"""
Slide 77: Slide 17
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-077/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 77 ===
// examples/middleware.py:1-18

  from langchain.agents.middleware import AgentMiddleware
  from deepagents import create_deep_agent

  class LoggerMiddleware(AgentMiddleware):
>     def before_model(self, state, runtime):
>         print(f"[model] {len(state['messages'])} msgs in")
>         return state
>  
>     def after_tool(self, state, runtime, tool_result):
>         print(f"[tool]  {tool_result.tool_call_id} -> {len(str(tool_result.content))} chars")
          return state

  agent = create_deep_agent(
      model="openai:gpt-4.1",
      middleware=[LoggerMiddleware(), HumanInTheLoopMiddleware(
>         interrupt_on={"execute": True},  # ask before running bash
>     )],
> )

// note: snippet has 18 lines, card fits ~16