No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by Jwuthri · MCP Server · ★ 1.4k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Tracely-ai safe to install? View the security audit →
Tracely Production failures become regression tests. Trace-native CI/CD for AI agents. Tracely grades every agent trace as it lands, clusters the failures into issues, freezes the bad runs into hermetic replayable cases, blocks the pull request that would ship them again — and tells you the moment any of it happens. production trace → failure detection → regression test → CI gate → alert Website · Docs · Product guide · Agent skill · Guided tour · 2-min demo · Design dossier Self-host the whole stack in
| Stars | 1,418 |
| Forks | 156 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 63.6600777730429/100 |
| Open Issues | 18 |
| Last Updated | 2026-10-03 |
| Created | 2026-06-03 |
| Platforms | cli, mcp, python |
| Est. Tokens | ~20k |
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Tracely-ai is Trace-native CI/CD for AI agents — production failures become regression tests that block the PR. Auto-detect, cluster, freeze into hermetic cases, replay in CI for $0.. It is categorized as a MCP Server with 1.4k GitHub stars.
Tracely-ai is primarily written in Python. It covers topics such as agent, agent-observability, ai-agents.
You can find installation instructions and usage details in the Tracely-ai GitHub repository at github.com/Jwuthri/Tracely-ai. The project has 1.4k stars and 156 forks, indicating an active community.
Tracely-ai is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to Tracely-ai on Agent Skills Hub include lmnr, trpc-agent-go, clawmetry. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: