What this error means

model is not supported (on agents.create() or sessions.create() via Managed Agents beta API, while same models work on Messages API) is a Anthropic API failure pattern reported for developers trying to fix anthropic managed agents api rejecting all claude models with 'model is not supported' error despite working on standard messages api with same key. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

GitHub Issue #1583 in anthropics/anthropic-sdk-python (open, May 21 2026). SDK version 0.103.1 on Python 3.13. Affects Managed Agents beta feature — developers building multi-agent systems hit blocking error on creation. High commercial value as this impacts agent orchestration workflows with paid API billing. Category: Anthropic API per exact mapping.

Common causes

  • GitHub Issue #1583 in anthropics/anthropic-sdk-python (open, May 21 2026). SDK version 0.103.1 on Python 3.13. Affects Managed Agents beta feature — developers building multi-agent systems hit blocking error on creation. High commercial value as this impacts agent orchestration workflows with paid API billing. Category: Anthropic API per exact mapping.

Quick fixes

  1. Confirm the exact error signature matches model is not supported (on agents.create() or sessions.create() via Managed Agents beta API, while same models work on Messages API).
  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.