Agentic Index
Mindgard vs Repello AI (2026)
Both score 9 of 14 and both run continuous AI red teaming with runtime protection, which makes this one of the closest comparisons in the lane. That verdict is the Agentic Index coverage score, graded from each vendor's own published materials.
Mindgard is research led, a Lancaster University spinout with eleven PhDs, mapping the production attack surface then running MITRE ATLAS and OWASP mapped attacks, deployed through GitHub Actions, CLI and Burp Suite. Repello runs a three phase loop, discover and build an AI BOM, red team with ARTEMIS across 15 million attack patterns and 270 plus vulnerability types, then deploy guardrails calibrated from those results, with multi modal and multi lingual coverage. Decide on the loop: Mindgard tests deeply, Repello closes the circle into guardrails.
On the Agentic Index AI SOC ranking, neither Mindgard nor Repello AI clears the bar, which asks for all five investigation loop capabilities documented in full. Neither documents human oversight and guardrails in full. 24 of the 94 vendors in the lane clear it. See the AI SOC ranking
This comparison is published by Agentic Index, an independent agentic AI vendor research platform. Mindgard and Repello AI are each graded against the same 14 capability Agentic Index taxonomy, from the vendor's own public materials under the Agentic Index verification standard, alongside 969 researched vendors. No vendor pays for placement and no vendor has reviewed this page. How this evidence is graded
Choose Mindgard if
- Research pedigree matters to how you evaluate security claims.
- Existing developer tooling integration, especially Burp Suite, is how your team works.
- Testing depth is the requirement and guardrails are already handled elsewhere.
Choose Repello AI if
- You want red team results automatically calibrating the guardrails, not a report to act on.
- Multi modal and multi lingual attack coverage matches how your systems are used.
- An AI BOM as the discovery step fits an environment where nobody knows what is deployed.
| At a glance | Mindgard | Repello AI |
|---|---|---|
| Category | Security / SOC agent | Security / SOC agent |
| Entry price | Contact sales | Contact sales |
| Free / trial | Not published | Open-source tooling available (Artemis GitHub Action, Whistleblower) |
| Pricing confidence | contact only | contact only |
| Feature | M Mindgard |
R Repello AI |
|---|---|---|
| Action & orchestration | ||
|
Integrations & Tool Calling Ability to connect agents to real systems through native integrations, OAuth-authenticated actions, custom tools, APIs, webhooks, or MCP-compatible tools. |
Full / Explicit | Full / Explicit |
|
Workflow Orchestration Ability to sequence, branch, retry, route, and combine deterministic workflow nodes with autonomous agent steps. |
Full / Explicit | Full / Explicit |
|
Triggers & Channel Coverage How agents wake up and where they work: schedules, webhooks, message events, CRM events, inbox events, chat, email, voice, and collaboration tools. |
Full / Explicit | Full / Explicit |
| Knowledge & context | ||
|
Knowledge Grounding & RAG Ability to ground agent behavior in company data through document ingestion, retrieval, external knowledge APIs, semantic search, or RAG layers. |
Partial | Full / Explicit |
|
Memory & State Persistence Ability to persist context across a run, conversation, workflow, user, team, or longer-term memory layer. |
Partial | Partial |
| Control & trust | ||
|
Human Oversight & Guardrails Approval steps, consent checkpoints, escalation rules, structured guardrails, policy constraints, and pause/resume controls. |
Partial | Partial |
|
Security, Identity & Governance RBAC, SSO, auditability, encryption, least-privilege tool access, compliance posture, and data handling policy. |
Full / Explicit | Partial |
|
Observability & Auditability Traces, logs, execution histories, metrics, audit events, and debugging detail for production agent behavior. |
Full / Explicit | Full / Explicit |
|
Deployment & Data Residency Deployment modes and options, including SaaS, dedicated cloud, VPC, on-prem, hybrid, local runtime, and self-hosting. |
Partial | Partial |
| Solution readiness | ||
|
Prebuilt Agents, Templates & Packs Ready-made workflows, packaged employees, templates, blueprints, industry solutions, and role-specific agents that reduce time-to-value. |
Partial | Partial |
| Platform extensibility | ||
|
Model Flexibility & Routing Ability to work across multiple foundation models, route tasks to different models, or let buyers bring their own providers and keys. |
No / Not documented | No / Not documented |
|
APIs, SDKs & MCP Extensibility Composability layer: stable APIs, SDKs, MCP tool consumption/serving, custom tools, and integration into internal systems. |
Full / Explicit | Full / Explicit |
|
Testing, Debugging & Optimization Testing, debugging, scoring, retries, fallbacks, quality gates, and optimization loops for improving agent workflows before and after deployment. |
Partial | Partial |
| Specialist automation | ||
|
Browser & Computer Use Browser, desktop, or remote/local computer control for workflows that cannot be handled through stable APIs alone. |
No / Not documented | No / Not documented |
Pricing snapshot
Sourced from the Index pricing dataset · open each vendor's profile for full detail.
| Pricing | M Mindgard |
R Repello AI |
|---|---|---|
|
Entry price Lowest public entry point |
Contact sales | Contact sales |
|
Pricing confidence How public the numbers are |
Contact only | Contact only |
|
Billing Primary billing axis |
scope of AI models, agents, and applications under continuous testing, plus expert services | scope of AI systems under continuous red teaming and runtime protection |
|
Variable cost Workload / overage exposure |
Medium variable cost | Medium variable cost |
|
Free tier / trial Try before you buy |
No free tier
|
No free tier
|
|
Buying motion Self-serve vs sales call |
Sales call | Sales call |
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