swarms-tools 0.2.1

Paper - Pytorch

Swarms Tools

Overview

Swarms Tools is an enterprise-grade Python package designed for seamless integration of cutting-edge APIs into multi-agent orchestration systems. Built for organizations requiring robust, scalable, and maintainable automation solutions, this toolkit provides standardized interfaces for financial data, social media integration, IoT connectivity, and more.

Key Features

Feature	Description
Unified API Integration	Production-ready Python functions for enterprise applications
Enterprise-Grade Architecture	Comprehensive type hints, structured outputs, and enterprise documentation standards
Multi-Agent System Compatibility	Optimized for seamless integration into Swarms' distributed agent orchestration platforms
Extensible Framework	Standardized schema for rapid tool development and deployment
Enterprise Security	Secure API key management and compliance-ready implementation patterns
Bleeding Edge Performance	Utilizes high-performance libraries such as httpx for async HTTP and orjson for ultra-fast serialization

Installation

pip3 install -U swarms-tools

Project Structure

swarms-tools/
├── swarms_tools/
│   ├── finance/
│   │   ├── htx_tool.py
│   │   ├── eodh_api.py
│   │   ├── coingecko_tool.py
│   │   └── defillama_mcp_tools.py
│   ├── social_media/
│   │   └── telegram_tool.py
│   ├── utilities/
│   │   └── logging.py
├── tests/
│   ├── test_financial_data.py
│   └── test_social_media.py
└── README.md

Enterprise Use Cases

Financial Data Management

Our comprehensive financial tools enable organizations to streamline operations and gain actionable insights:

Tool Name	Function	Description
fetch_stock_news	fetch_stock_news	Retrieves real-time stock news and market updates
fetch_htx_data	fetch_htx_data	Accesses financial data from HTX trading platform
yahoo_finance_api	yahoo_finance_api	Comprehensive stock data including pricing and trend analysis
coin_gecko_coin_api	coin_gecko_coin_api	Cryptocurrency market data and pricing information
helius_api_tool	helius_api_tool	Blockchain account, transaction, and token data via Helius API
okx_api_tool	okx_api_tool	Detailed cryptocurrency data from OKX exchange
defillama_mcp_tools	get_protocol_tvl, get_chain_tvl, get_token_prices, make_request	DeFi ecosystem data including protocol TVL and token pricing

Financial Data Retrieval Examples

Historical Data Analysis

from swarms_tools import fetch_htx_data

# Retrieve historical trading data for analysis
response = fetch_htx_data("swarms")
print(response)

Market News Intelligence

from swarms_tools import fetch_stock_news

# Access latest market news for strategic decision-making
news = fetch_stock_news("AAPL")
print(news)

Cryptocurrency Market Analysis

from swarms_tools import coin_gecko_coin_api

# Real-time cryptocurrency market data
crypto_data = coin_gecko_coin_api("bitcoin")
print(crypto_data)

DeFi Protocol Analytics

from swarms_tools import get_protocol_tvl

# Protocol TVL data for investment analysis
protocol_tvl = await get_protocol_tvl("uniswap-v3")
print(protocol_tvl)

Social Media Automation

Streamline corporate communication and engagement strategies:

Telegram Integration Example

from swarms_tools import telegram_dm_or_tag_api

def send_corporate_alert(response: str):
    telegram_dm_or_tag_api(response)

# Automated corporate communication
send_corporate_alert("Critical business update from Swarms Corporation.")

Dex Screener Integration

Enterprise-grade tool for accessing decentralized exchange data across multiple blockchain networks:

from swarms_tools.finance.dex_screener import (
    fetch_latest_token_boosts,
    fetch_dex_screener_profiles,
)

# Retrieve token boost data
fetch_dex_screener_profiles()
fetch_latest_token_boosts()

Tool Orchestration Framework

The tool chainer enables sequential or parallel execution of multiple tools for complex workflow automation:

from loguru import logger
from swarms_tools.structs import tool_chainer

if __name__ == "__main__":
    logger.add("tool_chainer.log", rotation="500 MB", level="INFO")

