What this error means
RuntimeError: Cannot send a request, as the client has been closed. is a LiteLLM failure pattern reported for developers trying to fix litellm cannot send a request as the client has been closed runtimeerror openai sdk. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.
Why this happens
22 comments. Traceback shows httpx client closed before request sent. Affects GPT 5.2 via LiteLLM Python SDK. Previous versions work without issues. Confirmed regression in v1.81.1.
Common causes
- LiteLLM SDK v1.81.1 breaks OpenAI inference with 'RuntimeError: Cannot send a request, as the client has been closed.' Regression from previous working version. Affects production deployments using LiteLLM as OpenAI proxy.
- 22 comments. Traceback shows httpx client closed before request sent. Affects GPT 5.2 via LiteLLM Python SDK. Previous versions work without issues. Confirmed regression in v1.81.1.
Quick fixes
- Confirm the exact error signature matches
RuntimeError: Cannot send a request, as the client has been closed.. - Check the LiteLLM account, local tool state, and provider configuration involved in the failing workflow.
- 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
- 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.