What this error means
RemoteProtocolError: Stream dropped mid-request during long code_execution + skills runs is a Anthropic API, Python SDK failure pattern reported for developers trying to fix anthropic api streaming connection dropping during long-running code execution or skills runs. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.
Why this happens
Confirmed bug in anthropic-sdk-python: streaming /v1/messages endpoint drops connection mid-stream during extended code_execution + skills runs. No graceful error handling.
Common causes
- Developers using Anthropic code execution features experience silent stream drops on long operations, losing partial results with no retry mechanism
- Confirmed bug in anthropic-sdk-python: streaming /v1/messages endpoint drops connection mid-stream during extended code_execution + skills runs. No graceful error handling.
Quick fixes
- Confirm the exact error signature matches
RemoteProtocolError: Stream dropped mid-request during long code_execution + skills runs. - Check the Anthropic API, Python SDK 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.