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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.

🤖 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: 0.7.0 — 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.

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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

	storage	recall	human-readable	typed ontology	curation discipline	bounded index	infra
Engramory	md files	loaded index → open file	✅	✅ role-based (4)	✅ contract (model-run)	✅ 150/200 + hook	none
CC auto-memory	md files	loaded index → open file	✅	✅ same 4 types	partial (auto)	~200-line window*	none (built-in)
basic-memory	md + SQLite	semantic/FTS search	✅	✅ freeform type	schema + overwrite checks	❌ (no loaded index)	SQLite + embeddings
obsidian-second-brain	md vault	index-first + search	✅	folder-typed	✅ reconcile/lint	partial	none
mem0 / Zep	vector/graph DB	semantic	❌ (DB)	typed (prefs/episodic/proc.; Zep custom)	auto-extract	n/a	DB + embeddings
agentmemory	SQLite + vector index (+opt. graph)	hybrid BM25+vector (+opt. graph), RRF	❌ (DB/engine)	✅ 4-tier lifecycle (work./epis./sem./proc.)	auto (capture + dedup + decay)	n/a	iii engine (local) + opt. embeddings

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.

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Continuity without a second handoff store

Engramory uses one canonical store. It does not add a handoff type or a parallel handoff folder. A live project note may hold the current goal, status, decisions, constraints, blockers, and next concrete step needed to resume an unfinished task. 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.

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Install

Requires Python 3.9+ for the hook and the tools/ scripts (python3 on most systems).

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)

Use the dsh init helper (defaults to $DSH_HOME, i.e. ~/.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; not .agents/skills beneath it, which is not one of the roots it scans there — install into the wrong one and the copy lands but is never listed), and keeps a separate .engramory-memory/ store. 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. What blocks it today is dsh's own preview packaging, not the plugin.

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. Only Claude Code's is written and tested here; some other hosts expose one 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.

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 — no scope / project_id; one flat slug namespace, so 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.