Log user feedback using the SDK

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

This page details how to log feedback using the SDK. For the structure of feedback objects, refer to Feedback data format.

Use create_feedback() / createFeedback

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.

The following example creates a trace with two child runs, then logs feedback against the root run and against one of the child runs. The TypeScript snippet shows the equivalent createFeedback call shape, assuming a runId is already available from your application.

Python

from langsmith import Client, trace, traceable

@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,
)

import { Client } from "langsmith";
const client = new Client();

    // ... Run your application and get the run_id...
    // This information can be the result of a user-facing feedback form

await client.createFeedback(
    runId,
    "feedback-key",
    {
        score: 1.0,
        comment: "comment",
    }
);

You can even log feedback for in-progress runs using create_feedback() / createFeedback. See Access the current run (span) within a traced function for how to get the run ID of an in-progress run.

Collect feedback from client-side applications

If you need to collect feedback from a browser or other client-side environment without exposing your API key, use presigned feedback tokens. These generate a URL scoped to a specific run and feedback key that clients can call directly.

See Collect feedback with presigned URLs for the full guide.

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

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