What this error means

websearch_interception silently truncates streaming response on /v1/messages — follow-up call always uses stream=False is a LiteLLM failure pattern reported for developers trying to fix litellm websearch_interception truncating claude code streaming responses with no error logs. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

Official LiteLLM issue #27721 (2026-05-12) reports websearch_interception with Tavily via Claude Code using /v1/messages with stream=True causes final LLM output to be silently truncated mid-response with no error logs. Follow-up call always uses stream=False.

Common causes

  • Developers using LiteLLM proxy with Claude Code via /v1/messages find that websearch_interception causes the final LLM output to be silently truncated mid-response with no error logs. The follow-up call defaults to stream=False, breaking Claude Code's streaming workflow entirely.
  • Official LiteLLM issue #27721 (2026-05-12) reports websearch_interception with Tavily via Claude Code using /v1/messages with stream=True causes final LLM output to be silently truncated mid-response with no error logs. Follow-up call always uses stream=False.

Quick fixes

  1. Confirm the exact error signature matches websearch_interception silently truncates streaming response on /v1/messages — follow-up call always uses stream=False.
  2. Check the LiteLLM 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.