by databufflabs · Agent Tool · ★ 600
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DataBuff — AI-Native APM Built On Opentelemetry Mission: build the strongest OpenTelemetry APM backend AI-native · OTLP-native · multi-agent out of the box · self-hosted traces, metrics, topology Live Demo · Documentation · Open Source APM · 简体中文 · Community <img src="docs/images/databuff-demo-en.gif" alt="DataBuff demo: AI chat, servic
| Stars | 600 |
| Forks | 120 |
| Language | Java |
| Category | Agent Tool |
| License | AGPL-3.0 |
| Quality Score | 54.0431458849492/100 |
| Open Issues | 13 |
| Last Updated | 2026-08-22 |
| Created | 2026-06-18 |
| Platforms | java |
| Est. Tokens | ~16k |
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databuff is Databuff is an open-source, AI-native ,OpenTelemetry-native APM tool , with multi-agent root-cause analysis across traces, metrics, and service topology. It is categorized as a Agent Tool with 600 GitHub stars.
databuff is primarily written in Java. It covers topics such as ai, ai-native, aiops.
You can find installation instructions and usage details in the databuff GitHub repository at github.com/databufflabs/databuff. The project has 600 stars and 120 forks, indicating an active community.
databuff is released under the AGPL-3.0 license, making it free to use and modify according to the license terms.
The top alternatives to databuff on Agent Skills Hub include ongrid, trpc-agent-go, langtrace. 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: