Building a Dual-Tool Multimodal Financial Research Agent

       +-----------------------+
       |   User Query Prompt   |
       +-----------------------+
                   |
                   v
       +-----------------------+
       |  Financial Orchestrator| <---+ Memory DB (SQLite)
       +-----------------------+
          /                 \
         v                   v
+----------------+   +-----------------------+
|  hybrid_search |   |live_finance_researcher|
|  (Qdrant RAG)  |   |  (Yahoo Finance MCP)  |
+----------------+   +-----------------------+

import sys
import subprocess
from langchain_core.tools import tool

@tool
def live_finance_researcher(query: str) -> str:
    """Research live stock data using Yahoo Finance MCP.

    Use this tool to get:
    - Current stock prices and real-time market data
    - Latest financial news
    - Stock recommendations and analyst ratings
    - Option chains and recent splits/dividends
    """
    # execute async finance_research client in a sync python subprocess
    code = f"""
import asyncio
from scripts.yahoo_mcp import finance_research
asyncio.run(finance_research("{query}"))
"""
    result = subprocess.run([sys.executable, '-c', code], capture_output=True, text=True)
    return result.stdout

import sqlite3
from dotenv import load_dotenv
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.agents import create_agent
from langgraph.checkpoint.sqlite import SqliteSaver
from scripts.rag_tools import hybrid_search, live_finance_researcher
from scripts.prompts import MULTIMODEL_AGENT_PROMPT

load_dotenv()

# Instantiate reasoning LLM model
model = ChatGoogleGenerativeAI(model='gemini-3-pro-preview')

def get_agent():
    # Store checkpointer databases inside the data folder
    conn = sqlite3.connect('data/financial_research_agent.db', check_same_thread=False)
    checkpointer = SqliteSaver(conn=conn)

    agent = create_agent(
        model=model,
        tools=[hybrid_search, live_finance_researcher],
        system_prompt=MULTIMODEL_AGENT_PROMPT,
        checkpointer=checkpointer
    )
    return agent

agent = get_agent()

from langchain_core.messages import HumanMessage, AIMessage, ToolMessage

def stream_agent_response(agent, query: str, thread_id: str = "default_session"):
    config = {'configurable': {'thread_id': thread_id}}
    
    for chunk in agent.stream(
        {'messages': [HumanMessage(query)]},
        stream_mode='messages',
        config=config
    ):
        message = chunk[0] if isinstance(chunk, tuple) else chunk
        
        # Stream tool calls
        if isinstance(message, AIMessage) and message.tool_calls:
            for tool_call in message.tool_calls:
                print(f"\n  [Tool Triggered]: {tool_call['name']}")
                print(f"   Arguments: {tool_call['args']}\n")
        
        # Stream tool return logs
        elif isinstance(message, ToolMessage):
            print(f"\n  [Tool Completed] (returned {len(message.text)} chars)\n")
        
        # Stream text response tokens
        elif isinstance(message, AIMessage) and message.text:
            print(message.text, end='', flush=True)