commit 6de7a6c803e50a3a156b1319f1aa7b3c729ab98e
Author: Даниил Викторов <mr.face2002@gmail.com>
Date:   Tue Jun 30 17:37:18 2026 +0000

    add: main.py — Экзамен: Самокорректирующийся агент

diff --git a/main.py b/main.py
new file mode 100644
index 0000000..d750c96
--- /dev/null
+++ b/main.py
@@ -0,0 +1,177 @@
+import os
+import asyncio
+import random
+from typing import TypedDict, Annotated
+
+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
+
+from langgraph.graph import StateGraph, START, END
+from langgraph.graph.message import add_messages
+
+# ---------- 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,
+)
+
+# ---------- Backend ----------
+backend = CompositeBackend(
+    [
+        LocalShellBackend(workspace_dir="./workspace"),
+        FilesystemBackend(),
+    ]
+)
+
+# ---------- Unreliable tool ----------
+@tool
+def unreliable_tool(query: str) -> str:
+    """
+    Simulates an unreliable external tool.
+    With ~30% probability it raises a ValueError.
+    """
+    if random.random() < 0.3:
+        raise ValueError("Simulated tool failure")
+    return f"Result for '{query}'"
+
+
+# ---------- DeepAgent (used inside execute_task node) ----------
+deep_agent = create_deep_agent(
+    model=llm,
+    tools=[unreliable_tool],
+    backend=backend,
+    system_prompt="You are a helpful assistant that uses the provided tool to answer user queries.",
+)
+
+# ---------- State definition ----------
+class AgentState(TypedDict):
+    task: str
+    result: str
+    attempts: int
+    status: str  # pending | success | failed | max_attempts
+    error: str | None
+    max_attempts: int
+    messages: Annotated[list, add_messages]
+
+# ---------- Nodes ----------
+async def execute_task(state: AgentState):
+    """Run the task using the deep agent."""
+    try:
+        response = await deep_agent.ainvoke(
+            {"messages": [HumanMessage(content=state["task"])]},
+            {"configurable": {"thread_id": f"session-{state['attempts']}"}},
+        )
+        # The deep agent returns a dict with "messages"
+        result_msg = response["messages"][-1].content
+        return {
+            "result": result_msg,
+            "error": None,
+            "status": "pending",
+            "messages": response["messages"],
+        }
+    except Exception as e:
+        return {
+            "result": "",
+            "error": str(e),
+            "status": "failed",
+            "messages": [],
+        }
+
+
+async def verify_result(state: AgentState):
+    """Ask LLM to judge the result."""
+    judge_prompt = f"""You are a judge. Determine if the following result correctly solves the task.
+
+Task: {state['task']}
+Result: {state['result']}
+
+Respond with only one word: SUCCESS if the result is correct, otherwise FAILED."""
+    judge_response = await llm.ainvoke([HumanMessage(content=judge_prompt)])
+    verdict = judge_response.content.strip().lower()
+    if verdict == "success":
+        new_status = "success"
+    else:
+        new_status = "failed"
+    return {"status": new_status, "messages": [HumanMessage(content=judge_response.content)]}
+
+
+def handle_error(state: AgentState):
+    """Increase attempt counter and decide next step."""
+    attempts = state["attempts"] + 1
+    if attempts >= state["max_attempts"]:
+        return {
+            "attempts": attempts,
+            "status": "max_attempts",
+            "error": state.get("error"),
+        }
+    else:
+        return {
+            "attempts": attempts,
+            "status": "pending",
+            "error": None,
+        }
+
+# ---------- Graph ----------
+graph = StateGraph(AgentState)
+
+graph.add_node("execute_task", execute_task)
+graph.add_node("verify_result", verify_result)
+graph.add_node("handle_error", handle_error)
+
+graph.add_edge(START, "execute_task")
+graph.add_edge("execute_task", "verify_result")
+graph.add_conditional_edges(
+    "verify_result",
+    lambda state: state["status"],
+    {
+        "success": END,
+        "failed": "handle_error",
+        "max_attempts": END,
+    },
+)
+graph.add_edge("handle_error", "execute_task")
+
+graph.set_entry_point(START)
+
+app = graph.compile()
+
+# ---------- Main ----------
+async def main():
+    task_description = "Calculate 2+2."
+    initial_state: AgentState = {
+        "task": task_description,
+        "result": "",
+        "attempts": 0,
+        "status": "pending",
+        "error": None,
+        "max_attempts": 5,
+        "messages": [],
+    }
+
+    async for event in app.astream(initial_state):
+        # Print progress information
+        if "attempts" in event:
+            print(f"Attempt {event['attempts']}: status={event['status']}")
+        if event.get("error"):
+            print(f"Error: {event['error']}")
+        if event.get("result"):
+            print(f"Result: {event['result']}")
+
+    final = await app.ainvoke(initial_state)
+    print("\n=== Final Outcome ===")
+    print(f"Task: {task_description}")
+    print(f"Status: {final['status']}")
+    print(f"Attempts: {final['attempts']}")
+    if final["status"] == "success":
+        print(f"Successful result: {final['result']}")
+    else:
+        print("Failed to obtain a correct result.")
+
+if __name__ == "__main__":
+    asyncio.run(main())
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