What this error means
-32603 Internal error: Stream consumed is a LiteLLM failure pattern reported for developers trying to fix litellm a2a /a2a/{id}/message/send returning -32603 stream consumed error. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.
Why this happens
Bug report on BerriAI/litellm repo: POST /a2a/{agent_id}/message/send returns {"jsonrpc":"2.0","id":null,"error":{"code":-32603,"message":"Internal error: Stream consumed"}}. Affects LiteLLM main branch as of 2026-05-13 on Docker image ghcr.io/berriai/litellm-non_root:main-stable.
Common causes
- LiteLLM's A2A (Agent-to-Agent) endpoints return a JSON-RPC 2.0 error 'Stream consumed' when invoked via raw HTTP POST. This blocks developers building multi-agent systems that rely on the A2A protocol through LiteLLM proxy.
- Bug report on BerriAI/litellm repo: POST /a2a/{agent_id}/message/send returns {"jsonrpc":"2.0","id":null,"error":{"code":-32603,"message":"Internal error: Stream consumed"}}. Affects LiteLLM main branch as of 2026-05-13 on Docker image ghcr.io/berriai/litellm-non_root:main-stable.
Quick fixes
- Confirm the exact error signature matches
-32603 Internal error: Stream consumed. - 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.