Kye Gomez
2 weeks ago
parent
4979e6947a
commit
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# Swarms Tools Example with HTX + CoinGecko
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- `pip3 install swarms swarms-tools`
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- Add `OPENAI_API_KEY` to your `.env` file
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```python
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from swarms import Agent
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from swarms.prompts.finance_agent_sys_prompt import (
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FINANCIAL_AGENT_SYS_PROMPT,
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)
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from swarms_tools import (
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coin_gecko_coin_api,
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fetch_htx_data,
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)
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# Initialize the agent
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agent = Agent(
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agent_name="Financial-Analysis-Agent",
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agent_description="Personal finance advisor agent",
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system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
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max_loops=1,
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model_name="gpt-4o",
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dynamic_temperature_enabled=True,
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user_name="swarms_corp",
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return_step_meta=False,
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output_type="str", # "json", "dict", "csv" OR "string" "yaml" and
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auto_generate_prompt=False, # Auto generate prompt for the agent based on name, description, and system prompt, task
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max_tokens=4000, # max output tokens
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saved_state_path="agent_00.json",
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interactive=False,
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)
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agent.run(
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f"Analyze the $swarms token on HTX with data: {fetch_htx_data('swarms')}. Additionally, consider the following CoinGecko data: {coin_gecko_coin_api('swarms')}"
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)
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```
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# Swarms Tools
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Welcome to **Swarms Tools**, the ultimate package for integrating **cutting-edge APIs** into Python functions with seamless multi-agent system compatibility. Designed for enterprises at the forefront of innovation, **Swarms Tools** is your key to simplifying complexity and unlocking operational excellence.
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---
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## 🚀 Features
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- **Unified API Integration**: Ready-to-use Python functions for financial data, social media, IoT, and more.
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- **Enterprise-Grade Design**: Comprehensive type hints, structured outputs, and robust documentation.
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- **Agent-Ready Framework**: Optimized for seamless integration into Swarms' multi-agent orchestration systems.
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- **Expandable Architecture**: Easily extend functionality with a standardized schema for new tools.
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---
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## 🔧 Installation
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```bash
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pip3 install -U swarms-tools
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```
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---
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## 📂 Directory Structure
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```plaintext
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swarms-tools/
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├── swarms_tools/
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│ ├── finance/
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│ │ ├── htx_tool.py
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│ │ ├── eodh_api.py
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│ │ └── coingecko_tool.py
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│ ├── social_media/
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│ │ ├── telegram_tool.py
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│ ├── utilities/
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│ │ └── logging.py
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├── tests/
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│ ├── test_financial_data.py
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│ └── test_social_media.py
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└── README.md
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```
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---
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## 💼 Use Cases
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## Finance
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Explore our diverse range of financial tools, designed to streamline your operations. If you need a tool not listed, feel free to submit an issue or accelerate integration by contributing a pull request with your tool of choice.
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| **Tool Name** | **Function** | **Description** |
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|---------------------------|--------------------------|---------------------------------------------------------------------------------|
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| `fetch_stock_news` | `fetch_stock_news` | Fetches the latest stock news and updates. |
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| `fetch_htx_data` | `fetch_htx_data` | Retrieves financial data from the HTX platform. |
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| `yahoo_finance_api` | `yahoo_finance_api` | Fetches comprehensive stock data from Yahoo Finance, including prices and trends. |
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| `coin_gecko_coin_api` | `coin_gecko_coin_api` | Fetches cryptocurrency data from CoinGecko, including market and price information. |
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| `helius_api_tool` | `helius_api_tool` | Retrieves blockchain account, transaction, or token data using the Helius API. |
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| `okx_api_tool` | `okx_api_tool` | Fetches detailed cryptocurrency data for coins from the OKX exchange. |
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### Financial Data Retrieval
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Enable precise and actionable financial insights:
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#### Example 1: Fetch Historical Data
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```python
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from swarms_tools import fetch_htx_data
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# Fetch historical trading data for "Swarms Corporation"
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response = fetch_htx_data("swarms")
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print(response)
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```
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#### Example 2: Stock News Analysis
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```python
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from swarms_tools import fetch_stock_news
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# Retrieve latest stock news for Apple
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news = fetch_stock_news("AAPL")
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print(news)
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```
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#### Example 3: Cryptocurrency Metrics
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```python
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from swarms_tools import coin_gecko_coin_api
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# Fetch live data for Bitcoin
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crypto_data = coin_gecko_coin_api("bitcoin")
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print(crypto_data)
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```
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### Social Media Automation
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Streamline communication and engagement:
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#### Example: Telegram Bot Messaging
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```python
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from swarms_tools import telegram_dm_or_tag_api
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def send_alert(response: str):
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telegram_dm_or_tag_api(response)
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# Send a message to a user or group
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send_alert("Mission-critical update from Swarms.")
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```
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---
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## Dex Screener
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This is a tool that allows you to fetch data from the Dex Screener API. It supports multiple chains and multiple tokens.
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```python
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from swarms_tools.finance.dex_screener import (
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fetch_latest_token_boosts,
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fetch_dex_screener_profiles,
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)
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fetch_dex_screener_profiles()
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fetch_latest_token_boosts()
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```
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---
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## Structs
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The tool chainer enables the execution of multiple tools in a sequence, allowing for the aggregation of their results in either a parallel or sequential manner.
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```python
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# Example usage
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from loguru import logger
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from swarms_tools.structs import tool_chainer
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if __name__ == "__main__":
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logger.add("tool_chainer.log", rotation="500 MB", level="INFO")
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# Example tools
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def tool1():
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return "Tool1 Result"
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def tool2():
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return "Tool2 Result"
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# def tool3():
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# raise ValueError("Simulated error in Tool3")
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tools = [tool1, tool2]
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# Parallel execution
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parallel_results = tool_chainer(tools, parallel=True)
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print("Parallel Results:", parallel_results)
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# Sequential execution
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# sequential_results = tool_chainer(tools, parallel=False)
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# print("Sequential Results:", sequential_results)
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```
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---
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## 🧩 Standardized Schema
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Every tool in **Swarms Tools** adheres to a strict schema for maintainability and interoperability:
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### Schema Template
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1. **Functionality**:
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- Encapsulate API logic into a modular, reusable function.
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2. **Typing**:
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- Leverage Python type hints for input validation and clarity.
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Example:
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```python
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def fetch_data(symbol: str, date_range: str) -> str:
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"""
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Fetch financial data for a given symbol and date range.
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Args:
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symbol (str): Ticker symbol of the asset.
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date_range (str): Timeframe for the data (e.g., '1d', '1m', '1y').
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Returns:
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dict: A dictionary containing financial metrics.
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"""
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pass
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```
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3. **Documentation**:
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- Include detailed docstrings with parameter explanations and usage examples.
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4. **Output Standardization**:
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- Ensure consistent outputs (e.g., strings) for easy downstream agent integration.
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5. **API-Key Management**:
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- All API keys must be fetched with `os.getenv("YOUR_KEY")`
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---
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## 📖 Documentation
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Comprehensive documentation is available to guide developers and enterprises. Visit our [official docs](https://docs.swarms.world) for detailed API references, usage examples, and best practices.
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---
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## 🛠 Contributing
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We welcome contributions from the global developer community. To contribute:
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1. **Fork the Repository**: Start by forking the repository.
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2. **Create a Feature Branch**: Use a descriptive branch name: `feature/add-new-tool`.
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3. **Commit Your Changes**: Write meaningful commit messages.
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4. **Submit a Pull Request**: Open a pull request for review.
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---
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## 🛡️ License
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This project is licensed under the **MIT License**. See the [LICENSE](LICENSE) file for details.
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---
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## 🌠 Join the Future
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Explore the limitless possibilities of agent-based systems. Together, we can build a smarter, faster, and more interconnected world.
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**Visit us:** [Swarms Corporation](https://swarms.ai)
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**Follow us:** [Twitter](https://twitter.com/swarms_corp)
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---
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**"The future belongs to those who dare to automate it."**
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**— The Swarms Corporation**
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@ -0,0 +1,30 @@
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from swarms import Agent
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from swarms.prompts.finance_agent_sys_prompt import (
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FINANCIAL_AGENT_SYS_PROMPT,
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)
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from swarms_tools import (
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coin_gecko_coin_api,
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fetch_htx_data,
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)
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# Initialize the agent
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agent = Agent(
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agent_name="Financial-Analysis-Agent",
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agent_description="Personal finance advisor agent",
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system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
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max_loops=1,
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model_name="gpt-4o",
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dynamic_temperature_enabled=True,
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user_name="swarms_corp",
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return_step_meta=False,
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output_type="str", # "json", "dict", "csv" OR "string" "yaml" and
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auto_generate_prompt=False, # Auto generate prompt for the agent based on name, description, and system prompt, task
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max_tokens=4000, # max output tokens
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saved_state_path="agent_00.json",
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interactive=False,
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)
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agent.run(
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f"Analyze the $swarms token on HTX with data: {fetch_htx_data('swarms')}. Additionally, consider the following CoinGecko data: {coin_gecko_coin_api('swarms')}"
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)
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Loading…
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