Orchestrate swarms of agents for production-grade applications.
Individual agents are barely being deployd into production because of 5 suffocating challanges: short memory, single task threading, hallucinations, high cost, and lack of collaboration. With Multi-agent collaboration, you can effectively eliminate all of these issues. Swarms provides you with simple, reliable, and agile primitives to build your own Swarm for your specific use case. Now, Swarms is being used in production by RBC, John Deere, and many AI startups. To learn more about the unparalled benefits about multi-agent collaboration check out this github repository for research papers or book a call with me!
pip3 install -U swarms
Run example in Collab:
A fully plug-and-play autonomous agent powered by an LLM extended by a long-term memory database, and equipped with function calling for tool usage! By passing in an LLM, you can create a fully autonomous agent with extreme customization and reliability, ready for real-world task automation!
Features:
✅ Any LLM / Any framework
✅ Extremely customize-able with max loops, autosaving, import docs (PDFS, TXT, CSVs, etc), tool usage, etc etc
✅ Long term memory database with RAG (ChromaDB, Pinecone, Qdrant)
import os
from dotenv import load_dotenv
# Import the OpenAIChat model and the Agent struct
from swarms import Agent, OpenAIChat
# Load the environment variables
load_dotenv()
# Get the API key from the environment
api_key = os.environ.get("OPENAI_API_KEY")
# Initialize the language model
llm = OpenAIChat(
temperature=0.5, model_name="gpt-4", openai_api_key=api_key, max_tokens=4000
)
## Initialize the workflow
agent = Agent(llm=llm, max_loops=1, autosave=True, dashboard=True)
# Run the workflow on a task
agent.run("Generate a 10,000 word blog on health and wellness.")ToolAgent is an agent that can use tools through JSON function calling. It intakes any open source model from huggingface and is extremely modular and plug in and play. We need help adding general support to all models soon.
from pydantic import BaseModel, Field
from transformers import AutoModelForCausalLM, AutoTokenizer
from swarms import ToolAgent
from swarms.utils.json_utils import base_model_to_json
# Load the pre-trained model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
"databricks/dolly-v2-12b",
load_in_4bit=True,
device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("databricks/dolly-v2-12b")
# Initialize the schema for the person's information
class Schema(BaseModel):
name: str = Field(..., title="Name of the person")
agent: int = Field(..., title="Age of the person")
is_student: bool = Field(
..., title="Whether the person is a student"
)
courses: list[str] = Field(
..., title="List of courses the person is taking"
)
# Convert the schema to a JSON string
tool_schema = base_model_to_json(Schema)
# Define the task to generate a person's information
task = (
"Generate a person's information based on the following schema:"
)
# Create an instance of the ToolAgent class
agent = ToolAgent(
name="dolly-function-agent",
description="Ana gent to create a child data",
model=model,
tokenizer=tokenizer,
json_schema=tool_schema,
)
# Run the agent to generate the person's information
generated_data = agent.run(task)
# Print the generated data
print(f"Generated data: {generated_data}")The Worker is a simple all-in-one agent equipped with an LLM, tools, and RAG for low level tasks.
✅ Plug in and Play LLM. Utilize any LLM from anywhere and any framework
✅ Reliable RAG: Utilizes FAISS for efficient RAG but it's modular so you can use any DB.
✅ Multi-Step Parallel Function Calling: Use any tool
# Importing necessary modules
import os
from dotenv import load_dotenv
from swarms import OpenAIChat, Worker, tool
# Loading environment variables from .env file
load_dotenv()
# Retrieving the OpenAI API key from environment variables
api_key = os.getenv("OPENAI_API_KEY")
# Create a tool
@tool
def search_api(query: str):
pass
# Creating a Worker instance
worker = Worker(
name="My Worker",
role="Worker",
human_in_the_loop=False,
tools=[search_api],
temperature=0.5,
llm=OpenAIChat(openai_api_key=api_key),
)
# Running the worker with a prompt
out = worker.run("Hello, how are you? Create an image of how your are doing!")
# Printing the output
print(out)Agent equipped with quasi-infinite long term memory. Great for long document understanding, analysis, and retrieval.
from swarms import Agent, ChromaDB, OpenAIChat
# Making an instance of the ChromaDB class
memory = ChromaDB(
metric="cosine",
n_results=3,
output_dir="results",
docs_folder="docs",
)
# Initializing the agent with the Gemini instance and other parameters
agent = Agent(
agent_name="Covid-19-Chat",
agent_description=(
"This agent provides information about COVID-19 symptoms."
