SpreadSheetSwarm Documentation¶

Class Definition¶

class SpreadSheetSwarm:

Full Path¶

from swarms.structs.spreadsheet_swarm import SpreadSheetSwarm

Attributes¶

The SpreadSheetSwarm class contains several attributes that define its behavior and configuration. These attributes are initialized in the constructor (__init__ method) and are used throughout the class to manage the swarm's operations.

Attribute	Type	Description
`name`	`str`	The name of the swarm.
`description`	`str`	A description of the swarm's purpose.
`agents`	`Union[Agent, List[Agent]]`	The agents participating in the swarm. Can be a single agent or a list of agents.
`autosave_on`	`bool`	Flag indicating whether autosave is enabled.
`save_file_path`	`str`	The file path where the swarm data will be saved.
`task_queue`	`queue.Queue`	The queue that stores tasks to be processed by the agents.
`lock`	`threading.Lock`	A lock used for thread synchronization to prevent race conditions.
`metadata`	`SwarmRunMetadata`	Metadata for the swarm run, including start time, end time, tasks completed, and outputs.
`run_all_agents`	`bool`	Flag indicating whether to run all agents or just one.
`max_loops`	`int`	The number of times to repeat the task.
`workspace_dir`	`str`	The directory where the workspace is located, retrieved from environment variables.

Parameters¶

name (str, optional): The name of the swarm. Default is "Spreadsheet-Swarm".
description (str, optional): A brief description of the swarm. Default is "A swarm that processes tasks from a queue using multiple agents on different threads.".
agents (Union[Agent, List[Agent]], optional): The agents participating in the swarm. Default is an empty list.
autosave_on (bool, optional): A flag to indicate if autosave is enabled. Default is True.
save_file_path (str, optional): The file path where swarm data will be saved. Default is "spreedsheet_swarm.csv".
run_all_agents (bool, optional): Flag to determine if all agents should run. Default is True.
max_loops (int, optional): The number of times to repeat the task. Default is 1.
workspace_dir (str, optional): The directory where the workspace is located. Default is retrieved from environment variable WORKSPACE_DIR.

Constructor (`init`)¶

The constructor initializes the SpreadSheetSwarm with the provided parameters. It sets up the task queue, locks for thread synchronization, and initializes the metadata.

Methods¶

`reliability_check`¶

def reliability_check(self):

Description¶

The reliability_check method performs a series of checks to ensure that the swarm is properly configured before it begins processing tasks. It verifies that there are agents available and that a valid file path is provided for saving the swarm's data. If any of these checks fail, an exception is raised.

Raises¶

ValueError: Raised if no agents are provided or if no save file path is specified.

Example¶

swarm = SpreadSheetSwarm(agents=[agent1, agent2])
swarm.reliability_check()

`run`¶

def run(self, task: str, *args, **kwargs):

Description¶

The run method starts the task processing using the swarm. Depending on the configuration, it can either run all agents or a specific subset of them. The method tracks the start and end times of the task, executes the task multiple times if specified, and logs the results.

Parameters¶

task (str): The task to be executed by the swarm.
*args: Additional positional arguments to pass to the agents.
**kwargs: Additional keyword arguments to pass to the agents.

Example¶

swarm = SpreadSheetSwarm(agents=[agent1, agent2])
swarm.run("Process Data")

`export_to_json`¶

def export_to_json(self):

Description¶

The export_to_json method generates a JSON representation of the swarm's metadata. This can be useful for exporting the results to an external system or for logging purposes.

Returns¶

str: The JSON representation of the swarm's metadata.

Example¶

json_data = swarm.export_to_json()
print(json_data)

`data_to_json_file`¶

def data_to_json_file(self):

Description¶

The data_to_json_file method saves the swarm's metadata as a JSON file in the specified workspace directory. The file name is generated using the swarm's name and run ID.

Example¶

swarm.data_to_json_file()

`_track_output`¶

def _track_output(self, agent: Agent, task: str, result: str):

Description¶

The _track_output method is used internally to record the results of tasks executed by the agents. It updates the metadata with the completed tasks and their results.

Parameters¶

agent (Agent): The agent that executed the task.
task (str): The task that was executed.
result (str): The result of the task execution.

