/ crewai vs agent-swarm.dev

CrewAI vs agent-swarm.dev

Compare CrewAI, a Python framework for agentic apps, with Agent Swarm, a persistent, memory-backed operating swarm with scheduling and active workers.

Talk with us Self-host on GitHub
framework
CrewAI
CrewAI logo
vs
operating swarm
Agent Swarm

The real comparison is not which abstraction is nicer. It is whether you want to build an agent system or run a persistent agent team.

/ tldr

Choose CrewAI when your team wants to build a custom Python agent application from framework primitives. Choose agent-swarm.dev when you want a standing AI team that remembers prior work, runs recurring tasks, coordinates in Slack, and can be self-hosted free under MIT.

/ when do you want each one

Pick by operating model,
not hype.

Choose CrewAI if

You want a Python framework

CrewAI is a good fit when the job is to write Python code around agents, tasks, crews, and flows. Your team owns the application architecture and runtime.

You need framework-level primitives

Crews, Flows, role-based agents, and deterministic orchestration are useful when you are building a product or internal workflow that needs custom control logic.

You are evaluating AMP governance

CrewAI AMP adds managed deployment, tracing, GitHub integration, and enterprise controls around CrewAI applications.

Choose Agent Swarm if

You want a team, not another project

agent-swarm.dev is already an operating system for work: a lead agent, specialized workers, a task pool, dependencies, Slack updates, and review loops.

Memory needs to compound

agent-swarm.dev keeps file-based and embedding-backed memory across sessions, so the swarm recalls prior decisions, codebase patterns, and failed approaches instead of starting cold.

Recurring work should happen by itself

Heartbeats, schedules, and workflows let a swarm run follow-ups, checks, reports, and implementation loops while the team is offline.

/ side by side

The practical
comparison.

Dimension
Category
CrewAIFramework and managed cloud for building agentic apps.
Agent SwarmTurnkey persistent swarm for operating agent work.
Primary user
CrewAIPython developers building custom agent systems.
Agent SwarmTeams that want autonomous coding, research, review, ops, and content work to run continuously.
Agent roles
CrewAIAgents are Python definitions with a role, goal and backstory, grouped into a crew that runs a sequential or hierarchical process.
Agent SwarmA lead agent routes work to specialized workers, and each worker keeps a persistent profile with identity, role and capabilities across sessions.
Task and workflow state
CrewAITasks belong to a crew. Flows hold structured or unstructured state, and the @persist decorator saves it so a run can resume after a restart.
Agent SwarmA shared task pool tracks statuses, dependencies and progress history. Workflow runs keep state, including a waiting state while a person decides.
Runtime and model choice
CrewAIA Python framework. Models come from native provider SDKs such as OpenAI, Anthropic, Google, Azure and Bedrock, with other providers through LiteLLM.
Agent SwarmWorkers run the coding-agent harness you pick per worker: Claude Code, Codex, pi, opencode, Cursor, Amp, Grok, Devin or an ACP agent.
Integrations
CrewAITools come from crewai-tools or MCP servers, plus apps such as Gmail, Slack and Jira behind an integration token. AMP adds a managed tools repository.
Agent SwarmEvent sources include GitHub, GitLab, Bitbucket, Linear, Jira, Azure DevOps, Slack, WhatsApp and Chat SDK. Tool surfaces include Composio, Salesforce, Microsoft Graph, Sentry and Serply.
Approvals and human review
CrewAITasks can ask for human input, Flows add the @human_feedback decorator, and AMP documents webhook-based review.
Agent SwarmA human-in-the-loop workflow node pauses a run for approval or input, with named approvers, a timeout, a Slack notification and an approval card.
Operator responsibility
CrewAIYour team writes, hosts, schedules and monitors the Python application, or runs it on AMP for managed deployment and tracing.
Agent SwarmYou self-host it under MIT, set credentials per harness, and assign and review work. Scheduling and memory are built in; uptime and upgrades are yours.
Memory
CrewAIOpt-in short-term, long-term, and entity memory; storage choices matter in serverless environments.
Agent SwarmPersistent file and semantic memory shared across the swarm and indexed for future recall.
Long-running autonomy
CrewAIRuns are typically task-scoped; long-running operation depends on Flows plus external schedulers or queues.
Agent SwarmNative heartbeats, schedules, workflows, and self-evolution make recurring work a first-class behavior.
Deployment
CrewAISelf-host Python apps, use AMP, or deploy via enterprise options such as Factory.
Agent SwarmFree self-hosting under MIT, with enterprise support by conversation; keep credentials, memory, and runtime boundaries under your control.
Lock-in
CrewAIOpen-source core, with managed features in AMP.
Agent SwarmOpen-source under MIT, self-hosted, with multi-harness workers across Claude, Codex, and other agents.
Best short version
CrewAIBuild an agent application.
Agent SwarmRun an agent team.
/ proof-of-concept checklist

Test both on
one real workflow.

