Agent Memory Architecture

Agent Memory Architecture

Overview

Conversation history ≠ memory. Production agents need tiered stores: hot in-context, warm vector recall, cold archival, plus temporal knowledge graphs for facts that change over time. This skill picks the backend and tier layout.

When to use

• Agent needs to remember user preferences across sessions
• Multi-session task continuity (Ralph, long-running research)
• Personalization that survives restarts
• Multi-tenant agents with strict isolation
• Facts change over time (price, status, relationships) → temporal KG needed

Backend selection

Tier design

┌─────────────────────────────────────────────┐
│  Core (in context)      — identity, rules   │  <2k tokens
├─────────────────────────────────────────────┤
│  Recall (vector search) — last N sessions   │  100k+ items
├─────────────────────────────────────────────┤
│  Archival (cold store)  — full history      │  unbounded
└─────────────────────────────────────────────┘

Promotion rule: archival → recall on retrieval; recall → core on repeated use. Eviction rule: core → recall on token pressure; recall → archival on staleness.

Temporal vs snapshot

• Snapshot: latest value wins (mem0 default). Simple, loses history.
• Temporal (bi-temporal): every fact has valid_from, valid_to, recorded_at. Required when "what did the agent believe yesterday?" matters.

Graphiti / Zep give you bi-temporal out of the box. Hand-rolled needs (entity, relation, value, t_valid_start, t_valid_end, t_recorded) tuples.

Privacy patterns

• Per-tenant memory namespace, never share embeddings across users
• PII detection (Presidio) before embedding
• TTL on recall tier (e.g. 90 days), explicit retention for archival
• User-initiated forget: hard-delete from all tiers + reindex

Quick start — mem0

from mem0 import Memory
m = Memory()
m.add("User prefers concise answers in Korean.", user_id="jeo")
results = m.search("language preference", user_id="jeo")

Quick start — Graphiti (temporal)

from graphiti_core import Graphiti
g = Graphiti(neo4j_uri, neo4j_user, neo4j_pwd)
await g.add_episode(name="session_1", episode_body="User joined Acme Corp 2026-03-01", reference_time=datetime.now())
nodes = await g.search("where does the user work?")

Further reading

• mem0 — fact extraction, semantic search
• Letta (MemGPT) — OS-style memory blocks
• Graphiti / Zep — bi-temporal knowledge graph
• LangMem — LangGraph-native memory primitives