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Engramory

An opinionated, zero-infrastructure memory protocol for small-scale, local,
file-based agent memory — a strict curation discipline plus a validator
(tools/engramory_doctor.py), loaded as standing rules (CLAUDE.md /
AGENTS.md / 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.

⚠️ Unrelated projects share this name. engram + memory is an obvious
coinage and at least one other repository arrived at it independently. This
project is only ever tinqiao-oss/engramory (npm: dsh-engramory); a same-named
repo under a different owner is not a fork, a mirror, or a newer version of it.

🤖 Are you an AI agent, asked to install or check this? Start at
AGENT-SETUP.md, not at the install steps below. It is the
procedure for working out what your host can actually enforce, whether a store
already exists, what you must not touch, and what to tell the user — the parts
agents reliably get wrong when improvising.

Status: 0.10.0 — experimental. The hard index cap (a PreToolUse hook) is
deterministic for the matched direct-edit tools (Edit | Write | MultiEdit) but
NOT a global write guard (shell tools — Bash, PowerShell, a background Monitor
command — plus MCP file tools, external editors, and 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
single writer / serialized writes. Don't rely on it as a "mandatory, reliable,
cross-agent" memory layer yet.

What this is — and is NOT

Engramory is not a new memory architecture. The "markdown files + a small index
loaded into context + the model curates it" pattern is now the mainstream shape
for agent memory, and it ships in several places already. Engramory stands on:

• Claude Code native auto-memory — the same markdown-MEMORY.md-index +
  lazy detail-file pattern; its system prompt even uses the same
  user | feedback | project | reference type vocabulary (per
  anthropics/claude-code#58840;
  the public docs describe only the index + topic files). Engramory is a
  disciplined superset of this default.
• basic-memory — markdown
  source-of-truth, YAML frontmatter type, [[wikilink]] graph, local-first.
• obsidian-second-brain,
  claude-memory-compiler
  ("a loaded index beats vector search at personal scale"), and the broader family
  of markdown-memory skills.

What Engramory contributes is the opinionated bundle + the discipline, not the
primitives. Do not claim novelty on markdown, frontmatter, wikilinks, a loaded
index, one-file-per-fact notes, or curation hygiene — all are prior art.

What's actually differentiated

1. A role/purpose ontology, headed by feedback = procedural memory. The
   semantic / episodic / procedural split is established prior art — the CoALA
   taxonomy, and a named procedural type in LangMem and mem0 — so Engramory does not
   claim the category. What it does is make procedural feedback the spine of a
   deliberately tiny, hand-authored, human-readable set, with required Why: /
   How to apply: lines, instead of auto-extracting it into a vector/graph store.
   The contribution is the packaging and discipline, not the ontology.

2. The curation contract as concrete behaviour the protocol applies (model-followed, not a hard gate): dedup-before-write,
   update-don't-duplicate, delete-when-wrong, and a negative-scope rule ("don't
   store what git/CLAUDE.md/the code already records"). Surveys consistently name
   modify/delete/forget as the most under-implemented memory operation — Engramory
   makes it the spine.

3. A bounded index designed not to silently rot. The index loads every session and
   Claude Code reads the first 200 lines / 25 KB (documented behavior), so an unbounded index silently
   drops memories off the end. Engramory warns at 150 lines / 20 KB, compacts-or-asks
   before 200 / 25 KB, and ships a hard PreToolUse hook backstop (it blocks only
   growth past the cap — shrinking/compaction edits always pass). Both the line and
   byte caps apply — whichever is hit first triggers (an index can be under the line
   count yet over on bytes when the lines run long).

   Claude Code has since followed up on this natively: v2.1.186 (released
   2026-06-22) reminds the agent to compact the index when it nears the cap, and
   v2.1.210 (released 2026-07-14) turned an over-cap write into an explicit error
   instead of a silent truncation. Both are after-the-fact alerts, though — the
   write still lands, and entries past the cap stay invisible until someone
   compacts. Engramory's hook denies the write before it happens, so a write
   through the matched edit tools never leaves the index over-cap in the first
   place (writes outside them — a shell, an MCP file tool — are not gated; see the
   status note above and SKILL.md §8). The native alerts validate the direction
   and make a welcome second layer — and older versions and other hosts still
   have neither.

