commit 0838fa603ae6258b5ee8d7b1a5eebf17432be89f
Author: Илья 5f1b81b8-4f5d-11e8-9c2d-fa7ae01bbebc <gurevich1104@mail.ru>
Date:   Sat Jun 27 13:53:26 2026 +0000

    add: main.py

diff --git a/main.py b/main.py
new file mode 100644
index 0000000..2c4507a
--- /dev/null
+++ b/main.py
@@ -0,0 +1,130 @@
+import os
+import asyncio
+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 CompositeBackend, LocalShellBackend, FilesystemBackend
+
+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(),
+])
+
+# ---------- Planning State ----------
+class PlanningState(TypedDict):
+    messages: Annotated[list, add_messages]
+    plan: list[str]
+    current_step: int
+    results: list[str]
+
+# ---------- Planner Node ----------
+async def planner_node(state: PlanningState):
+    task = state["messages"][-1].content
+    prompt = (
+        "You are a task planner.\n"
+        "Task: {task}\n"
+        "Break the task into 3–6 concrete steps.\n"
+        "Return the steps as a numbered list or JSON array.\n"
+        "Do not include any other text."
+    ).format(task=task)
+    plan_text = await llm.ainvoke([HumanMessage(content=prompt)])
+    plan_str = plan_text.content.strip()
+    # Try to parse JSON
+    plan = []
+    try:
+        import json
+        plan = json.loads(plan_str)
+        if not isinstance(plan, list):
+            raise ValueError
+    except Exception:
+        # Fallback to numbered list parsing
+        import re
+        plan = [line.strip() for line in plan_str.splitlines() if re.match(r"^\s*\d+\.", line)]
+    return {
+        "plan": plan,
+        "current_step": 0,
+        "results": [],
+    }
+
+# ---------- Executor Node ----------
+async def executor_node(state: PlanningState):
+    step = state["plan"][state["current_step"]]
+    prompt = (
+        "You are an executor.\n"
+        "Step: {step}\n"
+        "Provide a concise result for this step."
+    ).format(step=step)
+    result_text = await llm.ainvoke([HumanMessage(content=prompt)])
+    result = result_text.content.strip()
+    new_results = state["results"] + [result]
+    return {
+        "results": new_results,
+        "current_step": state["current_step"] + 1,
+    }
+
+# ---------- Should Continue ----------
+def should_continue(state: PlanningState):
+    if state["current_step"] >= len(state["plan"]):
+        return "finish"
+    return "execute"
+
+# ---------- Graph ----------
+graph = StateGraph(PlanningState)
+graph.add_node("planner", planner_node)
+graph.add_node("executor", executor_node)
+graph.add_conditional_edges("planner", lambda _: "execute")
+graph.add_conditional_edges("executor", should_continue)
+graph.set_entry_point("planner")
+graph.add_edge("execute", "executor")
+graph.add_edge("finish", END)
+planner_chain = graph.compile()
+
+# ---------- Tool that runs the planner graph ----------
+@tool
+def run_planner(task: str) -> str:
+    """Run the planning graph on the given task and return the final summary."""
+    # Initialize state with the task as a message
+    init_state = {"messages": [HumanMessage(content=task)], "plan": [], "current_step": 0, "results": []}
+    final_state = planner_chain.invoke(init_state)
+    # Build final output
+    plan_lines = [f"{i+1}. {step}" for i, step in enumerate(final_state["plan"])]
+    results = final_state["results"]
+    summary = "\n".join(results)
+    return (
+        f"План:\n" + "\n".join(plan_lines) + "\n\n[Шаги]" + "\n".join([f"[Шаг {i+1}] {r}" for i, r in enumerate(results)]) + "\n\nИтог: " + summary
+    )
+
+# ---------- Deep Agent ----------
+agent = create_deep_agent(
+    model=llm,
+    tools=[run_planner],
+    backend=backend,
+    system_prompt="You are a helpful agent that can plan and execute tasks.",
+)
+
+# ---------- Demo ----------
+async def main():
+    task = "Сравни Python и JavaScript"
+    result = await agent.ainvoke(
+        {"messages": [HumanMessage(content=task)]},
+        {"configurable": {"thread_id": "session-1"}},
+    )
+    print(result["messages"][-1].content)
+
+if __name__ == "__main__":
+    asyncio.run(main())