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 0arm · Agent Tool · ★ 75
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
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Emergent Emergent is a library designed to enhance LLMs like GPT-4 by providing an easy way to integrate external tools and long-term memory capabilities. With Emergent, you can effortlessly transform any Python function into a tool that LLMs can utilize. The library also includes a built-in implementation of a long-term memory system called HMCS (WIP), which seeks to emulate human-like memory organization and recall. https://user-images.githubusercontent.com/29310830/229719121-4d1dae6e-9282-4353-8d54-008bbc35f78e.MOV Usage Making your own tools Using the long-term memory system Overview The HMCS consists of several key components and processes: Log-based memory: Records thoughts, inputs, and outputs as individual logs. Rollup summaries: Periodically consolidates logs into higher-level summaries to reduce memory load. KB articles: Organizes rollup summaries based on semantic similarity to create or update knowledge base (KB) articles. Clustering or gat
| Stars | 75 |
| Forks | 7 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 68.6570170312/100 |
| Open Issues | 7 |
| Last Updated | 2023-04-09 |
| Created | 2023-03-20 |
| Platforms | python |
| Est. Tokens | ~10k |
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emergent is An implementation of long term memory and external tools for LLMs. It is categorized as a Agent Tool with 75 GitHub stars.
emergent is primarily written in Python.
You can find installation instructions and usage details in the emergent GitHub repository at github.com/0arm/emergent. The project has 75 stars and 7 forks, indicating an active community.
emergent is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to emergent on Agent Skills Hub include repren. 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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