),
llm=OpenAIChat(),
max_loops="auto",
autosave=True,
verbose=True,
long_term_memory=memory,
stopping_condition="finish",
)
# Defining the task and image path
task = ("What are the symptoms of COVID-19?",)
# Running the agent with the specified task and image
out = agent.run(task)
print(out)An LLM equipped with long term memory and tools, a full stack agent capable of automating all and any digital tasks given a good prompt.
from swarms import Agent, ChromaDB, OpenAIChat, tool
# Making an instance of the ChromaDB class
memory = ChromaDB(
metric="cosine",
n_results=3,
output_dir="results",
docs_folder="docs",
)
# Initialize a tool
@tool
def search_api(query: str):
# Add your logic here
return query
# Initializing the agent with the Gemini instance and other parameters
agent = Agent(
agent_name="Covid-19-Chat",
agent_description=(
"This agent provides information about COVID-19 symptoms."
),
llm=OpenAIChat(),
max_loops="auto",
autosave=True,
verbose=True,
long_term_memory=memory,
stopping_condition="finish",
tools=[search_api],
)
# Defining the task and image path
task = ("What are the symptoms of COVID-19?",)
# Running the agent with the specified task and image
out = agent.run(task)
print(out)Sequential Workflow enables you to sequentially execute tasks with Agent and then pass the output into the next agent and onwards until you have specified your max loops. SequentialWorkflow is wonderful for real-world business tasks like sending emails, summarizing documents, and analyzing data.
✅ Save and Restore Workflow states!
✅ Multi-Modal Support for Visual Chaining
✅ Utilizes Agent class
import os
from dotenv import load_dotenv
from swarms import Agent, OpenAIChat, SequentialWorkflow
load_dotenv()
# Load the environment variables
api_key = os.getenv("OPENAI_API_KEY")
# Initialize the language agent
llm = OpenAIChat(
temperature=0.5, model_name="gpt-4", openai_api_key=api_key, max_tokens=4000
)
# Initialize the agent with the language agent
agent1 = Agent(llm=llm, max_loops=1)
# Create another agent for a different task
agent2 = Agent(llm=llm, max_loops=1)
# Create another agent for a different task
agent3 = Agent(llm=llm, max_loops=1)
# Create the workflow
workflow = SequentialWorkflow(max_loops=1)
# Add tasks to the workflow
workflow.add(
agent1,
"Generate a 10,000 word blog on health and wellness.",
)
# Suppose the next task takes the output of the first task as input
workflow.add(
agent2,
"Summarize the generated blog",
)
# Run the workflow
workflow.run()
# Output the results
for task in workflow.tasks:
print(f"Task: {task.description}, Result: {task.result}")ConcurrentWorkflow runs all the tasks all at the same time with the inputs you give it!
import os
from dotenv import load_dotenv
from swarms import Agent, ConcurrentWorkflow, OpenAIChat, Task
# Load environment variables from .env file
load_dotenv()
# Load environment variables
llm = OpenAIChat(openai_api_key=os.getenv("OPENAI_API_KEY"))
agent = Agent(llm=llm, max_loops=1)
# Create a workflow
workflow = ConcurrentWorkflow(max_workers=5)
# Create tasks
task1 = Task(agent, "What's the weather in miami")
task2 = Task(agent, "What's the weather in new york")
task3 = Task(agent, "What's the weather in london")
# Add tasks to the workflow
workflow.add(tasks=[task1, task2, task3])
# Run the workflow
workflow.run()RecursiveWorkflow will keep executing the tasks until a specific token like is located inside the text!
import os
from dotenv import load_dotenv
from swarms import Agent, OpenAIChat, RecursiveWorkflow, Task
# Load environment variables from .env file
load_dotenv()
# Load environment variables
llm = OpenAIChat(openai_api_key=os.getenv("OPENAI_API_KEY"))
agent = Agent(llm=llm, max_loops=1)
# Create a workflow
workflow = RecursiveWorkflow(stop_token="<DONE>")
# Create tasks
task1 = Task(agent, "What's the weather in miami")
task2 = Task(agent, "What's the weather in new york")
task3 = Task(agent, "What's the weather in london")
# Add tasks to the workflow
workflow.add(task1)
workflow.add(task2)
workflow.add(task3)
# Run the workflow
workflow.run()The ModelParallelizer allows you to run multiple models concurrently, comparing their outputs. This feature enables you to easily compare the performance and results of different models, helping you make informed decisions about which model to use for your specific task.
