In this cookbook, you’ll learn how to build a multi-agent financial conversational assistant using Agno for agent orchestration and Maxim for observability and tracing. The agent can answer questions about stocks, companies, and financial data by leveraging both web search and financial data tools, with all interactions traced in Maxim.

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Prerequisites

  • Python 3.8+
  • Agno (pip install agno)
  • Maxim Python SDK (pip install maxim-py)
  • python-dotenv (pip install python-dotenv)
  • API keys for OpenAI and Maxim (and optionally Google Gemini)
  • (Optional) Set up a .env file with your API keys

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1. Import Required Libraries

from dotenv import load_dotenv
from agno.agent import Agent
# from agno.models.google.gemini import Gemini
from agno.models.openai import OpenAIChat
from agno.tools.googlesearch import GoogleSearchTools
from agno.tools.yfinance import YFinanceTools
from maxim import Maxim
from maxim.logger.agno import instrument_agno

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2. Load Environment Variables and Instrument Agno with Maxim

# Load environment variables from .env file
load_dotenv()

# Instrument Agno with Maxim's logger for observability
maxim = Maxim(api_key=os.getenv("MAXIM_API_KEY"))
instrument_agno(maxim.logger())

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3. Define the Web Search Agent

This agent uses an LLM and Google Search tools to fetch financial information from the web.

web_search_agent = Agent(
    name="Web Agent",
    role="Search the web for information",
    # model=Gemini(id="gemini-2.0-flash-001"),
    model=OpenAIChat(id="gpt-4o"),
    tools=[GoogleSearchTools()],
    instructions="Always include sources",
    show_tool_calls=True,
    markdown=True,
)

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4. Define the Finance Agent

This agent uses an LLM and YFinance tools to retrieve financial data such as stock prices, analyst recommendations, and company information.

finance_agent = Agent(
    name="Finance Agent",
    role="Get financial data",
    # model=Gemini(id="gemini-2.0-flash-001"),
    model=OpenAIChat(id="gpt-4o"),
    tools=[YFinanceTools(stock_price=True, analyst_recommendations=True, company_info=True)],
    instructions="Use tables to display data",
    markdown=True,
)

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5. Aggregate Agents into a Multi-Agent System

Combine both agents into a multi-agent system that can answer user questions by leveraging both web search and financial data tools.

multi_ai_agent = Agent(
    team=[web_search_agent, finance_agent],
    # model=Gemini(id="gemini-2.0-flash-001"),
    model=OpenAIChat(id="gpt-4o"),
    instructions="You are a helpful financial assistant. Answer user questions about stocks, companies, and financial data.",
    show_tool_calls=True,
    markdown=True
)

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6. Interactive Loop for the Financial Conversational Agent

This loop allows users to input queries and receive responses from the multi-agent system.

if __name__ == "__main__":
    print("Welcome to the Financial Conversational Agent! Type 'exit' to quit.")
    messages = []
    while True:
        print("********************************")
        user_input = input("You: ")
        if user_input.strip().lower() in ["exit", "quit"]:
            print("Goodbye!")
            break
        messages.append({"role": "user", "content": user_input})
        conversation = "\n".join([
            ("User: " + m["content"]) if m["role"] == "user" else ("Agent: " + m["content"]) for m in messages
        ])
        response = multi_ai_agent.run(
            f"Conversation so far:\n{conversation}\n\nRespond to the latest user message."
        )
        agent_reply = getattr(response, "content", response)
        print("---------------------------------")
        print("Agent:", agent_reply)
        messages.append({"role": "agent", "content": str(agent_reply)})

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7. Observability with Maxim

All agent interactions, tool calls, and responses are automatically traced and can be visualized in your Maxim dashboard. This provides deep insights into agent reasoning, tool usage, and user interactions.

For more details, see the Agno documentation and the Maxim Python SDK documentation.