One memory layer for every AI agent in your stack
MemLab is the production-grade memory layer for AI-native companies — built so a single unified pipeline can capture, structure, and retrieve context across every conversation, document, and event the agent ever sees.
INGEST
One pipeline for every signal
Conversations, documents, app events, webhooks, and external APIs all flow through a single ingestion endpoint. Sub-50ms p99 with backpressure-safe Kafka claim-check semantics — payloads stay in storage, the bus carries IDs.
EXTRACT
Facts, entities, and relations — automatic
In-process extraction turns raw input into facts, named entities, and typed relations. Embeddings, type labels, and provenance are attached at the same time. Five-tier dedup keeps near-duplicates from polluting retrieval.
RETRIEVE
Hybrid search, fused by default
Vector similarity, graph traversal, and lexical search fan out in parallel. RRF fusion ranks the result set so the strongest signal always wins. Sub-200ms p99 across the fan-out.
EVOLVE
Memory that compounds, not collects
Continuous dedup, relevance pruning, and feedback loops sharpen the store over time. Bi-temporal facts carry valid_at and invalid_at — stale beliefs stop bleeding into fresh answers without rewriting history.
TENANCY
Multi-tenant from row zero
Org and project scopes are enforced four layers deep — Postgres row-level security, per-project Qdrant collections, partition-keyed Kafka, and signed gateway claims. A missing scope can never cross customer boundaries.
OBSERVABILITY
Built for production traffic
Performance budgets, distributed tracing, and append-only audit trails on every memory mutation. You can answer "where did this answer come from" down to the page, the turn, and the model run.