Swarms

Swarms is an Apache 2.0 Python framework for building multi-agent systems where large groups of agents cooperate to solve complex tasks. It ships multiple pre-built swarm architectures, from sequential pipelines to mixture- of-agents patterns, with a single Agent class that handles tool use, memory, and context management. Positioned for enterprise workloads that need many agents working in parallel.

Most agent frameworks are built around the assumption that a team of four to eight agents is a reasonable scale for a workflow. Swarms starts from a different assumption: what if you needed a hundred agents working on different parts of the same problem, and you wanted that to feel like writing normal Python rather than distributed systems engineering?

That is the problem space Swarms is designed for. It is not the right tool for building a simple research assistant with a planner and an executor. It is a tool for orchestrating agent workloads that are large enough to warrant thinking about swarm topology as a first-class design concern.

What Swarms is

Swarms is an open-source Python framework maintained by Kye Gomez and contributors at kyegomez/swarms. The Apache 2.0 license means commercial use is unrestricted. The framework has accumulated roughly 4,800 GitHub stars and ships frequent releases, currently at version 7.7.0.

The project also operates Swarms Cloud, a hosted API for running swarms without managing your own infrastructure. The open-source library and the cloud product are separate: you can use the framework entirely without the cloud.

The framework is organized around two core ideas. The first is the Agent class, a batteries-included single-agent building block that handles tool calling, memory management, context window management, and LLM provider configuration. The second is Swarm architectures, pre-built patterns for how groups of agents coordinate, which you pick based on the shape of your problem.

The Agent class

Before you can build swarms, you need agents. The Swarms Agent class is designed to minimize boilerplate for a single agent definition:

from swarms import Agent
from swarms.models import OpenAIChat

model = OpenAIChat(openai_api_key="your-key", model_name="gpt-4o", temperature=0.1)

researcher = Agent(
    agent_name="Financial-Researcher",
    system_prompt="You are a financial analyst. Analyze the provided data and identify key trends.",
    llm=model,
    max_loops=3,
    autosave=True,
    dashboard=False,
    verbose=True,
    context_length=200000,
)

result = researcher.run("Analyze the Q1 2026 earnings data for NVIDIA")
print(result)

The max_loops parameter controls how many times the agent can iterate before stopping. context_length sets the maximum context window to manage. autosave saves the agent's state after each run, which helps with long-running tasks where you might want to resume.

The agent handles tool calling through a tools parameter:

def search_web(query: str) -> str:
    """Search the web for current information."""
    # your search implementation
    return f"Search results for: {query}"

def calculate_metrics(data: str) -> str:
    """Calculate financial metrics from raw data."""
    # your calculation logic
    return "Calculated metrics"

researcher = Agent(
    agent_name="Financial-Researcher",
    system_prompt="You analyze financial data using available tools.",
    llm=model,
    tools=[search_web, calculate_metrics],
    max_loops=5,
)

Tools are plain Python functions with docstrings. The docstring becomes the tool description the LLM sees when deciding which tool to call. This is similar to how most frameworks handle tool definition, and it keeps the barrier to adding tools low.

Swarm architectures

The distinguishing feature of Swarms is the range of pre-built coordination patterns. You pick the architecture that matches how your agents need to work together.

SequentialWorkflow

The simplest swarm type. Agents run in order, each receiving the previous agent's output:

from swarms import Agent, SequentialWorkflow

agents = [
    Agent(agent_name="Researcher", llm=model, system_prompt="Research the topic thoroughly."),
    Agent(agent_name="Analyst", llm=model, system_prompt="Analyze the research findings."),
    Agent(agent_name="Writer", llm=model, system_prompt="Write a clear summary of the analysis."),
]

workflow = SequentialWorkflow(agents=agents, max_loops=1)
result = workflow.run("What are the economic implications of AI automation?")

This is the right choice for pipelines where each step genuinely depends on the previous one's output. The overhead is minimal, and the execution is predictable. For many real-world use cases, a well-designed sequential workflow is more reliable than a more complex swarm.

