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Agent with Tools

Learn how to create powerful agents that can use external tools to interact with APIs, search the web, process data, and perform complex operations.

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Overview

Tools extend agent capabilities beyond language generation, allowing them to:
  • Search the web for real-time information
  • Execute code and scripts
  • Query databases
  • Make API calls
  • Process files and data
  • Perform calculations

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Basic Tool Integration

Here’s a simple example of creating a tool and using it with an agent: 
from swarms import Agent

def calculate_roi(investment: float, return_amount: float) -> str:
    """
    Calculate Return on Investment (ROI)
    
    Args:
        investment (float): Initial investment amount
        return_amount (float): Return amount received
    
    Returns:
        str: ROI percentage and analysis
    """
    roi = ((return_amount - investment) / investment) * 100
    return f"ROI: {roi:.2f}%. Investment: ${investment:,.2f}, Return: ${return_amount:,.2f}"

# Create agent with the tool
agent = Agent(
    agent_name="Financial-Analyst",
    system_prompt="You are a financial analyst. Use the calculate_roi tool to analyze investments.",
    model_name="gpt-4o-mini",
    max_loops=1,
    tools=[calculate_roi],  # Add tools here
)

# Agent will automatically use the tool when needed
response = agent.run(
    "What's the ROI if I invest $10,000 and get back $15,000?"
)
print(response)

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Real-World Example: Web Search Agent

Here’s a production-ready example using the Exa search API: 
from swarms import Agent
import os
import httpx
from loguru import logger
from swarms.utils.any_to_str import any_to_str

def exa_search(query: str) -> str:
    """
    Exa Web Search Tool
    
    Advanced web search using the Exa.ai API for research agents.
    Retrieves up-to-date information from documentation, articles, and more.
    
    Args:
        query (str): Natural language search query
        
    Returns:
        str: JSON-formatted search results with summaries
        
    Example:
        exa_search("Latest PyTorch 2.2.0 quantization APIs")
    """
    api_key = os.getenv("EXA_API_KEY")
    
    if not api_key:
        raise ValueError("EXA_API_KEY environment variable is not set")
    
    headers = {
        "x-api-key": api_key,
        "content-type": "application/json",
    }
    
    payload = {
        "query": query,
        "type": "auto",
        "numResults": 5,
        "contents": {
            "text": True,
            "summary": {
                "schema": {
                    "type": "object",
                    "required": ["answer"],
                    "properties": {
                        "answer": {
                            "type": "string",
                            "description": "Key insights from the search result",
                        }
                    },
                }
            },
        },
    }
    
    try:
        logger.info(f"[SEARCH] Executing Exa search for: {query[:50]}...")
        
        response = httpx.post(
            "https://api.exa.ai/search",
            json=payload,
            headers=headers,
            timeout=30,
        )
        
        response.raise_for_status()
        json_data = response.json()
        
        return any_to_str(json_data)
        
    except Exception as e:
        logger.error(f"Exa search failed: {e}")
        return f"Search failed: {str(e)}. Please try again."

# Initialize agent with search tool
agent = Agent(
    agent_name="Quantitative-Trading-Agent",
    agent_description="Advanced quantitative trading and algorithmic analysis agent",
    model_name="gpt-4.1",
    dynamic_temperature_enabled=True,
    max_loops=1,
    tools=[exa_search],
)

out = agent.run(
    "Create a report on the most undervalued and high potential energy stocks"
)
print(out)

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Multiple Tools Example

Agents can use multiple tools in a single workflow: 
from swarms import Agent
import httpx
import json

def search_stocks(query: str) -> str:
    """
    Search for stock information
    
    Args:
        query (str): Stock symbol or company name
        
    Returns:
        str: Stock information
    """
    # Implementation here
    return f"Stock information for {query}"

def analyze_financials(stock_symbol: str) -> str:
    """
    Analyze financial statements for a stock
    
    Args:
        stock_symbol (str): Stock ticker symbol
        
    Returns:
        str: Financial analysis
    """
    # Implementation here
    return f"Financial analysis for {stock_symbol}"

def calculate_metrics(revenue: float, expenses: float) -> str:
    """
    Calculate financial metrics
    
    Args:
        revenue (float): Total revenue
        expenses (float): Total expenses
        
    Returns:
        str: Calculated metrics including profit margin
    """
    profit = revenue - expenses
    margin = (profit / revenue) * 100 if revenue > 0 else 0
    return f"Profit: ${profit:,.2f}, Margin: {margin:.2f}%"

# Agent with multiple tools
multi_tool_agent = Agent(
    agent_name="Investment-Analyst",
    system_prompt="You are an investment analyst. Use the available tools to research and analyze stocks.",
    model_name="gpt-4o-mini",
    max_loops=3,
    tools=[search_stocks, analyze_financials, calculate_metrics],
)

result = multi_tool_agent.run(
    "Analyze Apple (AAPL) stock and provide investment recommendation"
)
print(result)

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Tool Types

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1. Python Functions

Simple Python functions with type hints and docstrings: 
def get_weather(city: str, units: str = "celsius") -> str:
    """
    Get current weather for a city
    
    Args:
        city (str): City name
        units (str): Temperature units (celsius or fahrenheit)
        
    Returns:
        str: Weather information
    """
    # API call here
    return f"Weather in {city}: 22°C, Sunny"

agent = Agent(
    model_name="gpt-4o-mini",
    tools=[get_weather],
)

