What this error means

langchain-anthropic: base64 file blocks with non-PDF text mime types fail with media_type validation error is a Anthropic API failure pattern reported for developers trying to fix anthropic document block translation in langchain that rejects csv/text base64 content with invalid media_type validation errors. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

Open issue #37576 on langchain-ai/langchain (created May 20, 2026). When sending base64-encoded text files (CSV, etc.) through ChatAnthropic, the SDK hard-codes mime_type='application/pdf' for all document blocks. Any non-PDF text mime type triggers a media_type validation error. This breaks multimodal workflows where users need to upload documents with various text-based mime types. Directly related to core-level bug #36939.

Common causes

  • Open issue #37576 on langchain-ai/langchain (created May 20, 2026). When sending base64-encoded text files (CSV, etc.) through ChatAnthropic, the SDK hard-codes mime_type='application/pdf' for all document blocks. Any non-PDF text mime type triggers a media_type validation error. This breaks multimodal workflows where users need to upload documents with various text-based mime types. Directly related to core-level bug #36939.

Quick fixes

  1. Confirm the exact error signature matches langchain-anthropic: base64 file blocks with non-PDF text mime types fail with media_type validation error.
  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.