What this error means

Streaming tool_call deltas with duplicate indexes in first chunk are accumulated incorrectly — openai-python accumulate_delta bug is a OpenAI API failure pattern reported for developers trying to fix openai-python sdk streaming tool_call delta duplication bug in first chunk. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

openai-python accumulate_delta assumes unique indexes per chunk. When the first streaming chunk contains duplicate tool_call indexes, the delta accumulation produces incorrect tool call arguments. Affects all AI agent frameworks using streaming tool calls.

Common causes

  • The openai-python SDK's accumulate_delta function incorrectly handles duplicate indexes in the first streaming chunk of tool_call deltas. This causes malformed tool call arguments in AI agent applications.
  • openai-python accumulate_delta assumes unique indexes per chunk. When the first streaming chunk contains duplicate tool_call indexes, the delta accumulation produces incorrect tool call arguments. Affects all AI agent frameworks using streaming tool calls.

Quick fixes

  1. Confirm the exact error signature matches Streaming tool_call deltas with duplicate indexes in first chunk are accumulated incorrectly — openai-python accumulate_delta bug.
  2. Check the OpenAI API 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.