How to collect user feedback for Agent Server runs ↗

    from langgraph_sdk import get_client

    client = get_client(url="<DEPLOYMENT_URL>", api_key="<API_KEY>")

    thread = await client.threads.create()
    thread_id = thread["thread_id"]

    feedback_urls = {}

    async for event in client.runs.stream(
        thread_id,
        "agent",
        input={
            "messages": [
                {"role": "user", "content": "Tell me a joke about databases."}
            ]
        },
        stream_mode="updates",
        feedback_keys=["user_liked", "user_disliked"],
    ):
        if event.event == "feedback":
            # Example: {"user_liked": ".../feedback/tokens/<id>", "user_disliked": "..."}
            feedback_urls = event.data
            print("Feedback URLs:", feedback_urls)
        elif event.event == "updates":
            print(event.data)
    ```
  <span class="tab-end"></span>

  <span class="tab-start" data-tab-title="JavaScript SDK"></span>
```javascript
    import { Client } from "@langchain/langgraph-sdk";

    const client = new Client({ apiUrl: "<DEPLOYMENT_URL>", apiKey: "<API_KEY>" });

    const thread = await client.threads.create();
    const threadId = thread.thread_id;

    let feedbackUrls = {};

    const streamResponse = client.runs.stream(threadId, "agent", {
      input: {
        messages: [{ role: "user", content: "Tell me a joke about databases." }],
      },
      streamMode: "updates",
      feedbackKeys: ["user_liked", "user_disliked"],
    });

    for await (const event of streamResponse) {
      if (event.event === "feedback") {
        // Example: { user_liked: ".../feedback/tokens/<id>", user_disliked: "..." }
        feedbackUrls = event.data;
        console.log("Feedback URLs:", feedbackUrls);
      } else if (event.event === "updates") {
        console.log(event.data);
      }
    }
    ```
  <span class="tab-end"></span>

  <span class="tab-start" data-tab-title="cURL"></span>
```bash
    curl --request POST \
      --url "<DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/stream" \
      --header "Content-Type: application/json" \
      --header "x-api-key: <API_KEY>" \
      --data '{
        "assistant_id": "agent",
        "input": {
          "messages": [
            {
              "role": "user",
              "content": "Tell me a joke about databases."
            }
          ]
        },
        "stream_mode": "updates",
        "feedback_keys": ["user_liked", "user_disliked"]
      }'
    ```
  <span class="tab-end"></span>
<span class="tab-group-end"></span>

## Handle the streamed `feedback` event

The stream emits a `feedback` event like the following:

```text
event: feedback
data: {"user_liked":"https://api.smith.langchain.com/api/v1/feedback/tokens/ef19fedf-dcac-4cbb-a59c-00661efd6425", "user_disliked": "https://api.smith.langchain.com/api/v1/feedback/tokens/e952734e-c0a0-417b-a04d-fc2209691ed5"}

    curl --request POST \
      --url "https://api.smith.langchain.com/api/v1/feedback/tokens/e952734e-c0a0-417b-a04d-fc2209691ed5" \
      --header "Content-Type: application/json" \
      --data '{
        "score": 1,
        "value": 0,
        "comment": "I didn't like this joke because it didn't make me laugh.",
        "correction": {},
        "metadata": {}
      }'
    ```
  <span class="tab-end"></span>

  <span class="tab-start" data-tab-title="GET"></span>
`metadata` is not supported with `GET`.

```bash
    curl --request GET \
      --url "https://api.smith.langchain.com/api/v1/feedback/tokens/e952734e-c0a0-417b-a04d-fc2209691ed5?score=1&value=0&comment=I%20didn%27t%20like%20this%20joke%20because%20it%20didn%27t%20make%20me%20laugh.&correction=%7B%7D"
    ```
  <span class="tab-end"></span>
<span class="tab-group-end"></span>

After this request succeeds, LangSmith records feedback on the trace using the key `user_disliked`.

## Optimize feedback data model

The `user_liked` and `user_disliked` keys can also be modeled under a single key such as `user_score`.

For example:

* Use `key="user_score"` with `score=1` for `user_liked`
* Use `key="user_score"` with `score=-1` for `user_disliked`

This can simplify analysis because all user preference signals are grouped under one feedback key.

The feedback data model is flexible and should be designed for your use case. For example, some applications may prefer separate boolean-style keys (`user_liked`, `user_disliked`), while others may prefer a single numeric score (`user_score`) or a richer rubric with multiple feedback keys.

## Productionize in a client UI

A productionized solution will expose the generated feedback URLs through your frontend instead of calling them manually.

Example high-level implementation:

1. Create the run from your backend or frontend.
2. Capture the `feedback` object and store the returned URLs.
3. Render feedback controls such as thumbs up/down buttons and feedback forms.
4. On feedback submission, `POST` or `GET` a feedback URL based on the user's feedback intent.
5. Optionally disable the feedback controls after submission and show confirmation to the user.

***


  <span class="callout-start" data-callout-type="note"></span>
[Edit this page on GitHub](https://github.com/langchain-ai/docs/edit/main/src/langsmith/agent-server-feedback.mdx) or [file an issue](https://github.com/langchain-ai/docs/issues/new/choose).
  <span class="callout-end"></span>

  <span class="callout-start" data-callout-type="note"></span>
[Connect these docs](/use-these-docs) to Claude, VSCode, and more via MCP for real-time answers.
  <span class="callout-end"></span>