CrewAI
by CrewAI Inc.
Open-source Python framework for building and orchestrating multi-agent AI systems. CrewAI is an open-source Python framework for building and orchestrating multi-agent AI systems, created in 2023 by João Moura and distributed under the MIT license. It is developed in the open on GitHub, where the project has reached 54,242 stars as of June 2026.
Performance Scores
3 rankings evaluated
Score range: 7.2 – 8.0
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#1Best AI Agent Platforms in 2026
Score: 7.9 · Best for: Developers building code-first multi-agent systems
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#3Most Customizable AI Agent Platforms 2026
Score: 7.2 · Best for: Engineering teams that need an unrestricted code-level ceiling
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#3Best LLM App Platforms for Building AI Agents in 2026
Score: 8.0 · Best for: Python developer teams building multi-agent systems where agents have distinct roles and tools.
Key Facts
Technical
| Attribute | Value | As of | Source |
|---|---|---|---|
| Core concepts | Core concepts: Agents (role-playing units), Crews (teams of agents), and Flows (event-driven, stateful orchestration) | Sep 2026 | CrewAI Documentation |
| License | MIT licensed, open-source Python framework | Jun 2026 | CrewAI GitHub |
| Language | Python 3.10+; integrates with LangChain tools and any LLM via litellm | Apr 2026 | CrewAI Docs |
General
| Attribute | Value | As of | Source |
|---|---|---|---|
| GitHub stars (June 2026) | 59,125 GitHub stars as of September 2026 (also MIT-licensed per the GitHub API license field) | Sep 2026 | CrewAI GitHub (API) |
| Founded | 2023 | Mar 2026 | CrewAI |
| GitHub Stars | 55,000+ (as of July 2026) | Jul 2026 | GitHub API (crewAIInc/crewAI) |
| LLM Backends | OpenAI, Anthropic, Google Gemini, Azure, AWS Bedrock via native SDKs; Ollama, Hugging Face, Mistral, Groq and other providers via LiteLLM (as of July 2026) | Jul 2026 | CrewAI documentation — LLMs |
| Enterprise | Cloud platform launched 2024 | Jul 2026 | CrewAI press release (GlobeNewswire) |
| Founder | João Moura | Jul 2026 | CrewAI blog author page |
Company
| Attribute | Value | As of | Source |
|---|---|---|---|
| Adoption (July 2026) | CrewAI states it is "used by 65% of the Fortune 500," with customers including DocuSign, Experian, PepsiCo, IBM, EXL, CHG Healthcare, and AB InBev, as of September 2026. The open-source framework has approximately 59,100 GitHub stars as of September 2026 (GitHub API). | Sep 2026 | CrewAI pricing |
Pricing
| Attribute | Value | As of | Source |
|---|---|---|---|
| CrewAI pricing (July 2026) | CrewAI has two published commercial tiers (verified September 2026): Basic (Free, $0 -- 50 workflow executions/month, visual editor + AI copilot, GitHub integration) and Enterprise (custom pricing -- SSO, RBAC, workload identity, PII redaction, policy controls, deployment flexibility, 45-day onboarding, forward-deployed engineering and training available a la carte). The current pricing page no longer states a fixed '50 developer-hours/month' figure for Enterprise; that detail is not currently published. The core CrewAI Python framework remains separately free and MIT-licensed. No public paid mid-tier exists. | Sep 2026 | CrewAI pricing |
| Enterprise Plan | CrewAI Enterprise uses custom pricing as of July 2026 (no published rate); a free Basic tier includes 50 workflow executions/month | Jul 2026 | CrewAI Pricing |
Capabilities
| Attribute | Value | As of | Source |
|---|---|---|---|
| Platform Type | Multi-agent orchestration framework for role-based AI agent crews | Jul 2026 | crewAIInc/crewAI GitHub repository |
Strengths
- ●Code-first multi-agent control
- ●Open-source and extensible
- ●The Basic tier is free, and the enterprise platform adds a Control Plane, RBAC and human-in-the-loop (crewai.com, read 29 Sep 2026)
- ●Unrestricted Python, with hooks at kickoff, step, tool, LLM and guardrail level
- ●Hierarchical scoped memory with tunable composite scoring, plus checkpoint replay and fork
- ●Crew Studio provides a drag-and-drop canvas on the free tier; Flows in Studio from July 2026
- ●Self-hosts anywhere under MIT, and the Factory distribution deploys the platform via Helm with a customer identity provider