How it compares

Engramory's lane: minimalism + actionable role typing + curation discipline, zero
infra. It does not try to out-search basic-memory, out-scale mem0, or
out-capture agentmemory — those solve a different problem (auto-capture /
auto-ingest at volume) at a different cost point. agentmemory is the closest
heavyweight foil: also local-first, but it bets on automatic capture (lifecycle
hooks) + hybrid retrieval (BM25 + vectors + optional graph) on a SQLite/iii
engine, where Engramory bets on hand-curation + a tiny always-loaded index and
ships no engine at all.

* Claude Code's memory docs
document this exactly: "the first 200 lines of MEMORY.md, or the first 25KB,
whichever comes first, are loaded at the start of every conversation." Other hosts
vary, so the window stays configurable via the hook's env vars.

Where it fits — and the goal

Engramory is a portable memory discipline, not a product — not a database, not a
framework, not a relevance-loaded skill, not a Claude-Code-only plugin. The plumbing it rides on (a markdown index +
one-file-per-fact notes, the user | feedback | project | reference types, a bounded loaded index)
is increasingly shipped natively by the host — Claude Code's built-in auto-memory
already does it. So Engramory's value is the part hosts don't ship: the explicit
curation contract (dedup-before-write, delete-when-wrong, don't-store-what-the-repo-
already-has), procedural feedback notes with required Why/How, and a portable way to
enforce the size cap.

The goal is the same discipline on any agent — by riding the real cross-agent rails,
not by inventing a new standard. Paste rules-snippet.md into the
host's always-loaded rules so the discipline fires every task. On a host that only gives
you a flat rules file or a raw file store, that is a real upgrade; on a host that already
ships structured memory, Engramory is a thin discipline layer on top — and says so.

On MCP: deliberately not the route for a host that can already read files and
load standing rules. Serving memory over MCP would (a) open a second write
channel that bypasses the pre-write hook — the single deterministic guarantee
this project has, and one that already lists MCP file tools among the things that
slip past it — and (b) demote recall from an index the host loads every session
to a tool the model has to remember to call, i.e. back to the weakest rung in
§8. For a host that lacks files or standing rules, an MCP entry point is the only
way in and is worth adding as a supplement; it is not a replacement for the
protocol, and it is not the cross-agent plan.

Continuity without a second handoff store

Engramory uses one canonical store. It does not add a handoff type or a
parallel handoff folder. An unfinished task may keep at most one live project
note — a ceiling, not a quota — holding the current goal, status, decisions,
constraints, blockers, and next concrete step needed to resume it. That note is
updated in place: never a dated series of state files, and never a per-turn
handoff log indexed beside it. A feedback note is narrower: only a correction or workflow
that should be reused beyond that task.

Before a deliberate compact, clear, or move to a new thread, the agent performs
one continuity sync: scan the task, dedup/update existing notes, refresh project
state, promote reusable feedback, keep durable reference pointers, retire stale
or completed transient state, run the size check plus doctor, and verify that a
cold-started agent could continue from the repo and memory alone. Continuity
never duplicates code or git: a note may keep a stable pointer (branch name,
issue/PR number, file path) to re-check, and may record a settled fact
("2.0 shipped on 2026-01-15"), but never current state — the version you are
on now, the tip commit, the current test count. It records where to read those.

After a write, the agent reports what was added, updated, archived, and skipped
(with reasons, identifying any deletion under archived), plus the index size and
check result. Host lifecycle hooks can remind, mark a task dirty, or gate a
manual transition; they do not perform or guarantee this semantic sync.

Install

Requires Python 3.9+ for the hook and the tools/ scripts. The commands below
are written as python; on macOS and most Linux distributions a bare python does
not exist, so use python3 there (and python on Windows, where python3 is often
a Microsoft Store stub that does nothing).

