CrewAI
An open source Python framework for orchestrating multi-agent collaboration
- Source repo
- crewAIInc/crewAI
- Stars
- ★ 60k
- Last updated
- today
- License
- MIT
- Primary language
- Python
- FA score
- 40/100 · Major gaps
At a glance
- How it runs
- Cost
- Free tier plus a paid hosted plan
- Setup effort
- Medium · a few setup steps
- Typical use
- Split a complex task across multiple agents with clear divisions of labor (e.g. market research + copywriting + review)
- Not a fit if
- Teams that need fully offline operation with no model API
- Users who want zero-config setup without writing Python
- Enterprises requiring every capability to be open source, with no commercial cloud control plane
- Source review
- 40/100 · Major gaps 6 safety controls not found
What does this agent do, and when should you use it?
CrewAI is an open source Python framework for orchestrating role-playing, autonomous AI agents, offering both high-level abstractions and low-level APIs to build production-ready multi-agent workflows. It provides two complementary orchestration modes: Crews (autonomous agent collaboration) and Flows (event-driven, precise process control), which can be combined. The project is maintained by crewAI Inc., which also offers a commercial cloud trial.
Lets developers define multiple agents with distinct roles, goals, and tool permissions, then orchestrate them through a Crew (collaborating agent team) or a Flow (event-driven process) to jointly complete complex tasks — combining both to get workflows with both autonomy and deterministic control points.
- Split a complex task across multiple agents with clear divisions of labor (e.g. market research + copywriting + review)
- Build automation pipelines that need precise process control (Flows) while plugging in autonomous agents (Crews) at specific steps
- Teaching/prototyping scenarios: job-description writing, trip planning, stock analysis, and other official example use cases
- Teams already on a Python stack who want to embed multi-agent capability into an existing system
How do you install or deploy this agent?
Requires Python >=3.10 <3.14. Uses UV for dependency management: uv pip install crewai; for optional built-in tools and extras, see the official docs for the additional install command.
How do you use this agent?
Define a set of agents (role, goal, available tools) and tasks, compose them into a Crew or Flow, and run it; see docs.crewai.com for detailed API usage and several runnable examples (job descriptions, trip planning, stock analysis, and more).
What are this agent's strengths and limitations?
- Offers both autonomous collaboration (Crews) and deterministic process control (Flows), combinable as needed
- Extensive documentation, courses, and examples make the learning path clear
- Large community (50k+ stars) with a mature ecosystem and an optional official cloud service
- MIT-licensed and integrates well within a pure Python stack
- The open-core + commercial cloud dual-track model means some capability may only be available in the paid tier
- As an orchestration framework, safety depends heavily on the tools and permissions a developer configures for each agent — the framework itself doesn't provide a unified gate
- This review did not verify actual runtime behavior of any specific Crew/Flow configuration
How does this agent compare with similar options?
Compared to graph-based orchestration frameworks like LangGraph, CrewAI's "role-playing" abstraction (each agent has a clear role and goal) is more intuitive for developers familiar with team-collaboration metaphors, with a gentler learning curve; if you need finer-grained state-graph control, LangGraph may fit better, and the two can also be combined (CrewAI's own docs mention interoperating with other frameworks).
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| CrewAI This agent | 40 · Major gaps | CLIFreemium | ★ 60k | today | Python | — |
| CaveAgent | 62 · Some gaps | Library / SDKFree + model costs | ★ 204 | 1mo ago | Python | OpenAI API · Claude API |
| AG2: Open-Source AgentOS | 55 · Major gaps | Library / SDKFree + model costs | ★ 5k | today | Python | OpenAI API |
| PowerContext | 55 · Major gaps | CLIFree | ★ 1.4k | 1d ago | Python | Codex · Claude Code |
How does FollowAgents rate this agent?
Why each dimension lost points
Evidence shows: AGENTS.md provides only contribution guidelines, with no mention of permission management, user confirmation, data flow transparency, or sensitive data handling. For dependency security, pyproject.toml and .pre-commit-config.yaml show dependency overrides and pip-audit usage, but with a large ignore list and no mitigation details. External effects, rollback, and source attribution are not addressed in AGENTS.md. Deductions: lack of evidence for least privilege, user confirmation, data flow transparency, sensitive data handling, external effects, and rollback; dependency security deducted due to ignore list and lack of mitigation details; source attribution only partially evidenced via author info.