AgentRearrange

A more flexible routing pattern where you define explicit connections between agents:

from swarms import Agent, AgentRearrange

coordinator = Agent(agent_name="Coordinator", llm=model, system_prompt="Route tasks appropriately.")
market_analyst = Agent(agent_name="Market-Analyst", llm=model, system_prompt="Analyze market conditions.")
risk_analyst = Agent(agent_name="Risk-Analyst", llm=model, system_prompt="Assess financial risks.")
report_writer = Agent(agent_name="Report-Writer", llm=model, system_prompt="Write final reports.")

swarm = AgentRearrange(
    name="Financial-Analysis-Swarm",
    agents=[coordinator, market_analyst, risk_analyst, report_writer],
    flow="Coordinator -> Market-Analyst, Risk-Analyst -> Report-Writer",
)

result = swarm.run("Analyze the investment opportunity in renewable energy")

The flow string defines the routing: Coordinator runs first, then Market-Analyst and Risk-Analyst run in parallel (comma-separated means parallel), then Report-Writer receives both their outputs. This pattern covers the majority of multi-agent workflows that need more structure than pure sequential execution.

MixtureOfAgents

Multiple agents run on the same input independently, and their outputs get aggregated:

from swarms import MixtureOfAgents

specialist_agents = [
    Agent(agent_name="Technical-Expert", llm=model, system_prompt="Analyze technical feasibility."),
    Agent(agent_name="Business-Expert", llm=model, system_prompt="Analyze business viability."),
    Agent(agent_name="Risk-Expert", llm=model, system_prompt="Identify key risks."),
]

aggregator = Agent(
    agent_name="Aggregator",
    llm=model,
    system_prompt="Synthesize multiple expert perspectives into a unified recommendation.",
)

mixture = MixtureOfAgents(
    agents=specialist_agents,
    aggregator_agent=aggregator,
    layers=1,
)

result = mixture.run("Should we invest $5M in this fintech startup?")

This pattern is useful when you want multiple independent perspectives on the same question before synthesizing a final answer. The aggregator sees all the specialist outputs and produces a unified response. It is slower than other swarm types because all specialist agents run in parallel but the aggregator waits for them all, adding a second LLM call step.

SpreadSheetSwarm

This is the architecture that most distinguishes Swarms from other frameworks. It runs one agent per row (or data item) in a large dataset:

from swarms import Agent, SpreadSheetSwarm
import pandas as pd

task_template = "Analyze the financial health of: {company}"
companies_df = pd.DataFrame({
    "company": ["Apple", "Microsoft", "Google", "Amazon", "Meta"]
})

analyst_agent = Agent(
    agent_name="Company-Analyst",
    llm=model,
    system_prompt="You are a financial analyst. Provide a concise financial health summary.",
)

swarm = SpreadSheetSwarm(
    name="Company-Analysis-Swarm",
    agents=[analyst_agent],
    autosave_on=True,
    save_file_path="company_analysis_results.csv",
)

result = swarm.run(
    task=task_template,
    data=companies_df,
)

For each row in the data, Swarms creates an agent instance, substitutes the row values into the task template, and runs the agents concurrently. Results get saved to a CSV. This is the "swarm" pattern in the most literal sense: you have a problem that can be decomposed into many independent sub-problems, and you want each one solved in parallel.

The SpreadSheetSwarm is practically useful for any analysis task that processes a list: analyzing a portfolio of companies, processing a batch of support tickets, categorizing a dataset of articles, or generating summaries for a list of documents.

Memory and context management

Each Agent in Swarms can be configured with long-term memory via vector store integrations. The framework supports several vector store backends for persisting information across runs:

from swarms.memory import ChromaDB

memory = ChromaDB(
    metric="cosine",
    n_results=5,
    output_dir="./agent_memory",
    docs_folder="./reference_docs",
)

analyst = Agent(
    agent_name="Persistent-Analyst",
    llm=model,
    system_prompt="You analyze financial data with access to historical context.",
    long_term_memory=memory,
)

The agent automatically queries the vector store before each response to retrieve relevant context. This is useful for agents that need to recall information from previous runs or from a reference document set without injecting everything into every prompt.

The built-in context length management handles the case where conversation history would exceed the model's context window. The framework automatically trims older messages when the context gets too large, which keeps agents running on long tasks without token limit errors.

Multi-model support

Swarms supports several model providers:

from swarms.models import OpenAIChat
from swarms.models import Anthropic
from swarms.models.base_llm import BaseLLM

# OpenAI
gpt4o = OpenAIChat(model_name="gpt-4o")

# Anthropic
claude = Anthropic(model_name="claude-3-5-sonnet-20241022")

# OpenAI-compatible local models
local_model = OpenAIChat(
    model_name="llama3.2",
    openai_api_base="http://localhost:11434/v1",
    openai_api_key="ollama",
)

In a swarm, different agents can use different models, which is useful for cost optimization. You might run your coordinator on a capable model and your specialist agents on cheaper smaller models that are sufficient for their narrower tasks.

