agentspro 0.1.0

The AI Workflow Cross-Platform Engine

The AI Agent Decorator SDK

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Table of Contents

• Why Agentspro?
• Quick Start
• Core Features
• Examples
• Contributing
• License

Why Agentspro?

Agentspro is a powerful Python SDK that enables you to seamlessly connect your local Python functions to the Agentspro platform through elegant decorators. With just a simple @agent(name=...) decorator, you can transform any Python function into a cloud-accessible AI agent.

• Zero Configuration: Simply add decorators to your functions
• Seamless Integration: Direct connection to Agentspro platform APIs
• Type Safety: Complete type validation based on Pydantic
• Real-time Sync: Automatic function registration and updates

Quick Start

Prerequisites

• Python 3.11+
• Agentspro account (sign up at agentspro.com)

Installation

pip install agentspro

Basic Usage

from agentspro import agent, init_agentspro

# Initialize with your API credentials
init_agentspro(
    api_key="your_api_key",
    api_secret="your_api_secret"
)

# Transform any function into an AI agent
@agent(name="weather_checker")
def get_weather(city: str) -> str:
    """Get weather information for a specific city."""
    # Your function logic here
    return f"The weather in {city} is sunny and 25°C"

@agent(name="calculator")
def calculate(expression: str) -> float:
    """Calculate mathematical expressions."""
    return eval(expression)  # Note: Use safe_eval in production

Core Features

Decorator-Based Agent Registration

from agentspro import agent

@agent(
    name="data_processor",
    description="Process and analyze data",
    tags=["data", "analysis"],
    version="1.0.0"
)
def process_data(data: list, operation: str) -> dict:
    """Process data with specified operation."""
    # Your processing logic
    return {"result": "processed", "count": len(data)}

Automatic Type Validation

from typing import List, Dict
from agentspro import agent

@agent(name="user_manager")
def create_user(
    name: str, 
    age: int, 
    skills: List[str],
    metadata: Dict[str, str] = None
) -> Dict[str, any]:
    """Create a new user with validation."""
    return {
        "id": "user_123",
        "name": name,
        "age": age,
        "skills": skills,
        "metadata": metadata or {}
    }

Configuration Options

from agentspro import agent, AgentConfig

@agent(
    name="advanced_agent",
    config=AgentConfig(
        timeout=30,
        retry_attempts=3,
        cache_enabled=True,
        rate_limit=100
    )
)
def advanced_function(param: str) -> str:
    """An advanced agent with custom configuration."""
    return f"Processed: {param}"

Examples

Data Processing Agent

from agentspro import agent
import pandas as pd

@agent(name="csv_analyzer")
def analyze_csv(file_path: str) -> dict:
    """Analyze CSV file and return statistics."""
    df = pd.read_csv(file_path)
    return {
        "rows": len(df),
        "columns": len(df.columns),
        "summary": df.describe().to_dict()
    }

Web Scraping Agent

from agentspro import agent
import requests
from bs4 import BeautifulSoup

@agent(name="web_scraper")
def scrape_title(url: str) -> str:
    """Extract title from a webpage."""
    response = requests.get(url)
    soup = BeautifulSoup(response.content, 'html.parser')
    return soup.title.string if soup.title else "No title found"

AI Integration Agent

from agentspro import agent
from openai import OpenAI

client = OpenAI()

@agent(name="text_summarizer")
def summarize_text(text: str, max_length: int = 100) -> str:
    """Summarize text using AI."""
    response = client.chat.completions.create(
        model="gpt-3.5-turbo",
        messages=[
            {"role": "system", "content": "Summarize the following text concisely."},
            {"role": "user", "content": text}
        ],
        max_tokens=max_length
    )
    return response.choices[0].message.content

Platform Integration

Once you've decorated your functions with @agent, they automatically become available on the Agentspro platform where you can:

• Monitor Usage: Track function calls and performance
• Manage Versions: Deploy and rollback different versions
• Configure Access: Set permissions and rate limits
• View Analytics: Analyze usage patterns and optimization opportunities

Contributing

We welcome community contributions! Please check the contribution guidelines for detailed processes.

Development Workflow

1. Fork this project
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Create a Pull Request

Contribution Types

• Bug fixes
• New feature development
• Documentation improvements
• Test cases
• Platform integrations

License

This project is licensed under the MIT License - see the LICENSE file for details.