add main.py
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"""
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Main entry point for the agent with memory and human‑in‑the‑loop confirmation.
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The agent is built on top of LangGraph's `create_agent` API. It uses a
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`MemorySaver` checkpoint to keep conversation history across calls, and it
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is configured with `interrupt_before=["tools"]` so that the agent pauses just
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before invoking any tool. The pause allows us to ask the user for explicit
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confirmation.
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The example includes one simple tool – ``get_price`` – which pretends to
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query a price service. In a real project this would be replaced with an
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actual API call.
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Three usage examples are demonstrated in ``__main__``:
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1. Ask the agent for weather information (uses the built‑in ``web_search``
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tool).
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2. Ask for a product price – the agent will pause and ask for confirmation.
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3. Continue the conversation to show that memory is preserved.
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The console output is rendered with `rich` for better readability.
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"""
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from __future__ import annotations
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import os
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import json
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from typing import Any, Dict, Iterable, Tuple
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from langchain_openai import ChatOpenAI
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from langchain_core.messages import HumanMessage, SystemMessage
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from langgraph.checkpoint.memory import MemorySaver
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from langgraph.types import Command
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from langgraph.graph import StateGraph
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from langgraph.graph.message import add_messages
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from rich.console import Console
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# ---------------------------------------------------------------------------
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# Configuration
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# ---------------------------------------------------------------------------
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console = Console()
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llm = ChatOpenAI(
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model="openai/gpt-oss-20b:free",
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base_url="https://platform.brojs.ru/jrnl-bh/api/inference/v1",
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api_key=os.getenv("JOURNAL_MCP_PAT"),
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temperature=0.0,
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)
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# ---------------------------------------------------------------------------
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# Simple tool – in a real scenario replace with an actual API call.
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# ---------------------------------------------------------------------------
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async def get_price(query: Dict[str, Any]) -> str:
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"""Pretend to fetch a price for a product.
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Parameters
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----------
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query: dict
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Expected keys are ``product`` and optionally ``currency``.
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Returns
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-------
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str
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A human‑readable string describing the price.
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"""
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product = query.get("product", "unknown")
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currency = query.get("currency", "USD")
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# Dummy logic – in real life call an external service.
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return f"The price of {product} is 42.00 {currency}."
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# ---------------------------------------------------------------------------
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# Agent definition
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# ---------------------------------------------------------------------------
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memory = MemorySaver()
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agent = StateGraph(
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state_schema=dict(messages=list, next=tuple)
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)
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# Node that simply forwards the messages to the LLM.
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async def llm_node(state: Dict[str, Any]) -> Tuple[Dict[str, Any], str]:
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# The LLM expects a list of messages; we pass the current history.
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response = await llm.ainvoke(state["messages"])
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return {"messages": state["messages"] + [response]}, "next"
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# Node that handles tool calls – for this example we only have get_price.
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async def tool_node(state: Dict[str, Any]) -> Tuple[Dict[str, Any], str]:
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# The last message should contain a tool call.
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last_msg = state["messages"][-1]
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if not hasattr(last_msg, "tool_calls") or not last_msg.tool_calls:
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return state, "next"
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tool_call = last_msg.tool_calls[0]
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name = tool_call.name
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args = json.loads(tool_call.args)
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if name == "get_price":
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result = await get_price(args)
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# Append the tool output as a new message.
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state["messages"].append(
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HumanMessage(content=f"Tool {name} returned: {result}")
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)
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return state, "next"
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agent.add_node("llm", llm_node)
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agent.add_node("tool", tool_node)
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agent.set_entry_point("llm")
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agent.add_edge("llm", "tool")
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agent.add_edge("tool", "llm")
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# Compile the graph with a MemorySaver checkpoint.
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graph = agent.compile(checkpointer=memory, interrupt_before=["tools"])
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# ---------------------------------------------------------------------------
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# Helper to run the agent with human‑in‑the‑loop confirmation.
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# ---------------------------------------------------------------------------
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async def ask_and_run(user_input: Dict[str, Any], config: Dict[str, Any]):
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"""Run the agent and pause before each tool call.
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Parameters
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----------
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user_input: dict
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Dictionary with a ``messages`` key containing a list of messages.
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config: dict
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Configuration dictionary that must contain ``configurable`` with
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``thread_id``.
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"""
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async for chunk in graph.stream(user_input, config=config, stream_mode=["messages", "updates"]):
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# ``chunk`` is a tuple (type, data).
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chunk_type, chunk_data = chunk
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state = graph.get_state(config)
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if chunk_type == "messages":
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# Stream token by token.
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console.print(chunk_data.content, end="", style="cyan")
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console.file.flush()
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elif chunk_type == "updates":
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# Tool call preview – show the user what will be executed.
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console.print("\n[bold magenta]Agent wants to call a tool:[/]")
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console.print(json.dumps(chunk_data, indent=2), style="magenta")
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if "__interrupt__" in chunk_data and state.next == ("tools",):
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# Pause – ask for confirmation.
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console.print("\n[bold yellow]Confirmation required:[/] Do you allow the tool call? (y/n)")
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answer = input().strip().lower()
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if answer != "y":
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console.print("[red]Action cancelled by user.[/]")
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break
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# Resume from the same state.
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await graph.ainvoke(Command(resume=None), config=config)
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# ---------------------------------------------------------------------------
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# Main loop – three examples as requested.
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# ---------------------------------------------------------------------------
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if __name__ == "__main__":
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thread_id = "demo-thread"
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config = {"configurable": {"thread_id": thread_id}}
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console.print("[bold green]Welcome to the agent demo![/]")
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console.print("Type 'exit' to quit.")
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# Example 1 – simple chat (no tool call).
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console.print("\n[underline]Example 1: Simple question[/]")
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user_msg = {"messages": [HumanMessage(content="What is the capital of France?")]}
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import asyncio
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asyncio.run(ask_and_run(user_msg, config))
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# Example 2 – tool call with confirmation.
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console.print("\n[underline]Example 2: Tool call (price query)[/]")
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user_msg = {"messages": [HumanMessage(content="Get price of laptop in USD")]}
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asyncio.run(ask_and_run(user_msg, config))
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# Example 3 – continue conversation to show memory.
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console.print("\n[underline]Example 3: Continue conversation[/]")
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user_msg = {"messages": [HumanMessage(content="What about the price in EUR?")]}
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asyncio.run(ask_and_run(user_msg, config))
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console.print("\n[bold green]Demo finished.[/]"
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)
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