> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-srmult-1765395526-473a2ea.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Log user feedback using the SDK

<Tip>
  **Key concepts**

  * [Conceptual guide on tracing and feedback](/langsmith/observability-concepts)
  * [Reference guide on feedback data format](/langsmith/feedback-data-format)
</Tip>

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()](https://docs.smith.langchain.com/reference/python/client/langsmith.client.Client#langsmith.client.Client.create_feedback) / [createFeedback()](https://docs.smith.langchain.com/reference/js/classes/client.Client#createfeedback)

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

<Info>
  **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.
</Info>

<Tip>
  **Non-blocking creation (Python only)**
  The Python client will automatically background feedback creation if you pass `trace_id=` to [create\_feedback()](https://docs.smith.langchain.com/reference/python/client/langsmith.client.Client#langsmith.client.Client.create_feedback).
  This is essential for low-latency environments, where you want to make sure your application isn't blocked on feedback creation.
</Tip>

<CodeGroup>
  ```python Python theme={null}
  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,
  )
  ```

  ```typescript TypeScript theme={null}
  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",
      }
  );
  ```
</CodeGroup>

You can even log feedback for in-progress runs using `create_feedback() / createFeedback()`. See [this guide](/langsmith/access-current-span) 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](/langsmith/filter-traces-in-application).

***

<Callout icon="pen-to-square" iconType="regular">
  [Edit the source of this page on GitHub.](https://github.com/langchain-ai/docs/edit/main/src/langsmith/attach-user-feedback.mdx)
</Callout>

<Tip icon="terminal" iconType="regular">
  [Connect these docs programmatically](/use-these-docs) to Claude, VSCode, and more via MCP for real-time answers.
</Tip>