ai-memory-developer

AI Memory Developer

When to Use

• Building persistent memory for copilots, agents, or conversational AI
• Designing memory APIs (read/write/consolidate/forget)
• Choosing between vector stores, graph databases, or structured DBs for memory
• Implementing memory write/read policies and ACLs
• Debugging wrong, stale, or hallucinated memories
• Tuning what the model should remember across sessions (episodic vs semantic)
• Planning GDPR deletion paths and privacy retention for stored memories
• Evaluating memory quality (recall, precision, isolation)

When NOT to Use

• General RAG document search or indexing pipelines → ai-engineer
• Context window packing, token budgets, or compression → ai-context-engineer
• AI team operations, release governance, or SLOs → ai-lead-ops
• Org-wide token cost improvement roadmaps → ai-token-improvement-plan-engineer

Related skills

Need	Skill
End-to-end LLM app, RAG, agents	ai-engineer
Context assembly and compression	ai-context-engineer
Prompt and tool message design	prompt-engineer
PII retention and governance	ai-risk-governance
Production monitoring and incidents	ai-lead-ops
Token cost program and phased savings plan	ai-token-improvement-plan-engineer

Core Workflows

1. Memory model design

Classify memory types:

Type	Lifetime	Examples	Store
Working	Single turn / tool loop	Tool results, scratchpad	In-context only
Session	Chat session	Current task state	Redis / thread store
User long-term	Cross-session	Preferences, facts user stated	Vector + structured DB
Organizational	Shared	Docs, policies	RAG index (see ai-engineer)

Design decisions:

1. What may be written automatically vs requires user confirmation?
2. Per-tenant isolation and ACL on every read/write
3. TTL and deletion (GDPR erase path)
4. Conflict resolution when new fact contradicts old

See references/memory_architecture.md for patterns and anti-patterns.

2. Write path (ingestion to memory)

observe → extract candidates → score importance → dedupe → persist → index

Checklist:

• Extract only durable facts, not transient chit-chat
• Attach provenance (message ID, timestamp, source)
• Dedupe with embedding similarity + entity linking
• Never store secrets, raw payment data, or full medical records unless required and approved

See references/write_consolidation.md for extraction prompts and consolidation jobs.

3. Read path (retrieval for generation)

1. Build query from current user message + session summary
2. Retrieve top-k memories with metadata filters (user_id, tenant_id)
3. Rerank; drop below relevance threshold
4. Inject into context in structured block (see ai-context-engineer)
5. Cite memory IDs in logs for debugging

See references/read_retrieval.md for ranking and injection formats.

4. Forgetting and maintenance

Trigger	Action
User delete request	Hard delete all user memories
TTL expired	Archive or purge
Low usefulness score	Decay or summarize away
Contradiction	Supersede old record; keep audit trail

Run nightly consolidation: merge episodic notes into semantic summaries.

See references/write_consolidation.md for consolidation algorithms.

5. Evaluation

Test	Pass criteria
Write accuracy	Gold facts appear in store after session
Recall	Question answerable from prior session
Precision	Irrelevant memories not retrieved
Isolation	Tenant A never sees tenant B
Forgetting	Deleted user has zero retrievable memories

See references/memory_eval.md for datasets and regression harness.

When to load references

• Architecture and stores → references/memory_architecture.md
• Write and consolidation → references/write_consolidation.md
• Read and ranking → references/read_retrieval.md
• Evaluation → references/memory_eval.md