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.
| 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
Retrieve historical trading data and market analysis from HTX platform.
from swarms_tools import fetch_htx_data
response = fetch_htx_data("swarms")
print(response)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)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)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 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)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)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.")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")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 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")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')")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