Memory Engineering

Memory Engineering — engineer the forgetting, not just the remembering

Portability: 4 stdlib scripts, no APIs/LLM calls/network. They measure and gate; you decide.

What this does

Anyone can give an agent memory: vector store, pipe in the history, retrieve top-k. That works until the history outgrows the context window, the write path costs more than every query it serves, and the store fills with stale state nobody removes. Memory is not a bucket — it is a system with a metabolism.

The shift: a storer optimizes what a system remembers; a memory engineer optimizes what it forgets. The problem was never that an agent forgets — it is that it never forgets on purpose.

The four lenses

Workflow

# 1 - Price it first. Never quote a quality number without a cost number.
python scripts/memory_cost_profiler.py --print-sample-spec > workload.json
python scripts/memory_cost_profiler.py --spec workload.json
# 2 - Pick which cost to pay. No "best" verdict; on a tie it asks, exit 2.
python scripts/memory_architecture_picker.py --constraints workload.json
# 3 - Audit what the store actually holds (skip if greenfield).
python scripts/memory_density_auditor.py --dir ~/.claude/memory
# 4 - Gate on forgetting. Exit 4 is a stop, not a suggestion.
python scripts/forgetting_policy_linter.py --policy design.json
# 5 - No command. Prove each pass by hand before scheduling it.

Step 1 reports the construction/query split, cost per correct answer, and amortization — if construction dominates, cut construction tokens before touching retrieval. Step 2 names the cost the winning family makes you pay. Step 3 classifies records FACT / SKILL / LOG / PROSE (LOG-HEAVY = archiving events; PROSE-HEAVY = docs, not memory).

Step 4 is the gate: F1 (explicit forgetting rule) and F4 (contradictions surfaced, never auto-merged) are blocking. Retrofitting forgetting onto two years of records is a migration nobody does; auto-merging disagreeing memories destroys the evidence the conflict existed.

Step 5 has no script — prove each pass by hand, then automate. Run it once against real history and ask whether it changed a decision. If not, scheduling it only makes noise. Ship order: forgetting_policy_design.md §7.

Hard rules

1. Never quote accuracy without cost per correct answer.
2. Never return a "best" memory system — name the cost the choice makes you pay.
3. Never auto-merge contradictions. The system surfaces; the human decides.
4. Never call a design done without a forgetting rule. No evaluated system provides one by default.
5. Never schedule a pass not yet run by hand.
6. Report findings as findings. A non-zero exit is a result to surface, not an error to swallow.
7. Attribute every number with its confidence level. Vendor customer figures are testimonials, not benchmarks.

Scripts

All support --output json and --sample (no input file needed).

References and assets

• references/memory_cost_canon.md — construction dominance, energy per correct answer, the four families, ten recommendations (7 sources)
• references/what_to_keep.md — PlugMem and MEMENTO: facts over logs, density over volume (7 sources)
• references/memory_control_and_governance.md — memory as files, scope/audit/rollback, poisoning, reading vendor numbers (7 sources)
• references/forgetting_policy_design.md — forgetting mechanisms, contradiction discipline, KV cache, ship order (7 sources)
• assets/memory_engineer_worksheet.md — seven forcing questions with recommended answers + citations; walk one at a time
• assets/memory_design_spec.example.json — one file covering every script's input
• assets/forgetting_policy_template.md — fillable policy covering F1–F8

Provenance

Framing from "How to be a Memory Engineer" by @N01ennn; every number is cited to a primary source instead, and two paraphrases are corrected — memory_cost_canon.md §2, memory_control_and_governance.md §4.