AI Finance Agent Team

Overview

The AI Finance Agent Team demonstrates how to build a collaborative team of AI agents that work together as financial analysts. This system combines web search capabilities with real-time financial data analysis tools to provide comprehensive financial insights in just 20 lines of Python code.

Architecture

Multi-Agent Team Structure

The Finance Agent Team uses a team coordination pattern where specialized agents collaborate under a team lead:

Agent Roles

Web Agent

Role: Search the web for informationTools:

DuckDuckGo search

Capabilities:

General internet research
News and articles
Market sentiment

Finance Agent

Role: Get financial dataTools:

Current stock prices
Analyst recommendations
Company information
Company news

Display: Always uses tables for data

Team Agent

Role: Coordinate between agentsResponsibilities:

Route queries to appropriate agents
Combine insights from both agents
Present unified analysis

Implementation

Complete System (20 Lines)
Individual Agents
Team Configuration

from agno.agent import Agent
from agno.team import Team
from agno.models.openai import OpenAIChat
from agno.db.sqlite import SqliteDb
from agno.tools.duckduckgo import DuckDuckGoTools
from agno.tools.yfinance import YFinanceTools
from agno.os import AgentOS

# Setup database for storage
db = SqliteDb(db_file="agents.db")

# Web Agent
web_agent = Agent(
    name="Web Agent",
    role="Search the web for information",
    model=OpenAIChat(id="gpt-4o"),
    tools=[DuckDuckGoTools()],
    db=db,
    add_history_to_context=True,
    markdown=True,
)

# Finance Agent
finance_agent = Agent(
    name="Finance Agent",
    role="Get financial data",
    model=OpenAIChat(id="gpt-4o"),
    tools=[YFinanceTools(include_tools=[
        "get_current_stock_price", 
        "get_analyst_recommendations", 
        "get_company_info", 
        "get_company_news"
    ])],
    instructions=["Always use tables to display data"],
    db=db,
    add_history_to_context=True,
    markdown=True,
)

# Team Agent
agent_team = Team(
    name="Agent Team (Web+Finance)",
    model=OpenAIChat(id="gpt-4o"),
    members=[web_agent, finance_agent],
    debug_mode=True,
    markdown=True,
)

# AgentOS
agent_os = AgentOS(teams=[agent_team])
app = agent_os.get_app()

if __name__ == "__main__":
    agent_os.serve(app="finance_agent_team:app", reload=True)

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.duckduckgo import DuckDuckGoTools
from agno.tools.yfinance import YFinanceTools
from agno.db.sqlite import SqliteDb

# Initialize database
db = SqliteDb(db_file="agents.db")

# Web Agent - General research
web_agent = Agent(
    name="Web Agent",
    role="Search the web for information",
    model=OpenAIChat(id="gpt-4o"),
    tools=[DuckDuckGoTools()],
    db=db,
    add_history_to_context=True,
    markdown=True,
)

# Finance Agent - Market data
finance_agent = Agent(
    name="Finance Agent",
    role="Get financial data",
    model=OpenAIChat(id="gpt-4o"),
    tools=[YFinanceTools(include_tools=[
        "get_current_stock_price",
        "get_analyst_recommendations",
        "get_company_info",
        "get_company_news"
    ])],
    instructions=["Always use tables to display data"],
    db=db,
    add_history_to_context=True,
    markdown=True,
)

from agno.team import Team
from agno.models.openai import OpenAIChat
from agno.os import AgentOS

# Create Team with both agents
agent_team = Team(
    name="Agent Team (Web+Finance)",
    model=OpenAIChat(id="gpt-4o"),
    members=[web_agent, finance_agent],
    debug_mode=True,
    markdown=True,
)

# Create AgentOS for serving
agent_os = AgentOS(teams=[agent_team])
app = agent_os.get_app()

# Serve the application
if __name__ == "__main__":
    agent_os.serve(
        app="finance_agent_team:app", 
        reload=True
    )

Key Features

Real-Time Financial Data

YFinance Integration:

Current stock prices
Analyst recommendations
Company information and fundamentals
Latest company news
Historical data analysis

Data Presentation:

Always formatted in tables
Clear, readable format
Supports multiple tickers

Web Search Capabilities

DuckDuckGo Search:

