CrewAI vs Kritt

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

	CrewAI Agents	Kritt Agents
Tagline	Python framework for multi-agent orchestration.	AI-assisted code security research with parallel agents and verified findings
Category	Agents	Agents
Pricing	Freemium· Free open-source core; cloud platform paid	Freemium· open-kritt: Free (self-hosted, AGPL-3.0) / Single managed scan: $5,000 (up to 200k LoC) / Enterprise: custom pricing for CI/CD integration and dedicated support
Model	BYO (Claude / GPT / open)	Codex and Claude Code (as agent harnesses)
Editorial score	8.4 / 10	—
Use cases	multi-agentorchestrationPython	Pre-launch security audit of a new serviceContinuous vulnerability scanning in CI/CDBug bounty research on open-source targetsAttack-surface mapping for acquired codebasesVerified proof-of-concept generation for reported bugsCustom-rule appsec review at scaleTriage reduction for existing SAST pipelinesDeep-dive review of security-sensitive modules (auth, crypto, parsers)
Pros	Clean Python API Strong role/goal abstractions Active community Hosted platform for deployment	Parallel-agent architecture covers large codebases faster than sequential AI review passes Verification stage attempts to confirm exploitability, cutting the LLM false-positive rate that plagues single-shot audits Open-source self-hosted build (AGPL-3.0) lets teams run it against private code with their own model keys Bring-your-own harness: works with Codex and Claude Code so you can pick the frontier model that fits your budget Custom severity rules and verification scripts let security teams encode their own threat model Fixed-price managed scan option ($5k up to 200k LoC) gives a predictable path for teams without in-house AI-sec expertise
Cons	Production observability still maturing Debugging multi-agent flows is hard	Managed scan pricing is steep for indie developers or small startups compared to traditional SAST tools Self-hosting still requires paying for the underlying frontier model API usage, which can be substantial on large repos AGPL-3.0 licensing on the open-source build is incompatible with some proprietary product integrations No standalone REST API documented publicly; automation lives at the CLI/workflow layer Effectiveness is bounded by the underlying LLMs' understanding of the language and framework; niche stacks may see weaker coverage Newer entrant relative to established SAST/DAST vendors, so tooling maturity and integrations are still evolving
Website	www.crewai.com	kritt.ai

Pick CrewAI if

✅ Clean Python API
✅ Strong role/goal abstractions
✅ Active community
✅ Hosted platform for deployment

Pick Kritt if

✅ Parallel-agent architecture covers large codebases faster than sequential AI review passes
✅ Verification stage attempts to confirm exploitability, cutting the LLM false-positive rate that plagues single-shot audits
✅ Open-source self-hosted build (AGPL-3.0) lets teams run it against private code with their own model keys
✅ Bring-your-own harness: works with Codex and Claude Code so you can pick the frontier model that fits your budget

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