daemon-blockint-tech 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:
- What may be written automatically vs requires user confirmation?
- Per-tenant isolation and ACL on every read/write
- TTL and deletion (GDPR erase path)
- 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)
- Build query from current user message + session summary
- Retrieve top-k memories with metadata filters (
user_id,tenant_id) - Rerank; drop below relevance threshold
- Inject into context in structured block (see
ai-context-engineer) - 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