Example¶

swarm._track_output(agent1, "Process Data", "Success")

`_save_to_csv`¶

def _save_to_csv(self):

Description¶

The _save_to_csv method saves the swarm's metadata to a CSV file. It logs each task and its result before writing them to the file. The file is saved in the location specified by save_file_path.

Example¶

swarm._save_to_csv()

Usage Examples¶

Example 1: Basic Swarm Initialization¶

import os

from swarms import Agent
from swarm_models import OpenAIChat
from swarms.prompts.finance_agent_sys_prompt import (
    FINANCIAL_AGENT_SYS_PROMPT,
)
from swarms.structs.spreadsheet_swarm import SpreadSheetSwarm

# Example usage:
api_key = os.getenv("OPENAI_API_KEY")

# Model
model = OpenAIChat(
    openai_api_key=api_key, model_name="gpt-4o-mini", temperature=0.1
)


# Initialize your agents (assuming the Agent class and model are already defined)
agents = [
    Agent(
        agent_name=f"Financial-Analysis-Agent-spreesheet-swarm:{i}",
        system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
        llm=model,
        max_loops=1,
        dynamic_temperature_enabled=True,
        saved_state_path="finance_agent.json",
        user_name="swarms_corp",
        retry_attempts=1,
    )
    for i in range(10)
]

# Create a Swarm with the list of agents
swarm = SpreadSheetSwarm(
    name="Finance-Spreadsheet-Swarm",
    description="A swarm that processes tasks from a queue using multiple agents on different threads.",
    agents=agents,
    autosave_on=True,
    save_file_path="financial_spreed_sheet_swarm_demo.csv",
    run_all_agents=False,
    max_loops=1,
)

# Run the swarm
swarm.run(
    task="Analyze the states with the least taxes for LLCs. Provide an overview of all tax rates and add them with a comprehensive analysis"
)

Example 2: QR Code Generator¶

import os
from swarms import Agent
from swarm_models import OpenAIChat
from swarms.structs.spreadsheet_swarm import SpreadSheetSwarm

# Define custom system prompts for QR code generation
QR_CODE_AGENT_1_SYS_PROMPT = """
You are a Python coding expert. Your task is to write a Python script to generate a QR code for the link: https://lu.ma/jjc1b2bo. The code should save the QR code as an image file.
"""

QR_CODE_AGENT_2_SYS_PROMPT = """
You are a Python coding expert. Your task is to write a Python script to generate a QR code for the link: https://github.com/The-Swarm-Corporation/Cookbook. The code should save the QR code as an image file.
"""

# Example usage:
api_key = os.getenv("OPENAI_API_KEY")

# Model
model = OpenAIChat(
    openai_api_key=api_key, model_name="gpt-4o-mini", temperature=0.1
)

# Initialize your agents for QR code generation
agents = [
    Agent(
        agent_name="QR-Code-Generator-Agent-Luma",
        system_prompt=QR_CODE_AGENT_1_SYS_PROMPT,
        llm=model,
        max_loops=1,
        dynamic_temperature_enabled=True,
        saved_state_path="qr_code_agent_luma.json",
        user_name="swarms_corp",
        retry_attempts=1,
    ),
    Agent(
        agent_name="QR-Code-Generator-Agent-Cookbook",
        system_prompt=QR_CODE_AGENT_2_SYS_PROMPT,
        llm=model,
        max_loops=1,
        dynamic_temperature_enabled=True,
        saved_state_path="qr_code_agent_cookbook.json",
        user_name="swarms_corp",
        retry_attempts=1,
    ),
]

# Create a Swarm with the list of agents
swarm = SpreadSheetSwarm(
    name="QR-Code-Generation-Swarm",
    description="A swarm that generates Python scripts to create QR codes for specific links.",
    agents=agents,
    autosave_on=True,
    save_file_path="qr_code_generation_results.csv",
    run_all_agents=False,
    max_loops=1,
)

# Run the swarm
swarm.run(
    task="Generate Python scripts to create QR codes for the provided links and save them as image files."
)

import os
from swarms import Agent
from swarm_models import OpenAIChat
from swarms.structs.spreadsheet_swarm import SpreadSheetSwarm

# Define custom system prompts for each social media platform
TWITTER_AGENT_SYS_PROMPT = """
You are a Twitter marketing expert. Your task is to create engaging, concise tweets and analyze trends to maximize engagement. Consider hashtags, timing, and content relevance.
"""