General market research
News and sentiment analysis
Industry trends
Competitive intelligence
Economic indicators

Privacy-Focused:

No tracking
Anonymous searches
Reliable results

Persistent Storage

SQLite Database:

Stores agent interactions
Maintains conversation history
Context awareness across queries
Historical analysis

Benefits:

Faster follow-up queries
Consistent analysis
Learning from past interactions

Team Coordination

Intelligent Routing:

Automatically selects appropriate agent
Combines insights from multiple agents
Unified response format

Collaboration:

Agents share context
Complementary analysis
Comprehensive insights

Agent Coordination Patterns

Query Routing

The Team Agent intelligently routes queries based on the type of information needed:

# Routed to Finance Agent
"What is the current stock price of AAPL?"
"Show me analyst recommendations for Tesla"
"Get company information for Microsoft"

Agent Handoff

# Example flow for combined query
User: "Analyze TSLA stock and recent news"

1. Team Agent receives query
2. Routes to Finance Agent first
   - Gets stock price
   - Gets analyst recommendations
   - Gets company financials
3. Routes to Web Agent
   - Searches for recent news
   - Finds market sentiment
4. Team Agent combines insights
5. Returns comprehensive analysis

YFinance Tools

Available Functions

get_current_stock_price
get_analyst_recommendations
get_company_info
get_company_news

# Get current stock price
def get_current_stock_price(ticker: str) -> dict:
    """
    Get the current stock price for a ticker symbol.
    
    Args:
        ticker: Stock ticker symbol (e.g., 'AAPL', 'TSLA')
        
    Returns:
        dict: Current price, day high/low, volume, etc.
    """

Example:

Query: "What is AAPL stock price?"
Response: 
| Metric | Value |
|--------|-------|
| Price  | $178.32 |
| High   | $179.50 |
| Low    | $177.20 |
| Volume | 45.2M |

# Get analyst recommendations
def get_analyst_recommendations(ticker: str) -> dict:
    """
    Get analyst recommendations and ratings.
    
    Args:
        ticker: Stock ticker symbol
        
    Returns:
        dict: Buy/Sell/Hold recommendations, price targets
    """

Example:

Query: "Show analyst recommendations for TSLA"
Response:
| Rating | Count |
|--------|-------|
| Strong Buy | 12 |
| Buy | 8 |
| Hold | 5 |
| Sell | 2 |
| Target Price | $245 |

# Get company information
def get_company_info(ticker: str) -> dict:
    """
    Get detailed company information.
    
    Args:
        ticker: Stock ticker symbol
        
    Returns:
        dict: Company details, sector, market cap, etc.
    """

Example:

Query: "Get information about Microsoft"
Response:
| Field | Value |
|-------|-------|
| Name | Microsoft Corporation |
| Sector | Technology |
| Industry | Software |
| Market Cap | $2.8T |
| Employees | 221,000 |

# Get company news
def get_company_news(ticker: str) -> list:
    """
    Get recent news articles about the company.
    
    Args:
        ticker: Stock ticker symbol
        
    Returns:
        list: Recent news articles with links
    """

Example:

Query: "What's the latest news on NVDA?"
Response:
- NVIDIA Announces New AI Chip (2 hours ago)
- Q3 Earnings Beat Expectations (1 day ago)
- Partnership with Major Cloud Provider (2 days ago)

Installation

Clone Repository

git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
cd advanced_ai_agents/multi_agent_apps/agent_teams/ai_finance_agent_team

Install Dependencies

pip install -r requirements.txt

Required packages:

agno>=2.2.10
openai
yfinance
duckduckgo-search
sqlalchemy

Set OpenAI API Key

export OPENAI_API_KEY='your-api-key-here'

Get your API key from platform.openai.com

Run Application

python3 finance_agent_team.py

Open your browser to the URL shown in the console to access the playground interface.

Usage Examples

Stock Analysis

User: "Analyze Apple stock (AAPL)"

Finance Agent:
- Current Price: $178.32
- 52-Week Range: $124.17 - $198.23
- Market Cap: $2.8T
- P/E Ratio: 29.5
- Dividend Yield: 0.52%

Web Agent:
- Recent product launches
- Market sentiment: Positive
- Industry position: Market leader

Team Analysis:
AAPL shows strong fundamentals with consistent growth.
Recent iPhone launch driving positive sentiment.
Analyst consensus: Buy with $195 target price.