Claude Code

1. Load the discipline as standing rules (primary): paste
   rules-snippet.md into your always-loaded rules —
   ~/.claude/CLAUDE.md (all projects) or the project CLAUDE.md — so the protocol
   fires on every task, not just when a skill happens to load by relevance.
2. (Optional) register the full spec as a skill: copy or symlink this folder
   into your Claude Code skills directory as engramory/, so SKILL.md
   is available on demand as the detailed reference (path in hooks/INSTALL.md).
3. Add the hard-cap hook: register the hook from hooks/ in your settings.json
   (snippet in hooks/settings.snippet.json).
4. Point <MEMORY_ROOT> at your memory directory; ensure it's .gitignored if
   inside a repo.

Codex

Use the Codex init helper to wire the discipline into AGENTS.md, create the
memory template, optionally install the full protocol as a Codex skill, and add a
.gitignore entry when the store lives inside the project:

python tools/engramory_init.py codex --project-root /path/to/project --install-skill

Optional Codex lifecycle assistance can also be installed with
--install-hooks --mode explicit (default), or --mode assisted to ask for the
same agent-run sync at meaningful milestones. Neither mode silently creates a
semantic summary; review/trust project hooks and confirm them with /hooks.
The hook's bounded .engramory-codex-state.json stores only synchronization
bookkeeping, never prompts, transcripts, or note bodies.

By default this creates <project>/.engramory-memory/. Pass --memory-root to
use an existing folder. Keep this store separate from Codex native Memories:
Codex Memories are generated state, while Engramory is a user-auditable plain
folder and the canonical store for the Engramory protocol. Full Codex notes,
including explicit sync versus optional lifecycle-hook assistance, are in
adapters/codex/README.md.

Read-only readers (recall another agent's memory)

Point any host at a store another agent owns and writes (e.g. Claude Code's native
auto-memory) so a delegated run is grounded in the same project memory — read-only, so the
owner stays the sole writer (Engramory assumes a single writer; many readers are fine):

python tools/engramory_init.py codex-reader   --project-root ~/.codex \
  --memory-root ~/.claude/projects/<project>/memory
# same shape for any host — it lands in that host's own rules file:
python tools/engramory_init.py cursor-reader  --project-root /path/to/repo --memory-root <store>

Reader hosts: codex-reader and dsh-reader (both dogfooded) plus claude-reader,
cursor-reader, kiro-reader, cline-reader, windsurf-reader, openclaw-reader,
hermes-reader (wired from each host's documented rules-file format, printed with an
"unverified" note). It creates no store and never
writes; --memory-root must be an existing store. See
adapters/reader/README.md (incl. the tested-host table + data-egress note).

OpenClaw

Use the OpenClaw init helper (defaults to the workspace ~/.openclaw/workspace):

python tools/engramory_init.py openclaw --install-skill

It writes a marked Engramory block into the workspace AGENTS.md (auto-loaded every
session), installs the protocol under .agents/skills/engramory (OpenClaw
auto-discovers it), and keeps a separate .engramory-memory/ store. The index cap on
OpenClaw is rules + engramory_check.py, not a deterministic deny hook (that would
need a before_tool_call plugin) — see
adapters/openclaw/README.md.

Kiro

Kiro (AWS's agentic IDE/CLI) is a strong host — always-loaded steering files, an agent
that reads/writes workspace markdown, and a real pre-write deny hook. Wiring is manual
(no init helper yet): copy
adapters/kiro/steering-engramory.md to
.kiro/steering/engramory.md (it is inclusion: always and pulls in the live index via
#[[file:.engramory-memory/MEMORY.md]]), and keep your notes in a non-steering
.engramory-memory/ folder.

⚠️ Do not drop notes into .kiro/steering/. A steering file with no inclusion
front-matter defaults to inclusion: always, so every note would load into every
request and blow up your context — the #1 Kiro install mistake. Only the index
belongs in always-loaded steering; notes stay in .engramory-memory/ and open on
demand. Cap is rules + engramory_check.py for now (a deterministic Kiro PreToolUse
hook is possible but not yet shipped/tested). Full notes:
adapters/kiro/README.md.

