swarms-tools 0.3.1

Paper - Pytorch

Swarms Tools

Overview

Swarms Tools provides a vast array of pre-built tools for your agents, MCP servers, and multi-agent systems. It is built from the ground up for bleeding-edge performance, leveraging packages like HTTPX, orjson, and other production-grade libraries. Our goal with this package is to make it easier for agent creators to integrate tools into their agents.

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

Tools Examples

HTX Trading Data

Retrieve historical trading data and market analysis from HTX platform.

from swarms_tools import fetch_htx_data

response = fetch_htx_data("swarms")
print(response)

Stock News

Access real-time stock news and market updates for strategic decision-making.

from swarms_tools import fetch_stock_news

news = fetch_stock_news("AAPL")
print(news)

Yahoo Finance API

Comprehensive stock data including pricing, trends, and historical analysis.

from swarms_tools import yahoo_finance_api

stock_data = yahoo_finance_api("AAPL")
print(stock_data)

CoinGecko API

Real-time cryptocurrency market data and pricing information.

from swarms_tools import coin_gecko_coin_api

crypto_data = coin_gecko_coin_api("bitcoin")
print(crypto_data)

DeFi Protocol Analytics

DeFi ecosystem data including protocol TVL and token pricing.

from swarms_tools import get_protocol_tvl

protocol_tvl = await get_protocol_tvl("uniswap-v3")
print(protocol_tvl)

Web Scraper

Enterprise-grade web scraping for content extraction and data mining.

from swarms_tools.search.web_scraper import scrape_single_url_sync

content = scrape_single_url_sync("https://example.com")
print(content.title, content.text)

Telegram API

Automated messaging and communication through Telegram platform.

from swarms_tools import telegram_dm_or_tag_api

telegram_dm_or_tag_api("Critical business update from Swarms Corporation.")

Twitter Tool

Comprehensive Twitter automation for enterprise social media management.

from swarms_tools.social_media.twitter_tool import TwitterTool

twitter_plugin = TwitterTool(options)
post_tweet = twitter_plugin.get_function("post_tweet")
post_tweet("Enterprise update from Swarms Corp")

Dex Screener

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,
)

fetch_dex_screener_profiles()
fetch_latest_token_boosts()

GitHub Tool

GitHub repository management and automation capabilities for development workflows.

from swarms_tools.devs.github import GitHubTool

github_tool = GitHubTool()
repo_info = github_tool.get_repository("swarms-corp/swarms-tools")

Code Executor

Secure code execution environment for development and automation workflows.

from swarms_tools.devs.code_executor import CodeExecutor

executor = CodeExecutor()
result = executor.execute("print('Hello from Swarms Tools')")

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)

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