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 radimsem · MCP Server · ★ 124
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is remindb safe to install? View the security audit →
remindb Agentic memory in a single SQLite file. Stop letting your agent re-read the same notes every session. Why I built this Coding agents already have memory. , , your notes folder, that growing pile of project READMEs. Stuff persists just fine. The problem is how the agent consumes it. Every session starts by re-reading the whole pile from scratch — every , every , scanning raw prose the agent has already processed dozens of times. Big context windows don't fix it. A 1M-token window is still paid per call, and still can't tell yesterday's stale note from today's relevant one. Raw mar
| Stars | 124 |
| Forks | 12 |
| Language | Go |
| Category | MCP Server |
| License | MIT |
| Quality Score | 65.685324467317/100 |
| Open Issues | 17 |
| Last Updated | 2026-08-03 |
| Created | 2026-04-15 |
| Platforms | claude-code, cli, codex, gemini, go, mcp |
| Est. Tokens | ~177k |
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remindb is An agentic memory database that cuts session tokens by 82–99%. One portable SQLite file — your agent's memory, anywhere.. It is categorized as a MCP Server with 124 GitHub stars.
remindb is primarily written in Go. It covers topics such as agent-memory, ai-agents, ast.
You can find installation instructions and usage details in the remindb GitHub repository at github.com/radimsem/remindb. The project has 124 stars and 12 forks, indicating an active community.
remindb is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to remindb on Agent Skills Hub include roam-code, agent-skills, fossil-mcp. 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: