"""
Slide 72: Slide 12
Section 3: Deep Agents
Source: slides/section3-deepagents/section3.pptx

Сгенерировано автоматически из slide-NN.js / sectionN.pptx.
Паттерн провайдера скопирован из
bro-js/agents/teacher/assistant/src/angry_teacher/llm.py.

Запуск:
  cd langchain-evolution-deck
  cp .env.example .env   # заполни OPENAI_API_KEY
  python examples/slide-072/script.py
"""
import os
from dotenv import load_dotenv
from langchain_openai import ChatOpenAI

load_dotenv()

llm = ChatOpenAI(
    # model="openrouter/free",
    # model="qwen/qwen3.5-35b-a3b",
    model="openai/gpt-oss-20b",
    # model="nvidia/nemotron-3-nano",
    # model="qwen/qwen3.5-9b",
    base_url="https://llm.brojs.ru/v1",
    # base_url="https://api.minimax.io/v1",
    # base_url="http://0.0.0.0:8090/v1",
    # base_url="https://openrouter.ai/api/v1",
    # api_key=os.getenv("MINIMAX_API_KEY"),
    api_key=(os.getenv("OPENAI_API_KEY") or "").strip() or None,
    temperature=0.7,
    stream_usage=True,
)


# === Code from slide 72 ===
// examples/task_call.py:1-10

  # When the main agent emits a tool call like:
> task(
>     subagent_type="researcher",
>     description="Find papers on RAG evaluation",
>     prompt="Search arXiv for 2025-2026 RAG evaluation surveys. 
>              Return a 150-word summary with 3 citations.",
  )

  # Deep Agents spins up a fresh deep agent with the researcher profile,
  # runs it to completion, and returns only the final message.