vektori — security grade SAFE, quality 68/100

Security audit verdict: SAFE · quality 68/100

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 vektori-ai · Agent Tool · ★ 132

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

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About vektori

Vektori Memory that remembers the story, not just the facts. GitHub · Issues · Docs 👋 Questions, ideas, bugs → GitHub Issues · [Discussions](https

ai-agentsai-memoryanthropiccontext-engineeringknowledge-graphllmlong-term-memorymemoryollamaopen-source

Quick Facts

Stars132
Forks10
LanguagePython
CategoryAgent Tool
LicenseApache-2.0
Quality Score68.1945621730708/100
Open Issues26
Last Updated2026-06-18
Created2026-03-23
Platformspython
Est. Tokens~17k

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Frequently Asked Questions

What is vektori?

vektori is Memory that remembers the story not just the facts. File System Memory and Three layer sentence graph for AI agents -> Facts, Episodes, raw Sentences. One DB. Zero config.. It is categorized as a Agent Tool with 132 GitHub stars.

What programming language is vektori written in?

vektori is primarily written in Python. It covers topics such as ai-agents, ai-memory, anthropic.

How do I install or use vektori?

You can find installation instructions and usage details in the vektori GitHub repository at github.com/vektori-ai/vektori. The project has 132 stars and 10 forks, indicating an active community.

What license does vektori use?

vektori is released under the Apache-2.0 license, making it free to use and modify according to the license terms.

What are the best alternatives to vektori?

The top alternatives to vektori on Agent Skills Hub include pensyve, mira-OSS, captain-claw. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

How this security grade is produced

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:

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