Finance Agent

pip install praisonaiagents praisonai yfinance
export OPENAI_API_KEY="your-key"

from praisonaiagents import Agent
from praisonaiagents import get_stock_price, get_stock_info, get_historical_data

agent = Agent(
    name="FinanceAnalyst",
    instructions="You are a financial analyst. Analyze stocks and provide insights.",
    tools=[get_stock_price, get_stock_info, get_historical_data]
)

result = agent.start("Analyze Apple (AAPL) stock - current price and 6-month trend")
print(result)

praisonai "Analyze Tesla stock performance" --tools yfinance

framework: praisonai
topic: Stock Analysis
roles:
  finance_analyst:
    role: Financial Analyst
    goal: Analyze stocks and provide investment insights
    backstory: You are an expert financial analyst
    tools:
      - get_stock_price
      - get_stock_info
      - get_historical_data
    tasks:
      analyze_stock:
        description: Analyze Apple (AAPL) stock - current price and 6-month trend
        expected_output: A comprehensive stock analysis

praisonai agents.yaml

from praisonaiagents import Agent
from praisonaiagents import get_stock_price, get_stock_info, get_historical_data

agent = Agent(
    name="FinanceAnalyst",
    instructions="You are a financial analyst.",
    tools=[get_stock_price, get_stock_info, get_historical_data]
)

agent.launch(port=8080)

curl -X POST http://localhost:8080/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Compare AAPL and GOOGL stocks"}'

pip install praisonaiagents praisonai yfinance pydantic
export OPENAI_API_KEY="your-key"

from praisonaiagents import Agent, Task, AgentTeam, Session
from praisonaiagents import get_stock_price, get_stock_info, get_historical_data
from pydantic import BaseModel

# Structured output schema
class StockAnalysis(BaseModel):
    symbol: str
    current_price: float
    recommendation: str
    key_metrics: list[str]
    risk_factors: list[str]

# Create session for portfolio tracking
session = Session(session_id="portfolio-001", user_id="user-1")

# Agent with memory and tools
agent = Agent(
    name="FinanceAnalyst",
    instructions="Analyze stocks and return structured investment reports.",
    tools=[get_stock_price, get_stock_info, get_historical_data],
    memory=True
)

# Task with structured output
task = Task(
    description="Analyze Apple (AAPL) stock with buy/sell recommendation",
    expected_output="Structured stock analysis",
    agent=agent,
    output_pydantic=StockAnalysis
)

# Run with SQLite persistence
agents = AgentTeam(
    agents=[agent],
    tasks=[task],
    memory=True
)

result = agents.start()
print(result)

# Resume later for portfolio review
session2 = Session(session_id="portfolio-001", user_id="user-1")
history = session2.search_memory("AAPL")

praisonai "Analyze AAPL stock" --tools yfinance --memory --verbose

framework: praisonai
topic: Stock Analysis
memory: true
memory_config:
  provider: sqlite
  db_path: finance.db
roles:
  finance_analyst:
    role: Financial Analyst
    goal: Analyze stocks with structured output
    backstory: You are an expert financial analyst
    tools:
      - get_stock_price
      - get_stock_info
      - get_historical_data
    memory: true
    tasks:
      analyze_stock:
        description: Analyze Apple (AAPL) stock with buy/sell recommendation
        expected_output: Structured stock analysis
        output_json:
          symbol: string
          current_price: number
          recommendation: string
          key_metrics: array
          risk_factors: array

praisonai agents.yaml --verbose

from praisonaiagents import Agent
from praisonaiagents import get_stock_price, get_stock_info, get_historical_data

agent = Agent(
    name="FinanceAnalyst",
    instructions="Analyze stocks and return structured reports.",
    tools=[get_stock_price, get_stock_info, get_historical_data],
    memory=True
)

agent.launch(port=8080)

curl -X POST http://localhost:8080/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Analyze TSLA", "session_id": "portfolio-001"}'

praisonai "test finance" --tools yfinance --verbose

rm -f finance.db