The Enterprise-Grade Production-Ready Multi-Agent Orchestration Framework

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", interactive=True, tools=[terminal, browser, file_editor, create_file], streaming=True, long_term_memory=memory, ) # Run the agent out = agent( "Create a CSV file with the latest tax rates for C corporations in the following ten states and the District of Columbia: Alabama, California, Florida, Georgia, Illinois, New York, North Carolina, Ohio, Texas, and Washington." ) print(out) ``` ------- ### Misc Agent Settings We provide vast array of features to save agent states using json, yaml, toml, upload pdfs, batched jobs, and much more! ```python # # Convert the agent object to a dictionary print(agent.to_dict()) print(agent.to_toml()) print(agent.model_dump_json()) print(agent.model_dump_yaml()) # Ingest documents into the agent's knowledge base agent.ingest_docs("your_pdf_path.pdf") # Receive a message from a user and process it agent.receive_message(name="agent_name", message="message") # Send a message from the agent to a user agent.send_agent_message(agent_name="agent_name", message="message") # Ingest multiple documents into the agent's knowledge base agent.ingest_docs("your_pdf_path.pdf", "your_csv_path.csv") # Run the agent with a filtered system prompt agent.filtered_run( "How can I establish a ROTH IRA to buy stocks and get a tax break? What are the criteria?" ) # Run the agent with multiple system prompts agent.bulk_run( [ "How can I establish a ROTH IRA to buy stocks and get a tax break? What are the criteria?", "Another system prompt", ] ) # Add a memory to the agent agent.add_memory("Add a memory to the agent") # Check the number of available tokens for the agent agent.check_available_tokens() # Perform token checks for the agent agent.tokens_checks() # Print the dashboard of the agent agent.print_dashboard() # Fetch all the documents from the doc folders agent.get_docs_from_doc_folders() # Activate agent ops agent.activate_agentops() agent.check_end_session_agentops() # Dump the model to a JSON file agent.model_dump_json() print(agent.to_toml()) ``` ### `Agent`with Pydantic BaseModel as Output Type The following is an example of an agent that intakes a pydantic basemodel and outputs it at the same time: ```python from pydantic import BaseModel, Field from swarms import Anthropic, Agent # 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 = Schema( name="Tool Name", agent=1, is_student=True, courses=["Course1", "Course2"], ) # Define the task to generate a person's information task = "Generate a person's information based on the following schema:" # Initialize the agent agent = Agent( agent_name="Person Information Generator", system_prompt=( "Generate a person's information based on the following schema:" ), # Set the tool schema to the JSON string -- this is the key difference tool_schema=tool_schema, llm=Anthropic(), max_loops=3, autosave=True, dashboard=False, streaming_on=True, verbose=True, interactive=True, # Set the output type to the tool schema which is a BaseModel output_type=tool_schema, # or dict, or str 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 to generate the person's information generated_data = agent.run(task) # Print the generated data print(f"Generated data: {generated_data}") ``` ### Multi Modal Autonomous Agent Run the agent with multiple modalities useful for various real-world tasks in manufacturing, logistics, and health. ```python import os from dotenv import load_dotenv from swarms import GPT4VisionAPI, Agent # Load the environment variables load_dotenv() # Initialize the language model llm = GPT4VisionAPI( openai_api_key=os.environ.get("OPENAI_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( agent_name = "Multi-ModalAgent", llm=llm, max_loops="auto", autosave=True, dashboard=True, multi_modal=True ) # Run the workflow on a task agent.run(task, img) ``` ---- ### `ToolAgent` 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. ```python 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}") ``` ### `Task` For deeper control of your agent stack, `Task` is a simple