Evidence shows: AGENTS.md guidelines are consistent with repository structure, but contain typos (e.g., 'behaivor'). Dependency availability: pyproject.toml specifies Python version range and dependency overrides, but no availability guarantees. Failure messages: AGENTS.md provides no error handling or failure message guidance. Deductions: self-consistency deducted for typos; dependency availability deducted for lack of guarantees; failure messages deducted for absence.
Evidence shows: AGENTS.md targets contributors, providing documentation translation and code contribution guidelines, but does not explicitly define target audience or use cases. Capability boundaries are not clearly stated. Trigger precision: guidelines do not define trigger conditions. Environment fit: Python version and installation instructions provided, but not covering all environments. Deductions: audience and scenarios deducted for lack of explicit definition; capability boundaries and trigger precision deducted for absence; environment fit deducted for incomplete coverage.
Evidence shows: AGENTS.md provides information architecture (e.g., docs structure), installation notes in README, naming stability not explicit, examples and FAQ in README, known limitations not mentioned, license is MIT, versioning via commitizen and changelog config, maintenance responsibility not explicit. Deductions: naming stability, known limitations, and maintenance responsibility deducted for absence; versioning deducted for config presence but not explicit.
Evidence shows: Output usability: README provides examples and output file instructions, but no explicit output format. Marginal value: framework offers multi-agent orchestration, but no comparison with alternatives. Cost-benefit: no performance or resource usage data. Deductions: output usability deducted for lack of explicit format; marginal value deducted for lack of comparison; cost-benefit deducted for lack of data.
Evidence shows: Claim traceability: README claims lack specific evidence. Cross-source corroboration: no external validation provided. Fact-inference separation: not explicitly distinguished. Deductions: all sub-criteria deducted for lack of evidence.
- Not found in source: least-privilege scopingGrant only what the task needs: a dedicated account or read-only token, scoped to specific directories and repos.
- Not found in source: confirmation before actingTurn on (or add) a confirmation step before it acts, and try it in a sandbox or test environment before real data.
- Not found in source: data-flow disclosureWatch which external services it contacts (proxy or firewall logs) and keep sensitive data out until you know where it goes.
- Not found in source: sensitive-data handlingUse dedicated, low-privilege, revocable API keys — never production credentials — and keep secrets out of logs.
- Not found in source: disclosed external effectsEstablish which external systems it writes to, sends to or changes, and verify with test accounts or repos before production.
- Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
- AGENTS.md provides only contribution guidelines, with no coverage of security, permissions, or data handling; assess before use.
- Dependency security has a large ignore list and lacks mitigation details; handle with caution.
- Publisher identity is unverified; source attribution is only partially clear.
Recent releases
Latest entries from the project's GitHub Releases. Notes are quoted from upstream as written, not rewritten or tested by FollowAgents.
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Release notes excerpt
What's Changed Features - Add crewai eval command to report where a stopped model broke. - Use LiteLLM catalog for context windows. Bug Fixes - Fix flow to learn from resumed human feedback and preserve reviewed output types. - Warn when non-empty apps resolve to zero platform tools. - Fix flow add-crew to correctly find the package from pyproject. - Show reasons for deployment failures and fix deploy remove and lockfile checks. - Fix trigger list and payload requests timeout to 60 seconds. - Read AMP's source_type during deploy push to ensure ZIP deployments use the new code. …View on GitHub ↗ -
Release notes excerpt
What's Changed Features - Add XPU to OpenCLIP provider device options - Add deepinfra as an OpenAI-compatible provider - Record why an evaluation stopped before its verdict - Record what the run app's Deploy met before an attempt - Implement per-run cost and time tracking in crewai eval --models without repeated progress lines Bug Fixes - Fix issue where stopSequences were sent to OpenAI GPT-6/GPT-5.6/gpt-oss - Preserve GitHub loader source attribution - Fix SQLite flow initialization contention Documentation - Add snapshot and changelog for v1.15.26 Contributors …View on GitHub ↗ -
Release notes excerpt
What's Changed Bug Fixes - Fix ownership retention of rwlock when acquisition is interrupted. - Resolve issue with documentation site source URLs not being preserved. - Fix task output display in log-tasks-outputs to show actual output. Documentation - Update snapshot and changelog for version v1.15.25. Contributors @Velmet44, @Vidit-Ostwal, @joaomdmoura, @leilei3167, @lorenzejay, @lux-liangView on GitHub ↗