Swarms: Multi-Agent Orchestration Framework

# pip
$ pip3 install -U swarms

# uv (recommended): https://github.com/astral-sh/uv
$ uv pip install swarms

# poetry
$ poetry add swarms

# Clone the repository
$ git clone https://github.com/kyegomez/swarms.git
$ cd swarms
$ pip install -r requirements.txt

# Pull the image
$ docker pull swarmscorp/swarms:latest

# Run a script from the current directory, passing your API key from the shell
$ docker run --rm -e OPENAI_API_KEY -v "$PWD:/app" swarmscorp/swarms python agent.py

# Open a Python shell with swarms installed
$ docker run -it --rm -e OPENAI_API_KEY swarmscorp/swarms

OPENAI_API_KEY=""
WORKSPACE_DIR="agent_workspace"
ANTHROPIC_API_KEY=""
GROQ_API_KEY=""
SWARMS_TELEMETRY_ON

from swarms import Agent

# Initialize a new agent
agent = Agent(
    model_name="gpt-5.4", # Specify the LLM
    max_loops="auto",              # Set the number of interactions
    interactive=True,         # Enable interactive mode for real-time feedback
    temperature=None,
)

# Run the agent with a task
agent.run("What are the key benefits of using a multi-agent system?")

from swarms import Agent, SequentialWorkflow

# Agent 1: The Researcher
researcher = Agent(
    agent_name="Researcher",
    system_prompt="Your job is to research the provided topic and provide a detailed summary.",
    model_name="gpt-5.4",
)

# Agent 2: The Writer
writer = Agent(
    agent_name="Writer",
    system_prompt="Your job is to take the research summary and write a beautiful, engaging blog post about it.",
    model_name="gpt-5.4",
)

# Create a sequential workflow where the researcher's output feeds into the writer's input
workflow = SequentialWorkflow(agents=[researcher, writer])

# Run the workflow on a task
final_post = workflow.run("The history and future of artificial intelligence")
print(final_post)