Memory Architecture
NEW IN 2026
Agent memory matured from "pick a vector database" into a benchmarked production discipline in 2026, with dedicated evaluation suites (LoCoMo (Long Conversation Memory)), MemBench, MemoryAgentBench, MemoryArena) and an ecosystem spanning roughly 20+ frameworks and vector stores across managed-cloud, self-hosted, and local-MCP hosting models.
Type |
Storage |
Layer |
Purpose |
|
Short-Term |
Session |
In-memory |
Conversation state |
|
Long-Term |
Cross-session |
Vector DB |
Semantic recall |
|
Graph Memory |
Persistent |
Graph DB |
Relationships |
|
Episodic |
Persistent |
DB |
Prior tasks / outcomes |
|
Procedural |
Persistent |
DB |
Learned workflows |
|
Policy Memory |
Persistent |
DB |
Rules & restrictions |
|
Audit Memory |
Permanent |
SQL |
Logs & traceability |
Three Patterns for Memory Control
● Pattern A — Context-resident: everything lives in the context window with compression. Simple, but caps out fast on long-running agents.
● Pattern B — Retrieval-augmented (workhorse pattern): working memory in-context, long-term records in a vector or structured store, injected each step. Recommended default, the engineering burden is manageable and the main challenge is retrieval quality.
● Pattern C — Tiered memory with learned control: multiple tiers (context, structured DB, vector store, cold archive) managed by a learned or prompted controller. Highest headroom, highest engineering cost — graduate to this only when data shows Pattern B is the bottleneck.
Best Practice
Memory entries should include:
● source
● timestamp
● confidence
● owner
● retention policy
● trust score
Known open problem across 2026 memory systems: selective forgetting. Most benchmarked systems handle retrieval and test-time learning reasonably well but still fail conspicuously at deciding what to evict. Budget explicit engineering time for eviction policy, not just ingestion.