What this error means

RemoteProtocolError — Streaming /v1/messages drops mid-stream during long code_execution + skills runs, losing partial responses is a Anthropic API failure pattern reported for developers trying to fix intermittent streaming disconnections (remoteprotocolerror) on long-running /v1/messages api calls with code_execution and tools. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

GitHub Issue #1470 opened Apr 29, 2026 by @ypang-anytimeai on anthropics/anthropic-sdk-python. Long-running streaming sessions with code_execution skill consistently drop with WebSocket-level protocol errors. Affects production AI agent pipelines running multi-step reasoning. 3 weeks ago still open. Not in covered-errors.md.

Common causes

  • GitHub Issue #1470 opened Apr 29, 2026 by @ypang-anytimeai on anthropics/anthropic-sdk-python. Long-running streaming sessions with code_execution skill consistently drop with WebSocket-level protocol errors. Affects production AI agent pipelines running multi-step reasoning. 3 weeks ago still open. Not in covered-errors.md.

Quick fixes

  1. Confirm the exact error signature matches RemoteProtocolError — Streaming /v1/messages drops mid-stream during long code_execution + skills runs, losing partial responses.
  2. Check the Anthropic API 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.