Multi-agent framework and agent management platform for orchestrating role-playing AI agents and event-driven workflows.

Free; Enterprise custom quoteFree planOpen-source multi-agent framework with enterprise management platform

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Quick Facts

DeveloperCrewAI Inc.
Websitecrewai.com/open-source
Agent typeOpen-source multi-agent framework with enterprise management platform
Best forPython developers and enterprise AI teams building multi-agent workflows where several specialized agents share a task.
Not ideal forNon-technical users who want a finished agent, or teams that need single-agent simplicity.
Pricing modelFree open-source framework; free cloud tier; custom enterprise pricing
Starting priceFree; Enterprise custom quote
Pricing detailsThe framework is free and MIT-licensed. The hosted free tier includes 50 workflow executions and 2 automations a month. Enterprise is custom and can run in CrewAI's cloud, your own VPC or your own infrastructure, with SSO, RBAC, PII redaction and a 45-day onboarding program.
Free planYes
ChannelsPython SDK, YAML configuration, hosted platform, APIs
What it does on its ownCrews let role-based agents plan and divide work on their own; Flows give developers step-by-step, event-driven control; the two can be combined.
IntegrationsMCP, A2A, E2B, Daytona, major LLM providers, hundreds of open-source tools
PlatformsPython; CrewAI cloud, private VPC or on-premises
Company HQSan Francisco, USA
Company founded2024
Details last verified28 October 2026
CategoriesAgent Frameworks for Developers, Enterprise, Multi-Agent Platforms, Open-Source AI Agents
User ratingNo reviews yet

CrewAI’s open-source framework is free, and the company says it is used at more than 60% of Fortune 500 companies. It lets developers build a crew of AI agents, each with a role such as researcher, writer or reviewer, that divide a job between them and pass work along until it is done.

The commercial side, the Agent Management Platform (AMP), adds hosting, monitoring and controls for companies running those crews in production.

How It Works

CrewAI is a standalone Python framework with its own building blocks and no dependency on LangChain. Developers define agents in YAML, code or both, giving each a role, goal, model, tools and knowledge sources. There are two ways to organize work. Crews let agents collaborate and make decisions with some freedom. Flows give exact, event-driven control over each step and the state passed between them. Most real projects mix the two, using a Flow as the backbone and dropping in Crews where judgment is needed.

Agents can plan long tasks and recover from errors. A memory layer stores what agents learn, and checkpointing records state at each step so a run can be replayed or forked with new inputs. Tools connect through MCP, agents can talk to other agents through the A2A protocol, and code runs in E2B or Daytona sandboxes. Async execution and streaming are built in.

Pricing

The framework is free under the MIT license. CrewAI’s hosted free tier allows 50 workflow executions and two automations a month. Enterprise pricing is custom and sized to the workload, with overage options. It adds SSO, role-based access, PII redaction, enterprise connectors, dedicated support and a 45-day onboarding program, and it can run in CrewAI’s cloud, a private VPC or on your own servers. Model costs are paid to your LLM provider.

What to Watch Out For

CrewAI is a developer framework, so it needs Python skills to use well. Multi-agent designs call the model many times, which raises costs and makes failures harder to trace than in a single-agent setup. The free hosted tier is small, and enterprise prices are not published. The company’s own adoption figures vary across its pages, with both 63% and 65% of the Fortune 500 cited, so treat them as marketing numbers. The pricing page lists no specific compliance certification and links to a trust center.

Features

  • Crews of role-based agents that collaborate on tasks
  • Flows for precise, event-driven control of execution and state
  • Agents defined in YAML, code or both
  • Memory layer that resolves contradictions and forgets on purpose
  • Checkpointing to replay or fork runs from any step
  • MCP tool support and A2A agent collaboration
  • Code execution sandboxes through E2B and Daytona
  • AMP enterprise control plane for deployment and monitoring

Pros and Cons

Pros

  • Free, MIT-licensed core that runs without LangChain
  • Simple role-based model that is easy to reason about
  • Large community, with more than 100,000 developers certified through its courses
  • Enterprise deployment options including private VPC and on-premises
  • Used by large companies such as PepsiCo, IBM and DocuSign

Cons

  • Python only
  • Enterprise pricing is not published
  • Free hosted tier is small at 50 executions a month
  • Multi-agent setups can be harder to debug and cost more in model calls than a single agent

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