structure for task execution with the `Agent`. Imagine zapier like 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. ```python import os from dotenv import load_dotenv from swarms 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}") ``` --- ---- # Multi-Agent Orchestration: Swarms was designed to facilitate the communication between many different and specialized agents from a vast array of other frameworks such as langchain, autogen, crew, and more. In traditional swarm theory, there are many types of swarms usually for very specialized use-cases and problem sets. Such as Hiearchical and sequential are great for accounting and sales, because there is usually a boss coordinator agent that distributes a workload to other specialized agents. | **Name** | **Description** | **Code Link** | **Use Cases** | |-------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------|---------------------------------------------------------------------------------------------------| | Hierarchical Swarms | A system where agents are organized in a hierarchy, with higher-level agents coordinating lower-level agents to achieve complex tasks. | [Code Link](#) | Manufacturing process optimization, multi-level sales management, healthcare resource coordination | | Agent Rearrange | A setup where agents rearrange themselves dynamically based on the task requirements and environmental conditions. | [Code Link](https://docs.swarms.world/en/latest/swarms/structs/agent_rearrange/) | Adaptive manufacturing lines, dynamic sales territory realignment, flexible healthcare staffing | | Concurrent Workflows | Agents perform different tasks simultaneously, coordinating to complete a larger goal. | [Code Link](#) | Concurrent production lines, parallel sales operations, simultaneous patient care processes | | Sequential Coordination | Agents perform tasks in a specific sequence, where the completion of one task triggers the start of the next. | [Code Link](https://docs.swarms.world/en/latest/swarms/structs/sequential_workflow/) | Step-by-step assembly lines, sequential sales processes, stepwise patient treatment workflows | | Parallel Processing | Agents work on different parts of a task simultaneously to speed up the overall process. | [Code Link](#) | Parallel data processing in manufacturing, simultaneous sales analytics, concurrent medical tests | ### `SequentialWorkflow` 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. ```python from swarms import Agent, SequentialWorkflow, Anthropic # Initialize the language model agent (e.g., GPT-3) llm = Anthropic() # Initialize agents for individual tasks agent1 = Agent( agent_name="Blog generator", system_prompt="Generate a blog post like stephen king", llm=llm, max_loops=1, dashboard=False, tools=[], ) agent2 = Agent( agent_name="summarizer", system_prompt="Sumamrize the blog post", llm=llm, max_loops=1, dashboard=False, tools=[], ) # Create the Sequential workflow workflow = SequentialWorkflow( agents=[agent1, agent2], max_loops=1, verbose=False ) # Run the workflow workflow.run( "Generate a blog post on how swarms of agents can help businesses grow." ) ``` ------ ## `AgentRearrange` Inspired by Einops and einsum, this orchestration techniques enables you to map out the relationships between various agents. For example you specify linear and sequential relationships like `a -> a1 -> a2 -> a3` or concurrent relationships where the first agent will send a message to 3 agents all at once: `a -> a1, a2, a3`. You can customize your workflow to mix sequential and concurrent relationships. [Docs Available:](https://swarms.apac.ai/en/latest/swarms/structs/agent_rearrange/) ```python from swarms import Agent, AgentRearrange, Anthropic # Initialize the director agent director = Agent( agent_name="Director", system_prompt="Directs the tasks for the workers", llm=Anthropic(), max_loops=1, dashboard=False, streaming_on=True, verbose=True, stopping_token="", state_save_file_type="json", saved_state_path="director.json", ) # Initialize worker 1 worker1 = Agent( agent_name="Worker1", system_prompt="Generates a transcript for a youtube video on what swarms are", llm=Anthropic(), max_loops=1, dashboard=False, streaming_on=True, verbose=True, stopping_token="", state_save_file_type="json", saved_state_path="worker1.json", ) # Initialize worker 2 worker2 = Agent( agent_name="Worker2", system_prompt="Summarizes the transcript generated by Worker1", llm=Anthropic(), max_loops=1, dashboard=False, streaming_on=True, verbose=True, stopping_token="", state_save_file_type="json", saved_state_path="worker2.json", ) # Create a list of agents agents = [director, worker1, worker2] # Define the flow pattern flow = "Director -> Worker1 -> Worker2" # Using AgentRearrange class agent_system = AgentRearrange(agents=agents, flow=flow) output = agent_system.run( "Create a format to express and communicate swarms of llms in a structured manner for youtube" ) print(output) ``` ## `HierarhicalSwarm` Coming soon... ## `GraphSwarm` ```python import os from dotenv import load_dotenv from swarms import Agent, Edge, GraphWorkflow, Node, NodeType, OpenAIChat load_dotenv() api_key = os.environ.get("OPENAI_API_KEY") llm = OpenAIChat( temperature=0.5, openai_api_key=api_key, max_tokens=4000 ) agent1 = Agent(llm=llm, max_loops=1, autosave=True, dashboard=True) agent2 = Agent(llm=llm, max_loops=1, autosave=True, dashboard=True) def sample_task(): print("Running sample task") return "Task completed" wf_graph = GraphWorkflow() wf_graph.add_node(Node(id="agent1", type=NodeType.AGENT, agent=agent1)) wf_graph.add_node(Node(id="agent2", type=NodeType.AGENT, agent=agent2)) wf_graph.add_node( Node(id="task1", type=NodeType.TASK, callable=sample_task) ) wf_graph.add_edge(Edge(source="agent1", target="task1")) wf_graph.add_edge(Edge(source="agent2", target="task1")) wf_graph.set_entry_points(["agent1", "agent2"]) wf_graph.set_end_points(["task1"]) print(wf_graph.visualize()) # Run the workflow results = wf_graph.run() print("Execution results:", results) ``` ## `MixtureOfAgents` This is an implementation from the paper: "Mixture-of-Agents Enhances Large Language Model Capabilities" by together.ai, it achieves SOTA on AlpacaEval 2.0, MT-Bench and FLASK, surpassing GPT-4 Omni. Great for tasks that need to be parallelized and then sequentially fed into another loop ```python from swarms import Agent, OpenAIChat, MixtureOfAgents # Initialize the director agent director = Agent( agent_name="Director", system_prompt="Directs the tasks for the accountants", llm=OpenAIChat(), max_loops=1, dashboard=False, streaming_on=True, verbose=True, stopping_token="", state_save_file_type="json", saved_state_path="director.json", ) # Initialize accountant 1 accountant1 = Agent( agent_name="Accountant1", system_prompt="Prepares financial statements", llm=OpenAIChat(), max_loops=1, dashboard=False, streaming_on=True, verbose=True, stopping_token="", state_save_file_type="json", saved_state_path="accountant1.json", ) # Initialize accountant 2 accountant2 = Agent( agent_name="Accountant2", system_prompt="Audits financial records", llm=OpenAIChat(), max_loops=1, dashboard=False, streaming_on=True, verbose=True, stopping_token="", state_save_file_type="json", saved_state_path="accountant2.json", ) # Create a list of agents agents = [director, accountant1, accountant2] # Swarm swarm = MixtureOfAgents( name="Mixture of Accountants", agents=agents, layers=3, final_agent=director, ) # Run the swarm out = swarm.run("Prepare financial statements and audit financial records") print(out) ``` ## SpreadSheetSwarm An all-new swarm architecture that makes it easy to manage and oversee the outputs of thousands of agents all at once! [Learn more at the docs here:](https://docs.swarms.world/en/latest/swarms/structs/spreadsheet_swarm/) ```python import os from swarms import Agent, 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 specializing in real estate. Your task is to create engaging, concise tweets to promote properties, analyze trends to maximize engagement, and use appropriate hashtags and timing to reach potential buyers. """ INSTAGRAM_AGENT_SYS_PROMPT = """ You are an Instagram marketing expert focusing on real estate. Your task is to create visually appealing posts with