What this error means

anthropic.APIStatusError: {'type': 'error', 'error': {'details': None, 'type': 'overloaded_error', 'message': 'Overloaded'}} during streaming is a Anthropic API failure pattern reported for developers trying to understand why max_retries doesn't prevent overloaded_error interruptions during streaming with chatanthropic in langchain, and find workaround patterns. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

GitHub issue #688 in anthropics/anthropic-sdk-python (Kevin-McIsaac, Oct 2024). User building RAG pipeline with LangChain gets 1-in-100 stream interruptions from overloaded_error. max_retries applies only to initial HTTP request, not mid-stream events. Officially confirmed intended behavior — callers must wrap stream iteration in try/except and retry the full request. Still relevant for SEO because many developers search for how to handle this exact pattern.

Common causes

  • GitHub issue #688 in anthropics/anthropic-sdk-python (Kevin-McIsaac, Oct 2024). User building RAG pipeline with LangChain gets 1-in-100 stream interruptions from overloaded_error. max_retries applies only to initial HTTP request, not mid-stream events. Officially confirmed intended behavior — callers must wrap stream iteration in try/except and retry the full request. Still relevant for SEO because many developers search for how to handle this exact pattern.

Quick fixes

  1. Confirm the exact error signature matches anthropic.APIStatusError: {'type': 'error', 'error': {'details': None, 'type': 'overloaded_error', 'message': 'Overloaded'}} during streaming.
  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.