Plug-and-Play Integration: The structure provides a seamless integration with various models, including OpenAIChat, Anthropic, Mixtral, and Gemini. You can easily plug in any of these models and start using them without the need for extensive modifications or setup.
import os
from dotenv import load_dotenv
from swarms import Anthropic, Gemini, Mixtral, ModelParallelizer, OpenAIChat
load_dotenv()
# API Keys
anthropic_api_key = os.getenv("ANTHROPIC_API_KEY")
openai_api_key = os.getenv("OPENAI_API_KEY")
gemini_api_key = os.getenv("GEMINI_API_KEY")
# Initialize the models
llm = OpenAIChat(openai_api_key=openai_api_key)
anthropic = Anthropic(anthropic_api_key=anthropic_api_key)
mixtral = Mixtral()
gemini = Gemini(gemini_api_key=gemini_api_key)
# Initialize the parallelizer
llms = [llm, anthropic, mixtral, gemini]
parallelizer = ModelParallelizer(llms)
# Set the task
task = "Generate a 10,000 word blog on health and wellness."
# Run the task
out = parallelizer.run(task)
# Print the responses 1 by 1
for i in range(len(out)):
print(f"Response from LLM {i}: {out[i]}")A Plug in and play conversational agent with GPT4, Mixytral, or any of our models
- Reliable conversational structure to hold messages together with dynamic handling for long context conversations and interactions with auto chunking
- Reliable, this simple system will always provide responses you want.
import os
from dotenv import load_dotenv
from swarms import Conversation, OpenAIChat
conv = Conversation(
time_enabled=True,
)
# Load the environment variables
load_dotenv()
# Get the API key from the environment
api_key = os.environ.get("OPENAI_API_KEY")
# Initialize the language model
llm = OpenAIChat(openai_api_key=api_key, model_name="gpt-4")
# Run the language model in a loop
def interactive_conversation(llm):
conv = Conversation()
while True:
user_input = input("User: ")
conv.add("user", user_input)
if user_input.lower() == "quit":
break
task = conv.return_history_as_string() # Get the conversation history
out = llm(task)
conv.add("assistant", out)
print(
f"Assistant: {out}",
)
conv.display_conversation()
conv.export_conversation("conversation.txt")
# Replace with your LLM instance
interactive_conversation(llm)SwarmNetwork provides the infrasturcture for building extremely dense and complex multi-agent applications that span across various types of agents.
✅ Efficient Task Management: SwarmNetwork's intelligent agent pool and task queue management system ensures tasks are distributed evenly across agents. This leads to efficient use of resources and faster task completion.
✅ Scalability: SwarmNetwork can dynamically scale the number of agents based on the number of pending tasks. This means it can handle an increase in workload by adding more agents, and conserve resources when the workload is low by reducing the number of agents.
✅ Versatile Deployment Options: With SwarmNetwork, each agent can be run on its own thread, process, container, machine, or even cluster. This provides a high degree of flexibility and allows for deployment that best suits the user's needs and infrastructure.
import os
from dotenv import load_dotenv
# Import the OpenAIChat model and the Agent struct
from swarms import Agent, OpenAIChat, SwarmNetwork
# Load the environment variables
load_dotenv()
# Get the API key from the environment
api_key = os.environ.get("OPENAI_API_KEY")
# Initialize the language model
llm = OpenAIChat(
temperature=0.5,
openai_api_key=api_key,
)
## Initialize the workflow
agent = Agent(llm=llm, max_loops=1, agent_name="Social Media Manager")
agent2 = Agent(llm=llm, max_loops=1, agent_name=" Product Manager")
agent3 = Agent(llm=llm, max_loops=1, agent_name="SEO Manager")
# Load the swarmnet with the agents
swarmnet = SwarmNetwork(
agents=[agent, agent2, agent3],
)
# List the agents in the swarm network
out = swarmnet.list_agents()
print(out)
# Run the workflow on a task
out = swarmnet.run_single_agent(
agent2.id, "Generate a 10,000 word blog on health and wellness."
)
print(out)
# Run all the agents in the swarm network on a task
out = swarmnet.run_many_agents("Generate a 10,000 word blog on health and wellness.")
print(out)Task is a simple structure for task execution with the Agent. Imagine zapier for LLM-based workflow automation
✅ Task is a structure for task execution with the Agent.
✅ Tasks can have descriptions, scheduling, triggers, actions, conditions, dependencies, priority, and a history.
✅ The Task structure allows for efficient workflow automation with LLM-based agents.
import os
from dotenv import load_dotenv
from swarms.structs import Agent, OpenAIChat, Task
# Load the environment variables
load_dotenv()
# Define a function to be used as the action
def my_action():
print("Action executed")
# Define a function to be used as the condition
def my_condition():
print("Condition checked")
return True
# Create an agent
agent = Agent(
llm=OpenAIChat(openai_api_key=os.environ["OPENAI_API_KEY"]),
max_loops=1,
dashboard=False,
)
# Create a task
task = Task(
description=(
"Generate a report on the top 3 biggest expenses for small"
" businesses and how businesses can save 20%"
),
agent=agent,
)
# Set the action and condition
task.set_action(my_action)
task.set_condition(my_condition)
# Execute the task
print("Executing task...")
task.run()
# Check if the task is completed
if task.is_completed():
print("Task completed")
else:
print("Task not completed")
# Output the result of the task
print(f"Task result: {task.result}")-
Modularity and Flexibility: BlocksList allows users to create custom swarms by adding or removing different classes or functions as blocks. This means users can easily tailor the functionality of their swarm to suit their specific needs.
-
Ease of Management: With methods to add, remove, update, and retrieve blocks, BlocksList provides a straightforward way to manage the components of a swarm. This makes it easier to maintain and update the swarm over time.
-
Enhanced Searchability: BlocksList offers methods to get blocks by various attributes such as name, type, ID, and parent-related properties. This makes it easier for users to find and work with specific blocks in a large and complex swarm.
import os
from dotenv import load_dotenv
from transformers import AutoModelForCausalLM, AutoTokenizer
from pydantic import BaseModel
from swarms import BlocksList, Gemini, GPT4VisionAPI, Mixtral, OpenAI, ToolAgent
# Load the environment variables
load_dotenv()
# Get the environment variables
openai_api_key = os.getenv("OPENAI_API_KEY")
gemini_api_key = os.getenv("GEMINI_API_KEY")
# Tool Agent
model = AutoModelForCausalLM.from_pretrained("databricks/dolly-v2-12b")
tokenizer = AutoTokenizer.from_pretrained("databricks/dolly-v2-12b")
# Initialize the schema for the person's information
class Schema(BaseModel):
name: str = Field(..., title="Name of the person")
agent: int = Field(..., title="Age of the person")
is_student: bool = Field(
..., title="Whether the person is a student"
)
courses: list[str] = Field(
..., title="List of courses the person is taking"
)
# Convert the schema to a JSON string
json_schema = base_model_to_json(Schema)
toolagent = ToolAgent(model=model, tokenizer=tokenizer, json_schema=json_schema)
# Blocks List which enables you to build custom swarms by adding classes or functions
swarm = BlocksList(
"SocialMediaSwarm",
"A swarm of social media agents",
[
OpenAI(openai_api_key=openai_api_key),
Mixtral(),
GPT4VisionAPI(openai_api_key=openai_api_key),
Gemini(gemini_api_key=gemini_api_key),
],
)
# Add the new block to the swarm
swarm.add(toolagent)
# Remove a block from the swarm
swarm.remove(toolagent)
# Update a block in the swarm
swarm.update(toolagent)
# Get a block at a specific index
block_at_index = swarm.get(0)
# Get all blocks in the swarm
all_blocks = swarm.get_all()
# Get blocks by name
openai_blocks = swarm.get_by_name("OpenAI")
# Get blocks by type
gpt4_blocks = swarm.get_by_type("GPT4VisionAPI")
# Get blocks by ID
block_by_id = swarm.get_by_id(toolagent.id)
# Get blocks by parent
blocks_by_parent = swarm.get_by_parent(swarm)
# Get blocks by parent ID
blocks_by_parent_id = swarm.get_by_parent_id(swarm.id)
# Get blocks by parent name
blocks_by_parent_name = swarm.get_by_parent_name(swarm.name)
# Get blocks by parent type
blocks_by_parent_type = swarm.get_by_parent_type(type(swarm).__name__)
# Get blocks by parent description
blocks_by_parent_description = swarm.get_by_parent_description(swarm.description)
# Run the block in the swarm
inference = swarm.run_block(toolagent, "Hello World")
print(inference)Multiple-agents will evaluate an idea based off of an parsing or evaluation function. From papers like "More agents is all you need
from swarms import Agent, MajorityVoting, ChromaDB, Anthropic
# Initialize the llm
llm = Anthropic()
# Agents
agent1 = Agent(
llm = llm,
system_prompt="You are the leader of the Progressive Party. What is your stance on healthcare?",
agent_name="Progressive Leader",
agent_description="Leader of the Progressive Party",
long_term_memory=ChromaDB(),
max_steps=1,
)
agent2 = Agent(
llm=llm,
agent_name="Conservative Leader",
agent_description="Leader of the Conservative Party",
long_term_memory=ChromaDB(),
max_steps=1,
)
agent3 = Agent(
llm=llm,
agent_name="Libertarian Leader",
agent_description="Leader of the Libertarian Party",
long_term_memory=ChromaDB(),
max_steps=1,
)
# Initialize the majority voting
mv = MajorityVoting(
agents=[agent1, agent2, agent3],
output_parser=llm.majority_voting,
autosave=False,
verbose=True,
)
# Start the majority voting
mv.run("What is your stance on healthcare?")Here's a production grade swarm ready for real-world deployment in a factory and logistics settings like warehouses. This swarm can automate 3 costly and inefficient workflows, safety checks, productivity checks, and warehouse security.
