What this error means

Bedrock pass-through /converse returns HTTP 200 OK with empty response body on v1.85.0 — callers receive no content despite successful status code is a LiteLLM failure pattern reported for developers trying to fix regression in litellm v1.85.0 where bedrock aws pass-through through the /converse endpoint returns 200 status but body is empty, causing silent data loss for clients consuming ai responses through litellm proxy. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

GitHub Issue #28388 (BerriAI/litellm): llm translation + proxy labels confirm Bedrock /converse endpoint regression. HTTP 200 with empty body looks like success but delivers nothing. Affects enterprise teams using AWS Bedrock via LiteLLM proxy.

Common causes

  • GitHub Issue #28388 (BerriAI/litellm): llm translation + proxy labels confirm Bedrock /converse endpoint regression. HTTP 200 with empty body looks like success but delivers nothing. Affects enterprise teams using AWS Bedrock via LiteLLM proxy.

Quick fixes

  1. Confirm the exact error signature matches Bedrock pass-through /converse returns HTTP 200 OK with empty response body on v1.85.0 — callers receive no content despite successful status code.
  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.