The audit-grade memory engine for enterprise multi-agent fleets. Human-like memory, shared across projects, customers, and organizations. Five dimensions of meaning (STARE). Deterministic compile. Every memory traces to source. You run it in your infrastructure (Docker Compose, K8s+Helm, or bare-metal). OTEL-native; adapter-friendly.
Vector stores answer "is this memory relevant?" Meaning Memory answers "what did your agent know, when did it know it, and how did that knowledge change?" The first is a search problem. The second is a governance problem. Production AI deployments need both.
Every entry carries source, mem_id, run_id, and full lineage from observation to compile. Trace any line in the compiled snapshot back to the journal entry that produced it. No surface-level metadata timestamps, full audit chain.
Same ledger + same config → same MEMORY.md, byte-for-byte. Run manifest captures every input. Replay any past run and verify the output matches. Regression-test agent memory the way you regression-test code.
Drain → snapshot → extract → compile → finalize. Each phase has explicit success/failure semantics, watchdogs, and observability surfaces. SLO-eligible by design, not a black-box recall layer.
Three-layer config inheritance lets you pick a vertical (Developer / Customer Service / Legal) and override only what differs. Same engine, different significance calibration per industry. Ship in hours, not weeks of tuning.
When your support, research, and editorial agents work for the same customer, they share what matters via typed-scope memory groups. Per-agent significance on the same fact: Pris's "high" can be Molty's "low", same memory, different priorities. Multi-tenant SQL-layer isolation, GIN-indexed.
26 MCP tools instrumented with OpenTelemetry. Tenant-aware spans, tool-call lineage, configurable exporters. Phoenix, Datadog, Honeycomb, Tempo. The audit trail is not a separate system; it's the same telemetry your platform team already reads.
Vector stores treat memory as a single dimension: relevance. Real memory has five. Meaning Memory is the only memory layer with all five as first-class primitives, grounded in cognitive-science principles, shipped at GA maturity in v3.10.
STARE is a mnemonic, the easy way to remember the 5 dimensions in pitches, demos, and partner conversations.
STARE 5D, each vertex is a first-class architectural primitive in the engine, not a marketing label. Schema columns, retrieval filters, scoring components.
Each dimension is an architectural primitive, schema column, retrieval filter, scoring component, not a marketing claim. See the engine architecture for the full per-dimension implementation map.
Every run = (ledger snapshot + config) → byte-identical MEMORY.md. Each phase has explicit success/failure semantics, watchdogs, and observability surfaces.
Meaning Memory is the engine, not the storage layer. Run with zero dependencies (FileBackend) or sit on Postgres (PostgresBackend, production default). An adapter layer integrates with the agent-memory and storage tools you already run; observability is OpenTelemetry-native and exporter-agnostic.
Already running an agent-memory layer for your customer-service fleet? Keep it. Layer Meaning Memory's audit-grade consolidation pipeline on top via the adapter API. Your storage stays. The governance layer is what's new.
FileBackend (SQLite, 30-second setup) for evaluation and single-host deployments. PostgresBackend (pgvector + GIN-indexed scope_groups) for multi-tenant production. Same engine, same STARE primitives, different durability profile.
26 MCP tools instrumented with OpenTelemetry. Tenant-aware spans. Tool-call lineage. Export anywhere your platform already reads, Phoenix, Datadog, Honeycomb, Tempo. The audit trail is the same telemetry your SRE team already trusts.
You deploy the engine in your infrastructure (Docker Compose, K8s+Helm, or bare-metal). SQL-layer + auth-layer isolation between tenants, your data never leaves your VPC. Industry analog: Neo4j Enterprise / Elasticsearch (Elastic License) / Stardog. Optional vendor-hosted managed cloud is a Phase 2+ option; not v3.10 RC scope.
Significance floor, decay curves, vertical playbooks, retrieval blends, configured in plain TOML and env vars, not buried in code. Every knob has a default; every default is documented; every change is observable.
Three vertical playbooks ship today: Developer, Customer Service, Legal. 3-layer config inheritance (base < playbook < agent < env). Pick a starting point, override what differs. Same engine, different significance calibration per industry.
Playbooks are enforced compile-time contracts. A Legal playbook can
declare "all decision entries require party
and status metadata before compile." Phase 4 enforces
the policy at compile time.
Vector stores and KV caches are excellent at storage and retrieval. Meaning Memory is what sits between your agents and that storage layer: the audit-grade consolidation pipeline. Different problem, complementary solution.
| Capability | Vector Memory Layer | + Meaning Memory |
|---|---|---|
| Audit trail | Metadata timestamps | Full provenance, every fact traces to source |
| Reproducibility | Best-effort top-K (drift over time) | Byte-identical compile (manifest-replayable) |
| Operator semantics | Black-box recall | 5-phase pipeline, SLO-eligible, observable |
| What to keep | Everything (or recency-based) | Significance-weighted with operator-controlled floor |
| Vertical specialization | Generic / one-size config | Playbooks (Developer / Customer Service / Legal) |
| Multi-agent | Shared storage (same view for all) | Per-agent significance (same facts, different priorities) |
| Human feedback | Binary remember/forget | Gradient scoring that trains the calibration |
| Deployment | Vendor-specific backend | Standalone (SQLite) or layered on Postgres + pgvector / Qdrant / your vector store |
Category snapshot of capability gaps Meaning Memory addresses on top of typical vector-store / KV-cache memory layers.
Production telemetry from our internal agent fleet on continuous consolidation. Tracking what the engine actually does, not what we wish it did.
Production telemetry as of May 18, 2026. Numbers track engine main; licensed-self-host engine distribution launching with v3.11.0-rc1 (private beta, request a license below).
Three-layer stack: your agent fleet → MM coordinator + MCP → STARE engine + storage. Detailed architecture diagram and API reference ship with private-beta onboarding.
Ten capabilities that distinguish Meaning Memory from a vector store + a Postgres install.
A summary of what the engine provides.
Private beta is invite-based, we're onboarding a small cohort of enterprise multi-agent fleets while STARE 5D calibration matures. Tell us about your fleet and we'll get back to you with a license, the customer-kit (wheel + INSTALL.md + reference deployments), and integration guidance. Licensed self-host engine (you deploy in your infrastructure) · audit-grade · OTEL-native. Industry analog: Neo4j Enterprise / Elasticsearch (Elastic License) / Stardog.
Want to kick the tires before contacting us? A sales-demo instance is available on request, single-tenant, hosted on cloud (Hetzner / GCP), synthetic data only, no SLA. Not a production environment.