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 Scale3-Labs · LLM Plugin · ★ 1.2k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is langtrace safe to install? View the security audit →
Langtrace Open Source Observability for LLM Applications [ is an open-source, cloud-native runtime for agentic AI. Secure, modular,
A database of SDKs, frameworks, libraries, and tools for creating, monitoring, debugging and deploying autonom
The only fully local production-grade Super SDK that provides a simple, unified, and powerful interface for ca
Build, Improve Performance, and Productionize your LLM Application with an Integrated Framework
Zero-Config Code Flow for Claude code & Codex
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langtrace is Langtrace 🔍 is an open-source, Open Telemetry based end-to-end observability tool for LLM applications, providing real-time tracing, evaluations and metrics for popular LLMs, LLM frameworks, vectorD. It is categorized as a LLM Plugin with 1.2k GitHub stars.
langtrace is primarily written in TypeScript. It covers topics such as ai, datasets, evaluations.
You can find installation instructions and usage details in the langtrace GitHub repository at github.com/Scale3-Labs/langtrace. The project has 1.2k stars and 127 forks, indicating an active community.
langtrace is released under the AGPL-3.0 license, making it free to use and modify according to the license terms.
The top alternatives to langtrace on Agent Skills Hub include aide, sre, awesome-ai-sdks. 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: