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Make conversation simulator public and easily subclassable#20243

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smoorjani merged 11 commits intomlflow:masterfrom
smoorjani:gwt-refactor-simulator
Jan 29, 2026
Merged

Make conversation simulator public and easily subclassable#20243
smoorjani merged 11 commits intomlflow:masterfrom
smoorjani:gwt-refactor-simulator

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Related Issues/PRs

#xxx

What changes are proposed in this pull request?

This PR does two main things:

  1. Makes the conversation simulator simulate method public as we have validated quality
  2. Makes the simulator easily customizable by allowing easy subclassing of the user agent

How is this PR tested?

  • Existing unit/integration tests
  • New unit/integration tests
  • Manual tests
from mlflow.genai.simulators import (
    BaseSimulatedUserAgent,
    ConversationSimulator,
    SimulatorContext,
)


class ImpatientUserAgent(BaseSimulatedUserAgent):
    def generate_message(self, context: SimulatorContext) -> str:
        if context.is_first_turn:
            return f"I need help RIGHT NOW with: {context.goal}"
        return self.invoke_llm(
            f"You are impatient. Respond briefly and push for faster answers. "
            f"Goal: {context.goal}. Last response: {context.last_assistant_response}"
        )


def predict_fn(input: list[dict], **kwargs) -> dict:
    return {"role": "assistant", "content": "I'll help you with that."}


simulator = ConversationSimulator(
    test_cases=[{"goal": "Reset my password"}],
    user_agent_class=ImpatientUserAgent,
    max_turns=3,
)

trace_ids = simulator.simulate(predict_fn)

result:
image

Does this PR require documentation update?

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

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.

Make simulate method of ConversationSimulator public and make the user agent easily subclassable to customize conversation simulation..

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.

What is a minor/patch release?
  • Minor release: a release that increments the second part of the version number (e.g., 1.2.0 -> 1.3.0).
    Bug fixes, doc updates and new features usually go into minor releases.
  • Patch release: a release that increments the third part of the version number (e.g., 1.2.0 -> 1.2.1).
    Bug fixes and doc updates usually go into patch releases.
  • 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)

Signed-off-by: Samraj Moorjani <samraj.moorjani@databricks.com>
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🛠 DevTools 🛠

Install mlflow from this PR

# mlflow
pip install git+https://github.com/mlflow/mlflow.git@refs/pull/20243/merge
# mlflow-skinny
pip install git+https://github.com/mlflow/mlflow.git@refs/pull/20243/merge#subdirectory=libs/skinny

For Databricks, use the following command:

%sh curl -LsSf https://raw.githubusercontent.com/mlflow/mlflow/HEAD/dev/install-skinny.sh | sh -s pull/20243/merge

@github-actions github-actions bot added area/evaluation MLflow Evaluation rn/feature Mention under Features in Changelogs. labels Jan 23, 2026
.
Signed-off-by: Samraj Moorjani <samraj.moorjani@databricks.com>
Signed-off-by: Samraj Moorjani <samraj.moorjani@databricks.com>
.
Signed-off-by: Samraj Moorjani <samraj.moorjani@databricks.com>
.
Signed-off-by: Samraj Moorjani <samraj.moorjani@databricks.com>
Signed-off-by: Samraj Moorjani <samraj.moorjani@databricks.com>
Signed-off-by: Samraj Moorjani <samraj.moorjani@databricks.com>
.
Signed-off-by: Samraj Moorjani <samraj.moorjani@databricks.com>
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github-actions bot commented Jan 24, 2026

Documentation preview for 205a258 is available at:

More info
  • Ignore this comment if this PR does not change the documentation.
  • The preview is updated when a new commit is pushed to this PR.
  • This comment was created by this workflow run.
  • The documentation was built by this workflow run.

return "\n".join(formatted)


def _fetch_traces(all_trace_ids: list[list[str]]) -> list[list["Trace"]]:
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Would it be better to return traces instead of traceids in _run_conversation directly so that we can remove this transformation?

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Good callout! The reason I do not is because we need to flush the traces (which are logged asynchronously) before retrieving the traces. Instead of doing this in multiple threads, it's much easier to flush all at once and then retrieve the traces.

The generated user message string.
"""

def invoke_llm(self, prompt: str, system_prompt: str | None = None) -> str:
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qq: is this provided in the abstract class for user's convenience or do we expect the users to use this for inference?

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yes, just for convenience

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@xsh310 xsh310 left a comment

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Overall LGTM

Signed-off-by: Samraj Moorjani <samraj.moorjani@databricks.com>
Signed-off-by: Samraj Moorjani <samraj.moorjani@databricks.com>

mlflow.flush_trace_async_logging()

client = TracingClient()
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not to reviewer: the alternative to this code is using search traces with a specific bit of metadata, but the logic to reconstruct the order would be more complex than what is presently here. There is no performance difference between using search traces and parallelizing get trace

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@AveshCSingh AveshCSingh left a comment

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I took a quick pass and left a few small comments, but overall this LGTM. Please feel free to merge after addressing my comments.

progress_bar.close()

return all_trace_ids
return _fetch_traces(all_trace_ids)
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👍

Signed-off-by: Samraj Moorjani <samraj.moorjani@databricks.com>
@smoorjani smoorjani enabled auto-merge January 29, 2026 01:35
@smoorjani smoorjani disabled auto-merge January 29, 2026 01:36
@smoorjani smoorjani added this pull request to the merge queue Jan 29, 2026
Merged via the queue into mlflow:master with commit c4c6c19 Jan 29, 2026
47 checks passed
@smoorjani smoorjani deleted the gwt-refactor-simulator branch January 29, 2026 03:02
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3 participants