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 pydantic · Agent Tool · ★ 4.5k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is logfire safe to install? View the security audit →
Pydantic Logfire — Know more. Build faster. From the team behind Pydantic Validation, Pydantic Logfire is an observability platform built on the same belief as our open source library — that the most powerful tools can be easy to use. What sets Logfire apart: Simple and Powerful: Logfire's dashboard is simple relative to the power it provides, ensuring your entire engineering team will actually use it. Python-centric Insights: From rich display of Python objects, to event-loop telemetry, to profiling Python code
| Stars | 4,487 |
| Forks | 292 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 65.9670167258598/100 |
| Open Issues | 207 |
| Last Updated | 2026-09-23 |
| Created | 2024-04-23 |
| Platforms | python |
| Est. Tokens | ~14k |
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logfire is AI observability platform for production LLM and agent systems.. It is categorized as a Agent Tool with 4.5k GitHub stars.
logfire is primarily written in Python. It covers topics such as agent-observability, ai, ai-observability.
You can find installation instructions and usage details in the logfire GitHub repository at github.com/pydantic/logfire. The project has 4.5k stars and 292 forks, indicating an active community.
logfire is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to logfire on Agent Skills Hub include lmnr, voltagent, honcho. 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: