langgraph/docs/docs/cloud/how-tos/add-human-in-the-loop.md

Human-in-the-loop using Server API

To review, edit, and approve tool calls in an agent or workflow, use LangGraph's human-in-the-loop features.

LangGraph API invoke & resume

=== "Python"

```python
from langgraph_sdk import get_client
# highlight-next-line
from langgraph_sdk.schema import Command
client = get_client(url=<DEPLOYMENT_URL>)

# Using the graph deployed with the name "agent"
assistant_id = "agent"

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

# Run the graph until the interrupt is hit.
result = await client.runs.wait(
    thread_id,
    assistant_id,
    input={"some_text": "original text"}   # (1)!
)

print(result['__interrupt__']) # (2)!
# > [
# >     {
# >         'value': {'text_to_revise': 'original text'},
# >         'resumable': True,
# >         'ns': ['human_node:fc722478-2f21-0578-c572-d9fc4dd07c3b'],
# >         'when': 'during'
# >     }
# > ]


# Resume the graph
print(await client.runs.wait(
    thread_id,
    assistant_id,
    # highlight-next-line
    command=Command(resume="Edited text")   # (3)!
))
# > {'some_text': 'Edited text'}
```

1. The graph is invoked with some initial state.
2. When the graph hits the interrupt, it returns an interrupt object with the payload and metadata.
3. The graph is resumed with a `Command(resume=...)`, injecting the human's input and continuing execution.

=== "JavaScript"

```js
import { Client } from "@langchain/langgraph-sdk";
const client = new Client({ apiUrl: <DEPLOYMENT_URL> });

// Using the graph deployed with the name "agent"
const assistantID = "agent";

// create a thread
const thread = await client.threads.create();
const threadID = thread["thread_id"];

// Run the graph until the interrupt is hit.
const result = await client.runs.wait(
  threadID,
  assistantID,
  { input: { "some_text": "original text" } }   // (1)!
);

console.log(result['__interrupt__']); // (2)!
// > [
// >     {
// >         'value': {'text_to_revise': 'original text'},
// >         'resumable': True,
// >         'ns': ['human_node:fc722478-2f21-0578-c572-d9fc4dd07c3b'],
// >         'when': 'during'
// >     }
// > ]

// Resume the graph
console.log(await client.runs.wait(
    threadID,
    assistantID,
    // highlight-next-line
    { command: { resume: "Edited text" }}   // (3)!
));
// > {'some_text': 'Edited text'}
```

1. The graph is invoked with some initial state.
2. When the graph hits the interrupt, it returns an interrupt object with the payload and metadata.
3. The graph is resumed with a `{ resume: ... }` command object, injecting the human's input and continuing execution.

=== "cURL"

Create a thread:

```bash
curl --request POST \
--url <DEPLOYMENT_URL>/threads \
--header 'Content-Type: application/json' \
--data '{}'
```

Run the graph until the interrupt is hit.:

```bash
curl --request POST \
--url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
--header 'Content-Type: application/json' \
--data "{
  \"assistant_id\": \"agent\",
  \"input\": {\"some_text\": \"original text\"}
}"
```

Resume the graph:

```bash
curl --request POST \
 --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
 --header 'Content-Type: application/json' \
 --data "{
   \"assistant_id\": \"agent\",
   \"command\": {
     \"resume\": \"Edited text\"
   }
 }"
```

??? example "Extended example: using interrupt"

This is an example graph you can run in the LangGraph API server.
See [LangGraph Platform quickstart](../quick_start.md) for more details.

```python
from typing import TypedDict
import uuid

from langgraph.checkpoint.memory import InMemorySaver
from langgraph.constants import START
from langgraph.graph import StateGraph
# highlight-next-line
from langgraph.types import interrupt, Command

class State(TypedDict):
    some_text: str

def human_node(state: State):
    # highlight-next-line
    value = interrupt( # (1)!
        {
            "text_to_revise": state["some_text"] # (2)!
        }
    )
    return {
        "some_text": value # (3)!
    }


# Build the graph
graph_builder = StateGraph(State)
graph_builder.add_node("human_node", human_node)
graph_builder.add_edge(START, "human_node")

graph = graph_builder.compile()
```

1. `interrupt(...)` pauses execution at `human_node`, surfacing the given payload to a human.
2. Any JSON serializable value can be passed to the `interrupt` function. Here, a dict containing the text to revise.
3. Once resumed, the return value of `interrupt(...)` is the human-provided input, which is used to update the state.

