add: main.py

main.py

@@ -0,0 +1,106 @@
+import os
+import asyncio
+from typing import TypedDict, Annotated
+
+import questionary
+from langgraph.graph import StateGraph, START, END
+from langgraph.constants import interrupt, Command
+from langgraph.checkpoint.memory import InMemorySaver
+from langgraph.types import State
+
+from langchain_openai import ChatOpenAI
+from langchain_core.messages import HumanMessage
+from langchain.tools import tool
+from deepagents import create_deep_agent
+from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend
+
+# ---------- LLM ----------
+llm = ChatOpenAI(
+    model="openai/gpt-oss-20b:free",
+    base_url="https://openrouter.ai/api/v1",
+    api_key=os.getenv("OPENAI_API_KEY"),
+    temperature=0.0,
+)
+
+# ---------- State ----------
+class GraphState(TypedDict):
+    human_value: Annotated[str | None, "value chosen by user"]
+    foo: Annotated[str, "initial data"]
+
+# ---------- Node with interrupt ----------
+async def interrupt_node(state: GraphState) -> GraphState:
+    # Trigger interrupt with structured payload
+    payload = {
+        "type": "confirm",
+        "question": "Уверены, что хотите продолжить?",
+        "allow_responds": ["approve", "reject"],
+    }
+    # The node will pause here; after resume, the payload will be updated with answer
+    await interrupt(payload)
+    # After resume, payload will contain 'answer'
+    answer = payload.get("answer")
+    state["human_value"] = answer
+    return state
+
+# ---------- Graph ----------
+builder = StateGraph(GraphState)
+builder.add_node("interrupt_node", interrupt_node)
+builder.set_entry_point("interrupt_node")
+builder.add_edge("interrupt_node", END)
+
+checkpoint = InMemorySaver()
+graph = builder.compile(checkpointer=checkpoint)
+
+# ---------- Tool to run the graph ----------
+@tool
+def run_graph() -> str:
+    """Run the LangGraph with human‑in‑the‑loop interrupt."""
+    thread_id = "thread-1"
+    config = {"configurable": {"thread_id": thread_id}}
+    # Initial state
+    state: GraphState = {"human_value": None, "foo": "initial"}
+    # Start streaming
+    stream = graph.stream(state, config)
+    try:
+        for chunk in stream:
+            if "__interrupt__" in chunk:
+                # Extract payload
+                payload = chunk["__interrupt__"][0].value
+                # Show question to user
+                answer = questionary.select(
+                    payload["question"],
+                    choices=payload["allow_responds"],
+                ).ask()
+                # Attach answer and resume
+                payload["answer"] = answer
+                stream = graph.stream(Command(resume=payload), config)
+                continue
+            # When stream ends, return final state
+            if "node" in chunk:
+                return f"Final state: {chunk['node']}"
+    except Exception as e:
+        return f"Error: {e}"
+    return "Graph finished"
+
+# ---------- DeepAgent ----------
+backend = CompositeBackend([
+    LocalShellBackend(workspace_dir="./workspace"),
+    FilesystemBackend(),
+])
+
+agent = create_deep_agent(
+    model=llm,
+    tools=[run_graph],
+    backend=backend,
+    system_prompt="You are a helpful agent that can run a graph with human interrupt.",
+)
+
+async def main():
+    result = await agent.ainvoke(
+        {"messages": [HumanMessage(content="run_graph")]},
+        {"configurable": {"thread_id": "session-1"}},
+    )
+    print(result["messages"][-1].content)
+
+if __name__ == "__main__":
+    asyncio.run(main())