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swarms
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35
docs/requirements.txt
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mkdocs
mkdocs-material
mkdocs-glightbox
mkdocs-git-authors-plugin
mkdocs-git-revision-date-plugin
mkdocs-git-committers-plugin
mkdocstrings
mike
mkdocs-jupyter
mkdocs-git-committers-plugin-2
mkdocs-git-revision-date-localized-plugin
mkdocs-redirects
mkdocs-material-extensions
mkdocs-simple-hooks
mkdocs-awesome-pages-plugin
mkdocs-versioning
mkdocs-mermaid2-plugin
mkdocs-include-markdown-plugin
mkdocs-enumerate-headings-plugin
mkdocs-autolinks-plugin
mkdocs-minify-html-plugin
mkdocs-autolinks-plugin
# Requirements for core
jinja2~=3.1
markdown~=3.7
mkdocs-material-extensions~=1.3
pygments~=2.18
pymdown-extensions~=10.13
# Requirements for plugins
babel~=2.16
colorama~=0.4
paginate~=0.5
regex>=2022.4

28
example.py
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from swarms import Agent
from swarms.prompts.finance_agent_sys_prompt import (
FINANCIAL_AGENT_SYS_PROMPT,
)
# Initialize the agent
agent = Agent(
agent_name="Financial-Analysis-Agent",
agent_description="Personal finance advisor agent",
system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
max_loops=1,
model_name="gpt-4o",
dynamic_temperature_enabled=True,
user_name="swarms_corp",
retry_attempts=3,
context_length=8192,
return_step_meta=False,
output_type="str", # "json", "dict", "csv" OR "string" "yaml" and
auto_generate_prompt=False, # Auto generate prompt for the agent based on name, description, and system prompt, task
max_tokens=4000, # max output tokens
saved_state_path="agent_00.json",
interactive=False,
)
agent.run(
"Create a table of super high growth opportunities for AI. I have $40k to invest in ETFs, index funds, and more. Please create a table in markdown.",
all_cores=True,
)

143
pyproject.toml
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[build-system]
requires = ["poetry-core>=1.0.0"]
build-backend = "poetry.core.masonry.api"
[tool.poetry]
name = "swarms"
version = "6.8.3"
description = "Swarms - TGSC"
license = "MIT"
authors = ["Kye Gomez <kye@apac.ai>"]
homepage = "https://github.com/kyegomez/swarms"
documentation = "https://docs.swarms.world"
readme = "README.md"
repository = "https://github.com/kyegomez/swarms"
keywords = [
"artificial intelligence",
"deep learning",
"optimizers",
"Prompt Engineering",
"swarms",
"agents",
"llms",
"transformers",
"multi-agent",
"swarms of agents",
"Enterprise-Grade Agents",
"Production-Grade Agents",
"Agents",
"Multi-Grade-Agents",
"Swarms",
"Transformers",
"LLMs",
"Prompt Engineering",
"Agents",
"Generative Agents",
"Generative AI",
"Agent Marketplace",
"Agent Store",
"quant",
"finance",
"algorithmic trading",
"portfolio optimization",
"risk management",
"financial modeling",
"machine learning for finance",
"natural language processing for finance",
]
classifiers = [
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3.10",
]
[tool.poetry.dependencies]
python = ">=3.10,<4.0"
# torch = ">=2.1.1,<3.0"
# transformers = ">= 4.39.0, <5.0.0"
asyncio = ">=3.4.3,<4.0"
toml = "*"
pypdf = "5.1.0"
swarm-models = "*"
loguru = "*"
pydantic = "*"
tenacity = "*"
psutil = "*"
sentry-sdk = "*"
python-dotenv = "*"
PyYAML = "*"
docstring_parser = "0.16" # TODO:
tiktoken = "*"
networkx = "*"
aiofiles = "*"
clusterops = "*"
# chromadb = "*"
rich = "*"
