Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add instructions/coding-dev-tools/engraphis/agents-mdgit clone --depth 1 https://github.com/Coding-Dev-Tools/engraphisWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.04851 | $0.04851 |
| Opus 5 | $0.02426 | $0.02426 |
| Sonnet 5 | $0.00970 | $0.00970 |
| Haiku 4.5 | $0.00485 | $0.00485 |
Grade A, and why
engraphis AGENTS.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 297 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md — Engraphis
Engraphis is a local-first, open AI memory engine for agents — Ebbinghaus decay,
interaction-aware reinforcement, bi-temporal facts, hybrid recall, and a native
workspace → repo → session → memory hierarchy. Python 3.9+ for the core (Python 3.10+
for the server/MCP stack), FastAPI, SQLite, and local embeddings; the external LLM is optional
and pluggable.
This is the canonical operating manual for any AI agent working in this repo. CLAUDE.md
imports it. Read §0 before editing anything.
0. Read this first — two architectures live in one package
There are two parallel codebases under engraphis/. Confusing them is the single
most common mistake here.
| v2 — current architecture (build here) | v1 — legacy reference server | |
|---|---|---|
| Status | Primary scoped, bi-temporal, interface-driven implementation. | Compatibility/reference implementation with flat namespaces. |
| Model | Scoped + bi-temporal + typed; interface-driven. | Single flat namespace string per memory. |
| Code | engraphis/core/, engraphis/backends/, eval/, tests/, scripts/migrate_to_v2.py |
engraphis/app.py, config.py, models.py, routes/, stores/, engines/, llm/, static/ |
| Data | new v2 schema (SCHEMA_VERSION = 16) |
engraphis_v1.db |
| Entry | engraphis.MemoryEngine.create() / engraphis.create_memory_engine() → engraphis/factory.py → core/engine.py |
Internal reference only; never a public launcher |
Rule: build new capability on v2 (core/ + backends/) behind the interfaces.
Only touch the v1 server for compatibility fixes or to keep the reference running. When a
task is ambiguous, decide which side it belongs to before editing.
1. Commands
# ── Install ──────────────────────────────────────────────────────────────────
pip install numpy pytest # v2 core + tests, fully offline (Python 3.9 floor job)
pip install -e ".[test]" # full offline CI test/lint/typecheck dependencies
pip install -e ".[all,dev]" # complete local stack: dashboard, MCP, embeddings, dev tools
# Config: process environment or owner-private ~/.engraphis/config.env; never a searched CWD .env
# ── Primary offline gate (no API key — KEEP THIS GREEN; mirrors CI's full-stack job) ──
ruff check . # pinned lint rules
python scripts/check_commercial_manifest.py # source/service boundary
python scripts/externalize_dashboard_assets.py # strict-CSP asset drift
python -m pytest tests/ -q # full offline unit suite
python -m eval.harness --dataset eval/datasets/sample.jsonl --k 5 # retrieval eval gate
python -m eval.harness --dataset eval/datasets/codemem.jsonl --k 5 # coding/conflict gate
python -m eval.ablation # vector-only vs hybrid
python -m eval.reinforcement # bounded retention trajectory
python -m eval.adversarial_memory_security # prompt/graph boundary
python -m eval.grounded # grounded-abstain decision gate
python -m eval.code_arm # coding-agent arm gate
pyright # core + backends typecheck
# ── External benchmarks (real numbers need torch + the dataset; see eval/external.py) ──
python -m eval.external --dataset locomo10.json --format locomo --k 10 # LoCoMo
python -m eval.external --dataset longmemeval_s.json --format longmemeval # LongMemEval
python -m eval.external --dataset locomo10.json --format locomo --offline --limit 2 # plumbing check
# ── Unified dashboard + memory inspector ──
python -m scripts.start_dashboard # http://127.0.0.1:8700
# Use this unified launcher; there is no separate Inspector service.
# ── Onboarding (writes owner-private ~/.engraphis/config.env; doctor verifies install) ──
engraphis-init # or: python -m scripts.init
engraphis-init --check
# ── Customer-side hosted session ───────────────────────────────────────────
ENGRAPHIS_CLOUD_CONTROL_URL=https://api.engraphis.com
ENGRAPHIS_CLOUD_REFRESH_CREDENTIAL=... # secret; prefer the owner-only session file
ENGRAPHIS_CLOUD_TOKEN_SUBJECT=member # device or member, fixed at bootstrap
# Authorization, billing, relay, compute, and worker implementations are private services.
# ── Sleep-time consolidation (schedulable local job; also an MCP tool) ────────
python -m scripts.consolidate --db engraphis.db --workspace acme --dry-run
# ── Sync (local shared-folder transport or hosted Cloud Sync — see docs/SYNC.md) ──
python -m scripts.sync --db engraphis.db --workspace acme --remote ~/Dropbox/engraphis --dry-run
python -m scripts.sync --db engraphis.db --workspace acme --relay https://relay.engraphis.com # or bare --relay + ENGRAPHIS_RELAY_URL
# ── Compatibility server alias (v2, headless; needs the full install) ────────
python -m scripts.start_server # same v2 app as engraphis-dashboard, without opening a browser
python -m scripts.cli recall "what do we know about X" -n vault # CLI: ingest/recall/chat/thoughts/list
# ── v2 data migration (v1 flat namespaces → v2 scoped/bi-temporal) ───────────
python -m scripts.migrate_to_v2 --old engraphis_v1.db --new engraphis_v2.db --dry-run
python -m scripts.migrate_to_v2 --old engraphis_v1.db --new engraphis_v2.db
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 297 lines · 4,851 tokens per session scan A 3dc629864c59
engraphis AGENTS.md is an instructions file published in the GitHub repository Coding-Dev-Tools/engraphis (170 stars, last pushed 2d ago), licensed Apache-2.0. It adds 4,851 tokens to every session, about $0.0243 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other instructions, from other repositories
Binder AGENTS.md
Instructions for mpazik/Binder, covering binder, tech stack, monorepo structure, testing and development.
agentmemory-codex-windows AGENTS.md
Instructions for M-T-D-N/agentmemory-codex-windows, covering agentmemory — agent instructions, architecture, windows/codex downstream profile, consistency rules and code patterns.
cli AGENTS.md
Instructions for konteks/cli, covering agent instructions, project overview, tech stack, repository layout and common commands.
mnemic CLAUDE.md
Claude Code instructions for dongtang3/mnemic: This project includes a project-level .mcp.json for the Mnemic memory MCP server.
mnemic AGENTS.md
AGENTS.md instructions for dongtang3/mnemic: Mnemic is a graph-backed long-term memory substrate for coding agents and LLM applications.
m_flow AGENTS.md
AGENTS.md instructions for FlowElement-xinliuyuansu/m_flow, covering m-flow — developer & agent reference, 1. repository map, extension points, 2. local development and python backend (requires python 3.10 – 3.13).