A workflow demonstrating how to create and use class-based tools that can be integrated with AI agents to extend their capabilities with custom functionality.

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Quick Start

1

Install Package

First, install the Praison Labs Agents package:

pip install praisonaiagents
2

Set API Key

Set your OpenAI API key and EXA API key as environment variables in your terminal: 

export OPENAI_API_KEY=your_api_key_here
export EXA_API_KEY=your_exa_api_key_here
3

Create a file

Create a new file app.py with the basic setup: 

from praisonaiagents import Agent, Task, Praison LabsAgents
import os
import requests
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Field

class EXASearchTool(BaseModel):
    """Wrapper for EXA Search API."""
    search_url: str = "https://api.exa.ai/search"
    headers: Dict = {
        "accept": "application/json",
        "content-type": "application/json",
    }
    max_results: Optional[int] = None

    def run(self, query: str) -> str:
        """Run query through EXA and return concatenated results."""
        payload = {
            "query": query,
            "type": "magic",
        }

        headers = self.headers.copy()
        headers["x-api-key"] = os.environ['EXA_API_KEY']

        response = requests.post(self.search_url, json=payload, headers=headers)
        results = response.json()
        
        if 'results' in results:
            return self._parse_results(results['results'])
        return ""

    def results(self, query: str, max_results: Optional[int] = None) -> List[Dict[str, Any]]:
        """Run query through EXA and return metadata."""
        payload = {
            "query": query,
            "type": "magic",
        }

        headers = self.headers.copy()
        headers["x-api-key"] = os.environ['EXA_API_KEY']

        response = requests.post(self.search_url, json=payload, headers=headers)
        results = response.json()
        
        if 'results' in results:
            return results['results'][:max_results] if max_results else results['results']
        return []

    def _parse_results(self, results: List[Dict[str, Any]]) -> str:
        """Parse results into a readable string format."""
        strings = []
        for result in results:
            try:
                strings.append('\n'.join([
                    f"Title: {result['title']}",
                    f"Score: {result['score']}",
                    f"Url: {result['url']}",
                    f"ID: {result['id']}",
                    "---"
                ]))
            except KeyError:
                continue

        content = '\n'.join(strings)
        return f"\nSearch results: {content}\n"

# Create an agent with the tool
agent = Agent(
    name="SearchAgent",
    role="Research Assistant",
    goal="Search for information about 'AI Agents Framework'",
    backstory="I am an AI assistant that can search GitHub.",
    tools=[EXASearchTool],
    self_reflect=False
)

# Create task to demonstrate the tool
task = Task(
    name="search_task",
    description="Search for information about 'AI Agents Framework'",
    expected_output="Information about AI Agents Framework",
    agent=agent
)

# Create and start the workflow
agents = Praison LabsAgents(
    agents=[agent],
    tasks=[task],
    verbose=True
)

agents.start()
4

Start Agents

Type this in your terminal to run your agents:

python app.py

Requirements

  • Python 3.10 or higher
  • OpenAI API key. Generate OpenAI API key here
  • EXA API key for search functionality
  • Basic understanding of Python and Pydantic

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Understanding Tools as Class

What are Class-based Tools?

Class-based tools enable:

  • Custom functionality encapsulation
  • Reusable tool components
  • Type-safe tool interfaces
  • Complex API integrations

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Features

Pydantic Integration

Built-in validation and type safety with Pydantic models.

API Wrapping

Easily wrap external APIs as agent tools.

Method Flexibility

Support for multiple methods within a single tool.

Type Hints

Strong typing for better code reliability.

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Configuration Options 

# Create a custom tool class
class CustomTool(BaseModel):
    """Custom tool with configuration options."""
    api_url: str = Field(default="https://api.example.com")
    headers: Dict[str, str] = Field(default_factory=dict)
    max_retries: int = Field(default=3)

    def run(self, input_data: str) -> str:
        """Main execution method."""
        # Tool implementation
        return "Result"

    def configure(self, **kwargs):
        """Update tool configuration."""
        for key, value in kwargs.items():
            if hasattr(self, key):
                setattr(self, key, value)

# Use the tool with an agent
agent = Agent(
    name="CustomAgent",
    role="Tool User",
    goal="Use custom tool functionality",
    tools=[CustomTool],
    verbose=True
)

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Troubleshooting

Tool Issues

If tool execution fails:

  • Check API credentials
  • Verify network connectivity
  • Enable verbose logging

Type Errors

If type validation fails:

  • Review input types
  • Check Pydantic model
  • Verify method signatures

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Next Steps

Function Tools

Learn about function-based tools

API Tools

Explore API integration tools

For optimal results, ensure your tool classes are well-documented and follow Pydantic best practices for model definition.