Unified memory for enterprise AI.
The memory layer for AI-native companies.
Capture, structure, and retrieve context across every conversation, document, and event — in one production-grade memory layer.
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What you ship on day one
A memory layer built for production agents
Three guarantees that make MemLab safe to put in front of real users from day one.
Ingest → memory
Turn any signal into structured memory
Conversations, documents, app events, and APIs flow into one pipeline. MemLab extracts what matters automatically.
Sub-50ms p99 ingestion API
Hybrid retrieval
Always surface the right context
Vector similarity, graph traversal, and lexical search fan out in parallel. RRF fuses the results so the best signal wins.
Sub-200ms p99 search API
Compounding impact
Memory that actually improves
Continuous dedup, bi-temporal facts, and relevance pruning mean your agents get sharper with every interaction — without retraining.
5-tier dedup + bi-temporal graph
The lifecycle
Everything you need to ship memory
From the first ingest event to the thousandth retrieval — MemLab handles the full lifecycle of agent memory so you can focus on the product around it.
01 · Integrate
Plug into any agent framework
Wrap your agent with withMemLab(...) — and it gets persistent memory instantly.
Works with LangGraph, CrewAI, AutoGen, or your own runtime. No rewrites. No glue code. Every interaction is automatically captured, indexed, and retrieved — so memory just shows up between calls.
02 · Extract
Extract what matters, automatically
Raw input becomes facts, entities, and relationships — with embeddings, types, and provenance attached.
A single in-process extraction pipeline turns transcripts into compact, high-signal memory units. No bespoke prompts, no fragile JSON parsing.
03 · Retrieve
Hybrid retrieval, fused by default
Vector + graph + lexical search fan out in parallel; RRF fusion ranks the result set so the best signal always wins.
Stop choosing between semantic search and keyword recall. MemLab runs both, plus graph traversal over your knowledge layer, and merges them into one ranked list.
04 · Graph
A bi-temporal knowledge graph
Entities and relations carry valid_at and invalid_at — so stale facts stop bleeding into fresh answers.
When facts change, MemLab marks the old ones invalid instead of overwriting them. Time-travel queries stay coherent and audit trails stay intact.
05 · Tenancy
Multi-tenant from the first row
Org and project scopes are baked into every layer — RLS in Postgres, per-project Qdrant collections, partition-keyed Kafka.
No bolted-on tenancy story. Defense-in-depth means a single missing scope can never cross customer boundaries; every gRPC call and Kafka event carries both IDs.
06 · Evolve
Memory that compounds, not collects
Continuous dedup, relevance pruning, and feedback loops mean your store gets sharper, not heavier, over time.
Five-tier dedup catches near-duplicates before they pollute retrieval. Feedback events tighten relevance. Old context fades when it stops mattering.
07 · Production
Engineered for production traffic
Performance budgets, observability, and audit trails — not a notebook prototype dressed up as a service.
Sub-50ms ingestion p99, sub-200ms retrieval p99, full tracing, and end-to-end audit. Built so you can ship memory to real users on day one.
A complete memory platform
The full memory lifecycle, in one platform
Ready to give your agent a memory?
MemLab is open source and production-tested. Spin up the stack in minutes, or talk to us about your deployment.
© 2026 MemLab — the memory layer for AI-native companies. Auth by Ory.