What this error means

httpx.RemoteProtocolError: peer closed connection without sending complete message body (incomplete chunked read) is a Anthropic Python SDK failure pattern reported for developers trying to fix remoteprotocolerror peer closed connection during anthropic streaming with code_execution and skills. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

Open issue on official repo. Server closes connection mid-stream during active content_block_delta events. Confirmed not an idle timeout issue. Each retry burns full input tokens and partial output tokens. High cost impact for agentic workflows.

Common causes

  • Streaming requests to /v1/messages using code_execution + skills consistently drop mid-stream with RemoteProtocolError. Each retry costs full input tokens plus previously-billed partial output tokens, making this a significant cost issue for developers running long agentic workflows.
  • Open issue on official repo. Server closes connection mid-stream during active content_block_delta events. Confirmed not an idle timeout issue. Each retry burns full input tokens and partial output tokens. High cost impact for agentic workflows.

Quick fixes

  1. Confirm the exact error signature matches httpx.RemoteProtocolError: peer closed connection without sending complete message body (incomplete chunked read).
  2. Check the Anthropic Python SDK 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.