What this error means

max_parallel_requests Current limit: N, Remaining: 0 is a LiteLLM failure pattern reported for developers trying to fix litellm max_parallel_requests counter not decrementing on cancelled streams. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

Detailed root cause analysis in issue. Claude Code sends 2 HTTP POSTs per turn (speculative + confirmation), cancels POST A when POST B starts. CancelledError skips the decrement path. Counter grows by 1 per turn. Workaround provided (try/finally with async_log_failure_event).

Common causes

  • LiteLLM's parallel request limiter has a counter leak when Claude Code's dual-POST pattern cancels mid-stream. Eventually all requests hit 429 Budget Exceeded. Affects production deployments using Claude Code through LiteLLM proxy.
  • Detailed root cause analysis in issue. Claude Code sends 2 HTTP POSTs per turn (speculative + confirmation), cancels POST A when POST B starts. CancelledError skips the decrement path. Counter grows by 1 per turn. Workaround provided (try/finally with async_log_failure_event).

Quick fixes

  1. Confirm the exact error signature matches max_parallel_requests Current limit: N, Remaining: 0.
  2. Check the LiteLLM account, local tool state, and provider configuration involved in the failing workflow.
  3. Compare the failing environment with a known working setup, then change one configuration value at a time.

Platform/tool-specific checks

  • Verify the command, editor, extension, or API client that produced the error.
  • Compare local settings with CI, deployment, or editor-level settings when the error appears in only one environment.
  • Avoid deleting credentials, local model data, or project settings until the failing scope is clear.

Step-by-step troubleshooting

  1. Capture the exact error message and the command, editor action, or request that triggered it.
  2. Check whether the failure is account/auth, quota/rate, model/provider, local runtime, or deployment configuration.
  3. Review the source evidence below and compare it with your environment.
  4. Apply one change at a time and rerun the smallest failing action.
  5. Keep the working fix documented for the team or deployment environment.

How to prevent it

  • Keep provider/tool configuration documented.
  • Record non-secret diagnostics such as tool version, provider name, model name, and command path.
  • Add a lightweight check before CI or production workflows depend on the tool.