INSTAGRAM_AGENT_SYS_PROMPT = """
You are an Instagram marketing expert. Your task is to create visually appealing and engaging content, including captions and hashtags, tailored to a specific audience.
"""

FACEBOOK_AGENT_SYS_PROMPT = """
You are a Facebook marketing expert. Your task is to craft posts that are optimized for engagement and reach on Facebook, including using images, links, and targeted messaging.
"""

EMAIL_AGENT_SYS_PROMPT = """
You are an Email marketing expert. Your task is to write compelling email campaigns that drive conversions, focusing on subject lines, personalization, and call-to-action strategies.
"""

# Example usage:
api_key = os.getenv("OPENAI_API_KEY")

# Model
model = OpenAIChat(
    openai_api_key=api_key, model_name="gpt-4o-mini", temperature=0.1
)

# Initialize your agents for different social media platforms
agents = [
    Agent(
        agent_name="Twitter-Marketing-Agent",
        system_prompt=TWITTER_AGENT_SYS_PROMPT,
        llm=model,
        max_loops=1,
        dynamic_temperature_enabled=True,
        saved_state_path="twitter_agent.json",
        user_name="swarms_corp",
        retry_attempts=1,
    ),
    Agent(
        agent_name="Instagram-Marketing-Agent",
        system_prompt=INSTAGRAM_AGENT_SYS_PROMPT,
        llm=model,
        max_loops=1,
        dynamic_temperature_enabled=True,
        saved_state_path="instagram_agent.json",
        user_name="swarms_corp",
        retry_attempts=1,
    ),
    Agent(
        agent_name="Facebook-Marketing-Agent",
        system_prompt=FACEBOOK_AGENT_SYS_PROMPT,
        llm=model,
        max_loops=1,
        dynamic_temperature_enabled=True,
        saved_state_path="facebook_agent.json",
        user_name="swarms_corp",
        retry_attempts=1,
    ),
    Agent(
        agent_name="Email-Marketing-Agent",
        system_prompt=EMAIL_AGENT_SYS_PROMPT,
        llm=model,
        max_loops=1,
        dynamic_temperature_enabled=True,
        saved_state_path="email_agent.json",
        user_name="swarms_corp",
        retry_attempts=1,
    ),
]

# Create a Swarm with the list of agents
swarm = SpreadSheetSwarm(
    name="Social-Media-Marketing-Swarm",
    description="A swarm that processes social media marketing tasks using multiple agents on different threads.",
    agents=agents,
    autosave_on=True,
    save_file_path="social_media_marketing_spreadsheet.csv",
    run_all_agents=False,
    max_loops=2,
)

# Run the swarm
swarm.run(
    task="Create posts to promote hack nights in miami beach for developers, engineers, and tech enthusiasts. Include relevant hashtags, images, and engaging captions."
)

Additional Information and Tips¶

Thread Synchronization: When working with multiple agents in a concurrent environment, it's crucial to ensure that access to shared resources is properly synchronized using locks to avoid race conditions.
Autosave Feature: If you enable the autosave_on flag, ensure that the file path provided is correct and writable. This feature is handy for long-running tasks where you want to periodically save the state.
Error Handling

Implementing proper error handling within your agents can prevent the swarm from crashing during execution. Consider catching exceptions in the run method and logging errors appropriately.

Custom Agents: You can extend the Agent class to create custom agents that perform specific tasks tailored to your application's needs.

SpreadSheetSwarm Documentation¶

Class Definition¶

Full Path¶

Attributes¶

Parameters¶

Constructor (__init__)¶

Methods¶

reliability_check¶

Description¶

Raises¶

Example¶

run¶

Description¶

Parameters¶

Example¶

export_to_json¶

Description¶

Returns¶

Example¶

data_to_json_file¶

Description¶

Example¶

_track_output¶

Description¶

Parameters¶

Example¶

_save_to_csv¶

Description¶

Example¶

Usage Examples¶

Example 1: Basic Swarm Initialization¶

Example 2: QR Code Generator¶

Example 3: Social Media Marketing¶

Additional Information and Tips¶

References and Resources¶

Constructor (`init`)¶

`reliability_check`¶

`run`¶

`export_to_json`¶

`data_to_json_file`¶

`_track_output`¶

`_save_to_csv`¶