Designing Memory Infrastructure for LLM Applications

The Problem with LLM Memory

Large language models have no persistent memory. Every conversation starts from zero. Every context window is a blank slate that gets filled, used, and discarded.

At Maximem AI, we asked: what if memory was a first-class infrastructure component — not an afterthought bolted onto a prompt?

---

Architecture

We designed the Memory Infrastructure around three layers:

1. Episodic Memory Store

Short-term, conversation-specific memories stored as compressed embedding clusters. Each episode captures:

• The raw interaction
• Extracted entities and relationships
• Emotional valence (surprisingly useful for prioritization)
• Temporal metadata

2. Semantic Memory Graph

Long-term knowledge represented as a directed graph. Nodes are concepts, edges are relationships, and edge weights decay over time (inspired by our research on differential memory decay).

User preference: "likes dark themes"
  ├── Context: UI/design discussions
  ├── Strength: 0.92 (reinforced 4x)
  └── Last accessed: 2 hours ago

3. Cross-Application Sync Layer

The novel piece. A pub/sub system that propagates memory updates across applications in real-time. When a user tells App A their name, App B knows it within 50ms.

---

Retrieval Pipeline

The retrieval pipeline needed to be fast — under 300ms for the full round trip:

1. Query embedding — 15ms (cached model)
2. Episodic search — 40ms (HNSW index)
3. Semantic graph traversal — 80ms (2-hop max)
4. Ranking & fusion — 30ms
5. Context assembly — 20ms

Total: ~185ms average. Well within our budget.

---

Lessons Learned

1. Decay is a feature, not a bug. Memories that never fade lead to context pollution.
2. Embeddings alone aren't enough. The graph structure captures relationships that vector similarity misses.
3. Cross-app sync is harder than it sounds. Conflict resolution for contradictory memories is still an open problem.

The full system processes over 10M memory operations per day with p99 latency under 300ms.