"""
Slide 83: Slide 23
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-083/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 83 ===
// examples/research_agent.py:1-24

  from langchain.tools import tool
  from deepagents import create_deep_agent

  @tool
  def arxiv_search(query: str, max_results: int = 5) -> str:
      """Search arXiv for papers matching the query."""
      import arxiv
      client = arxiv.Client()
      results = list(client.results(arxiv.Search(query=query, max_results=max_results)))
      return "\n\n".join(
          f"{r.title}\n{r.summary[:300]}..." for r in results
      )

  agent = create_deep_agent(
      model="openai:gpt-4.1",
      tools=[arxiv_search],
      system_prompt=("You are a research assistant. Always cite paper titles and arXiv IDs."),
>     subagents=[{
>         "name": "summarizer",
>         "description": "Compresses paper abstracts into a paragraph",
>         "system_prompt": "You are a precise summarizer.",
>         "tools": [],
>     }],
> )

// note: snippet has 24 lines, card fits ~16