What this error means
Anthropic proxied response is buffered: delta JSON lines sent in one large batch despite stream=true configuration is a LiteLLM failure pattern reported for developers trying to fix litellm proxy buffering anthropic sse streams instead of sending real-time deltas, when both openai and anthropic share same proxy config. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.
Why this happens
GitHub issue #28384 on BerriAI/litellm opened May 20, 2026. User runs LiteLLM v1.85.0 in Kubernetes with Anthropic + OpenAI models. Streaming works for OpenAI but Anthropic responses arrive as single batch. Config includes proxy_buffering off in nginx, Redis cache, Prometheus callbacks. Not a network issue — pure LiteLLM proxy behavior difference between providers. High-value P1 tech with 47.9k GitHub stars.
Common causes
- GitHub issue #28384 on BerriAI/litellm opened May 20, 2026. User runs LiteLLM v1.85.0 in Kubernetes with Anthropic + OpenAI models. Streaming works for OpenAI but Anthropic responses arrive as single batch. Config includes proxy_buffering off in nginx, Redis cache, Prometheus callbacks. Not a network issue — pure LiteLLM proxy behavior difference between providers. High-value P1 tech with 47.9k GitHub stars.
Quick fixes
- Confirm the exact error signature matches
Anthropic proxied response is buffered: delta JSON lines sent in one large batch despite stream=true configuration. - Check the LiteLLM account, local tool state, and provider configuration involved in the failing workflow.
- 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
- Capture the exact error message and the command, editor action, or request that triggered it.
- Check whether the failure is account/auth, quota/rate, model/provider, local runtime, or deployment configuration.
- Review the source evidence below and compare it with your environment.
- Apply one change at a time and rerun the smallest failing action.
- 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.