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

  1. Confirm the exact error signature matches -32603 Internal error: Stream consumed.
  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.