Prompt for human feedback in LangGraph HITL sample

langgraph_plugin/graph_api/human_in_the_loop/README.md

@@ -14,7 +14,7 @@ Demonstrates pausing a graph with LangGraph's `interrupt()` and waiting indefini
 1. The Workflow starts and the `generate_draft` node produces a response.
 2. The `human_review` node calls `interrupt(draft)`, pausing execution.
 3. The Workflow stores the draft (visible via the query) and calls `workflow.wait_condition()` — blocking durably until the signal sets `_human_input`. This can wait indefinitely; Temporal persists the state.
-4. An external process (UI, CLI, etc.) queries the draft and sends approval via signal.
+4. An external process (UI, CLI, etc.) queries the draft and sends the human's feedback via signal.
 5. The graph resumes — `interrupt()` returns the signal value and the node completes.
 
 ## Running the Sample
@@ -25,14 +25,16 @@ Prerequisites: `uv sync --group langgraph` and a running Temporal dev server (`t
 # Terminal 1: start the worker
 uv run langgraph_plugin/graph_api/human_in_the_loop/run_worker.py
 
-# Terminal 2: start the workflow (polls for draft, then auto-approves)
+# Terminal 2: start the workflow (polls for the draft, then prompts you for feedback)
 uv run langgraph_plugin/graph_api/human_in_the_loop/run_workflow.py
 ```
 
+When the draft is ready, you'll be prompted at the terminal. Type `approve` to accept it as-is, or type revision feedback and the draft will be regenerated by an LLM incorporating your notes.
+
 ## Files
 
 | File | Description |
 |------|-------------|
 | `workflow.py` | Graph node functions, graph definition, and `ChatbotWorkflow` definition |
 | `run_worker.py` | Builds graph, registers with `LangGraphPlugin`, starts worker |
-| `run_workflow.py` | Starts workflow, polls draft via query, sends approval via signal |
+| `run_workflow.py` | Starts workflow, polls draft via query, prompts for human feedback, sends it via signal |

langgraph_plugin/graph_api/human_in_the_loop/run_workflow.py

@@ -27,8 +27,11 @@ async def main() -> None:
 
     print(f"Draft for review: {draft}")
 
-    # Send approval via signal (a UI would trigger this)
-    await handle.signal(ChatbotWorkflow.provide_feedback, "approve")
+    # Prompt for human feedback instead of auto-approving.
+    feedback = await asyncio.to_thread(
+        input, "Enter 'approve' to accept, or type revision feedback: "
+    )
+    await handle.signal(ChatbotWorkflow.provide_feedback, feedback)
 
     result = await handle.result()
     print(f"Final response: {result}")

langgraph_plugin/graph_api/human_in_the_loop/workflow.py

@@ -6,6 +6,7 @@
 
 from datetime import timedelta
 
+from langchain.chat_models import init_chat_model
 from langchain_core.runnables import RunnableConfig
 from langgraph.checkpoint.memory import InMemorySaver
 from langgraph.graph import START, StateGraph
@@ -20,21 +21,25 @@ class State(TypedDict):
 
 
 async def generate_draft(state: State) -> dict[str, str]:
-    """Generate a draft response. Replace with an LLM call in production."""
-    return {
-        "value": (
-            f"Here's my response to '{state['value']}': "
-            "The answer is 42. Let me know if this helps!"
-        )
-    }
+    """Generate a draft response with an LLM."""
+    response = await init_chat_model("claude-sonnet-4-6").ainvoke(
+        f"Please respond concisely to: {state['value']}"
+    )
+    return {"value": str(response.content)}
 
 
 async def human_review(state: State) -> dict[str, str]:
-    """Present draft to human for review via interrupt."""
+    """Present draft to human for review via interrupt; revise with LLM on feedback."""
     feedback = interrupt(state["value"])
     if feedback == "approve":
         return {"value": state["value"]}
-    return {"value": f"[Revised] {state['value']} (incorporating feedback: {feedback})"}
+    response = await init_chat_model("claude-sonnet-4-6").ainvoke(
+        "Revise the following draft according to the reviewer's feedback. "
+        "Output only the revised draft, with no preamble.\n\n"
+        f"Draft:\n{state['value']}\n\n"
+        f"Feedback:\n{feedback}"
+    )
+    return {"value": str(response.content)}
 
 
 def make_chatbot_graph() -> StateGraph:

tests/langgraph_plugin/human_in_the_loop_test.py

@@ -1,6 +1,7 @@
 import asyncio
 import sys
 import uuid
+from unittest.mock import patch
 
 import pytest
 from temporalio.client import Client
@@ -18,36 +19,59 @@
 )
 
 
+class _FakeMessage:
+    def __init__(self, content: str) -> None:
+        self.content = content
+