# sentence-transformers = "*"
# [tool.poetry.extras]
# # Extra for NLP-related functionalities
# nlp = [
# "torch>=2.1.1,<3.0",
# "transformers>=4.39.0,<5.0.0",
# "sentence-transformers",
# "swarm-models",
# ]
# # Extra for database-related functionalities
# db = ["chromadb"]
# # All optional dependencies for convenience
# all = [
# "torch>=2.1.1,<3.0",
# "transformers>=4.39.0,<5.0.0",
# "sentence-transformers",
# "chromadb",
# "swarm-models"
# ]
[tool.poetry.scripts]
swarms = "swarms.cli.main:main"
[tool.poetry.group.lint.dependencies]
black = ">=23.1,<25.0"
ruff = ">=0.5.1,<0.8.5"
types-toml = "^0.10.8.1"
types-pytz = ">=2023.3,<2025.0"
types-chardet = "^5.0.4.6"
mypy-protobuf = "^3.0.0"
[tool.poetry.group.test.dependencies]
pytest = "^8.1.1"
[tool.ruff]
line-length = 70
[tool.black]
target-version = ["py38"]
line-length = 70
include = '\.pyi?$'
exclude = '''
/(
\.git
| \.hg
| \.mypy_cache
| \.tox
| \.venv
| _build
| buck-out
| build
| dist
| docs
)/
'''

29
requirements.txt
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torch>=2.1.1,<3.0
transformers>=4.39.0,<5.0.0
asyncio>=3.4.3,<4.0
toml
pypdf==5.1.0
ratelimit==2.2.1
loguru
pydantic==2.8.2
tenacity
rich
psutil
sentry-sdk
python-dotenv
PyYAML
docstring_parser==0.16
black>=23.1,<25.0
ruff>=0.0.249,<0.8.5
types-toml>=0.10.8.1
types-pytz>=2023.3,<2025.0
types-chardet>=5.0.4.6
mypy-protobuf>=3.0.0
pytest>=8.1.1
networkx
aiofiles
clusterops
gradio
litellm
python-dotenv

414
swarms/structs/spreadsheet_swarm.py
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import asyncio
import csv
from datetime import datetime
import os
import uuid
from typing import Dict, List, Union
import aiofiles
from pydantic import BaseModel, Field
from swarms.structs.agent import Agent
from swarms.structs.base_swarm import BaseSwarm
from swarms.telemetry.capture_sys_data import log_agent_data
from swarms.utils.file_processing import create_file_in_folder
from swarms.utils.loguru_logger import initialize_logger
logger = initialize_logger(log_folder="spreadsheet_swarm")
# Replace timestamp-based time with a UUID for file naming
run_id = uuid.uuid4().hex # Unique identifier for each run
class AgentOutput(BaseModel):
agent_name: str
task: str
result: str
timestamp: str
class SwarmRunMetadata(BaseModel):
run_id: str = Field(
default_factory=lambda: f"spreadsheet_swarm_run_{run_id}"
)
name: str
description: str
agents: List[str]
start_time: str = Field(
default_factory=lambda: str(datetime.now().timestamp()), # Numeric timestamp
description="The start time of the swarm run.",
)
end_time: str
tasks_completed: int
outputs: List[AgentOutput]
number_of_agents: int = Field(
...,
description="The number of agents participating in the swarm.",
)
class SpreadSheetSwarm(BaseSwarm):
"""
A swarm that processes tasks concurrently using multiple agents.
Args:
name (str, optional): The name of the swarm. Defaults to "Spreadsheet-Swarm".
description (str, optional): The description of the swarm. Defaults to "A swarm that processes tasks concurrently using multiple agents.".
agents (Union[Agent, List[Agent]], optional): The agents participating in the swarm. Defaults to an empty list.
autosave_on (bool, optional): Whether to enable autosave of swarm metadata. Defaults to True.
save_file_path (str, optional): The file path to save the swarm metadata as a CSV file. Defaults to "spreedsheet_swarm.csv".
max_loops (int, optional): The number of times to repeat the swarm tasks. Defaults to 1.
workspace_dir (str, optional): The directory path of the workspace. Defaults to the value of the "WORKSPACE_DIR" environment variable.
*args: Additional positional arguments.
