Buying snapshot
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| Decision point | What CrewAI states |
|---|---|
Framework license | MIT License. |
Requirements | Python 3.10 or later, below 3.14. The CLI installs with the uv package manager (uv tool install crewai). |
Project setup | crewai create crew makes a JSON-first project, with agents in agents/*.jsonc and tasks in crew.jsonc. The --classic option gives the older Python and YAML layout. |
Models | Agents use the OpenAI API by default. Other providers can be configured, including local models through Ollama or LM Studio. |
Our take
Choose CrewAI if your team writes Python and wants agents that collaborate inside workflows you can control step by step. Start with the free framework; consider the paid platform only when you need CrewAI to host, monitor and govern those agents for you.
Evaluate CrewAI
Popular comparisons
Where it stands out
Autonomy and control in one framework. Crews let role-based agents delegate and collaborate. Flows add event-driven steps with state, branching and routing, and can run Crews as steps. You keep business logic in regular Python and use agents only where they help.
Production features in the open-source core. The framework supports tools, memory, knowledge, checkpointing, async execution, MCP and A2A, human input and structured outputs. Tasks run in sequence, or under a manager agent that CrewAI assigns automatically in the hierarchical process.
Model testing from the CLI. Recent releases added a crewai eval command. Its --models option, with llm_overlay, lets you swap the models behind agent roles and tracks cost and time per run, which helps you pick a model for each step.
Ways out of the visual builder. Workflows built in the Studio visual editor can be exported to Python, and both platform plans list export as an MCP server or as a UI component.
Product screenshot
Trade-offs to weigh
Telemetry is on unless you turn it off. The framework sends anonymous usage data, such as CrewAI and Python versions, agent and task counts, the model used and tool names, unless you set OTEL_SDK_DISABLED=true. Task descriptions, agent goals and task outputs are collected only if you enable share_crew.
Frequent releases. Ten releases, 1.15.19 to 1.15.28, came out between 4 September and 14 October, mixing new features, provider fixes and security updates to dependencies. Pin the version and test each upgrade.
Pricing and plan conditions
The framework itself is free under the MIT License. The platform plans differ mainly in run volume, deployment and governance: Basic is free but capped, while Enterprise is priced as Custom and sizes executions to your workflows with flexible overage, so its cost depends on how many runs you need.
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| Plan | Price | Workflow runs | Deployment | What the plan includes |
|---|---|---|---|---|
Basic | Free | 2 automations; 50 executions a month, which is also the maximum. | CrewAI cloud. | Studio visual editor and AI copilot, GitHub integration, standard tools and triggers, private agent and tool repositories, workflow templates, tracing and OpenTelemetry, guardrails, human-in-the-loop input, cron scheduling and community support. |
Enterprise | Custom | Custom automations and maximum executions; included executions sized to your workflow, with flexible overage for additional ones. | CrewAI cloud, your own VPC or your own infrastructure. Dedicated VPC and NAT run by CrewAI or the customer. | Everything in Free, plus enterprise connectors, SSO (Microsoft Entra, Okta), role-based access control, workload identity, PII redaction and policies, 45-day onboarding, and dedicated, Slack/Teams and on-site support with a support contact, training, deployment, onboarding and development help. Forward deployed engineering and training are available a la carte. |
Public signals
About these signals
Verified factual signals from public sources. They indicate observable activity or interest, not total adoption, product quality, or cost.
369 GitHub commits 90d60.1k GitHub stars0 vulnerabilities across 1 package
See all signals from 6 sources
PyPI · crewai@1.15.30
Alternatives to CrewAI
The reviewed substitutes for CrewAI among the agent frameworks, and what would make each one the better answer.
Direct alternatives
Reviewed substitutes: products bought for the same job, where a team picks one.
