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by tinqiao-oss · Codex Skill · ★ 160
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English | 简体中文 Engramory An opinionated, zero-infrastructure memory protocol for small-scale, local, file-based agent memory — a strict curation discipline plus a validator (), loaded as standing rules ( / / your host's rules file). It is not a database, a framework, or a relevance-loaded skill. Memory is a folder of small, human-readable markdown files plus one always-loaded index. No database, no embeddings, no server — just plain-text files you can open, read, edit, and diff in any editor (the live store itself stays git-ignored). Engramory — coined from engram (the physical trace a memory leaves in the brain) + memory. Here: one file = one fact. Status: 0.5.0 — experimental. The hard index cap (a hook) is deterministic for the matched direct-edit tools () but NOT a global write guard (Bash / MCP file tools / external editors / sync clients bypass it); the discipline loads as standing rules the model follows, so it's best-effort, not guaranteed on every task (see SKILL.md §8). Assumes a
| Stars | 160 |
| Forks | 11 |
| Language | Python |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 67.5539072644472/100 |
| Open Issues | 2 |
| Last Updated | 2026-08-19 |
| Created | 2026-06-13 |
| Platforms | claude-code, codex, python |
| Est. Tokens | ~17k |
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engramory is A portable memory protocol for AI agents — load it as standing rules; a curation discipline + reference spec + optional cap hook.. It is categorized as a Codex Skill with 160 GitHub stars.
engramory is primarily written in Python. It covers topics such as agent-memory, ai-agents, claude-code.
You can find installation instructions and usage details in the engramory GitHub repository at github.com/tinqiao-oss/engramory. The project has 160 stars and 11 forks, indicating an active community.
engramory is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to engramory on Agent Skills Hub include Myco, mnemon, memtrace-public. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.