Log user feedback using the SDK

Key concepts

• Conceptual guide on tracing and feedback
• Reference guide on feedback data format

LangSmith makes it easy to attach feedback to traces. This feedback can come from users, annotators, automated evaluators, etc., and is crucial for monitoring and evaluating applications.

Use create_feedback() / createFeedback

Here we’ll walk through how to log feedback using the SDK.

Child runs You can attach user feedback to ANY child run of a trace, not just the trace (root run) itself. This is useful for critiquing specific steps of the LLM application, such as the retrieval step or generation step of a RAG pipeline.

Non-blocking creation (Python only) The Python client will automatically background feedback creation if you pass trace_id= to create_feedback(). This is essential for low-latency environments, where you want to make sure your application isn’t blocked on feedback creation.

Python

from langsmith import trace, traceable, Client

    @traceable
    def foo(x):
        return {"y": x * 2}

    @traceable
    def bar(y):
        return {"z": y - 1}

    client = Client()

    inputs = {"x": 1}
    with trace(name="foobar", inputs=inputs) as root_run:
        result = foo(**inputs)
        result = bar(**result)
        root_run.outputs = result
        trace_id = root_run.id
        child_runs = root_run.child_runs

    # Provide feedback for a trace (a.k.a. a root run)
    client.create_feedback(
        key="user_feedback",
        score=1,
        trace_id=trace_id,
        comment="the user said that ..."
    )

# Provide feedback for a child run
foo_run_id = [run for run in child_runs if run.name == "foo"][0].id
client.create_feedback(
    key="correctness",
    score=0,
    run_id=foo_run_id,
    # trace_id= is optional but recommended to enable batched and backgrounded
    # feedback ingestion.
    trace_id=trace_id,
)

You can even log feedback for in-progress runs using create_feedback() / createFeedback. See this guide for how to get the run ID of an in-progress run.

To learn more about how to filter traces based on various attributes, including user feedback, see this guide.

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