Swarms vs CrewAI: Which Multi-Agent Framework Should You Use?

Both are Python frameworks for building teams of agents. Both install with pip, both run in your process, and both will get a two-agent prototype working this afternoon. The question this post answers is what happens after that, when the prototype becomes a system you maintain.

Credit where it is due first. CrewAI's role, goal, backstory and expected_output vocabulary reads well, and a new engineer can follow a crew definition without a tutorial. Mapping a business process onto roles and tasks is a genuinely good on-ramp, and for a linear pipeline of three agents it is hard to beat for legibility.

This comparison is about the framework you grow into. Everything below is pip install swarms, running locally, with no hosted service required.

pip install -U swarms

The same pipeline, both ways

A researcher hands off to an analyst, sequentially.

CrewAI

from crewai import Agent, Task, Crew, Process

researcher = Agent(
    role="Researcher",
    goal="Gather the facts on the given market",
    backstory="A thorough analyst who cites sources.",
)

analyst = Agent(
    role="Analyst",
    goal="Turn research into a decision-ready brief",
    backstory="A skeptical reviewer who flags weak claims.",
)

research_task = Task(
    description="Research the EV battery supply chain in 2026.",
    expected_output="A factual summary with sources.",
    agent=researcher,
)

analysis_task = Task(
    description="Write a decision brief from the research.",
    expected_output="A one-page brief with a recommendation.",
    agent=analyst,
    context=[research_task],
)

crew = Crew(
    agents=[researcher, analyst],
    tasks=[research_task, analysis_task],
    process=Process.sequential,
)

print(crew.kickoff())

Swarms

from swarms import Agent, SequentialWorkflow

researcher = Agent(
    agent_name="Researcher",
    system_prompt="Research thoroughly and cite sources.",
    model_name="claude-sonnet-5",
    max_loops=1,
)

analyst = Agent(
    agent_name="Analyst",
    system_prompt="Write a one-page brief. Flag weak claims.",
    model_name="gpt-4.1",
    max_loops=1,
)

workflow = SequentialWorkflow(agents=[researcher, analyst])
print(workflow.run("Assess the EV battery supply chain in 2026."))

Comparable in length, and the agents are the same idea in both. Two differences are already visible: an agent carries its model rather than inheriting a crew-wide one, and the pipeline is a structure you pass agents to rather than a set of task objects wired by context.

That second difference is the one that compounds.

Topology is a parameter, not a rewrite

In CrewAI the shape of your system is expressed through tasks, their context links, and the crew's Process. Changing the shape means editing those relationships.

In Swarms the agents are separate from the structure that runs them, so the same roster runs under a different topology by swapping one class:

from swarms import Agent, SequentialWorkflow
from swarms.structs.concurrent_workflow import ConcurrentWorkflow

agents = [researcher, analyst, risk_reviewer]

# One after another, each seeing the last one's output
SequentialWorkflow(agents=agents).run(task)

# All three at once, on the same task
ConcurrentWorkflow(agents=agents).run(task)

Or you keep one entry point and choose the topology by name with SwarmRouter:

from swarms.structs.swarm_router import SwarmRouter

router = SwarmRouter(
    name="research-swarm",
    agents=agents,
    swarm_type="ConcurrentWorkflow",  # or SequentialWorkflow, MajorityVoting, ...
)

print(router.run("Assess the EV battery supply chain in 2026."))

The catalog is 15+ structures, and they are not variations on sequential and hierarchical. MajorityVoting runs several agents on one task and reconciles the answers. CouncilAsAJudge scores an output against criteria. MixtureOfAgents aggregates many opinions into one synthesis. DebateWithJudge runs an adversarial exchange resolved by a third agent. HeavySwarm, GroupChat, RoundRobinSwarm, AgentRearrange, HierarchicalSwarm and AutoSwarmBuilder each encode a different collaboration pattern. In CrewAI, most of these are patterns you hand-build out of tasks and callbacks.

Here is a quality gate that takes four lines because the structure already exists:

from swarms.structs.majority_voting import MajorityVoting

vote = MajorityVoting(agents=[analyst_a, analyst_b, analyst_c])
print(vote.run("Is this contract clause enforceable? Answer yes or no, then justify."))