- LangGraph
- CrewAI and LangGraph are open-source Python frameworks for orchestrating several AI agents on complex tasks. CrewAI organises agents as a crew of role-playing members who delegate work and adds a visual editor for enterprises, while LangGraph is a lower-level runtime from the LangChain team for stateful agent graphs with cycles, persistence and human-in-the-loop checks.Applies to: Choosing between two products of the same kind for one job.
- PydanticAI
- Both are Python agent frameworks a team adopts as its primary way of building agents. PydanticAI organises around typed output, CrewAI around collaborating roles, but they are alternatives, not layers.
Other approaches
A different approach to the same problem. Each substitutes only for the workload named beside it.
- Agno
- Both build applications where a model calls tools and takes several steps, from different starting points: framework breadth, explicit orchestration, role decomposition or an SDK inside an existing application. Teams pick one to build on, and the choice follows the shape of the workflow rather than a feature count.Applies to: Choosing the framework an agent or multi-step LLM application will be built on.
- AutoGen
- Microsoft's framework for building multi-agent conversational AI systems with customizable and composable agents.Applies to: for organizations that want to distribute agent-building beyond a Python-focused development workflow while retaining enterprise-oriented controls.
- AutoGPT
- AutoGPT is used rather than CrewAI for digital workflow automation workloads built around intelligent assistants and hosted automation credits. **AutoGPT** focuses on intelligent assistants that streamline digital workflows, with a self-hosted open-source option and hosted cloud plans.
- Google Agent Development Kit
- Google's open agent framework — hierarchical multi-agent composition, graph workflows that mix deterministic code with model reasoning, a local dev UI and evaluation harness, and a deployment path onto Vertex AI Agent Engine. Python, TypeScript, Go, Java and Kotlin.
- Haystack
- CrewAI is used rather than Haystack for role-based multi-agent collaboration workloads. **CrewAI** orchestrates role-playing autonomous agents that collaborate on complex tasks, with both an API-oriented builder experience and a visual editor with an AI copilot.
- LangChain
- Both build applications where a model calls tools and takes several steps, from different starting points: framework breadth, explicit orchestration, role decomposition or an SDK inside an existing application. Teams pick one to build on, and the choice follows the shape of the workflow rather than a feature count.Applies to: Choosing the framework an agent or multi-step LLM application will be built on.
- LlamaIndex
- CrewAI and LlamaIndex are both frameworks for building LLM applications with agents. CrewAI organises the work as a crew of agents with roles that collaborate on a task; LlamaIndex centres on ingesting, indexing and querying a private document corpus, with a simpler agent loop on top, and CrewAI has no indexing layer of its own.Applies to: Building an LLM application where the work divides into roles. CrewAI stands in for LlamaIndex when the job is role-based multi-agent collaboration a product team can read; LlamaIndex stands in when the job is indexing and querying a private corpus and the agent loop is simple.
- MetaGPT
- MetaGPT is chosen instead of CrewAI for AI-assisted product commercialization workflows that begin with an idea and move toward building. **MetaGPT** originated as an open-source framework and has evolved through MGX to Atoms, which it describes as an AI-powered commercialization engine.Applies to: when the evaluation is driven by the product-development journey it presents, while treating operational governance requirements as a separate validation area.
- OpenAI Agents SDK
- CrewAI and the OpenAI Agents SDK are both ways to build multi-agent applications. CrewAI organises role-based crews that a product team can read and carries no model provider commitment, while the OpenAI Agents SDK is the shortest path to production on OpenAI, with agents, handoffs and tracing and less abstraction to learn.Applies to: Coordinating several agents on one task. CrewAI stands in when the division of labour is the design and must be legible; the OpenAI Agents SDK stands in when the team is on OpenAI and wants handoffs and tracing without a new paradigm.
- Semantic Kernel
- Semantic Kernel is preferred over CrewAI for application development workloads that need an open-source SDK with planners and plugin architecture. **Semantic Kernel** is Microsoft’s open-source SDK for incorporating LLMs into applications through AI agents, planners, and a plugin architecture.