Fabraix vs Ninjō AI: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Fabraix and Ninjō AI — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Fabraix
Fabraix
An adversarial staging environment and open playground to find gaps in AI agents through live red-teaming and verification.
Key features
- Live Adversarial Playground: Deploys fully functional AI agents in live challenge environments so researchers and attackers can probe real capabilities rather than toy or mocked scenarios.
- Published System Prompts: System prompts and agent configurations are published openly to ensure transparency and reproducibility of challenges and defenses.
- Versioned Challenge Configs: Challenge definitions and configuration files are stored and versioned in public repositories, enabling traceability and collaborative iteration on tests and fixes.
- Autonomous Red‑Teaming Agents: Provides or links to autonomous agents and tooling that systematically probe target systems to discover failure modes and bypasses.
- Exploit Documentation and Remediation Sharing: When a technique succeeds, the winning method is documented and shared so defenders can learn common weaknesses and implement fixes.
- Community Contribution Model: Encourages external contributors to submit new challenges, attacks, and mitigations to expand coverage and collective understanding.
- Open-Source Repositories and Licensing: Maintains public GitHub repositories (Playground and related tools) with code, challenges, and license files to support adoption and auditing.
- Runtime Security Focus: Orients testing and tooling toward protecting live agent behavior and interactions, not just static model evaluation.
- Live deployment of AI agents for real-world adversarial testing
- Publicly published system prompts and versioned challenge configurations
- Community-driven challenges with documented winning techniques
- Open-source repository containing frontend, challenge configs, and tooling
- Ability to reproduce attacks and defenses for shared learning
- Designed to surface runtime vulnerabilities and failure modes
Best for
- Pre-release Red-Teaming: Run live adversarial challenges against an AI agent prior to product launch to identify prompt-injection, data-exfiltration, or policy-bypass vulnerabilities.
- Security Research and Failure-Mode Analysis: Researchers use the Playground to reproduce, analyze, and document novel agent attacks and their root causes.
- Defensive Engineering and Patch Verification: Developers apply documented winning techniques to validate fixes and confirm that mitigations prevent previously successful exploits.
- Benchmarking Defenses: Operations teams compare different defense strategies or system-prompt configurations against the same community challenges to evaluate robustness.
- Training Security Teams: Security engineers and incident responders practice detection and mitigation in realistic, live-agent scenarios to build operational readiness.
- Community Knowledge Sharing: Open publication of challenges and solutions enables cross-organization learning and dissemination of best practices for agent runtime safety.
- Automated Vulnerability Discovery: Use the provided autonomous probing agents to continuously scan deployed agents for regressions or new vulnerabilities as code and prompts evolve.
- Security validation and hardening of autonomous agents before production rollout
- Red-team exercises to discover prompt- and runtime-based bypasses
- Research and education on agent failure modes and defenses
- Auditing agent behavior by reproducing attacks from community-documented challenges
- Continuous integration of agent defenses by tracking challenge regressions
Ninjō AI
Ninjo
Infrastructure for AI sales agents on Instagram, WhatsApp and other DM channels, built and improved by talking to an LLM over MCP.
Key features
- MCP Server Control Surface: Exposes agent creation, testing, analysis and improvement as MCP tools, so Claude, Claude Code, Codex or ChatGPT becomes the interface instead of a dashboard.
- Cortex Playbook Library: Ships prompt templates, KPI rubrics and anti-patterns distilled from agents that ran in production, so a new agent inherits patterns that already converted rather than starting blank.
- Multi-Channel DM Deployment: Connects agents to Instagram, WhatsApp and other direct-message channels where the selling actually happens, without a separate build per channel.
- Versioned Changes with Rollback: Every edit to an agent is versioned and instantly reversible, so a bad prompt change during a live launch can be undone rather than debugged under pressure.
- Synthetic Conversation Testing: Runs an agent against generated conversations before it reaches a real inbox, surfacing broken qualification logic ahead of launch.
- Follow-Ups and Keyword Triggers: Fires scheduled follow-up sequences and keyword-based branches so stalled conversations get reopened automatically.
- Built-In CRM and Funnel Analytics: Ninjo Studio provides real-time conversation views, contact records and funnel reporting in one panel for when you want direct oversight.
- Payment Recovery Flows: Agents can chase declined payments conversation by conversation, a pattern the team credits for recovering 47 declined payments in a single four-day launch.
Best for
- Creator and Coach Launches: Running a short high-volume launch where an agent qualifies inbound DMs, handles objections and sends payment links at a pace a human team cannot match.
- Instagram Lead Qualification: Filtering hundreds of daily inbound Instagram messages down to the prospects worth a human sales call.
- WhatsApp Sales Follow-Up: Reopening conversations that went quiet with timed follow-up sequences instead of leaving them to decay.
- Agency Multi-Client Operations: Managing many client agents from a chat interface so a three or four person team can operate over a hundred agents.
- Declined Payment Recovery: Having an agent work through failed transactions individually to recover revenue that would otherwise be written off.
- Rapid Agent Iteration: Rewriting an agent's qualification logic mid-campaign and rolling back immediately if conversion drops.