Maxim

This example shows how to instrument your agno agent and send traces to Maxim AI. We are building a simple Financial Conversation Agent.

Code

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.team.team import Team
from agno.tools.duckduckgo import DuckDuckGoTools
from agno.tools.yfinance import YFinanceTools

try:
    from maxim import Maxim
    from maxim.logger.agno import instrument_agno
except ImportError:
    raise ImportError(
        "`maxim` not installed. Please install using `pip install maxim-py`"
    )

# Instrument Agno with Maxim for automatic tracing and logging
instrument_agno(Maxim().logger())

# Web Search Agent: Fetches financial information from the web
web_search_agent = Agent(
    name="Web Agent",
    model=OpenAIChat(id="gpt-4o"),
    tools=[DuckDuckGoTools()],
    instructions="Always include sources",
    markdown=True,
)

# Finance Agent: Gets financial data using YFinance tools
finance_agent = Agent(
    name="Finance Agent",
    model=OpenAIChat(id="gpt-4o"),
    tools=[YFinanceTools()],
    instructions="Use tables to display data",
    markdown=True,
)

# Aggregate both agents into a multi-agent system
multi_ai_team = Team(
    members=[web_search_agent, finance_agent],
    model=OpenAIChat(id="gpt-4o"),
    instructions="You are a helpful financial assistant. Answer user questions about stocks, companies, and financial data.",
    markdown=True,
)

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_team.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)})