# CrewAI Open-source Python framework for role-based multi-agent orchestration ## About CrewAI CrewAI is an open-source Python framework for building multi-agent AI systems. Their cloud platform (CrewAI Enterprise) adds a visual studio, managed hosting, and compliance features (SOC 2, HIPAA). Sistava is a fully managed platform where you hire and manage AI employees without writing code. ## Platform details - Pricing: Open-source (free). Cloud: Free tier, then $25/mo (Pro). Enterprise from $120K/year. - Founded: 2023, San Francisco - Funding: $18M+ raised. 60% Fortune 500 adoption. 100K+ developers. - Last reviewed: 2026-03-22 ## Official website - [Visit CrewAI](https://crewai.com) ## What does CrewAI actually do? CrewAI is a Python framework for building teams of AI agents that collaborate on tasks. You define each agent by role, goal, and backstory, hand them tools, and write tasks the agents pass between each other. The mental model is closer to a small org chart than a workflow diagram. It is open source and free. There is also a paid platform called AMP that adds visual building, deployment, tracing, and managed hosting. AMP starts around $99 per month and is metered by crew executions, where one execution is one full run of a crew. CrewAI is one of the cleanest entry points to multi agent thinking. You write much less boilerplate than equivalent LangChain code, often 40 to 60 percent less, and the role and task abstractions are easy to teach to new developers. Sistava is downstream of CrewAI in concept. We use multi agent ideas internally, but the product you see is an AI employee, not a framework. You hire it, give it a goal, and it runs. There is no crew to define. ## How much does CrewAI cost? The open source CrewAI framework is free. You pay only for the LLM API calls each agent makes. That is the hidden trap of multi agent systems: a crew of five specialized agents can easily 10x the token spend of a single well prompted model. The paid AMP plans start around $99 per month for Basic with 50 workflow executions, and Professional adds more executions and an extra seat. Enterprise pricing is custom. Executions are charged regardless of how many tokens each agent burns, which means token bills sit on top. On a real workflow, expect three line items: the AMP subscription if you use it, the LLM provider, and any external tools the agents call. Real monthly spend for a small production crew is usually in the low hundreds of dollars before you scale. Sistava charges a single credit quota that covers all of that, including the underlying multi agent reasoning when the employee delegates. Direct cost per task is higher than raw CrewAI, but there is no token spike when an agent loop misbehaves. ## When does CrewAI beat the alternatives? CrewAI shines when you have a defined process that maps cleanly to a small team of roles. Research, write, edit, publish is the canonical example. The role and task model is intuitive, the docs are good, and the open source community is large. It is also the best pick when you want a fast prototype of a multi agent system without writing a graph from scratch. You can describe the agents and tasks in a few dozen lines of Python and have something running in a single sitting. It is weaker than LangGraph for long running, stateful, or branching workflows. CrewAI assumes a linear or hierarchical task flow. Pure graph orchestration with checkpointing and human approval steps is not its strength. And it is weaker than Sistava as a finished product. CrewAI is a way to build an agent crew. Sistava is the crew, already wired into a workspace, with memory, schedules, and reporting. ## Where does CrewAI fall short for non-engineers? CrewAI is a developer tool. The free open source path requires Python, an LLM key, and a willingness to debug agent loops in the terminal. Non engineers cannot productively use the open source version without a developer wrapping it first. The paid AMP product narrows that gap with a studio UI for designing crews and viewing runs, but you still think in terms of agents, tasks, tools, and triggers. It is closer to Airflow than to Notion. The mental model is technical. There is also the cost surprise. Multi agent systems are inherently chatty, and CrewAI does not protect you from runaway loops or from agents that re prompt each other indefinitely. You set those guardrails yourself. Sistava trades flexibility for safety. You cannot redesign the agent graph from the UI, but you also cannot accidentally rack up a four figure token bill from a malformed crew. ## How does CrewAI handle production deployment? Open source CrewAI runs anywhere Python runs. Most teams ship it inside a FastAPI service, a Celery worker, or a Lambda. There is no native deployment story for the free library, which means logging, retries, and tracing are your job. AMP adds managed deployment, run tracing, and integrated triggers, so a production crew can sit behind a webhook or schedule without you operating the infrastructure. That is the main reason teams move from open source to paid. Even with AMP, you are still operating crews. You watch executions, tune prompts, add guardrails, and write evals for each task. CrewAI is a serious framework, not a turnkey product. Sistava handles deployment, scheduling, retries, and evals as the default behavior of the employee. You give up the ability to reshape the underlying crew, but you also skip the production hardening work. ## Comparison - [Compare CrewAI with Sistava](/en/compare/dev-frameworks/crewai) — See the two platforms side by side when you are ready to evaluate them.