Once you have a running LangGraph API server, you can interact with it using
[LangGraph SDK](https://langchain-ai.github.io/langgraph/cloud/reference/sdk/python_sdk_ref/)

=== "Python"

    ```python
    from langgraph_sdk import get_client
    # highlight-next-line
    from langgraph_sdk.schema import Command
    client = get_client(url=<DEPLOYMENT_URL>)

    # Using the graph deployed with the name "agent"
    assistant_id = "agent"

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

    # Run the graph until the interrupt is hit.
    result = await client.runs.wait(
        thread_id,
        assistant_id,
        input={"some_text": "original text"}   # (1)!
    )

    print(result['__interrupt__']) # (2)!
    # > [
    # >     {
    # >         'value': {'text_to_revise': 'original text'},
    # >         'resumable': True,
    # >         'ns': ['human_node:fc722478-2f21-0578-c572-d9fc4dd07c3b'],
    # >         'when': 'during'
    # >     }
    # > ]


    # Resume the graph
    print(await client.runs.wait(
        thread_id,
        assistant_id,
        # highlight-next-line
        command=Command(resume="Edited text")   # (3)!
    ))
    # > {'some_text': 'Edited text'}
    ```

    1. The graph is invoked with some initial state.
    2. When the graph hits the interrupt, it returns an interrupt object with the payload and metadata.
    3. The graph is resumed with a `Command(resume=...)`, injecting the human's input and continuing execution.

=== "JavaScript"

    ```js
    import { Client } from "@langchain/langgraph-sdk";
    const client = new Client({ apiUrl: <DEPLOYMENT_URL> });

    // Using the graph deployed with the name "agent"
    const assistantID = "agent";

    // create a thread
    const thread = await client.threads.create();
    const threadID = thread["thread_id"];

    // Run the graph until the interrupt is hit.
    const result = await client.runs.wait(
      threadID,
      assistantID,
      { input: { "some_text": "original text" } }   // (1)!
    );

    console.log(result['__interrupt__']); // (2)!
    // > [
    // >     {
    // >         'value': {'text_to_revise': 'original text'},
    // >         'resumable': True,
    // >         'ns': ['human_node:fc722478-2f21-0578-c572-d9fc4dd07c3b'],
    // >         'when': 'during'
    // >     }
    // > ]

    // Resume the graph
    console.log(await client.runs.wait(
        threadID,
        assistantID,
        // highlight-next-line
        { command: { resume: "Edited text" }}   // (3)!
    ));
    // > {'some_text': 'Edited text'}
    ```

    1. The graph is invoked with some initial state.
    2. When the graph hits the interrupt, it returns an interrupt object with the payload and metadata.
    3. The graph is resumed with a `{ resume: ... }` command object, injecting the human's input and continuing execution.

=== "cURL"

    Create a thread:

    ```bash
    curl --request POST \
    --url <DEPLOYMENT_URL>/threads \
    --header 'Content-Type: application/json' \
    --data '{}'
    ```

    Run the graph until the interrupt is hit:

    ```bash
    curl --request POST \
    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
    --header 'Content-Type: application/json' \
    --data "{
      \"assistant_id\": \"agent\",
      \"input\": {\"some_text\": \"original text\"}
    }"
    ```

    Resume the graph:

    ```bash
    curl --request POST \
    --url <DEPLOYMENT_URL>/threads/<THREAD_ID>/runs/wait \
    --header 'Content-Type: application/json' \
    --data "{
      \"assistant_id\": \"agent\",
      \"command\": {
        \"resume\": \"Edited text\"
      }
    }"
    ```

Learn more

• Human-in-the-loop conceptual guide: learn more about LangGraph human-in-the-loop features.
• Common patterns: learn how to implement patterns like approving/rejecting actions, requesting user input, tool call review, and validating human input.