84 lines
3.2 KiB
Python
84 lines
3.2 KiB
Python
# DESIGN DECISION: We use langgraph's create_agent instead of deepagents' create_deep_agent because the assignment's technical analysis requires replacing deepagents agent with LangGraph's create_agent. This satisfies the updated requirement and ensures compatibility with MemorySaver and interrupt_before features.
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# NECESSITY: The course's latest guidelines explicitly state to replace deepagents agent with LangGraph's create_agent. Using deepagents would violate this instruction and could lead to failing tests.
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# OPTIMALITY: LangGraph's create_agent provides native support for MemorySaver, interrupt_before, and stream_mode, simplifying implementation and reducing dependencies.
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# ALTERNATIVES CONSIDERED: Keeping deepagents would require additional wrappers to emulate MemorySaver and interrupt behavior, increasing complexity and risk of bugs.
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import os
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from typing import Optional
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from langchain_openai import ChatOpenAI
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from langchain.tools import tool
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from langgraph import create_agent
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from langgraph.checkpoint.memory import MemorySaver
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from rich.console import Console
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# Initialize LLM
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llm = ChatOpenAI(
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model="openai/gpt-oss-20b:free",
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base_url="https://openrouter.ai/api/v1",
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api_key=os.getenv("OPENAI_API_KEY"),
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temperature=0.0,
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)
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# Define a simple tool
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@tool
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def get_price(query: str) -> str:
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"""Get price for a city and date."""
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return f"Price for {query} is $100"
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# Create agent with memory and interrupt before tools
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agent = create_agent(
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model=llm,
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tools=[get_price],
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system_prompt="You are a helpful agent.",
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checkpointer=MemorySaver(),
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interrupt_before=["tools"],
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)
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console = Console()
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config = {"configurable": {"thread_id": "conversation-1"}}
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def ask_and_run(user_input: Optional[dict], cfg: dict) -> None:
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"""
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Stream agent output, handle pauses before tool calls, and ask for user confirmation.
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"""
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for chunk in agent.stream(
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user_input,
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config=cfg,
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stream_mode=["messages", "updates"],
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):
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chunk_type, chunk_data = chunk
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# Handle message tokens
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if chunk_type == "messages":
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content = chunk_data.get("content", "")
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console.print(content, end="")
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# Handle tool call results or other updates
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if chunk_type == "updates":
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console.print(chunk_data)
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# Detect pause before tool invocation
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if "__interrupt__" in chunk_data and agent.get_state(cfg).next == ("tools",):
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state = agent.get_state(cfg)
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last_msg = state.values["messages"][-1]
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tool_call = last_msg.tool_calls[0]
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name = tool_call["name"]
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args = tool_call["arguments"]
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console.print(f"{name}({args})")
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console.print(f"Агент хочет вызвать утилиту {name}({args})")
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answer = input("Разрешить? (Y/n): ")
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if answer.lower().strip() == "y":
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ask_and_run(None, cfg)
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else:
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console.print("Отменено")
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break
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while True:
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user_input = input("\nВы: ")
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if user_input.lower() == "exit":
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break
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ask_and_run(
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{"messages": [{"role": "human", "content": user_input}]},
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config,
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) |