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.
| 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 |
pip3 install -U swarms-toolsswarms-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
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 |
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)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.")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()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)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}")Every tool in Swarms Tools adheres to enterprise-grade development standards:
- Modular Architecture: Encapsulate API logic into reusable, maintainable functions
- Type Safety: Comprehensive Python type hints for input validation and code clarity
- Documentation: Detailed docstrings with parameter specifications and usage examples
- Output Standardization: Consistent return formats for seamless system integration
- Security Compliance: Secure API key management using environment variables
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
"""
passComprehensive enterprise documentation is available at docs.swarms.world, providing detailed API references, implementation guides, and best practices for enterprise deployment.
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 |
| 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 |
| 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 |
We welcome enterprise contributions and partnerships. To contribute:
- Fork the Repository: Begin by forking the main repository
- Create Feature Branch: Use descriptive naming:
feature/enterprise-tool-name - Implement Standards: Follow enterprise development guidelines
- Submit Pull Request: Open pull request for technical review
This project is licensed under the MIT License. See the LICENSE file for complete terms and conditions.
"The future belongs to those who dare to automate it."
— The Swarms Corporation