pip install upsonic

from upsonic.tasks.tasks import Task
from upsonic.agent.agent import Agent

# Create a task
task = Task(
    description="Research latest news in AI and summarize the key points",
    tools=[]  # We'll add tools next
)

# Create an agent
agent = Agent(
    model="openai/gpt-4o",
    name="Research Assistant"
)

# Execute the task
result = agent.print_do(task)

from upsonic.tools.builtin_tools import WebSearchTool, CodeExecutionTool

# Web search (works with Anthropic, OpenAI, Google, Groq)
web_search = WebSearchTool(
    search_context_size='high',
    max_uses=5
)

# Code execution (works with Anthropic, OpenAI, Google)
code_execution = CodeExecutionTool()

task = Task(
    description="Search for Python best practices and write a sample function",
    tools=[web_search, code_execution]
)

from upsonic.tools.common_tools import tavily_search_tool, duckduckgo_search_tool

# Tavily search (requires API key)
tavily_search = tavily_search_tool(api_key="your-tavily-key")

# DuckDuckGo search (free)
ddg_search = duckduckgo_search_tool()

task = Task(
    description="Get current stock price for AAPL and search for recent news",
    tools=[tavily_search, ddg_search]
)

from upsonic.tools import tool

@tool
def calculate_tip(bill_amount: float, tip_percentage: float = 15.0) -> str:
    """Calculate tip amount and total bill.
    
    Args:
        bill_amount: The total bill amount
        tip_percentage: Tip percentage (default 15%)
    
    Returns:
        Formatted string with tip and total
    """
    tip = bill_amount * (tip_percentage / 100)
    total = bill_amount + tip
    return f"Tip: ${tip:.2f}, Total: ${total:.2f}"

@tool(requires_confirmation=True, cache_results=True)
def send_email(to: str, subject: str, body: str) -> str:
    """Send an email (requires confirmation).
    
    Args:
        to: Recipient email address
        subject: Email subject
        body: Email body content
    
    Returns:
        Confirmation message
    """
    # Your email sending logic here
    return f"Email sent to {to}"

task = Task(
    description="Calculate tip for a $50 bill and send a thank you email",
    tools=[calculate_tip, send_email]
)

agent = Agent(
    model="openai/gpt-4o",
    name="Advanced Assistant",
    
    # Agent personality
    role="Senior Software Engineer",
    goal="Help developers write better code",
    instructions="Always provide code examples and best practices",
    
    # Advanced features
    enable_thinking_tool=True,      # Multi-step planning
    enable_reasoning_tool=True,     # Self-reflection
    tool_call_limit=10,            # Max tool calls per task
    show_tool_calls=True,          # Display tool execution
    
    # Memory and context
    memory=memory_instance,        # Conversation history
    compression_strategy="simple", # Context compression
    
    # Safety and reliability
    user_policy=safety_policy,     # Input validation
    agent_policy=output_policy,    # Output filtering
    retry=3,                       # Retry attempts
)

@tool(
    requires_user_input=True,      # Prompt for additional input
    user_input_fields=['api_key'], # Fields to collect
    show_result=True,              # Display result to user
    stop_after_tool_call=True,     # Stop agent after this tool
    sequential=True,               # No parallel execution
    cache_results=True,            # Cache results
    cache_ttl=3600,               # Cache for 1 hour
    max_retries=3,                # Retry on failure
    timeout=30.0                  # 30 second timeout
)
def advanced_tool(param: str) -> str:
    """An advanced tool with full configuration."""
    return f"Processed: {param}"

# Connect to any MCP server
class FetchMCP:
    command = "uvx"
    args = ["mcp-server-fetch"]

class GitHubMCP:
    url = "https://github.com/modelcontextprotocol/servers"
    # Additional MCP configuration

task = Task(
    description="Fetch data from GitHub and process it",
    tools=[FetchMCP, GitHubMCP]
)

from upsonic.tasks.tasks import Task
from upsonic.agent.agent import Agent
from upsonic.tools import tool
from upsonic.tools.builtin_tools import WebSearchTool
from upsonic.tools.common_tools import YFinanceTools


# Custom tool
@tool(cache_results=True)
def analyze_sentiment(text: str) -> str:
    """Analyze sentiment of given text."""
    positive_words = ['good', 'great', 'excellent', 'amazing']
    negative_words = ['bad', 'terrible', 'awful', 'horrible']
    
    text_lower = text.lower()
    positive_count = sum(1 for word in positive_words if word in text_lower)
    negative_count = sum(1 for word in negative_words if word in text_lower)
    
    if positive_count > negative_count:
        return "Positive sentiment"
    elif negative_count > positive_count:
        return "Negative sentiment"
    else:
        return "Neutral sentiment"

# Create tools
web_search = WebSearchTool()
finance_tools = YFinanceTools(stock_price=True, company_news=True)
finance_tools.enable_all_tools()

# Create task
task = Task(
    description="Get current AAPL stock price, search for recent news, and analyze sentiment",
    tools=[finance_tools, web_search, analyze_sentiment]
)

# Create agent
agent = Agent(
    model="openai/gpt-4o",
    name="Financial Analyst",
    role="Senior Financial Analyst",
    enable_thinking_tool=True,
    tool_call_limit=8
)

# Execute
result = agent.print_do(task)