Swarms Orchestration Framework

What does this agent do, and when should you use it?

Swarms is a Python multi-agent orchestration library installable with pip, uv, Poetry, or from source. Its core Agent combines an LLM, tools, and memory, with behavior configured through fields such as model_name, system_prompt, and max_loops before calling Agent.run. The repository documents SequentialWorkflow, ConcurrentWorkflow, GraphWorkflow, AgentRearrange, MixtureOfAgents, GroupChat, HierarchicalSwarm, HeavySwarm, and SwarmRouter. GraphWorkflow represents agents as DAG nodes and dependencies as edges, while SwarmRouter selects a workflow type through one interface. Runs return the output of the selected Agent or workflow; the AOP example additionally shows registered agents served from a configured port.

A developer creates an Agent with fields including agent_name, system_prompt, model_name, and max_loops, then calls agent.run(task) on a text task. Multiple agents can be passed to SequentialWorkflow to pass output down a chain, to ConcurrentWorkflow to work on one task simultaneously, or to GraphWorkflow as Node objects linked by Edge dependencies. AgentRearrange describes relationships with a flow string such as researcher -> writer, editor, while MixtureOfAgents runs expert agents in parallel and sends their results to aggregator_agent. Setting mcp_url or mcp_urls connects an Agent to MCP servers and makes their tools available automatically. AutoSwarmBuilder generates agents and workflow configurations from a task description, and AOP registers agents with add_agent before starting a server with run.

1. A content team that needs research, drafting, and editing in order can use SequentialWorkflow to pass each agent's output to the next one.
2. An analysis team producing market, financial, and risk assessments for the same prompt can run specialist agents with ConcurrentWorkflow.
3. A software or research workflow with fan-out, fan-in, or diamond-shaped dependencies can model its work as a GraphWorkflow DAG.
4. A Python application that wants to switch collaboration strategies without changing its calling pattern can use SwarmRouter with different swarm_type values.
5. A developer whose agent needs tools or external data from an MCP server can configure mcp_url or mcp_urls.
6. A team exposing research and analysis agents as services can follow the AOP pattern using server_name, port, add_agent, and run.

What are this agent's strengths and limitations?

How do you install or deploy this agent?

Install with:

uv pip install swarms

Alternatives:

pip3 install -U swarms
poetry add swarms

From source:

git clone https://github.com/kyegomez/swarms.git
cd swarms
pip install -r requirements.txt

The README lists OPENAI_API_KEY, ANTHROPIC_API_KEY, GROQ_API_KEY, and WORKSPACE_DIR as environment configuration variables.

How do you use this agent?

Minimal invocation:

from swarms import Agent

agent = Agent(

model_name="gpt-5.4",
max_loops="auto",
interactive=True,
temperature=None,
)

result = agent.run("What are the key benefits of using a multi-agent system?")
print(result)

For a chain, pass agents to SequentialWorkflow(agents=[...]) and call workflow.run(task). For MCP access, set mcp_url="https://mcp.deepwiki.com/mcp" or mcp_urls on an Agent.

How does this agent compare with similar options?

FAQ