HOL Guard vs Pragor
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HOL Guard is an Apache-2.0, local-first security tool for AI coding agents. On supported integrations it evaluates agent actions including shell commands, secret access, MCP changes and plugin or skill installs, then allows, asks or blocks based on policy. It records decisions locally, with optional cloud sync. Coverage depends on the supported harness and action surface; it is not a network firewall.
Pair Programmer
Pragor is an operations board for teams of human and AI coding agents. It coordinates project-scoped tasks, messages, approvals and evidence across models and frameworks through REST and MCP; agents do the coding in their own tools. A public demo is available. Pragor is an orchestration and governance layer, not an autonomous code generator.
Summary
- HOL Guard scores 1.6/5 and Pragor scores 2.0/5 on the Dark Factory Score, a difference of 0.4.
- The widest gap is Project Complexity: HOL Guard 2.0, Pragor 3.0.
- Pragor scores higher on code quality, integration, self-correction and project complexity.
- They score the same on autonomy.
- HOL Guard is listed as open source and Pragor as freemium.
- Both are listed under Orchestration and Full-Stack Agents.
Dimension by dimension
| Dimension | Weight | HOL Guard | Pragor |
|---|---|---|---|
| Autonomy | 28% | 1.5 | 1.5 |
| Code Quality | 20% | 0.5 | 1.0 |
| Integration | 17% | 3.0 | 3.5 |
| Self-Correction | 15% | 1.0 | 1.5 |
| Project Complexity | 20% | 2.0 | 3.0 |
| Dark Factory Score | 1.6 | 2.0 |
Why HOL Guard scores 1.6
HOL Guard is not a code-generating or autonomous-coding tool — it's a policy/gatekeeper layer that intercepts and arbitrates risky actions (shell commands, secret access, MCP/plugin changes) taken by other AI coding agents. Because it does not write, test, or ship code itself, it scores very low on code quality and self-correction, and low on autonomy: its 'decisions' are allow/ask/block policy evaluations rather than independent multi-step task execution. It has no role in generating, refactoring, or iterating on code output, so those dimensions are effectively out of scope for its function. Where it does have some relevance is integration — it explicitly plugs into agent harnesses (Codex, Claude Code, MCP ecosystems) and keeps a local/cloud-synced audit log of decisions, which is a meaningful form of tooling integration, though it's not a CI/CD or deployment pipeline tool per se. On project complexity, since it operates at the action/harness level rather than the codebase-structure level, its applicability is roughly flat across tiers: it can monitor a one-off script's shell commands just as easily as a multi-service app's MCP calls, but it doesn't meaningfully scale its value or sophistication with project size — it offers the same category of oversight regardless of tier, with reduced confidence at true distributed/multi-repo scale where action surfaces multiply beyond a single harness's visibility. Overall, HOL Guard should be understood as a security/governance complement to coding agents rather than a competitor in code generation or autonomy — its Dark Factory Score is necessarily low across the core 'build' dimensions, with modest credit for integration and consistent (if shallow) support across complexity tiers.
Scored by Claude AI — last evaluated October 29, 2026
Why Pragor scores 2.0
Pragor explicitly positions itself as an orchestration and governance layer, not an autonomous code generator — the actual coding happens in separate agent tools. This fundamentally caps autonomy and code_quality: Pragor itself never writes code, so it can't be judged on types/tests/error-handling, and its workflow is built around human-in-the-loop approvals rather than independent multi-step execution. Self-correction is similarly indirect — Pragor can surface failures via its audit trail and approval gates, but the actual fixing/iterating is delegated to whichever coding agent is plugged in, so Pragor's own self-correction capability is minimal. Where Pragor shines is integration: it exposes REST and MCP interfaces, works across multiple models/frameworks, and maintains an attributable audit trail with persistent memory — these are genuine devops/orchestration strengths suited to coordinating teams of agents. This also drives the project_complexity profile: Pragor is overkill for a single script or trivial one-off feature (low value at Tier 1-2), but becomes increasingly valuable as the number of agents, tasks, and cross-service coordination needs grow, making it reasonably well-suited to Tier 4 (multi-service apps) and especially Tier 5 (distributed, multi-repo systems) where governance, task-routing, and audit trails across many contributors (human or AI) matter most. Overall, Pragor should be understood as a force-multiplier/governance tool rather than a Dark-Factory autonomous coder, and its scores reflect strong infrastructure/coordination capability paired with no direct code-generation or self-correction capacity of its own.
Scored by Claude AI — last evaluated October 22, 2026
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Generated from Dark Factory Score data. How the score works. Comparing three or more tools at once is part of Pro.