engaging captions and hashtags to showcase properties, targeting specific demographics interested in real estate. """ FACEBOOK_AGENT_SYS_PROMPT = """ You are a Facebook marketing expert for real estate. Your task is to craft posts optimized for engagement and reach on Facebook, including using images, links, and targeted messaging to attract potential property buyers. """ LINKEDIN_AGENT_SYS_PROMPT = """ You are a LinkedIn marketing expert for the real estate industry. Your task is to create professional and informative posts, highlighting property features, market trends, and investment opportunities, tailored to professionals and investors. """ EMAIL_AGENT_SYS_PROMPT = """ You are an Email marketing expert specializing in real estate. Your task is to write compelling email campaigns to promote properties, focusing on personalization, subject lines, and effective call-to-action strategies to drive conversions. """ # 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-RealEstate-Agent", system_prompt=TWITTER_AGENT_SYS_PROMPT, llm=model, max_loops=1, dynamic_temperature_enabled=True, saved_state_path="twitter_realestate_agent.json", user_name="realestate_swarms", retry_attempts=1, ), Agent( agent_name="Instagram-RealEstate-Agent", system_prompt=INSTAGRAM_AGENT_SYS_PROMPT, llm=model, max_loops=1, dynamic_temperature_enabled=True, saved_state_path="instagram_realestate_agent.json", user_name="realestate_swarms", retry_attempts=1, ), Agent( agent_name="Facebook-RealEstate-Agent", system_prompt=FACEBOOK_AGENT_SYS_PROMPT, llm=model, max_loops=1, dynamic_temperature_enabled=True, saved_state_path="facebook_realestate_agent.json", user_name="realestate_swarms", retry_attempts=1, ), Agent( agent_name="LinkedIn-RealEstate-Agent", system_prompt=LINKEDIN_AGENT_SYS_PROMPT, llm=model, max_loops=1, dynamic_temperature_enabled=True, saved_state_path="linkedin_realestate_agent.json", user_name="realestate_swarms", retry_attempts=1, ), Agent( agent_name="Email-RealEstate-Agent", system_prompt=EMAIL_AGENT_SYS_PROMPT, llm=model, max_loops=1, dynamic_temperature_enabled=True, saved_state_path="email_realestate_agent.json", user_name="realestate_swarms", retry_attempts=1, ), ] # Create a Swarm with the list of agents swarm = SpreadSheetSwarm( name="Real-Estate-Marketing-Swarm", description="A swarm that processes real estate marketing tasks using multiple agents on different threads.", agents=agents, autosave_on=True, save_file_path="real_estate_marketing_spreadsheet.csv", run_all_agents=False, max_loops=2, ) # Run the swarm swarm.run( task=""" Create posts to promote luxury properties in North Texas, highlighting their features, location, and investment potential. Include relevant hashtags, images, and engaging captions. Property: $10,399,000 1609 Meandering Way Dr, Roanoke, TX 76262 Link to the property: https://www.zillow.com/homedetails/1609-Meandering-Way-Dr-Roanoke-TX-76262/308879785_zpid/ What's special Unveiling a new custom estate in the prestigious gated Quail Hollow Estates! This impeccable residence, set on a sprawling acre surrounded by majestic trees, features a gourmet kitchen equipped with top-tier Subzero and Wolf appliances. European soft-close cabinets and drawers, paired with a double Cambria Quartzite island, perfect for family gatherings. The first-floor game room&media room add extra layers of entertainment. Step into the outdoor sanctuary, where a sparkling pool and spa, and sunken fire pit, beckon leisure. The lavish master suite features stunning marble accents, custom his&her closets, and a secure storm shelter.Throughout the home,indulge in the visual charm of designer lighting and wallpaper, elevating every space. The property is complete with a 6-car garage and a sports court, catering to the preferences of basketball or pickleball enthusiasts. This residence seamlessly combines luxury&recreational amenities, making it a must-see for the discerning buyer. Facts & features Interior Bedrooms & bathrooms Bedrooms: 6 Bathrooms: 8 Full bathrooms: 7 1/2 bathrooms: 1 Primary bedroom Bedroom Features: Built-in Features, En Suite Bathroom, Walk-In