**kwargs: Additional keyword arguments.
"""
def __init__(
self,
name: str = "Spreadsheet-Swarm",
description: str = "A swarm that processes tasks concurrently using multiple agents and saves the metadata to a CSV file.",
agents: Union[Agent, List[Agent]] = [],
autosave_on: bool = True,
save_file_path: str = None,
max_loops: int = 1,
workspace_dir: str = os.getenv("WORKSPACE_DIR"),
load_path: str = None,
*args,
**kwargs,
):
super().__init__(
name=name,
description=description,
agents=agents if isinstance(agents, list) else [agents],
*args,
**kwargs,
)
self.name = name
self.description = description
self.save_file_path = save_file_path
self.autosave_on = autosave_on
self.max_loops = max_loops
self.workspace_dir = workspace_dir
# Create a timestamp without colons or periods
timestamp = datetime.now().isoformat().replace(":", "_").replace(".", "_")
# Use this timestamp in the CSV filename
self.save_file_path = f"spreadsheet_swarm_{timestamp}_run_id_{run_id}.csv"
self.metadata = SwarmRunMetadata(
run_id=f"spreadsheet_swarm_run_{run_id}",
name=name,
description=description,
agents=[agent.name for agent in agents],
start_time=str(datetime.now().timestamp()), # Numeric timestamp
end_time="",
tasks_completed=0,
outputs=[],
number_of_agents=len(agents),
)
self.reliability_check()
def reliability_check(self):
"""
Check the reliability of the swarm.
Raises:
ValueError: If no agents are provided or no save file path is provided.
"""
logger.info("Checking the reliability of the swarm...")
# if not self.agents:
# raise ValueError("No agents are provided.")
# if not self.save_file_path:
# raise ValueError("No save file path is provided.")
if not self.max_loops:
raise ValueError("No max loops are provided.")
logger.info("Swarm reliability check passed.")
logger.info("Swarm is ready to run.")
async def _load_from_csv(self):
"""
Load agent configurations from a CSV file.
Expected CSV format: agent_name,description,system_prompt,task
Args:
csv_path (str): Path to the CSV file containing agent configurations
"""
try:
csv_path = self.load_path
logger.info(
f"Loading agent configurations from {csv_path}"
)
async with aiofiles.open(csv_path, mode="r") as file:
content = await file.read()
csv_reader = csv.DictReader(content.splitlines())
for row in csv_reader:
config = AgentConfig(
agent_name=row["agent_name"],
description=row["description"],
system_prompt=row["system_prompt"],
task=row["task"],
)
# Create new agent with configuration
new_agent = Agent(
agent_name=config.agent_name,
system_prompt=config.system_prompt,
description=config.description,
model_name=(
row["model_name"]
if "model_name" in row
else "openai/gpt-4o"
),
docs=[row["docs"]] if "docs" in row else "",
dynamic_temperature_enabled=True,
max_loops=row["max_loops"] if "max_loops" in row else 1,
user_name=row["user_name"] if "user_name" in row else "user",
# output_type="str",
stopping_token=row["stopping_token"] if "stopping_token" in row else None,
)
# Add agent to swarm
self.agents.append(new_agent)
self.agent_configs[config.agent_name] = config
# Update metadata with new agents
self.metadata.agents = [
agent.name for agent in self.agents
]
self.metadata.number_of_agents = len(self.agents)
logger.info(
f"Loaded {len(self.agent_configs)} agent configurations"
)
except Exception as e:
logger.error(f"Error loading agent configurations: {e}")
def load_from_csv(self):
asyncio.run(self._load_from_csv())
async def run_from_config(self):
"""
Run all agents with their configured tasks concurrently
"""
logger.info("Running agents from configuration")
self.metadata.start_time = time
tasks = []
for agent in self.agents:
config = self.agent_configs.get(agent.agent_name)
if config:
for _ in range(self.max_loops):
tasks.append(
asyncio.to_thread(
self._run_agent_task, agent, config.task
)
)
# Run all tasks concurrently
results = await asyncio.gather(*tasks)
# Process the results
for result in results:
self._track_output(*result)
self.metadata.end_time = time
# Save metadata
logger.info("Saving metadata to CSV and JSON...")
await self._save_metadata()
if self.autosave_on:
self.data_to_json_file()
log_agent_data(self.metadata.model_dump())
return self.metadata.model_dump_json(indent=4)
async def _run(self, task: str = None, *args, **kwargs):
"""
Run the swarm either with a specific task or using configured tasks.
Args:
task (str, optional): The task to be executed by all agents. If None, uses tasks from config.
*args: Additional positional arguments.
**kwargs: Additional keyword arguments.
Returns:
str: The JSON representation of the swarm metadata.
"""
if task is None and self.agent_configs:
return await self.run_from_config()
else:
self.metadata.start_time = time
await self._run_tasks(task, *args, **kwargs)
self.metadata.end_time = time
await self._save_metadata()
if self.autosave_on:
self.data_to_json_file()
print(log_agent_data(self.metadata.model_dump()))
return self.metadata.model_dump_json(indent=4)
def run(self, task: str = None, *args, **kwargs):
"""
Run the swarm with the specified task.
