Pragor vs SalesTouch
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.
Manual
The LinkedIn MCP for AI agents. Connect Claude, Codex, and other MCP-compatible agents to LinkedIn prospect research and automated outreach. Find prospects, research profiles and companies, manage contextual conversations, and follow up with approval controls, scoped OAuth, pacing limits, and activity visibility.
Summary
- Pragor scores 2.0/5 and SalesTouch scores 0.8/5 on the Dark Factory Score, a difference of 1.2.
- The widest gap is Integration: Pragor 3.5, SalesTouch 1.0.
- Pragor scores higher on code quality, integration, self-correction and project complexity.
- They score the same on autonomy.
- Pragor is listed as freemium and SalesTouch as paid.
- Both are listed under Orchestration.
Dimension by dimension
| Dimension | Weight | Pragor | SalesTouch |
|---|---|---|---|
| Autonomy | 28% | 1.5 | 1.5 |
| Code Quality | 20% | 1.0 | 0.0 |
| Integration | 17% | 3.5 | 1.0 |
| Self-Correction | 15% | 1.5 | 0.5 |
| Project Complexity | 20% | 3.0 | 0.5 |
| Dark Factory Score | 2.0 | 0.8 |
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
Why SalesTouch scores 0.8
SalesTouch is not a software-development tool in the sense this rubric targets — it's an MCP connector that exposes LinkedIn prospecting and outreach actions (search, read, message, follow up, publish) to AI agents like Claude or Codex, gated by OAuth scopes, pacing limits, and human approval controls. It has no stated capability to write, test, or ship code, so code quality, CI/CD integration, and project-complexity handling (which are defined here in terms of scripts, modules, and applications) are essentially inapplicable — scores reflect that near-total mismatch rather than poor execution of a dev task. On autonomy, the tool explicitly emphasizes 'approval controls' and 'activity visibility,' meaning actions are designed to be human-gated rather than fully autonomous — appropriate for compliance/safety in outreach, but it caps autonomy scoring under a dev-tool rubric. Integration gets a small credit because it plugs into agent orchestration frameworks (MCP, Claude, Codex) via OAuth, which is a real technical integration pattern, but there's no evidence of git, deployment pipelines, or monitoring hooks relevant to software engineering. Self-correction is unaddressed in any materials (no mention of retry logic, error recovery, or iterative refinement), and project-complexity tiers (script through distributed system) don't meaningfully apply since this tool operates in the sales/outreach domain, not software construction — so the tier scores are flat and minimal across the board.
Scored by Claude AI — last evaluated October 22, 2026
More comparisons
Generated from Dark Factory Score data. How the score works. Comparing three or more tools at once is part of Pro.