CrewAI

integrator · United States , San Francisco · founded 2023

Employees: 1-50 · Stage: Series A · Funding: $18,000,000

35.5

moderate

default-balanced
composite score

Framework for orchestrating autonomous AI agents working together as a crew. Open-source Python library plus CrewAI Enterprise hosted platform. Focused on multi-agent collaboration for complex task automation.

Analyst summary

CrewAI is a popular open-source multi-agent framework (Insight Partners-led $18M Series A) with a paid CrewAI AMP Enterprise tier. The OSS side is genuinely production-grade in adoption; the Enterprise side claims SOC 2 and HIPAA compliance and offers VPC and on-prem deployment, but the vendor's public trust documentation is thin and at least one pricing-page claim (FedRAMP High) is not corroborated by an external attestation.

Solid as an OSS framework; treat the Enterprise compliance claims as in-progress and require written attestations before signing.

Rating: acceptable

Compliance posture

SOC 2 Type II	Yes (report 2025-09-01)
ISO 27001	No / not disclosed
ISO 42001 (AI management system)	No / not disclosed
FedRAMP authorized	No / not disclosed
GDPR compliant	Yes
CCPA compliant	Yes
HIPAA compliant	No / not disclosed
NIST AI RMF aligned	No / not disclosed
CSA STAR certified	No / not disclosed
EU AI Act classification	general_purpose

Data handling

Trains on user data	no
Outputs feed model improvement	no
Data retention period	Customer-controlled; enterprise configurable
Can delete user data on request	Yes (SLA 30 days)
Default data residency	US
Encryption at rest	Yes (AES-256 / TLS 1.2+)
Encryption in transit	Yes
DPA available	Yes
Public subprocessor list	Yes
HIPAA BAA available	No / not disclosed

IP profile

User owns outputs	yes
Vendor claims output rights	No / not disclosed
Input IP protection	strong
Indemnification offered	No / not disclosed
Copyright shield program	No / not disclosed
Commercial use permitted	Yes
Training data provenance	not_applicable
Known IP lawsuits	No / not disclosed

Jurisdiction

Incorporation country	United States
Incorporation jurisdiction risk	low
Subject to US jurisdiction	Yes
Subject to EU jurisdiction	Yes
Subject to China jurisdiction	No / not disclosed
Subject to Russia jurisdiction	No / not disclosed
Government data access risk	moderate
Five Eyes aligned	Yes
Adequate privacy jurisdiction	No / not disclosed

Governance

Publishes model cards	No / not disclosed
Publishes transparency reports	No / not disclosed
Has AI ethics board	No / not disclosed
Safety testing disclosed	No / not disclosed
Red-teaming program	No / not disclosed
Government contracts	No / not disclosed
Responsible-AI policy	https://www.crewai.com/terms
Terms of service	https://www.crewai.com/terms
Privacy policy	https://www.crewai.com/privacy

Incidents on record

No incidents on file.

OWASP LLM Top 10 cross-walk

TrustAtlas dimensions that materially address each OWASP risk. Use to translate this vendor's compliance posture and data-handling stance into the application-security vocabulary your security team already uses.

LLM01	Prompt Injection User-supplied prompts manipulate model behaviour to bypass intended controls. SecurityTransparencyDependency chain
LLM02	Sensitive Information Disclosure Models leak PII, PHI, secrets, or proprietary data through outputs. Data handlingIP exposureJurisdiction
LLM03	Supply Chain Risk propagates from upstream models, datasets, plug-ins, and vendors. Dependency chainBusiness stabilitySecurity
LLM04	Data and Model Poisoning Adversarial training data or fine-tuning input degrades model integrity. Data handlingTransparencySecurity
LLM05	Improper Output Handling Downstream systems blindly trust model output, enabling injection downstream. IP exposureTransparency
LLM06	Excessive Agency Agents granted overbroad tool, identity, or permission scopes cause harm. Dependency chainTransparencyJurisdiction
LLM07	System Prompt Leakage System prompts containing secrets or logic are extracted via crafted input. Data handlingTransparency
LLM08	Vector and Embedding Weaknesses Vector stores and RAG pipelines leak or contaminate retrieved context. Data handlingSecurity
LLM09	Misinformation Hallucinated, biased, or fabricated outputs treated as authoritative. TransparencyRegulatory complianceBusiness stability
LLM10	Unbounded Consumption Cost, denial-of-service, and resource-exhaustion attacks against LLM endpoints. SecurityBusiness stability

Full framework reference: https://trustatlas.pages.dev/framework/owasp-llm-top-10

NIST AI RMF cross-walk

How each NIST AI RMF function is supported by the dimensions TrustAtlas scores.

GOVERN	Govern Establish AI governance structure: policies, roles, accountability. Regulatory complianceJurisdictionTransparencyBusiness stability
MAP	Map Establish AI context: intended purpose, use cases, capabilities, and risks. TransparencyDependency chainData handlingIP exposure
MEASURE	Measure Quantitative + qualitative risk assessment: testing, benchmarks, monitoring. SecurityData handlingTransparency
MANAGE	Manage Treat identified risks: mitigation, controls, incident response, lifecycle. Regulatory complianceSecurityDependency chainBusiness stability

Full framework reference: https://trustatlas.pages.dev/framework/nist-ai-rmf

Cited sources

Field	Source
data_handling.compliance_documentation_gap	https://github.com/crewAIInc/crewAI/discussions/2404 Verified 2025-04-01 by analyst
vendors.deployment_options	https://crewai.com/pricing Verified 2026-01-15 by analyst
vendors.funding_stage	https://pulse2.com/crewai-multi-agent-platform-raises-18-million-series-a/ Verified 2024-10-22 by analyst
vendors.license_open_source	https://github.com/crewaiinc/crewai Verified 2025-12-01 by analyst

Questions to ask before signing

Vendor-agnostic baseline. Send these to the vendor and require written answers before contract.

01. Provide your most recent SOC 2 Type II report (with bridge letter if applicable).
02. Describe your training-data provenance and customer opt-out mechanics in writing.
03. List all sub-processors and confirm notification policy for material additions.
04. Confirm BAA availability and signed-BAA process if we process PHI.
05. Describe rate-limiting, quota, and circuit-breaker controls protecting our usage.
06. Provide your model card or equivalent disclosure documenting intended use, limitations, and known failure modes.
07. Describe your prompt-injection defences and red-team posture against OWASP LLM Top 10 risks.
08. Confirm data residency options and which sub-regions our data may touch.
09. Provide incident-response SLAs, security-event notification timelines, and the most recent pen-test report summary.
10. Confirm output ownership terms and any indemnification or copyright-shield programs available.
11. Describe acquisition-risk safeguards and what happens to our data on a change of control.
12. List foundation-model dependencies and how upstream-model risk is mitigated.

Methodology + caveats

Composite scores use the default-balanced weight profile (25% data handling, 20% IP exposure, 15% jurisdiction, 15% security, 10% regulatory compliance, 8% transparency, 5% business stability, 2% dependency chain). All facts are sourced from the vendor's own public disclosures, public regulatory filings, or reputable secondary reporting — see the cited sources table above. This pack is decision-support material, not legal advice or audit evidence.