A real graph, compiled once

Sequential and concurrent cover a lot, but branch-and-merge needs a graph. GraphWorkflow is a first-class structure with nodes, edges, and a compile step:

from swarms.structs.graph_workflow import GraphWorkflow

workflow = GraphWorkflow(name="market-analysis")

workflow.add_node(collector)
workflow.add_node(macro_analyst)
workflow.add_node(credit_analyst)
workflow.add_node(risk_analyst)
workflow.add_node(synthesizer)

# Fan out from one node to three, then fan back in
workflow.add_edges_from_source("collector", ["macro", "credit", "risk"])
workflow.add_edges_to_target(["macro", "credit", "risk"], "synthesizer")

workflow.compile()
print(workflow.run("Assess the EV battery supply chain in 2026."))

compile() is the part worth understanding. It infers entry and exit points from node degrees, computes topological generations, materializes adjacency in one pass, validates reachability, and freezes a per-layer execution plan with every node pre-resolved. Repeated runs sweep that frozen plan instead of re-deriving the order, and any mutation invalidates the cache so a stale plan cannot execute. There is a published systems paper on the engine, with an open benchmark harness and raw data you can rerun on your own hardware.

Structures also compose. A node in a graph can be an entire workflow:

triage = ConcurrentWorkflow(agents=[macro_analyst, credit_analyst, risk_analyst])

workflow.add_node(collector)
workflow.add_node(triage)        # a whole concurrent workflow as one node
workflow.add_node(synthesizer)

Real systems are rarely one pure pattern all the way down. They are usually a graph whose nodes are small sequential or concurrent workflows, which is exactly what this expresses.

Dynamic tool loading: fifty tools, one schema

This is the capability with no CrewAI equivalent, and on a large toolset it is the difference between an agent that works and one that is expensive and confused.

Tool definitions live in the prompt. They are re-sent on every call and they sit in the cached prefix, so a large toolset is paid for continuously. The 16 built-in harness tools alone are roughly 2,600 tokens per request, and a single MCP server can add forty more. Selection accuracy also degrades with list length: a model choosing among 80 tools chooses worse than one choosing among 8.

Swarms defers them. Tools stay registered and executable but absent from the schema list, discoverable through one tool_search tool:

from swarms import Agent

agent = Agent(
    agent_name="Researcher",
    model_name="gpt-4.1",
    max_loops="auto",
    tools=[...],          # fifty tools registered
    dynamic_tools=True,   # one schema sent, the rest searchable
)

The agent searches the catalog when it needs something, loads what it finds, and calls it on the next turn. Loaded tools stay loaded for the rest of the run. Control-flow tools are pinned and never deferred, because an agent that has to search for its own complete_task cannot finish. A missed search falls through to keyword matching and returns a capped catalog listing rather than silence, which turns a failed lookup into a productive retry.

In a framework without this, fifty tools means fifty schemas on every single request for the entire run.

An autonomous harness, not just a loop

max_loops="auto" is a working agent harness with 16 built-in tools spanning files, shell, grep, sub-agents and control flow:

agent = Agent(
    agent_name="Engineer",
    system_prompt="You fix failing tests in the repository you are given.",
    model_name="claude-sonnet-5",
    max_loops="auto",
)

agent.run("The auth test suite is failing. Find the cause and fix it.")

The agent plans, executes, revises the plan, and stops when the work is done. It gets a structured transcript and a mutable plan rather than a flattened string, so its own view of progress and the model's view of progress are the same object. Sub-agents are one of the built-in tools, so an autonomous agent can spawn helpers for subtasks without you wiring a second structure.

MCP is a constructor argument

Model Context Protocol support is built into the Agent class, not bolted on:

agent = Agent(
    agent_name="MCP-Agent",
    model_name="gpt-4.1",
    mcp_url="http://localhost:8000/mcp",
    max_loops=1,
)

Several servers at once, with auth, headers and a timeout:

agent = Agent(
    agent_name="Multi-MCP-Agent",
    model_name="gpt-4.1",
    mcp_urls=[
        "http://localhost:8000/mcp",
        "https://tools.internal.example.com/mcp",
    ],
    mcp_authorization_token="Bearer ...",
    mcp_headers={"X-Tenant-Id": "acme"},
    mcp_timeout=30,
)

OAuth flows are supported with token storage, for servers that require them. Combined with dynamic tool loading, an agent can attach several MCP servers exposing hundreds of tools and still send one tool schema per request.

Per-agent models, across providers

Every agent names its own model as a plain string, and the framework is provider-agnostic: OpenAI, Anthropic, Google, DeepSeek, Mistral, Together, OpenRouter, AWS Bedrock, Azure OpenAI, Hugging Face, and local models through LM Studio or Ollama.

writer = Agent(agent_name="Writer", model_name="gpt-4.1", system_prompt="...")
critic = Agent(agent_name="Critic", model_name="claude-sonnet-5", system_prompt="...")

SequentialWorkflow(agents=[writer, critic]).run(task)

Putting the critic on a different vendor from the writer is not a novelty. A critic that shares a model family with the writer also shares its blind spots and characteristic failure modes, so it nods along exactly where it should object. Mixed-vendor review is the only version of review that is actually adversarial, and here it costs one string.

Multi-agent context that stays correct

This one is invisible until it bites. When a structure hands the conversation to the next agent, the naive implementation flattens the whole transcript into one string and passes it as the next task. That costs three things at once: role attribution is destroyed, because an agent cannot tell its own prior output from a peer's; prompt caching never fires, because every turn produces a different single message and no request is a prefix of the next; and context grows superlinearly, because each agent's transcript gets folded back into the shared one.

Swarms passes typed chat turns instead. Every structure delivers real [{role, content}] message lists, where an agent's own output arrives as an assistant turn and each peer arrives as a labelled user turn. Role attribution survives, the cached prefix holds across turns, and transcripts stay linear. On one measured structure, flattening had grown context to 1,157 characters by turn six where 116 was correct.

You do not configure this. It is how every structure in the framework hands off, and it is the kind of correctness work that only shows up in your token bill.

Execution modes and observability

Structures ship the run modes you would otherwise write:

workflow = SequentialWorkflow(agents=agents)

workflow.run(task)                  # synchronous
await workflow.run_async(task)      # async
workflow.run_stream(task)           # token streaming
workflow.run_batched([t1, t2, t3])  # many tasks through the same pipeline
workflow.run_concurrent([t1, t2])   # several tasks at once through the chain

ConcurrentWorkflow takes show_dashboard=True for a live view of each agent's progress, which matters when several things run at once. output_type controls what comes back, from a final string to a dict keyed by agent name. Autosave is one WorkspaceManager for the whole framework, with non-blocking failures and atomic writes so a crash cannot leave corrupted state on disk. Telemetry is on by default and turns off with SWARMS_TELEMETRY_ON=false.

And when you do not know the right roster, AutoSwarmBuilder designs one from the task description:

from swarms.structs.swarm_router import SwarmRouter

router = SwarmRouter(name="auto", agents=[], swarm_type="AutoSwarmBuilder")
print(router.run("Produce a competitive analysis of the EV battery supply chain."))

Side by side

Where CrewAI still fits

If your system is one linear pipeline of three or four agents, it will not change shape, and the reason you chose a framework was legibility for non-specialists, CrewAI's role vocabulary is a reasonable place to stay. Nothing above makes that a mistake.

The argument for Swarms starts the moment any of these become true: you want to change topology without a rewrite, you need branch and merge, you have more than a handful of tools, you want agents on different providers, you want voting or adversarial review, or you have started noticing the token cost of the context your framework is assembling.

Getting started

pip install -U swarms
python -c "import swarms; print(swarms.__version__)"

Port one crew. Lift each role's text into a system_prompt, name the agent after the role, drop the task objects, and pass the agents to SequentialWorkflow. It is usually an afternoon. Then change the structure once, to ConcurrentWorkflow or MajorityVoting, and notice that nothing about the agents had to change.

From there, the three shapes of agent orchestration covers when each topology is correct, and Swarms v15 Akira is the full technical record of the dynamic tool loader, the autonomous harness and the typed-turn work described above.

The framework is open source at github.com/kyegomez/swarms, and the same structures are available behind an API on Swarms Cloud when you would rather not run them yourself 🦾