# mlflow
pip install git+https://github.com/mlflow/mlflow.git@refs/pull/19359/merge
# mlflow-skinny
pip install git+https://github.com/mlflow/mlflow.git@refs/pull/19359/merge#subdirectory=libs/skinny

%sh curl -LsSf https://raw.githubusercontent.com/mlflow/mlflow/HEAD/dev/install-skinny.sh | sh -s pull/19359/merge

Related Issues/PRs

N/A

What changes are proposed in this pull request?

This PR adds a --max-results option to the mlflow experiments search CLI command, allowing users to limit the number of experiments returned. The backend API already supports this parameter; this change exposes it through the CLI.

Key features:

• New --max-results CLI option with integer validation
• Validates non-negative values with clear error messages
• Default behavior unchanged (returns all experiments when not specified)
• Fully backward compatible

How is this PR tested?

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

New tests added:

• Parametrized tests for default, explicit values, and combined flags
• Edge case test for zero value
• Validation test for negative values
• Parameter introspection test

Manual testing:

# Test with Databricks backend
export MLFLOW_TRACKING_URI="..."

# Returns exactly 5 experiments
mlflow experiments search --max-results 5

# Returns exactly 3 experiments  
mlflow experiments search --max-results 3

# Validates negative values
mlflow experiments search --max-results -5
# Error: Invalid value for '--max-results': max-results must be a non-negative integer

Does this PR require documentation update?

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

The CLI option is self-documenting through --help text.

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.

Added --max-results option to mlflow experiments search command to limit the number of experiments returned, improving performance and helping avoid rate limits when querying backends with many experiments.

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