yantrikdb: Cognitive memory engine for AI agents — temporal decay, contradiction detection, autonomous consolidation

by yantrikos · MCP Server · ★ 72

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

How to install yantrikdb

pip install yantrikdb-mcp
pip install yantrikdb

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 62/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

agent-memoryai-agentsanthropicclaude-codecognitive-memorydatabaseembeddingshnswknowledge-graphllm

Quick Facts

Stars72
Forks25
LanguageRust
CategoryMCP Server
LicenseApache-2.0
Quality Score62/100
Open Issues32
Last Updated2026-11-02
Created2026-02-23
Platformsclaude-code, mcp, rust
Est. Tokens~27k

About yantrikdb

YantrikDB — A Cognitive Memory Engine for Persistent AI Systems The memory engine for AI that actually knows you. Get Started in 60 Seconds For AI agents (MCP — works with Claude, Cursor, Windsurf, Copilot) Add to your MCP client config: That's it. The agent auto-recalls context, auto-remembers decisions, and auto-detects contradictions — no prompting needed. See yantrikdb-mcp for full docs.

yantrikdb alternative? Top 6 similar tools

Looking for a yantrikdb alternative? If you're comparing yantrikdb with other mcp server tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.

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  • ctxvault by Filippo-Venturini · ⭐ 62

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  • Ori-Mnemos by aayoawoyemi · ⭐ 328

    Local-first persistent agentic memory powered by Recursive Memory Harness (RMH). Open source must win.

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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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