Memory Architect

Restructure flat MEMORY.md files into a tiered memory system with an entity graph. Use when: (1) MEMORY.md is growing unwieldy or exceeds 150 lines, (2) user asks to organize/restructure/tier memory, (3) context compaction is losing important details, (4) you need structured entity lookup (people, projects, properties, contacts) instead of scanning markdown. Triggers on organize my memory, memory is too big, restructure memory, tier my memory, make memory more efficient.

Split a monolithic MEMORY.md into three tiers plus a structured entity graph.

Architecture

text

MEMORY.md              → Router (30 lines max). Points to tiers.
memory/protocols.md    → HOT: Stable workflows, shortcuts, procedures. Read on session start.
memory/active.md       → WARM: Current projects, waiting-on, live context. Check before acting.
memory/archive.md      → COLD: Completed work, historical reference. Search when needed.
memory/ontology/graph.jsonl → Structured entities + relations (JSONL append-only)

Process

1. Analyze the existing MEMORY.md

Read the full file. Classify each section:

Content type	Tier	Examples
Stable workflow / procedure	protocols	Emoji shortcuts, deploy steps, tool usage rules
Active project / waiting-on	active	Current builds, pending replies, live URLs
Completed work / reference data	archive	Done tasks, contact lists, account tables, old decisions
Named entity with properties	ontology	People, orgs, projects, properties, locations

2. Create the tier files

Write each tier file with a header comment explaining its purpose and update frequency.

protocols.md rules:

• Only procedures that rarely change
• Include the exact commands (copy-pasteable)
• No project-specific state

active.md rules:

• Only things with a next action or pending status
• Include "Waiting On" section at bottom
• Prune completed items to archive on each update

archive.md rules:

• Completed work grouped by date or category
• Reference data (contacts, accounts, chat IDs)
• Keep searchable — use headers and tables

3. Extract entities to ontology

For each named person, organization, project, property, or location, create a JSONL entry:

jsonl

{"op":"create","entity":{"id":"p_alice","type":"Person","properties":{"name":"Alice","email":"[e-mail]","role":"Engineer"}},"timestamp":"2026-01-01T00:00:00Z"}
{"op":"relate","from":"p_alice","rel":"member_of","to":"org_acme","timestamp":"2026-01-01T00:00:00Z"}

ID conventions:

• People: p_shortname
• Organizations: grp_name or org_name
• Projects: proj_name
• Properties/locations: prop_name or loc_name

Relation types: member_of, owns, collaborates_on, interested_in, guides, uses, listed_by, located_at

4. Rewrite MEMORY.md as router

Replace MEMORY.md with a ~25-line index that:

• Lists the three tiers with one-line descriptions
• Notes the ontology location
• Preserves any system directives (NO_REPLY rules, heartbeat instructions)
• Contains zero project-specific content

5. Verify

bash

wc -l MEMORY.md memory/protocols.md memory/active.md memory/archive.md memory/ontology/graph.jsonl

Targets: MEMORY.md under 30, protocols under 100, active under 80, graph = 1 line per entity/relation.

Maintenance

On each session

• Read memory/protocols.md (always)
• Scan memory/active.md (always)
• memory/archive.md — only on memory_search or explicit request

When adding new information

• New procedure → protocols.md
• New project/active item → active.md
• Completed item → move active → archive
• New person/org/project → append to graph.jsonl

Entity queries

bash

grep "p_forrest" memory/ontology/graph.jsonl
grep '"type":"Project"' memory/ontology/graph.jsonl
cat memory/ontology/graph.jsonl | jq -r 'select(.entity?.type=="Person") | .entity.properties.name'