+
+class _EchoModel:
+    """Stand-in for a chat model that echoes the prompt back as its response."""
+
+    async def ainvoke(self, prompt: str) -> _FakeMessage:
+        return _FakeMessage(prompt)
+
+
+def _fake_init_chat_model(*args: object, **kwargs: object) -> _EchoModel:
+    return _EchoModel()
+
+
+_patch_llm = lambda: patch(
+    "langgraph_plugin.graph_api.human_in_the_loop.workflow.init_chat_model",
+    _fake_init_chat_model,
+)
+
+
 async def test_human_in_the_loop_approve(client: Client) -> None:
     task_queue = f"hitl-test-{uuid.uuid4()}"
     plugin = LangGraphPlugin(graphs={"chatbot": make_chatbot_graph()})
 
-    async with Worker(
-        client,
-        task_queue=task_queue,
-        workflows=[ChatbotWorkflow],
-        plugins=[plugin],
-    ):
-        handle = await client.start_workflow(
-            ChatbotWorkflow.run,
-            "test message",
-            id=f"hitl-{uuid.uuid4()}",
+    with _patch_llm():
+        async with Worker(
+            client,
             task_queue=task_queue,
-        )
-
-        # Poll for draft to be ready
-        draft = None
-        for _ in range(40):
-            await asyncio.sleep(0.25)
-            draft = await handle.query(ChatbotWorkflow.get_draft)
-            if draft is not None:
-                break
-        assert draft is not None
-        assert "test message" in draft
-
-        # Approve
-        await handle.signal(ChatbotWorkflow.provide_feedback, "approve")
-        result = await handle.result()
+            workflows=[ChatbotWorkflow],
+            plugins=[plugin],
+        ):
+            handle = await client.start_workflow(
+                ChatbotWorkflow.run,
+                "test message",
+                id=f"hitl-{uuid.uuid4()}",
+                task_queue=task_queue,
+            )
+
+            # Poll for draft to be ready
+            draft = None
+            for _ in range(40):
+                await asyncio.sleep(0.25)
+                draft = await handle.query(ChatbotWorkflow.get_draft)
+                if draft is not None:
+                    break
+            assert draft is not None
+            assert "test message" in draft
+
+            # Approve
+            await handle.signal(ChatbotWorkflow.provide_feedback, "approve")
+            result = await handle.result()
 
     assert result == draft  # approved draft returned as-is
 
@@ -56,31 +80,36 @@ async def test_human_in_the_loop_revise(client: Client) -> None:
     task_queue = f"hitl-revise-test-{uuid.uuid4()}"
     plugin = LangGraphPlugin(graphs={"chatbot": make_chatbot_graph()})
 
-    async with Worker(
-        client,
-        task_queue=task_queue,
-        workflows=[ChatbotWorkflow],
-        plugins=[plugin],
-    ):
-        handle = await client.start_workflow(
-            ChatbotWorkflow.run,
-            "test message",
-            id=f"hitl-revise-{uuid.uuid4()}",
+    with _patch_llm():
+        async with Worker(
+            client,
             task_queue=task_queue,
-        )
-
-        # Poll for draft
-        draft = None
-        for _ in range(40):
-            await asyncio.sleep(0.25)
-            draft = await handle.query(ChatbotWorkflow.get_draft)
-            if draft is not None:
-                break
-        assert draft is not None
-
-        # Send revision feedback
-        await handle.signal(ChatbotWorkflow.provide_feedback, "please be more concise")
-        result = await handle.result()
-
-    assert "[Revised]" in result
+            workflows=[ChatbotWorkflow],
+            plugins=[plugin],
+        ):
+            handle = await client.start_workflow(
+                ChatbotWorkflow.run,
+                "test message",
+                id=f"hitl-revise-{uuid.uuid4()}",
+                task_queue=task_queue,
+            )
+
+            # Poll for draft
+            draft = None
+            for _ in range(40):
+                await asyncio.sleep(0.25)
+                draft = await handle.query(ChatbotWorkflow.get_draft)
+                if draft is not None:
+                    break
+            assert draft is not None
+
+            # Send revision feedback
+            await handle.signal(
+                ChatbotWorkflow.provide_feedback, "please be more concise"
+            )
+            result = await handle.result()
+
+    # The revision node feeds the draft and feedback into the LLM; the echo
+    # stand-in returns the revision prompt, which contains both.
     assert "please be more concise" in result
+    assert "test message" in result