How Swarms compares to the alternatives

Swarms vs CrewAI

CrewAI is the more mature and more widely documented choice for small-to-medium multi-agent workflows. The crew-and-role model is intuitive, the community is larger, and there are more production examples. Swarms wins when you need the SpreadSheetSwarm pattern (parallel agents over large datasets) or when you want explicit swarm topology control through AgentRearrange. For most teams starting a new multi-agent project, CrewAI is the lower-risk default. For teams who've hit CrewAI's ceiling on parallel scale, Swarms is worth evaluating.

Swarms vs AutoGen

AutoGen has a larger community and a more sophisticated async runtime through its v0.4 event-driven Core. It also handles code execution better through Docker isolation. Swarms has more pre-built swarm architectures and a simpler API for getting a large number of agents working in parallel quickly. AutoGen is the better choice for code execution agents. Swarms is the better choice for large-scale data processing agents where you want many independent agents running without complex inter-agent communication.

Swarms vs LangGraph

LangGraph gives you explicit state machine control with typed state, conditional edges, and human-in-the-loop support that is more deterministic and easier to debug than any swarm-based approach. Swarms gives you faster setup for parallel multi-agent tasks where you don't need fine-grained control over branching logic. LangGraph is the right choice when correctness and auditability come first. Swarms is the right choice when you need to run many agents in parallel and the coordination pattern is relatively simple.

What to watch out for

The documentation is Swarms' most notable weakness. The README and official docs are extensive in breadth but uneven in depth. Core concepts like Agent configuration are well-documented. More advanced topics like custom swarm types, memory backend configuration, and error recovery have thinner coverage. Expect to read source code for non-trivial use cases.

The versioning and stability story is worth noting. At version 7.7.0, the framework has gone through significant changes. Code examples from tutorials more than a few months old may not run on the current version. Always check against the current docs and CHANGELOG when working from older resources.

Error handling in complex swarms is also something you need to manage explicitly. If one agent in a parallel swarm fails, the default behavior may not be what you expect. Adding explicit retry logic and error handling around individual agents is necessary for production use.

Getting started

pip install swarms

Set your API key:

import os
os.environ["OPENAI_API_KEY"] = "your-key"

A minimal two-agent sequential workflow:

from swarms import Agent, SequentialWorkflow
from swarms.models import OpenAIChat

model = OpenAIChat(model_name="gpt-4o-mini", temperature=0.1)

researcher = Agent(
    agent_name="Researcher",
    system_prompt="Research the given topic and provide detailed findings.",
    llm=model,
    max_loops=1,
)

summarizer = Agent(
    agent_name="Summarizer",
    system_prompt="Summarize the provided research into three bullet points.",
    llm=model,
    max_loops=1,
)

workflow = SequentialWorkflow(agents=[researcher, summarizer], max_loops=1)
result = workflow.run("Recent advances in battery technology")
print(result)

From here, the natural progression is trying AgentRearrange for workflows with parallel steps, then SpreadSheetSwarm for batch processing workloads. The swarms.world documentation has examples for each architecture type.

The verdict

Swarms occupies a real niche in the multi-agent landscape. The SpreadSheetSwarm pattern for large-scale parallel agent execution is something most other frameworks don't address directly. The multiple pre-built swarm architectures reduce the amount of custom orchestration code you need to write. The Apache 2.0 license and the active release cadence signal a project that is genuinely maintained.

The tradeoffs are real: smaller community than CrewAI or LangGraph, documentation gaps in advanced areas, and less battle-testing in production. Teams who need maximum reliability and the deepest documentation should start with CrewAI or LangGraph. Teams who specifically need large-scale parallel agent execution or who've found other frameworks too constrained in their coordination patterns should give Swarms a serious look. The framework does what it promises; the question is whether what it promises is what your workload needs.

Key features

• Multiple swarm architectures: SequentialWorkflow, AgentRearrange, MixtureOfAgents, SpreadSheetSwarm
• Agent class with built-in tool use, memory, and context management
• Long-term memory via vector store integrations
• Multi-model support across OpenAI, Anthropic, Cohere, and local models
• Concurrent and async agent execution
• Swarms Cloud for hosted multi-agent execution via API
• MCP tool support for extending agent capabilities