What are you using for agent memory that actually works across sessions?

Genuine question before I share what I built — curious

what others are actually doing.

Every standard approach I tried broke differently:

Stuffing history into system prompt — hits token limits

fast. Agent re-reads everything from scratch every call.

Pure vector search — no time ordering, no structure.

"What did Acme do in Q2?" returns semantically similar

noise, not actual Q2 events.

Metadata filtering — can't distinguish "Acme signed X"

from "X signed with Acme." Relationships destroyed.

What I built instead:

Decompose every piece of text into WHO + DID + WHAT +

WHEN before storing. Keep both the structured tuple

(PostgreSQL for temporal queries) AND the embedding

(pgvector for semantic search). Hybrid rank at retrieval.

"Acme Corp signed a $50,000 contract for Q2 2026"

↓

WHO: Acme Corp

DID: signed

WHAT: $50,000 contract

WHEN: Q2 2026

CONF: 0.95

Now "what did Acme do?" is a direct lookup. "What

happened in Q2?" is a timestamp filter. No fuzzy

guessing.

Running GLM 4.7 for the agent and Llama 3.1 8B for

the SVO parsing — fast enough that the extraction

overhead is negligible.

But genuinely more interested in

what others are using — knowledge graphs? Fine-tuned

retrievers? Something simpler I'm missing?

check the link on the comment.