) and all(isinstance(m, str) and '/' in m for m in models)

try {
  await litellm.completion({ model, fallbacks: [...], messages });
} catch (e) {
  // all fallbacks exhausted — degrade gracefully (queue, cached answer, user error)
}

When it happens

Trigger: Using the fallbacks parameter or Router with fallbacks where the primary model and all fallback models fail — e.g. all keys rate-limited, all endpoints unreachable, or an invalid shared parameter (bad max_tokens) failing identically on every model.

Common situations: Exhausted API quota across all providers, a shared invalid argument causing universal failure, misconfigured fallback model names, or a region-wide outage.

Related errors

• LLM Router not initialized. Ensure models added to proxy.
• LiteLLM Managed File object with id={file_id} has no file_ob
• Redis circuit breaker is open — skipping {name}
• {exception_provider} - {message} This error occurs when lo
• litellm.MidStreamFallbackError: {message}

AI-assisted analysis of BerriAI/litellm@6c2dcb801b (2026-08-15). Data as JSON: /api/errors/278719aba3bc0ab2. Report an issue: GitHub.