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2. BaseTool Class

For more complex tools, use the BaseTool class: 
from swarms.tools.base_tool import BaseTool
from pydantic import Field

class WebSearchTool(BaseTool):
    """
    Web search tool for finding information online
    """
    name: str = "web_search"
    description: str = "Search the web for information"
    api_key: str = Field(..., description="API key for search service")
    
    def run(self, query: str) -> str:
        """
        Execute web search
        
        Args:
            query (str): Search query
            
        Returns:
            str: Search results
        """
        # Implementation
        return f"Search results for: {query}"

# Use the tool
search_tool = WebSearchTool(api_key=os.getenv("SEARCH_API_KEY"))

agent = Agent(
    model_name="gpt-4o-mini",
    tools=[search_tool],
)

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3. External Tool Libraries

Integrate tools from external libraries: 
# Pip install swarms-tools
from swarms_tools import exa_search, browser_use
from swarms import Agent

# Agent with pre-built tools
agent = Agent(
    agent_name="Research-Agent",
    model_name="gpt-4o-mini",
    tools=[exa_search, browser_use],
)

result = agent.run("Find the latest AI research papers from 2024")

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Advanced Tool Patterns

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Tool with Error Handling

def safe_api_call(endpoint: str, params: dict) -> str:
    """
    Make API call with error handling
    
    Args:
        endpoint (str): API endpoint URL
        params (dict): Request parameters
        
    Returns:
        str: API response or error message
    """
    try:
        response = httpx.get(endpoint, params=params, timeout=10)
        response.raise_for_status()
        return json.dumps(response.json())
    except httpx.HTTPError as e:
        return f"API call failed: {str(e)}"
    except Exception as e:
        return f"Unexpected error: {str(e)}"

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Stateful Tool

class DatabaseTool:
    """
    Tool that maintains connection state
    """
    def __init__(self, connection_string: str):
        self.connection_string = connection_string
        self.connection = None
    
    def query(self, sql: str) -> str:
        """
        Execute SQL query
        
        Args:
            sql (str): SQL query to execute
            
        Returns:
            str: Query results
        """
        if not self.connection:
            self.connection = self._connect()
        
        # Execute query
        return "Query results"
    
    def _connect(self):
        # Establish connection
        return None

# Use stateful tool
db_tool = DatabaseTool("postgresql://localhost/mydb")

agent = Agent(
    model_name="gpt-4o-mini",
    tools=[db_tool.query],
)

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Best Practices

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1. Clear Tool Documentation

Always provide detailed docstrings: 
def analyze_sentiment(text: str) -> str:
    """
    Analyze sentiment of text
    
    This tool uses natural language processing to determine whether
    the sentiment of the input text is positive, negative, or neutral.
    
    Args:
        text (str): The text to analyze. Should be at least 10 characters.
        
    Returns:
        str: Sentiment analysis result with score and classification
        
    Example:
        >>> analyze_sentiment("I love this product!")
        "Sentiment: Positive (Score: 0.92)"
    """
    # Implementation
    pass

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2. Type Hints

Use proper type hints for better tool discovery: 
from typing import Dict, List, Optional

def search_database(
    query: str,
    filters: Optional[Dict[str, str]] = None,
    limit: int = 10
) -> List[Dict[str, Any]]:
    """
    Search database with filters
    """
    pass

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3. Error Handling

Handle errors gracefully: 
def robust_tool(input_data: str) -> str:
    """
    Tool with robust error handling
    """
    try:
        # Tool logic
        result = process(input_data)
        return result
    except ValueError as e:
        return f"Invalid input: {e}"
    except Exception as e:
        logger.error(f"Tool error: {e}")
        return "Tool execution failed. Please try again."

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4. Logging

Add logging for debugging: 
from loguru import logger

def logged_tool(param: str) -> str:
    """
    Tool with logging
    """
    logger.info(f"Tool called with param: {param}")
    
    try:
        result = execute(param)
        logger.success(f"Tool completed successfully")
        return result
    except Exception as e:
        logger.error(f"Tool failed: {e}")
        raise

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Tool Configuration

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Dynamic Tool Loading

from swarms import Agent

# Load tools dynamically
tools = []

if os.getenv("ENABLE_SEARCH"):
    tools.append(exa_search)

if os.getenv("ENABLE_DATABASE"):
    tools.append(db_query)

agent = Agent(
    model_name="gpt-4o-mini",
    tools=tools,
)

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Tool Registry

from swarms.tools.tool_registry import ToolRegistry

# Create tool registry
registry = ToolRegistry()

# Register tools
registry.register("search", exa_search)
registry.register("calculate", calculate_metrics)

# Use registered tools
agent = Agent(
    model_name="gpt-4o-mini",
    tools=registry.get_all_tools(),
)

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Output Examples

When an agent uses tools, you’ll see output like: 
🤖 Agent: Financial-Analyst
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

🔧 Using tool: calculate_roi
   Arguments: {"investment": 10000, "return_amount": 15000}

📊 Tool Result:
   ROI: 50.00%. Investment: $10,000.00, Return: $15,000.00

💡 Analysis:
   Based on the ROI calculation, this investment returned 50%, which is
   an excellent return. The investment of $10,000 generated a profit of
   $5,000, indicating strong performance.

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Next Steps

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