🐝 Agency Swarm

Overview

Agency Swarm started as a desire and effort of Arsenii Shatokhin (aka VRSEN) to fully automate his AI Agency with AI. By building this framework, we aim to simplify the agent creation process and enable anyone to create collaborative swarm of agents (Agencies), each with distinct roles and capabilities. By thinking about automation in terms of real world entities, such as agencies and specialized agent roles, we make it a lot more intuitive for both the agents and the users.

Key Features

• Customizable Agent Roles: Define roles like CEO, virtual assistant, developer, etc., and customize their functionalities with Assistants API.
• Full Control Over Prompts: Avoid conflicts and restrictions of pre-defined prompts, allowing full customization.
• Tool Creation: Tools within Agency Swarm are created using Instructor, which provides a convenient interface and automatic type validation.
• Efficient Communication: Agents communicate through a specially designed "send message" tool based on their own descriptions.
• State Management: Agency Swarm efficiently manages the state of your assistants on OpenAI, maintaining it in a special settings.json file.
• Deployable in Production: Agency Swarm is designed to be reliable and easily deployable in production environments.

Installation

pip install agency-swarm

Getting Started

1. Set Your OpenAI Key:

   from agency_swarm import set_openai_key
   set_openai_key("YOUR_API_KEY")

2. Create Tools: Define your custom tools with Instructor:

   from agency_swarm.tools import BaseTool
   from pydantic import Field
   
   class MyCustomTool(BaseTool):
       """
       A brief description of what the custom tool does. 
       The docstring should clearly explain the tool's purpose and functionality.
       """
   
       # Define the fields with descriptions using Pydantic Field
       example_field: str = Field(
           ..., description="Description of the example field, explaining its purpose and usage."
       )
   
       # Additional fields as required
       # ...
   
       def run(self):
           """
           The implementation of the run method, where the tool's main functionality is executed.
           This method should utilize the fields defined above to perform its task.
           Doc string description is not required for this method.
           """
   
           # Your custom tool logic goes here
           do_something(self.example_field)
   
           # Return the result of the tool's operation
           return "Result of MyCustomTool operation"

   or convert from OpenAPI schemas:

   from agency_swarm.tools import ToolFactory
   # using local file
   with open("schemas/your_schema.json") as f:
       tools = ToolFactory.from_openapi_schema(
           f.read(),
       )
   
   # using requests
   tools = ToolFactory.from_openapi_schema(
       requests.get("https://api.example.com/openapi.json").json(),
   )

3. Define Agent Roles: Start by defining the roles of your agents. For example, a CEO agent for managing tasks and a developer agent for executing tasks.

   from agency_swarm import Agent
   
   ceo = Agent(name="CEO",
               description="Responsible for client communication, task planning and management.",
               instructions="You must converse with other agents to ensure complete task execution.", # can be a file like ./instructions.md
               files_folder="./files", # files to be uploaded to OpenAI
               schemas_folder="./schemas", # OpenAPI schemas to be converted into tools
               tools=[MyCustomTool])

   Import from existing agents:

   agency-swarm import-agent --name "Devid" --destination "./"

   This will import Devid (Software Developer) Agent locally, including all source code files, so you have full control over your system. Currently, available agents are: Devid, BrowsingAgent.

4. Define Agency Communication Flows: Establish how your agents will communicate with each other.

   from agency_swarm import Agency
   # if importing from local files
   from Developer import Developer
   from VirtualAssistant import VirtualAssistant
   
   dev = Developer()
   va = VirtualAssistant()
   
   agency = Agency([
       ceo,  # CEO will be the entry point for communication with the user
       [ceo, dev],  # CEO can initiate communication with Developer
       [ceo, va],   # CEO can initiate communication with Virtual Assistant
       [dev, va]    # Developer can initiate communication with Virtual Assistant
   ], shared_instructions='agency_manifesto.md') # shared instructions for all agents

   In Agency Swarm, communication flows are directional, meaning they are established from left to right in the agency_chart definition. For instance, in the example above, the CEO can initiate a chat with the developer (dev), and the developer can respond in this chat. However, the developer cannot initiate a chat with the CEO. The developer can initiate a chat with the virtual assistant (va) and assign new tasks.

5. Run Demo: Run the demo to see your agents in action!

   Web interface:

   agency.demo_gradio(height=900)

   Terminal version:

   agency.run_demo()

   Backend version:

   completion_output = agency.get_completion("Please create a new website for our client.", yield_messages=False)

CLI

Genesis Agency

The genesis command starts the genesis agency in your terminal to help you create new agencies and agents.

Command Syntax:

agency-swarm genesis [--openai_key "YOUR_API_KEY"]

Make sure to include:

• Your mission and goals.
• The agents you want to involve and their communication flows.
• Which tools or APIs each agent should have access to, if any.

Importing Existing Agents

This CLI command allows you to import existing agents from local files into your agency.

Command Syntax:

agency-swarm import-agent --name "AgentName" --destination "/path/to/directory"

To check available agents, simply run this command without any arguments.

Creating Agent Templates Locally

This CLI command simplifies the process of creating a structured environment for each agent.

Command Syntax:

agency-swarm create-agent-template --name "AgentName" --description "Agent Description" [--path "/path/to/directory"] [--use_txt]

Folder Structure

When you run the create-agent-template command, it creates the following folder structure for your agent:

/your-specified-path/
│
├── agency_manifesto.md or .txt # Agency's guiding principles (created if not exists)
└── AgentName/                  # Directory for the specific agent
    ├── files/                  # Directory for files that will be uploaded to openai
    ├── schemas/                # Directory for OpenAPI schemas to be converted into tools
    ├── tools/                  # Directory for tools to be imported by default. 
    ├── AgentName.py            # The main agent class file
    ├── __init__.py             # Initializes the agent folder as a Python package
    ├── instructions.md or .txt # Instruction document for the agent
    └── tools.py                # Custom tools specific to the agent
    

This structure ensures that each agent has its dedicated space with all necessary files to start working on its specific tasks. The tools.py can be customized to include tools and functionalities specific to the agent's role.

Future Enhancements

1. Creation of agencies that can autonomously create other agencies.
2. Asynchronous communication and task handling.
3. Inter-agency communication for a self-expanding system.

Contributing

For details on how to contribute you agents and tools to Agency Swarm, please refer to the Contributing Guide.

License

Agency Swarm is open-source and licensed under MIT.

Need Help?

If you need help creating custom agent swarms for your business, check out our Agents-as-a-Service subscription, or schedule a consultation with me at https://calendly.com/vrsen/ai-project-consultation