"""
Minimal LangGraph ReAct agent using Qwen3-Coder(-Flash) via OpenAI-compatible API.

Prereqs:
  pip install -U langgraph langchain langchain-openai

Env:
  export QWEN_API_KEY=sk-...
  export QWEN_BASE_URL=https://dashscope-intl.aliyuncs.com/compatible-mode/v1  # or your gateway
  export QWEN_MODEL=qwen3-coder-flash  # use console's actual model name
"""
import sys,os


from typing import Optional

from langgraph.prebuilt import create_react_agent
from langchain_core.messages import HumanMessage

try:
    from langchain.tools import tool
except Exception:  # older versions
    from langchain_core.tools import tool  # type: ignore

from langgraph_qwen import ChatQwenOpenAICompat, bind_qwen_tools


@tool
def get_time(_: str = "") -> str:
    """Get current local time in ISO format."""
    import datetime as _dt
    return _dt.datetime.now().isoformat()


@tool
def add(x_and_y: str) -> str:
    """Add two integers given as 'x y'."""
    xs = [int(s) for s in x_and_y.strip().split()]
    if len(xs) != 2:
        return "Please provide two integers: 'x y'"
    return str(xs[0] + xs[1])


def main():
    # Prefer the custom adapter to avoid extra deps (langchain_openai)
    base = ChatQwenOpenAICompat(temperature=0)
    model = bind_qwen_tools(base, [get_time, add], tool_choice="auto")

    agent = create_react_agent(model, [get_time, add])

    # A prompt that can trigger multiple tool calls
    question = (
        "现在是几点？然后把 7 和 35 相加，并把两者结果合并成一句简洁中文回答。"
    )

    result = agent.invoke({"messages": [HumanMessage(content=question)]})

    # Unpack final message text
    last = result["messages"][-1]
    print("\n=== Agent Final Answer ===\n")
    print(getattr(last, "content", last))


if __name__ == "__main__":
    main()