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CrewAI vs OpenAI Agents SDK. Rows marked Differs have different values.
	Open source CrewAI Remove	Open source OpenAI Agents SDK Remove
Type	Open source	Open source
CategoriesDiffers	Orchestration & Frameworks	Orchestration & Frameworks Protocols & Interop
In one line	Role-based multi-agent teams with a simple mental model.	A lightweight, production-ready multi-agent SDK with tracing.
Stage & funding	—	—
GitHub starsDiffers	≈60k	≈30k
License	MIT	MIT
Language	Python	Python
Last pushDiffers	Oct 28, 2026	Oct 27, 2026
Works with	—	—
Built onDiffers	—	Model Context Protocol
Built on itDiffers	—	Agency Swarm any-agent DBOS
Similar toDiffers	AgentScope AutoGen CAMEL CrewAI Hive Microsoft Agent Framework myclaude PocketFlow	—
People linked	0	0
Events	0	0
Pros	User reportsUsers find it fast for prototyping, with role-based agents that are intuitive to set up.[1] User reportsSeparate agents, tasks and tools, plus YAML config for roles and models, keep behaviour changes out of code.[1] MetadataMIT licensed and released very often (1.15.33 on Oct 29, 2026).[1]	User reportsVery easy start for function calling and sub-agents-as-tools; with the Responses API you don't have to store input items yourself.[1] User reportsOne long-time user says they are largely happy with its abstractions: handoffs, tools, guardrails and structured output.[1][2] DocsProvider-agnostic despite the name (100+ LLMs), with built-in tracing, sessions, sandbox agents and realtime/voice agents.[1] User reportsCommenters see it as simpler than heavier frameworks like Google ADK or LangChain, and its backlog is small (about 12 open issues).[1][2]
Cons	User reportsUsers report the whole crew hanging when one agent or API call times out; one moved the pipeline to LangGraph.[1] User reportsThe early simplicity becomes a constraint: users report hard-to-reproduce drift on long tasks and a 'loose and underspecified' abstraction.[1][2] User reportsForum users report poor visibility into agent decisions, roughly 10-minute crew runs, and a virtualenv near 1 GB.[1]	User reportsInfra, observability and reliable scaling are left to you, and one user found those were the bigger pain.[1] User reportsOne user calls it messy internally, citing inconsistent use of its own enums.[1] User reportsSome teams used it extensively and then moved to their own framework.[1]
Best for	Demos and internal multi-agent prototypes where speed to a first result matters more than determinism.	Python teams that want lightweight multi-agent primitives (handoffs, guardrails, tracing) with first-class OpenAI Responses and realtime support.
Watch out for	Timeouts, hangs and limited observability; plan your own monitoring before production.	Production concerns like durability, scaling and approvals are on you; plan that layer early.
Links	GitHub Docs	GitHub Docs

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