- ●Code-first model fits teams that prefer Python over visual builders
- ●Multi-agent role pattern is well-documented with reference projects
- ●Open-source core means flows are portable across runtimes
- ●Managed Enterprise plan adds traces, dashboards, and team controls
Limitations
- ●Requires engineering effort
- ●Code-first core; the no-code visual editor exports to Python (crewai.com, read 29 Sep 2026)
- ●Roughly 28 documented enterprise app integrations: the smallest prebuilt catalogue in this ranking
- ●The free Studio tier caps at 50 executions per month and requires the user to supply a model key
- ●Anything beyond what Studio generates means downloading a ZIP and writing Python
- ●The public platform REST API exposes four endpoints with no management operations
- ●Audit logging appears in marketing but not documentation, and no compliance certification could be confirmed on crewai.com
- ●Less suitable for non-engineering builders — no drag-and-drop canvas
- ●Governance features concentrated on the paid Enterprise tier
- ●Performance depends heavily on the underlying model and tool choices
Based on evaluations in 3 rankings: Best AI Agent Platforms in 2026, Most Customizable AI Agent Platforms 2026, Best LLM App Platforms for Building AI Agents in 2026
Pricing Plans
Basic
Free plan; no paid upgrade path below Enterprise
- ✓Visual editor and AI copilot
- ✓GitHub integration
- ✓Access to CrewAI platform core features
- !50 workflow executions/month
Enterprise
Custom pricing; contact sales — no published dollar figure
- ✓SSO, RBAC, and workload identity
- ✓PII redaction and policy governance
- ✓Flexible deployment options
- ✓45-day onboarding with dedicated support
- ✓Forward-deployed engineering and training (a la carte)
- !Execution limits negotiated per contract
About CrewAI
CrewAI is an open-source Python framework for building and orchestrating multi-agent AI systems, created in 2023 by João Moura and distributed under the MIT license. It is developed in the open on GitHub, where the project has reached 54,242 stars as of June 2026. The framework targets Python developers who compose multiple language-model agents in code rather than through a visual canvas.
Its model rests on three concepts. Agents are role-playing units, each defined with a role, a goal, and a set of tools. Crews are teams of agents that collaborate on a task, dividing work by role. Flows add event-driven, stateful orchestration, giving developers explicit control over how steps are triggered and sequenced and how state passes between them. Crews suit collaborative, role-based problem solving, while Flows suit deterministic pipelines where order matters. CrewAI works with multiple model backends, including OpenAI, Anthropic, Google, and local models through Ollama, and its agents can call custom tools, read and write files, and execute code.
CrewAI follows an open-core structure. The framework is free and MIT-licensed, while a separate commercial CrewAI Enterprise (AMP) product adds managed deployment, monitoring dashboards, execution logging, and governance. Public pricing for the Enterprise tier is not independently verified, so prospective buyers should confirm figures with the vendor. In the wider landscape, CrewAI sits between lightweight single-agent scripts and heavier durable-workflow engines built for long-running, fault-tolerant execution, and it assumes a coding workflow rather than a drag-and-drop builder, which makes it a poor fit for non-engineering users.
Editor's Note: CrewAI is one of the more approachable entries in the multi-agent space: MIT-licensed, Python-native, and past 54,242 GitHub stars as of June 2026, so the developer mindshare is real. The role-and-crew pattern maps cleanly onto how we delegate sub-tasks, and the open-source core means nothing is locked behind a runtime you cannot inspect. The honest caveat, which applies to every open-source multi-agent framework we have used, is that production reliability is not a property of the framework: it follows the model and tools you wire in. Agents that demo well can turn non-deterministic and expensive under real load, so we treat retries, cost ceilings, and tight observability as mandatory before anything reaches production. — Rafal Fila, ShadowGen
Integrations (6)
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