  1. 01

    Pick one real task in one repository, such as a small refactor behind a review gate, and write the acceptance criteria before either run; then run it as a CrewAI crew and as an Agent Swarm task. Pass if you can judge both results against the same written criteria without inventing new ones.

  2. 02

    Start a new session and ask for a follow-up that depends on the first change without restating the decision; resume CrewAI from persisted Flow state where you enabled @persist, and hand the Agent Swarm follow-up to the same worker. Pass if the second run recovers the earlier decision on its own, or you can list exactly what you had to restate.

  3. 03

    Put the same check on a daily schedule, using a scheduler around the CrewAI app or AMP there and a built-in schedule in Agent Swarm. Pass if the run history shows each run's outcome, start time and changes in one place, without reading application logs.

  4. 04

    Gate one irreversible step, such as a merge or a deploy, behind a person using @human_feedback or a webhook review in CrewAI and a human-in-the-loop node in Agent Swarm. Pass if the run pauses, records who approved and when, and continues on the approved path.

  5. 05

    Break a credential or fail a test on purpose, then retry. Pass if the failure appears in the run's own record, the retry starts from a state you can name, and the failing step is identifiable without reconstructing it from logs.

  6. 06

    Record the time from install to first green run, then the work that repeats afterwards: upgrades, credential rotation, monitoring and triage. Pass if you can state a monthly operating cost in your own hours for each option, next to its inference spend.

/ the honest tradeoff

Understand
the tradeoffs.

A useful comparison says where each tool actually wins. agent-swarm.dev is for a persistent, owned operating team; the alternative wins when its shape fits your job better.

CrewAI is better if the job is SDK design

If your priority is the ergonomics of hand-building an agent app in Python, CrewAI's framework primitives are purpose-built for that.

The linked public sources were reviewed on October 26, 2026, and this page describes documented capabilities, not a controlled benchmark.

/ proof by trying

Try the operating swarm before you choose the framework path

agent-swarm.dev is open source, deployable in minutes, and easiest to judge from a real task. Run it beside a CrewAI evaluation for a day and compare the difference between building agents and handing work to a standing team.

Deploy agent-swarm.dev View the repo
/ faq

Common
questions.

Is agent-swarm.dev a CrewAI alternative?

agent-swarm.dev can replace a build-it-yourself agent orchestration project when the desired outcome is a running AI team. CrewAI is better described as a framework for building agentic applications; it is the operating product you run.

Can CrewAI and agent-swarm.dev coexist?

Yes. A team could build a specialized agentic app with CrewAI and still use agent-swarm.dev to coordinate broader coding, research, review, and operational tasks around it.

When should I choose CrewAI over agent-swarm.dev?

Choose CrewAI when you want to write and own a Python agent application. Choose agent-swarm.dev when you want persistent workers, memory, scheduling, Slack/GitHub workflows, and an operational surface out of the box.

/ sources

This page compares product categories and operating models from public product documentation and repositories. We do not claim the tools are interchangeable.

CrewAI homepageCrewAI docsCrewAI GitHubCrewAI AMP deployment docsagent-swarm.dev GitHubagent-swarm.dev docsCrewAI agentsCrewAI crews and processesCrewAI Flows and stateCrewAI LLM providersCrewAI toolsCrewAI MCP serversCrewAI agent capabilitiesCrewAI human feedback in FlowsCrewAI human input on executionCrewAI human-in-the-loop workflowsCrewAI tracingAgent Swarm task lifecycleAgent Swarm workflowsAgent Swarm agent identityAgent Swarm integrations
/ keep comparing

See how agent-swarm.dev stacks up against the rest.

All comparisons
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/ Install

Two ways in.

01 · THE EASY WAY

Ask your coding agent

It reads the skill, asks about your setup, then installs the swarm.

Read https://agent-swarm.dev/skill.md and help me install and operate Agent Swarm on our infra. Ask about my setup before running anything.

02 · THE FAST WAY

Run the installer

One command. Needs Docker.

$ curl -fsSL https://agent-swarm.dev/install.sh | bash

Or read the docs first

Rolling it out across a team? Book a free 15-min call