Closet(s) Cooling Central Air, Ceiling Fan(s), Electric Appliances Included: Built-In Gas Range, Built-In Refrigerator, Double Oven, Dishwasher, Gas Cooktop, Disposal, Ice Maker, Microwave, Range, Refrigerator, Some Commercial Grade, Vented Exhaust Fan, Warming Drawer, Wine Cooler Features Wet Bar, Built-in Features, Dry Bar, Decorative/Designer Lighting Fixtures, Eat-in Kitchen, Elevator, High Speed Internet, Kitchen Island, Pantry, Smart Home, Cable TV, Walk-In Closet(s), Wired for Sound Flooring: Hardwood Has basement: No Number of fireplaces: 3 Fireplace features: Living Room, Primary Bedroom Interior area Total interior livable area: 10,466 sqft Total spaces: 12 Parking features: Additional Parking Attached garage spaces: 6 Carport spaces: 6 Features Levels: Two Stories: 2 Patio & porch: Covered Exterior features: Built-in Barbecue, Barbecue, Gas Grill, Lighting, Outdoor Grill, Outdoor Living Area, Private Yard, Sport Court, Fire Pit Pool features: Heated, In Ground, Pool, Pool/Spa Combo Fencing: Wrought Iron Lot Size: 1.05 Acres Details Additional structures: Outdoor Kitchen Parcel number: 42232692 Special conditions: Standard Construction Type & style Home type: SingleFamily Architectural style: Contemporary/Modern,Detached Property subtype: Single Family Residence """ ) ``` ---------- ## Onboarding Session Get onboarded now with the creator and lead maintainer of Swarms, Kye Gomez, who will show you how to get started with the installation, usage examples, and starting to build your custom use case! [CLICK HERE](https://cal.com/swarms/swarms-onboarding-session) --- ## Documentation Documentation is located here at: [docs.swarms.world](https://docs.swarms.world) ---- ## Docker Instructions - [Learn More Here About Deployments In Docker](https://swarms.apac.ai/en/latest/docker_setup/) ----- ## Folder Structure 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`. ```sh ├── __init__.py ├── agents ├── artifacts ├── memory ├── schemas ├── models ├── prompts ├── structs ├── telemetry ├── tools ├── utils └── workers ``` ---- ## 🫶 Contributions: The easiest way to contribute is to pick any issue with the `good first issue` tag 💪. Read the Contributing guidelines [here](/CONTRIBUTING.md). Bug Report? [File here](https://github.com/swarms/gateway/issues) | Feature Request? [File here](https://github.com/swarms/gateway/issues) 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](https://github.com/kyegomez/swarms/blob/master/CONTRIBUTING.md) and our [contributing board](https://github.com/users/kyegomez/projects/1) to participate in Roadmap discussions! ---- ## Swarm Newsletter 🤖 🤖 🤖 📧 Sign up to the Swarm newsletter to receive updates on the latest Autonomous agent research papers, step by step guides on creating multi-agent app, and much more Swarmie goodiness 😊 [CLICK HERE TO SIGNUP](https://docs.google.com/forms/d/e/1FAIpQLSfqxI2ktPR9jkcIwzvHL0VY6tEIuVPd-P2fOWKnd6skT9j1EQ/viewform?usp=sf_link) ## Discovery Call 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!](https://calendly.com/swarm-corp/30min?month=2023-11) ## Accelerate Backlog Accelerate Bugs, Features, and Demos to implement by supporting us here: ## Community Join our growing community around the world, for real-time support, ideas, and discussions on Swarms 😊 - View our official [Blog](https://swarms.apac.ai) - Chat live with us on [Discord](https://discord.gg/kS3rwKs3ZC) - Follow us on [Twitter](https://twitter.com/kyegomez) - Connect with us on [LinkedIn](https://www.linkedin.com/company/the-swarm-corporation) - Visit us on [YouTube](https://www.youtube.com/channel/UC9yXyitkbU_WSy7bd_41SqQ) - [Join the Swarms community on Discord!](https://discord.gg/AJazBmhKnr) - 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](https://lu.ma/5p2jnc2v) --- # License Apache License # Citations Please cite Swarms in your paper or your project if you found it beneficial in any way! Appreciate you. ```bibtex @misc{swarms, author = {Gomez, Kye}, title = {{Swarms: The Multi-Agent Collaboration Framework}}, howpublished = {\url{https://github.com/kyegomez/swarms}}, year = {2023}, note = {Accessed: Date} } ```