Industry Research

User: "Research the semiconductor industry and check NVDA"

Web Agent:
- Industry growing at 8.6% CAGR
- AI chip demand driving growth
- Supply chain improving
- Major players: NVIDIA, AMD, Intel

Finance Agent:
NVIDIA (NVDA):
- Price: $495.50
- Market Cap: $1.2T
- Forward P/E: 35.2
- Revenue Growth: 126% YoY

Team Analysis:
Semiconductor sector shows strong growth driven by AI.
NVDA is industry leader with commanding market share.
Stock trading at premium but justified by growth.

Comparative Analysis

User: "Compare Tesla and traditional automakers"

Finance Agent:
| Company | Price | P/E | Market Cap |
|---------|-------|-----|------------|
| TSLA | $242.50 | 75.3 | $770B |
| F | $12.40 | 6.2 | $50B |
| GM | $32.10 | 5.8 | $42B |

Web Agent:
- EV market growing rapidly
- Tesla leads in innovation
- Traditional automakers catching up
- Battery technology key differentiator

Team Analysis:
Tesla commands premium valuation due to tech leadership.
Traditional automakers cheaper but slower EV transition.
Market favors growth and innovation in auto sector.

Technical Architecture

Database Storage

from agno.db.sqlite import SqliteDb

# Persistent storage for agent interactions
db = SqliteDb(db_file="agents.db")

# Benefits:
# - Conversation history
# - Context across sessions
# - Historical analysis
# - Performance optimization

Context Management

# Agents maintain conversation context
add_history_to_context=True

# Enables:
# - Follow-up questions
# - Reference to previous queries
# - Consistent analysis
# - Better understanding

Markdown Output

# All agents output in markdown
markdown=True

# Provides:
# - Formatted tables
# - Clear structure
# - Easy reading
# - Professional presentation

Best Practices

Query Formulation

Be specific about what you need
Mention ticker symbols explicitly
Combine requests for comprehensive analysis
Ask for comparisons when relevant

Data Interpretation

Review both quantitative and qualitative data
Consider market context
Look at historical trends
Verify with multiple sources

Cost Management

Use specific queries to reduce API calls
Leverage conversation history
Cache frequently accessed data
Monitor OpenAI usage

Error Handling

Verify ticker symbols
Check for market hours
Handle data unavailability
Validate financial data

Important Notes:

Financial data is for informational purposes only
Not investment advice
Always verify critical information
Market data may have slight delays
Past performance doesn’t guarantee future results

Investment Agent

Advanced investment analysis

Deep Research Agent

Comprehensive research capabilities

Legal Agent Team

Document analysis with teams

Starter Examples

Advanced Examples

RAG Examples

Overview

Architecture

Multi-Agent Team Structure

Agent Roles

Web Agent

Finance Agent

Team Agent

Implementation

Key Features

Agent Coordination Patterns

Query Routing

Agent Handoff

YFinance Tools

Available Functions

Installation

Usage Examples

Technical Architecture

Database Storage

Context Management

Markdown Output

Best Practices

Query Formulation

Data Interpretation

Cost Management

Error Handling

Investment Agent

Deep Research Agent

Legal Agent Team

Starter Examples

Advanced Examples

RAG Examples

​Overview

​Architecture

​Multi-Agent Team Structure

​Agent Roles

Web Agent

Finance Agent

Team Agent

​Implementation

​Key Features

​Agent Coordination Patterns

​Query Routing

​Agent Handoff

​YFinance Tools

​Available Functions

​Installation

​Usage Examples

​Technical Architecture

​Database Storage

​Context Management

​Markdown Output

​Best Practices

Query Formulation

Data Interpretation

Cost Management

Error Handling

​Related Examples

Investment Agent

Deep Research Agent

Legal Agent Team

Overview

Architecture

Multi-Agent Team Structure

Agent Roles

Implementation

Key Features

Agent Coordination Patterns

Query Routing

Agent Handoff

YFinance Tools

Available Functions

Installation

Usage Examples

Technical Architecture

Database Storage

Context Management

Markdown Output

Best Practices

Related Examples