
Free / Open-sourceAgent observability, evaluation, and continuous improvement for production AI

Mindgard records 153,455 monthly visits with +1.3% month-over-month growth and Contact for Pricing pricing. Roark records 9,226 monthly visits with +23.1% growth and Paid pricing.

Test, monitor and improve the voice AI agents you already run
| Dimension / Metric | ![]() | ![]() |
|---|---|---|
| Official Domain | mindgard.ai | roark.ai |
| Pricing Model | Contact for Pricing | Paid |
| Monthly Visits | 153.46K (153,455) | 9.23K (9,226) |
| MoM Traffic Delta | +1.96K (+1.3%) |
| +1.73K (+23.1%) |
| Primary Market & Share | 🇺🇸 United States (38.4%) | 🇺🇸 United States (75.7%) |
|---|
| Direct Visits / mo | 21.56K | 4.83K |
|---|
| User Rating | 4.3 | 4.4 |
|---|
| Supported Platforms | Website | Website |
|---|

Attack-led AI security testing and red teaming
Agent-native reconnaissance maps models, agents, tools, instructions, permissions and behaviours the way an attacker would before launching attacks.
Continuous, adaptive testing of AI agents and systems against evolving attack techniques, rather than one-off audits.
Findings are backed by a knowledge base built from more than 175 publicly disclosed AI vulnerabilities across major AI systems.
Runtime protections and remediation guidance help close defensive gaps and verify that fixes actually hold.

Test, monitor and improve the voice AI agents you already run
Run your agent against hundreds of realistic personas, accents, background noise and edge cases before a customer ever dials in.
Red-team callers attempt prompt injection, jailbreaks and social engineering to check your agent holds policy under pressure.
Every live call is scored as it lands on more than 500 metrics, with issues filed, alerts fired and traces attached.
Purpose-built audio models measure pronunciation, emotion, vocal stress, pace, interruptions and dead air, not just transcript quality.

Free / Open-sourceAgent observability, evaluation, and continuous improvement for production AI


Automates continuous red teaming and security reviews, but lacks Mindgard's dedicated machine learning vulnerability scanning suite.

Free / Open-sourceBuild, simulate, evaluate and monitor self-improving AI agents
#3
Free / Open-sourceEvaluate, monitor, and govern your AI applications in production

SpecialistSpecializes in real-time runtime security against prompt injections, but lacks comprehensive offline model vulnerability scanning.

Free / Open-sourceStandardize LLM evaluation, observability, and red teaming across your whole org

Automated QA, simulation and observability for voice and chat AI agents
Free / Open-sourceAI governance, testing, and observability for regulated enterprises

SpecialistDelivers enterprise-grade AI observability and model governance, but provides less automated vulnerability red teaming.

Free / Open-sourceFind and fix problems in your AI agents before users do

Free / Open-sourceThe security layer for voice and chat agents: test, red-team, and monitor every call

Agentic CX trust platform for testing, monitoring and observing every customer journey
Free / Open-sourceAgent observability, evaluation, and continuous improvement for production AI
Free / Open-sourceBuild, simulate, evaluate and monitor self-improving AI agents
Free / Open-sourceEvaluate, monitor, and govern your AI applications in production
SpecialistSpecializes in real-time runtime security against prompt injections, but lacks comprehensive offline model vulnerability scanning.
Free / Open-sourceStandardize LLM evaluation, observability, and red teaming across your whole org
Free / Open-sourceAI governance, testing, and observability for regulated enterprises
SpecialistDelivers enterprise-grade AI observability and model governance, but provides less automated vulnerability red teaming.
Free / Open-sourceFind and fix problems in your AI agents before users do
Free / Open-sourceThe security layer for voice and chat agents: test, red-team, and monitor every call
Agentic CX trust platform for testing, monitoring and observing every customer journey