add main.py

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2026-05-27 13:20:18 +00:00
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"""
Main entry point for the assignment.
This script demonstrates a deep agent that:
1. Uses `create_deep_agent` from the `deepagents` package.
2. Stores conversation history with `MemorySaver`.
3. Pauses before each tool call and asks the user for confirmation.
4. Can defend its decisions when a user contradicts the original task.
The script contains three example interactions that showcase:
- Normal operation.
- Tool usage with confirmation.
- Defending the agent's choice.
"""
import os
from typing import Dict, Any
# LLM configuration BroJS only
from langchain_openai import ChatOpenAI
from deepagents import create_deep_agent
from deepagents.backends import CompositeBackend, FilesystemBackend
from langgraph.checkpoint.memory import MemorySaver
from langgraph.prebuilt import create_react_agent as create_agent
from rich.console import Console
from rich.markdown import Markdown
from tools import get_price
# ---------------------------------------------------------------------------
# Configuration
# ---------------------------------------------------------------------------
LLM = ChatOpenAI(
# LLM configuration using BroJS
llm = ChatOpenAI(
model="openai/gpt-oss-20b:free",
base_url="https://platform.brojs.ru/jrnl-bh/api/inference/v1",
api_key=os.getenv("JOURNAL_MCP_PAT"),
temperature=0.0,
temperature=0.5,
)
# Backend virtual FS (real shell not needed for this demo).
backend = CompositeBackend(
default=FilesystemBackend(root_dir="./workspace", virtual_mode=True, inherit_env=True),
routes={},
)
# ---------------------------------------------------------------------------
# Helper tools
# ---------------------------------------------------------------------------
from langchain.tools import tool
@tool
def echo(text: str) -> str:
"""Return the same text useful for demonstration."""
return f"Echo: {text}"
@tool
def add(a: int, b: int) -> int:
"""Add two integers."""
return a + b
# ---------------------------------------------------------------------------
# Agent creation
# ---------------------------------------------------------------------------
# Memory checkpoint for interrupt handling
memory = MemorySaver()
agent = create_deep_agent(
llm=LLM,
tools=[echo, add],
backend=backend,
system_prompt="You are an assistant that must follow the users instructions and can use tools. You should ask for confirmation before calling a tool.",
# Create the agent with interrupt before tools
agent = create_agent(
model=llm,
tools=[get_price],
checkpointer=memory,
interrupt_before=["tools"], # pause before each tool call
interrupt_before=["tools"],
)
console = Console()
# ---------------------------------------------------------------------------
# Interaction helpers
# ---------------------------------------------------------------------------
async def run_interaction(messages: list[Dict[str, str]], thread_id: str) -> None:
"""Run a single interaction with the agent."""
config = {"configurable": {"thread_id": thread_id}}
async for chunk in agent.astream(messages, config=config, stream_mode="messages"):
if isinstance(chunk, str):
console.print(chunk, end="", style="bold cyan")
else:
# Handle possible interrupt
if "__interrupt__" in chunk and agent.get_state(config).next == ("tools",):
console.print("\n[bold yellow]Agent wants to use a tool:[/]", style="yellow")
console.print(Markdown(str(chunk)))
confirm = input("Allow? (y/n) ").strip().lower()
if confirm == "y":
await agent.ainvoke(Command(resume=None), config)
else:
console.print("[red]Tool call rejected by user.[/]\n", style="red")
break
else:
console.print(chunk, style="green")
console.print("\n--- End of interaction ---\n", style="bold magenta")
# ---------------------------------------------------------------------------
# Main demo loop three examples
# ---------------------------------------------------------------------------
def ask_and_run(user_input: str, config: dict):
"""Synchronously run the agent with streaming and handle interrupts."""
# Stream messages and updates
for chunk in agent.stream(
{"messages": [{"role": "user", "content": user_input}],
"configurable": config},
stream_mode=["messages", "updates"],
):
if isinstance(chunk, dict) and "__interrupt__" in chunk:
# Interrupt: ask for confirmation
console.print("[bold red]Agent requested tool execution. Confirm? (y/n): ", end="")
choice = input().strip().lower()
if choice != "y":
# Reject by sending a new message to the agent
config.update({"configurable": {"thread_id": config.get("thread_id", "default")}})
continue
if isinstance(chunk, dict) and "messages" in chunk:
for msg in chunk["messages"]:
console.print(Markdown(msg["content"]))
if __name__ == "__main__":
import asyncio
async def main():
# Example 1: Simple echo
await run_interaction(
[{"role": "human", "content": "Say hello."}],
thread_id="demo-echo",
)
# Example 2: Tool usage with confirmation
await run_interaction(
[{"role": "human", "content": "Add 7 and 5."}],
thread_id="demo-add",
)
# Example 3: Defending the agent when user contradicts task
await run_interaction(
[
{"role": "human", "content": "I think you should not use tools at all."},
{"role": "assistant", "content": "But I need to add numbers. Let me call the tool."},
],
thread_id="demo-contradiction",
)
asyncio.run(main())
thread_id = os.getenv("THREAD_ID", "session-1")
config = {"thread_id": thread_id}
console.print("[bold green]Welcome to the price agent. Type 'exit' to quit.")
while True:
user_input = input("Вы: ")
if user_input.lower() in ("exit", "quit"):
break
ask_and_run(user_input, config)
console.print("[bold blue]Goodbye!", style="bold")