import os
from dotenv import load_dotenv
from swarms.models import GPT4VisionAPI
from swarms.prompts.logistics import (
Efficiency_Agent_Prompt,
Health_Security_Agent_Prompt,
Productivity_Agent_Prompt,
Quality_Control_Agent_Prompt,
Safety_Agent_Prompt,
Security_Agent_Prompt,
Sustainability_Agent_Prompt,
)
from swarms.structs import Agent
# Load ENV
load_dotenv()
api_key = os.getenv("OPENAI_API_KEY")
# GPT4VisionAPI
llm = GPT4VisionAPI(openai_api_key=api_key)
# Image for analysis
factory_image = "factory_image1.jpg"
# Initialize agents with respective prompts
health_security_agent = Agent(
llm=llm,
sop=Health_Security_Agent_Prompt,
max_loops=1,
multi_modal=True,
)
# Quality control agent
quality_control_agent = Agent(
llm=llm,
sop=Quality_Control_Agent_Prompt,
max_loops=1,
multi_modal=True,
)
# Productivity Agent
productivity_agent = Agent(
llm=llm,
sop=Productivity_Agent_Prompt,
max_loops=1,
multi_modal=True,
)
# Initiailize safety agent
safety_agent = Agent(llm=llm, sop=Safety_Agent_Prompt, max_loops=1, multi_modal=True)
# Init the security agent
security_agent = Agent(
llm=llm, sop=Security_Agent_Prompt, max_loops=1, multi_modal=True
)
# Initialize sustainability agent
sustainability_agent = Agent(
llm=llm,
sop=Sustainability_Agent_Prompt,
max_loops=1,
multi_modal=True,
)
# Initialize efficincy agent
efficiency_agent = Agent(
llm=llm,
sop=Efficiency_Agent_Prompt,
max_loops=1,
multi_modal=True,
)
# Run agents with respective tasks on the same image
health_analysis = health_security_agent.run(
"Analyze the safety of this factory", factory_image
)
quality_analysis = quality_control_agent.run(
"Examine product quality in the factory", factory_image
)
productivity_analysis = productivity_agent.run(
"Evaluate factory productivity", factory_image
)
safety_analysis = safety_agent.run(
"Inspect the factory's adherence to safety standards",
factory_image,
)
security_analysis = security_agent.run(
"Assess the factory's security measures and systems",
factory_image,
)
sustainability_analysis = sustainability_agent.run(
"Examine the factory's sustainability practices", factory_image
)
efficiency_analysis = efficiency_agent.run(
"Analyze the efficiency of the factory's manufacturing process",
factory_image,
)Run the agent with multiple modalities useful for various real-world tasks in manufacturing, logistics, and health.
# Description: This is an example of how to use the Agent class to run a multi-modal workflow
import os
from dotenv import load_dotenv
from swarms.models.gpt4_vision_api import GPT4VisionAPI
from swarms.structs import Agent
# Load the environment variables
load_dotenv()
# Get the API key from the environment
api_key = os.environ.get("OPENAI_API_KEY")
# Initialize the language model
llm = GPT4VisionAPI(
openai_api_key=api_key,
max_tokens=500,
)
# Initialize the task
task = (
"Analyze this image of an assembly line and identify any issues such as"
" misaligned parts, defects, or deviations from the standard assembly"
" process. IF there is anything unsafe in the image, explain why it is"
" unsafe and how it could be improved."