Args:
task (str): The task to be executed by the swarm.
*args: Additional positional arguments.
**kwargs: Additional keyword arguments.
Returns:
str: The JSON representation of the swarm metadata.
"""
logger.info(f"Running the swarm with task: {task}")
self.metadata.start_time = str(datetime.now().timestamp()) # Numeric timestamp
# Check if we're already in an event loop
if asyncio.get_event_loop().is_running():
# If so, create and run tasks directly using `create_task` without `asyncio.run`
task_future = asyncio.create_task(self._run_tasks(task, *args, **kwargs))
asyncio.get_event_loop().run_until_complete(task_future)
else:
# If no event loop is running, run using `asyncio.run`
asyncio.run(self._run_tasks(task, *args, **kwargs))
self.metadata.end_time = str(datetime.now().timestamp()) # Numeric timestamp
# Synchronously save metadata
logger.info("Saving metadata to CSV and JSON...")
asyncio.run(self._save_metadata())
if self.autosave_on:
self.data_to_json_file()
print(log_agent_data(self.metadata.model_dump()))
return self.metadata.model_dump_json(indent=4)
async def _run_tasks(self, task: str, *args, **kwargs):
"""
Run the swarm tasks concurrently.
Args:
task (str): The task to be executed by the swarm.
*args: Additional positional arguments.
**kwargs: Additional keyword arguments.
"""
tasks = []
for _ in range(self.max_loops):
for agent in self.agents:
# Use asyncio.to_thread to run the blocking task in a thread pool
tasks.append(
asyncio.to_thread(
self._run_agent_task,
agent,
task,
*args,
**kwargs,
)
)
# Run all tasks concurrently
results = await asyncio.gather(*tasks)
# Process the results
for result in results:
self._track_output(*result)
def _run_agent_task(self, agent, task, *args, **kwargs):
"""
Run a single agent's task in a separate thread.
Args:
agent: The agent to run the task for.
task (str): The task to be executed by the agent.
*args: Additional positional arguments.
**kwargs: Additional keyword arguments.
Returns:
Tuple[str, str, str]: A tuple containing the agent name, task, and result.
"""
try:
result = agent.run(task=task, *args, **kwargs)
# Assuming agent.run() is a blocking call
return agent.agent_name, task, result
except Exception as e:
logger.error(
f"Error running task for {agent.agent_name}: {e}"
)
return agent.agent_name, task, str(e)
def _track_output(self, agent_name: str, task: str, result: str):
"""
Track the output of a completed task.
Args:
agent_name (str): The name of the agent that completed the task.
task (str): The task that was completed.
result (str): The result of the completed task.
"""
self.metadata.tasks_completed += 1
self.metadata.outputs.append(
AgentOutput(
agent_name=agent_name,
task=task,
result=result,
timestamp=str(datetime.now().timestamp()), # Numeric timestamp
)
)
def export_to_json(self):
"""
Export the swarm metadata to JSON.
Returns:
str: The JSON representation of the swarm metadata.
"""
return self.metadata.model_dump_json(indent=4)
def data_to_json_file(self):
"""
Save the swarm metadata to a JSON file.
"""
out = self.export_to_json()
create_file_in_folder(
folder_path=f"{self.workspace_dir}/Spreedsheet-Swarm-{self.name}/{self.name}",
file_name=f"spreedsheet-swarm-{self.metadata.run_id}_metadata.json",
content=out,
)
async def _save_metadata(self):
"""
Save the swarm metadata to CSV and JSON.
"""
if self.autosave_on:
await self._save_to_csv()
async def _save_to_csv(self):
"""
Save the swarm metadata to a CSV file.
"""
logger.info(f"Saving swarm metadata to: {self.save_file_path}")
run_id = uuid.uuid4()
# Check if file exists before opening it
file_exists = os.path.exists(self.save_file_path)
async with aiofiles.open(self.save_file_path, mode="a") as file:
# Write header if file doesn't exist
if not file_exists:
header = "Run ID,Agent Name,Task,Result,Timestamp\n"
await file.write(header)
# Write each output as a new row
for output in self.metadata.outputs:
row = f"{run_id},{output.agent_name},{output.task},{output.result},{output.timestamp}\n"
await file.write(row)
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