For developers building AI agent workflows, CrewAI is a multi-agent orchestration framework that lets you define teams of agents, called crews, to plan, delegate, and execute tasks together via API, MCP, SDK, or CLI.
Presence & Market Position
#10 of 22
Orchestrators & Agentic Workflow
Measures earned, engagement-weighted share of voice across the GTM voices panel. Methodology →
CrewAI is positioning itself as the production orchestration framework for developers building teams of agents that plan, delegate, and execute work together. Its market presence is rising quickly across agent-engineering conversation. Builders discuss CrewAI in the context of hackathons, enterprise agent stacks, framework choice, deployment pipelines, evaluation, security, and centralized governance across multiple runtimes. A recurring practitioner view is that framework selection matters less than the infrastructure surrounding production agents. CrewAI’s product agenda addresses that layer through visual construction, reusable flows, discovery tools, and authorization below the agent itself.
Relevance AI
Dust
Lindy
Vellum
InngestPepsiCo, Johnson & Johnson, IBM, DocuSign, AB InBev, Experian, EXL, Konecta, Gelato
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Business Profile
CrewAI was founded in 2023 and has grown into what its own materials call enterprise agent infrastructure, reporting adoption across a majority of the Fortune 500 alongside customers including PepsiCo, IBM, DocuSign, and AB InBev. The company has raised $20.9 million and was valued at $76 million as of 2026, reporting roughly $3.2 million in ARR for 2025. At 75 employees, it remains a small team relative to its enterprise customer base and market visibility, still operating independently. Its growth story centers on moving from an open-source multi-agent framework toward a governed runtime built for production deployments inside large, often regulated, organizations.
Agent Readiness
Measures how easily your agents can build on it — API, MCP, CLI, SDK, docs depth. Methodology →
CrewAI ships API, MCP, SDK, and CLI surfaces, and recent releases have focused on production hardening rather than initial capability: workflow checkpointing so a run survives a crash, new orchestration patterns for balancing determinism against agent autonomy, and Crew Studio for building and inspecting full agentic systems, not just single agents. It has also published guidance on keeping authorization decisions outside the model itself, treating write-access as the part of agent deployment that needs the most control.
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