Key concepts
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.

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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.
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.