Related Issues/PRs

https://github.com/mlflow/mlflow/pull/18562/changes
openai/openai-agents-python#1387 (comment)

What changes are proposed in this pull request?

• disable openai-agents-sdk tracing by default

How is this PR tested?

• Existing unit/integration tests
• New unit/integration tests
• Manual tests

Databricks LLM CUJ, export to both MLflow and OpenAI (set OPENAI_API_KEY):

before and after dont work due to schema misalignment:

ERROR:openai.agents:[non-fatal] Tracing client error 400: {
  "error": {
    "message": "Unknown parameter: 'data[0].span_data.usage.total_tokens'.",
    "type": "invalid_request_error",
    "param": "data[0].span_data.usage.total_tokens",
    "code": "unknown_parameter"
  }
}

Databricks LLM CUJ, export only to MLflow

before:

ERROR:openai.agents:[non-fatal] Tracing client error 401: {
  "error": {
    "message": "Incorrect API key provided: no-token. You can find your API key at https://platform.openai.com/account/api-keys.",
    "type": "invalid_request_error",
    "param": null,
    "code": "invalid_api_key"
  }
}

after: no errors in the logs bc the openai agents sdk is disabled by default

Use OpenAI LLM, export to both MLflow and OpenAI

before and after both work. manual test after:
my openai traces console:

mlflow traces:

Does this PR require documentation update?

• No. You can skip the rest of this section.
• Yes. I've updated:
  • Examples
  • API references
  • Instructions

Does this PR require updating the MLflow Skills repository?

• No. You can skip the rest of this section.
• Yes. Please link the corresponding PR or explain how you plan to update it.

Release Notes

Is this a user-facing change?

• No. You can skip the rest of this section.
• Yes. Give a description of this change to be included in the release notes for MLflow users.

What component(s), interfaces, languages, and integrations does this PR affect?

Components

• area/tracking: Tracking Service, tracking client APIs, autologging
• area/models: MLmodel format, model serialization/deserialization, flavors
• area/model-registry: Model Registry service, APIs, and the fluent client calls for Model Registry
• area/scoring: MLflow Model server, model deployment tools, Spark UDFs
• area/evaluation: MLflow model evaluation features, evaluation metrics, and evaluation workflows
• area/gateway: MLflow AI Gateway client APIs, server, and third-party integrations
• area/prompts: MLflow prompt engineering features, prompt templates, and prompt management
• area/tracing: MLflow Tracing features, tracing APIs, and LLM tracing functionality
• area/projects: MLproject format, project running backends
• area/uiux: Front-end, user experience, plotting, JavaScript, JavaScript dev server
• area/build: Build and test infrastructure for MLflow
• area/docs: MLflow documentation pages

How should the PR be classified in the release notes? Choose one:

• rn/none - No description will be included. The PR will be mentioned only by the PR number in the "Small Bugfixes and Documentation Updates" section
• rn/breaking-change - The PR will be mentioned in the "Breaking Changes" section
• rn/feature - A new user-facing feature worth mentioning in the release notes
• rn/bug-fix - A user-facing bug fix worth mentioning in the release notes
• rn/documentation - A user-facing documentation change worth mentioning in the release notes

Should this PR be included in the next patch release?

Yes should be selected for bug fixes, documentation updates, and other small changes. No should be selected for new features and larger changes. If you're unsure about the release classification of this PR, leave this unchecked to let the maintainers decide.

• Yes (this PR will be cherry-picked and included in the next patch release)
• No (this PR will be included in the next minor release)