    # Define enterprise tools
    def data_analysis_tool():
        return "Data Analysis Complete"

    def reporting_tool():
        return "Report Generated"

    tools = [data_analysis_tool, reporting_tool]

    # Parallel execution for performance optimization
    parallel_results = tool_chainer(tools, parallel=True)
    print("Parallel Results:", parallel_results)

    # Sequential execution for dependency management
    sequential_results = tool_chainer(tools, parallel=False)
    print("Sequential Results:", sequential_results)

Social Media Management

Twitter API Integration

Comprehensive Twitter automation for enterprise social media management:

import os
from time import time
from swarm_models import OpenAIChat
from swarms import Agent
from dotenv import load_dotenv
from swarms_tools.social_media.twitter_tool import TwitterTool

load_dotenv()

# Initialize enterprise AI model
model_name = "gpt-4o"
model = OpenAIChat(
    model_name=model_name,
    max_tokens=3000,
    openai_api_key=os.getenv("OPENAI_API_KEY"),
)

# Configure Twitter integration
options = {
    "id": "29998836",
    "name": "mcsswarm",
    "description": "Enterprise Twitter automation platform",
    "credentials": {
        "apiKey": os.getenv("TWITTER_API_KEY"),
        "apiSecretKey": os.getenv("TWITTER_API_SECRET_KEY"),
        "accessToken": os.getenv("TWITTER_ACCESS_TOKEN"),
        "accessTokenSecret": os.getenv("TWITTER_ACCESS_TOKEN_SECRET"),
    },
}

twitter_plugin = TwitterTool(options)
post_tweet = twitter_plugin.get_function("post_tweet")

# Automated content generation and posting
def generate_corporate_content():
    content_prompt = "Generate professional corporate content for social media engagement"
    tweet_text = model.run(content_prompt)
    
    try:
        post_tweet(tweet_text)
        print(f"Content posted successfully: {tweet_text}")
    except Exception as e:
        print(f"Error posting content: {e}")

Enterprise Development Standards

Every tool in Swarms Tools adheres to enterprise-grade development standards:

Development Schema

1. Modular Architecture: Encapsulate API logic into reusable, maintainable functions
2. Type Safety: Comprehensive Python type hints for input validation and code clarity
3. Documentation: Detailed docstrings with parameter specifications and usage examples
4. Output Standardization: Consistent return formats for seamless system integration
5. Security Compliance: Secure API key management using environment variables

Schema Template

def enterprise_data_function(parameter: str, date_range: str) -> str:
    """
    Enterprise-grade data retrieval function.

    Args:
        parameter (str): Business parameter for data retrieval
        date_range (str): Timeframe specification (e.g., '1d', '1m', '1y')

    Returns:
        str: Structured data response for enterprise systems
    """
    pass

Documentation and Support

Comprehensive enterprise documentation is available at docs.swarms.world, providing detailed API references, implementation guides, and best practices for enterprise deployment.

Community and Support

Join our enterprise community for technical support, platform updates, and exclusive access to advanced agent engineering insights:

Platform	Description	Link
Discord	Live technical support and community	Join Discord
Twitter	Platform updates and announcements	@swarms_corp
YouTube	Technical tutorials and demonstrations	Swarms Channel
Documentation	Official technical documentation	docs.swarms.world
Blog	Technical articles and platform insights	Medium
LinkedIn	Professional network and corporate updates	The Swarm Corporation
Events	Enterprise community events and workshops	Sign up here
Onboarding	Enterprise onboarding with platform experts	Book Session

Contributing

We welcome enterprise contributions and partnerships. To contribute:

1. Fork the Repository: Begin by forking the main repository
2. Create Feature Branch: Use descriptive naming: feature/enterprise-tool-name
3. Implement Standards: Follow enterprise development guidelines
4. Submit Pull Request: Open pull request for technical review

License

This project is licensed under the MIT License. See the LICENSE file for complete terms and conditions.

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"The future belongs to those who dare to automate it."
— The Swarms Corporation