)
img = "assembly_line.jpg"
## Initialize the workflow
agent = Agent(
llm=llm, max_loops="auto", autosave=True, dashboard=True, multi_modal=True
)
# Run the workflow on a task
agent.run(task=task, img=img)from swarms import AbstractLLM
class vLLMLM(AbstractLLM):
def __init__(self, model_name='default_model', tensor_parallel_size=1, *args, **kwargs):
super().__init__(*args, **kwargs)
self.model_name = model_name
self.tensor_parallel_size = tensor_parallel_size
# Add any additional initialization here
def run(self, task: str):
pass
# Example
model = vLLMLM("mistral")
# Run the model
out = model("Analyze these financial documents and summarize of them")
print(out)from swarms import Agent
class MyCustomAgent(Agent):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# Custom initialization logic
def custom_method(self, *args, **kwargs):
# Implement custom logic here
pass
def run(self, task, *args, **kwargs):
# Customize the run method
response = super().run(task, *args, **kwargs)
# Additional custom logic
return response`
# Model
agent = MyCustomAgent()
# Run the agent
out = agent("Analyze and summarize these financial documents: ")
print(out)from swarms import AutoSwarm, AutoSwarmRouter, BaseSwarm
# Build your own Swarm
class MySwarm(BaseSwarm):
def __init__(self, name="kyegomez/myswarm", *args, **kwargs):
super().__init__(*args, **kwargs)
self.name = name
def run(self, task: str, *args, **kwargs):
# Add your multi-agent logic here
# agent 1
# agent 2
# agent 3
return "output of the swarm"
# Add your custom swarm to the AutoSwarmRouter
router = AutoSwarmRouter(
swarms=[MySwarm]
)
# Create an AutoSwarm instance
autoswarm = AutoSwarm(
name="kyegomez/myswarm",
description="A simple API to build and run swarms",
verbose=True,
router=router,
)
# Run the AutoSwarm
autoswarm.run("Analyze these financial data and give me a summary")
Documentation is located here at: swarms.apac.ai
The easiest way to contribute is to pick any issue with the good first issue tag 💪. Read the Contributing guidelines here. Bug Report? File here | Feature Request? File here
Swarms is an open-source project, and contributions are VERY welcome. If you want to contribute, you can create new features, fix bugs, or improve the infrastructure. Please refer to the CONTRIBUTING.md and our contributing board to participate in Roadmap discussions!
Join our growing community around the world, for real-time support, ideas, and discussions on Swarms 😊
- View our official Blog
- Chat live with us on Discord
- Follow us on Twitter
- Connect with us on LinkedIn
- Visit us on YouTube
- Join the Swarms community on Discord!
- Join our Swarms Community Gathering every Thursday at 1pm NYC Time to unlock the potential of autonomous agents in automating your daily tasks Sign up here
Book a discovery call to learn how Swarms can lower your operating costs by 40% with swarms of autonomous agents in lightspeed. Click here to book a time that works for you!
Help us accelerate our backlog by supporting us financially! Note, we're an open source corporation and so all the revenue we generate is through donations at the moment ;)
The swarms package has been meticlously crafted for extreme use-ability and understanding, the swarms package is split up into various modules such as swarms.agents that holds pre-built agents, swarms.structs that holds a vast array of structures like Agent and multi agent structures. The 3 most important are structs, models, and agents.
├── __init__.py
├── agents
├── artifacts
├── chunkers
├── cli
├── loaders
├── memory
├── models
├── prompts
├── structs
├── telemetry
├── tokenizers
├── tools
├── utils
└── workersThis application uses Docker with CUDA support. To build and run the Docker container, follow these steps:
- Make sure you have Docker installed on your machine.
- Ensure your machine has an NVIDIA GPU and NVIDIA Docker support installed.
To build the Docker image, navigate to the root directory containing the Dockerfile and run the following command:
docker build --gpus all -t swarmsTo run the Docker container, use the following command:
docker run --gpus all -p 4000:80 swarms
Replace swarms with the name of your Docker image, and replace 4000:80 with your actual port mapping. The format is hostPort:containerPort.
Now, your application should be running with CUDA support!
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Apache License
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