What this error means
Rate limit exceeded for api_key: <sha256_hash>. Limit type: parallel_request_limit is a LiteLLM failure pattern reported for developers trying to fix litellm rate limit error exposing api key hash in response body. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.
Why this happens
LiteLLM parallel_request_limiter_v3.py line ~1261 includes full token hash in 429 response detail. When descriptor_key is 'api_key', descriptor_value is the SHA-256 hash from /key/generate. Visible to any HTTP client hitting rate limit. redact_user_api_key_info doesn't cover this path.
Common causes
- When parallel request limiter returns 429, the error body includes the full 64-character SHA-256 hash of the virtual key. redact_user_api_key_info setting doesn't affect this code path. Security concern for proxy deployments.
- LiteLLM parallel_request_limiter_v3.py line ~1261 includes full token hash in 429 response detail. When descriptor_key is 'api_key', descriptor_value is the SHA-256 hash from /key/generate. Visible to any HTTP client hitting rate limit. redact_user_api_key_info doesn't cover this path.
Quick fixes
- Confirm the exact error signature matches
Rate limit exceeded for api_key: <sha256_hash>. Limit type: parallel_request_limit. - Check the LiteLLM account, local tool state, and provider configuration involved in the failing workflow.
- Reduce request pressure, check quota or plan limits, and retry with backoff instead of immediate repeated requests.
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
- Capture the exact error message and the command, editor action, or request that triggered it.
- Check whether the failure is account/auth, quota/rate, model/provider, local runtime, or deployment configuration.
- Review the source evidence below and compare it with your environment.
- Apply one change at a time and rerun the smallest failing action.
- 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.