DeepSeek Harness (dsh)

That card is dsh-xray's static capability scan of
the plugin, and C2 reads worse than it is: the level fires on manifest.bundle.patch,
which 74.6% of the scanned ecosystem declares because a plugin that omits it silently
never mounts. That one flag is the entire card here. Across 4,092 scanned plugins 42.6%
ship exec, 14.0% base64_decode, 10.4% credential-like env reads — this plugin has none
of those, no eval, no install script, and no outbound domains.

Use the dsh init helper (defaults to $DSH_HOME — the env var wins when set, else ~/.dsh):

python tools/engramory_init.py dsh --install-skill

It writes a marked Engramory block into $DSH_HOME/AGENTS.md — dsh's agent-instructions
plugin loads a hardcoded ["AGENTS.md", "CLAUDE.md"] candidate list at the start of every
session — installs the protocol under <DSH_HOME>/skills/engramory (dsh's user skill
root; with --project-root it goes to <project>/.dsh/skills/engramory instead, the
root dsh scans there — not .agents/skills, which dsh does not scan in either place;
install into an unscanned root and the copy lands but is never listed), and keeps a
separate .engramory-memory/ store. The user-global block renders ABSOLUTE paths (dsh's
file tools resolve relative paths against the session cwd); a project block stays
relative so the repo can move. The index cap from those steps is rules + engramory_check.py,
not a deterministic deny hook — though adapters/dsh/plugin/
(dsh-engramory) implements one against ctx.tools.guard(), whose refusal is monotonic.
Install 0.2.1 or later: dsh's preview-era "cannot install third-party plugins"
bug is fixed upstream (rc.7), and 0.2.0 installs but never activates (issue #8 —
older-Cordis inject syntax).

Wiring and model behavior were dogfooded against deepseek-v4-flash: the block arrives
as a <system-reminder>, a question answerable only from a stored note made the model open
that note unprompted, and one durable fact came back as a conforming note plus index
pointer. See adapters/dsh/README.md.

Any other agent (Hermes, Cursor, Cline, Windsurf, …)

Engramory is model-agnostic (DeepSeek, GPT, Llama, …) and rides on the host's own
memory store. Full wiring is in PORTING.md; in short: paste
rules-snippet.md into the host's always-loaded rules (so the
discipline is always-on, not just a by-relevance skill), import SKILL.md
if the host supports skills, point <MEMORY_ROOT> at the host's memory dir when
that dir is plain files you control (against a host that manages its own memory —
Codex, OpenClaw, Hermes — use a separate folder instead), and
wire the size cap at the strongest rung the host supports: PreToolUse hook →
tools/engramory_check.py after each index write → model discipline, with
tools/engramory_doctor.py as a periodic backstop. A deterministic cap needs a
pre-write deny hook. Claude Code's is written, tested, and RUNNING here; dsh's shim
(adapters/dsh/plugin/, dsh-engramory 0.2.1+) installs and activates on current dsh
builds — 0.2.0 never activated (issue #8); some other hosts
expose an equivalent seam too (Hermes; Cursor, though its is newer/flaky), so the cap is portable with
a per-host I/O shim you write and verify yourself — while OpenClaw can only block via a
before_tool_call plugin and some hosts have none. See PORTING.md for the
per-host picture. Where no such hook exists (or plain chat), the cap degrades to
best-effort discipline (see SKILL.md §9).

First connecting a pre-existing store to the strict doctor surfaces a wall of
mechanical issues (missing created/updated, Why/How not yet in canonical form) —
don't blindly fix them. See PORTING.md's Adopting an existing store: run
--no-schema for structure first, batch-backfill dates with the snippet, then
hand-write Why/How.

A plain chat UI with no file access / no rules mechanism cannot run Engramory — it
needs a host that executes skills/rules and can read & write files.

Uninstall

Remove a host's wiring by re-running the same host with --uninstall:

python tools/engramory_init.py codex --uninstall --dry-run   # print the plan
python tools/engramory_init.py codex --uninstall             # do it

It removes only what the installer wrote — the marked block in the rules file, the
installed skill copy, and the managed Codex hooks (dropping this installer's handlers
while keeping anyone else's, and deleting .codex/hooks.json only when nothing but ours
was in it).

