No retention model
Tiered memory keeps architectural decisions longer than chat fluff.
Platform · Module
Agents that remember
what worked
Cross-session recall, tiered retention, and hive propagation, so the next chat starts warm, not from zero.
Proof beats promises
portfolio.nltlabs.ai ↗Every claim routes to a live URL, not a slide.
Every new session relearns the same lessons.
Siloed agents
Hive propagation shares what worked across the fleet.
Trust opacity
Operator dashboard surfaces health, capacity, and integrity.
Competitive advantage vs chat history and vector dumps
- Tiered retention so architectural decisions outlive disposable chat fluff.
- Hive propagation shares what worked across the fleet without folklore Slack threads.
- HTTP bridge only. No in-process embed that couples your repo to our memory process.
- Integrity and capacity dashboards so trust is visible to operators.
How to picture it
Authority rings
Read outside-in. Any arrow that skips a ring is an architecture violation.
Tier half-lives
Architectural 180d · pattern 60d · procedural 30d · context 14d.
Hive mesh
Propagate high-signal knowledge across agents without folklore.
Trust boundary
HTTP only. Consumer repos never embed the store.
Product surface
How it works
- 01
Store
Tiered entries with embeddings.
- 02
Recall
HTTP bridge into agent sessions.
- 03
Propagate
Hive shares high-signal knowledge.
What's included
- HTTP memory bridge
- Tiered retention
- Operator dashboard
- Eval against your recall requirements
Questions buyers ask
Is this the quality pipeline?
No. NLT Memory is runtime recall. NLT Quality Pipeline is the dev-time gate. Related, different product.
Where is the ROI?
Less session rework and faster ramp for new agents. Full ROI and team framing lives on the Platform page.
NLT Memory
One conversation. Honest fit. We'll tell you if this isn't the right path.