What this error means

BadRequestError: Error code: 400 - {'type': 'error', 'error': {'type': 'invalid_request_error', 'message': 'requests.0.custom_id: String should have at most 64 characters'}} is a Anthropic API failure pattern reported for developers trying to fix anthropic api batch processing error: custom_id character limit not documented, causing 400 badrequesterror. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

GitHub issue anthropics/anthropic-sdk-python#984 (open since Jun 2025, updated May 2026): custom_id parameter for Message Batch Processing has an undocumented 64-character limit. Users hit BadRequestError 400 when exceeding it. The limit is not mentioned anywhere in the API documentation. Category mapping: Anthropic API (direct API error).

Common causes

  • GitHub issue anthropics/anthropic-sdk-python#984 (open since Jun 2025, updated May 2026): custom_id parameter for Message Batch Processing has an undocumented 64-character limit. Users hit BadRequestError 400 when exceeding it. The limit is not mentioned anywhere in the API documentation. Category mapping: Anthropic API (direct API error).

Quick fixes

  1. Confirm the exact error signature matches BadRequestError: Error code: 400 - {'type': 'error', 'error': {'type': 'invalid_request_error', 'message': 'requests.0.custom_id: String should have at most 64 characters'}}.
  2. Check the Anthropic 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.