by kweaver-ai · Agent Tool · ★ 412
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中文 | English KWeaver Core is a harness-first foundation for enterprise decision agents. It turns fragmented data, knowledge, tools, and policies into governed context, safe execution, and verifiable feedback loops. With semantic modeling, real-time access, runtime control, and TraceAI, it helps AI systems reason, adapt, and act reliably in complex enterprises. On this page: 📚 Quick links · 🎬 Demo video · 🚀 Quick start · 🛠️ KWeaver SDK · 🛡️ KWeaver Admin CLI · 🏗️ KWeaver Core · 📐 BKN Lang · 📊 Benchmarks · 💬 Community Note: KWeaver Core is a backend-only framework — it does not include a web UI. All interactions are through the CLI, SDK, or API. If you need a graphical interface, please install KWeaver DIP. Want to have an intuitive experience of the core functions of KWeaver
| Stars | 412 |
| Forks | 50 |
| Language | Go |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 66.1019341348625/100 |
| Open Issues | 18 |
| Last Updated | 2026-04-08 |
| Created | 2022-09-05 |
| Platforms | go |
| Est. Tokens | ~12718k |
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kweaver is KWeaver Core is a harness-first foundation for enterprise decision agents. It turns fragmented data, knowledge, tools, and policies into governed context, safe execution, and verifiable feedback loops. It is categorized as a Agent Tool with 412 GitHub stars.
kweaver is primarily written in Go. It covers topics such as agents, bkn, bussiness-knowledge-network.
You can find installation instructions and usage details in the kweaver GitHub repository at github.com/kweaver-ai/kweaver. The project has 412 stars and 50 forks, indicating an active community.
kweaver is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to kweaver on Agent Skills Hub include aeon, modsearch, mcp_flutter. 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.
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