@ -1,8 +1,6 @@
### Swarms Tool Documentation Page
### Swarms Tool Documentation
Welcome to the Swarms Tool Documentation!
Here you will find examples and guidelines on how to build and use tools in the Swarms environment. A tool is a Python function designed to perform specific tasks, with clear type annotations and comprehensive docstrings. Below are examples of tools to help you get started.
A tool is a Python function designed to perform specific tasks, with clear type annotations and comprehensive docstrings. Below are examples of tools to help you get started.
# Rules
@ -31,9 +29,6 @@ To create a tool in the Swarms environment, follow these rules:
### Example Tools
### Examples and Anti-Examples
#### Example 1: Fetch Financial News
@ -351,3 +346,239 @@ By following the examples provided, you can create your own tools to perform var
## Integrate tools into Agent
To integrate tools into an agent, you'd simply just pass in a callable function with types and documentation into the agent class.
```python
from swarms import Agent, OpenAIChat # ChromaDB
import subprocess
# Model
llm = OpenAIChat(
temperature=0.1,
)
# Tools
def terminal(
code: str,
):
"""
Run code in the terminal.
Args:
code (str): The code to run in the terminal.
Returns:
str: The output of the code.
"""
out = subprocess.run(
code, shell=True, capture_output=True, text=True
).stdout
return str(out)
def browser(query: str):
"""
Search the query in the browser with the `browser` tool.
Args:
query (str): The query to search in the browser.
Returns:
str: The search results.
"""
import webbrowser
url = f"https://www.google.com/search?q={query}"
webbrowser.open(url)
return f"Searching for {query} in the browser."
def create_file(file_path: str, content: str):
"""
Create a file using the file editor tool.
Args:
file_path (str): The path to the file.
content (str): The content to write to the file.
Returns:
str: The result of the file creation operation.
"""
with open(file_path, "w") as file:
file.write(content)
return f"File {file_path} created successfully."
def file_editor(file_path: str, mode: str, content: str):
"""
Edit a file using the file editor tool.
Args:
file_path (str): The path to the file.
mode (str): The mode to open the file in.
content (str): The content to write to the file.
Returns:
str: The result of the file editing operation.
"""
with open(file_path, mode) as file:
file.write(content)
return f"File {file_path} edited successfully."
# Agent
agent = Agent(
agent_name="Devin",
system_prompt=(
"Autonomous agent that can interact with humans and other"
" agents. Be Helpful and Kind. Use the tools provided to"
" assist the user. Return all code in markdown format."
),
llm=llm,
max_loops="auto",
autosave=True,
dashboard=False,
streaming_on=True,
verbose=True,
stopping_token="< DONE > ",
interactive=True,
tools=[terminal, browser, file_editor, create_file],
# long_term_memory=chromadb,
metadata_output_type="json",
# List of schemas that the agent can handle
# list_base_models=[tool_schema],
function_calling_format_type="OpenAI",
function_calling_type="json", # or soon yaml
)
# Run the agent
agent.run("Create a new file for a plan to take over the world.")
```
## Example 2
```python
import os
import requests
from swarms import Agent, OpenAIChat
# Get the OpenAI API key from the environment variable
api_key = os.getenv("OPENAI_API_KEY")
# Create an instance of the OpenAIChat class
model = OpenAIChat(
api_key=api_key, model_name="gpt-4o-mini", temperature=0.1
)
def fetch_financial_news(
query: str = "Nvidia news", num_articles: int = 5
) -> str:
"""
Fetches financial news from the Google News API and returns a formatted string of the top news.
Args:
api_key (str): Your Google News API key.
query (str): The query term to search for news. Default is "financial".
num_articles (int): The number of top articles to fetch. Default is 5.
Returns:
str: A formatted string of the top financial news articles.
Raises:
ValueError: If the API response is invalid or there are no articles found.
requests.exceptions.RequestException: If there is an error with the request.
"""
url = "https://newsapi.org/v2/everything"
params = {
"q": query,
"apiKey": os.getenv("NEWSAPI_KEY"),
"pageSize": num_articles,
"sortBy": "relevancy",
}
try:
response = requests.get(url, params=params)
response.raise_for_status()
data = response.json()
if "articles" not in data or len(data["articles"]) == 0:
raise ValueError("No articles found or invalid API response.")
articles = data["articles"]
formatted_articles = []
for i, article in enumerate(articles, start=1):
title = article.get("title", "No Title")
description = article.get("description", "No Description")
url = article.get("url", "No URL")
formatted_articles.append(
f"{i}. {title}\nDescription: {description}\nRead more: {url}\n"
)
return "\n".join(formatted_articles)
except requests.exceptions.RequestException as e:
print(f"Request Error: {e}")
raise
except ValueError as e:
print(f"Value Error: {e}")
raise
# # Example usage:
# api_key = "ceabc81a7d8f45febfedadb27177f3a3"
# print(fetch_financial_news(api_key))
# Initialize the agent
agent = Agent(
agent_name="Financial-Analysis-Agent",
# system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
llm=model,
max_loops=2,
autosave=True,
# dynamic_temperature_enabled=True,
dashboard=False,
verbose=True,
streaming_on=True,
# interactive=True, # Set to False to disable interactive mode
dynamic_temperature_enabled=True,
saved_state_path="finance_agent.json",
tools=[fetch_financial_news],
# stopping_token="Stop!",
# interactive=True,
# docs_folder="docs", # Enter your folder name
# pdf_path="docs/finance_agent.pdf",
# sop="Calculate the profit for a company.",
# sop_list=["Calculate the profit for a company."],
user_name="swarms_corp",
# # docs=
# # docs_folder="docs",
retry_attempts=3,
# context_length=1000,
# tool_schema = dict
context_length=200000,
# tool_schema=
# tools
# agent_ops_on=True,
# long_term_memory=ChromaDB(docs_folder="artifacts"),
)
# Run the agent
response = agent("What are the latest financial news on Nvidia?")
print(response)
```
Kye Gomez
5 months ago