The memory store is never touched, nor is its .gitignore entry: the notes are the
one artefact here that cannot be regenerated from this repo, and the ignore rule is what
keeps a still-present store out of git. Delete the store yourself if you want the
memories gone.

That holds even for a store kept inside a directory the uninstall cleans up — it removes
its own files by name and leaves anything else in place, naming what it kept. If your
store is not at the default path, passing the same --memory-root to --uninstall is
still worth it: it lets the closing report name your real store instead of saying it
could not find one.

A rules file with stray or duplicated markers (a botched hand-edit) is left
byte-identical and reported, rather than guessed at — an Engramory block you must remove
by hand is recoverable; content deleted on a guess is not.

Configuration

• <MEMORY_ROOT> — where memory lives. Keep it somewhere you'll actually
  look; .gitignore it inside repos.
• Index limits — soft warn / hard cap default 150 / 200 lines and 20 / 25 KB;
  override via the hook's env vars (see hooks/).

Security & privacy

The store is plain, unencrypted text that any local process can read. .gitignore
keeps it out of git — it is not encryption, and it does nothing against
cloud-sync clients (Dropbox / iCloud / OneDrive), OS backups, or desktop search. If
your <MEMORY_ROOT> sits in a synced or backed-up folder, its contents leave your
machine.

• Never write a secret's value into memory — keys, tokens, passwords,
  cookies, recovery codes. Record only where the secret lives (e.g. "in the
  password manager / env var FOO"). An IP / path / serial used as a locator is
  fine; a credential value never is.
• Minimize partial PII (phone, email, address) — prefer a pointer.

This discipline is unenforced (no hook scans memory content — see
SKILL.md §5/§8); treat it as best-effort and be deliberate.

Known limitations

Engramory is a single-project, single-writer, personal-scale protocol. It does
not yet have:

• Versioning / migration — the semantic memory store has no
  schema_version; there is no defined upgrade path if the frontmatter format
  changes. (The optional Codex hook's bookkeeping file is versioned, but it
  contains no memory.) For onboarding a pre-existing store, PORTING.md's
  "Adopting an existing store" has a triage recipe + a date-backfill snippet.
• Provenance / trust — no source, confidence, last_verified, expiry, or
  superseded-by fields. Recalled memory is advisory and attacker-influenceable
  (see SKILL.md §4); there is no authentication of memory content.
• Scope / multi-project — a note CAN carry an optional scope: global | repo
  (SKILL.md §2.1, doctor-validated), but there is still no project_id, and one flat
  slug namespace means a store shared across projects/agents would hit slug
  collisions and project bleed. A store-level manifest (protocol version + scope +
  host config) is the planned first step — not built yet.
• Concurrency — semantic note/index writes assume one serialized writer and
  have no store-level lock. The optional Codex hook locks only its bookkeeping
  state; it does not make memory writes concurrent-safe.
• Scale — the always-loaded flat index bounds the active set to what fits the
  cap (~200 pointers). It is a personal / curated-scale tool, not a large corpus;
  above that, a retrieval-based system (basic-memory, mem0) is the right tool.

Prior art & credits

Andrej Karpathy's LLM Wiki / Knowledge Base (the markdown-over-RAG pattern, the
most prominent statement of this approach — note it targets a knowledge
encyclopedia, where Engramory targets agent working memory: who the user is,
how the agent should behave, project state) · Claude Code auto-memory · basic-memory ·
obsidian-second-brain · claude-memory-compiler (itself Karpathy-inspired) · the
Anthropic memory tool · OpenAI Codex memory (and its earlier topics-memory proposal
#19758) · agentmemory (a heavyweight,
local-first counterpart — auto-capture + SQLite/iii engine + hybrid BM25/vector
retrieval; the opposite design point to Engramory's zero-infra hand-curation) ·
the wider markdown-memory community.

License

MIT — see LICENSE.