Most AI memory is RAG over PDFs — a search index over text humans wrote about their work. InvariantDB stores the work itself: every access, every context, every decision, with bitemporal provenance. Train new agents from real behavior. Replay any past state. Ask what did the agent know when it acted? Memory without amnesia.
“Your agent made the wrong call yesterday. Can you replay exactly what it knew at 3:47pm when it decided?
Vector DBs say no. InvariantDB says AT RECORDED '2026-04-13T15:47:00Z'.”
Built into the database itself — not bolted on as a layer above embeddings.
Outlives context windows, process restarts, and multi-instance scaling. State lives in the graph, not in the agent process.
Real graph structure: entities, facts, sessions, contradictions. Query with Cypher. Not just “k-nearest neighbor on a blob.”
Bitemporal by default. Replay agent memory state at any past moment with AT RECORDED. The bitemporal moat: no other memory store can do this natively.
Tamper-evident hash chain over every memory mutation. Stamped _actor=agent:<id>. WORM mode optional. The audit trail comes for free.
Multi-agent collaboration through cross-graph queries. One agent's findings inform another's. Federated by design, isolated by default.
Agent memory can be projected into structured JSON documents. Define a template once, and episodic graph memory materializes into documents with full bitemporal replay.
npm install https://sdk.invariantdb.com/node/invariantdb-0.7.0.tgz — then record, recall, and time-travel.
Zep, Mem0, Redis-vector, pgvector. They're search engines. We're a brain.
| Capability | InvariantDB | Vector memory stores |
|---|---|---|
| Persistent recall across sessions | ✓ Yes | ✓ Yes |
| Structured entities & relationships | ✓ Native graph | × Embeddings only |
| Multi-hop reasoning over memory | ✓ Cypher MATCH | × No traversal |
| Time travel (replay memory at a past moment) | ✓ AT RECORDED |
× No bitemporal |
| Tamper-evident audit trail | ✓ Hash chain | × None |
| Per-agent isolation (auth model) | ✓ One graph + scoped key | × Namespace at best |
| Multi-agent collaboration | ✓ Cross-graph query | × Manual sync |
| Reversible auto-corrections | ✓ Append-only versioning | × Mutate in place |
We dogfood the SDK. These are real agents shipping in the platform.
Nightly agent that audits a graph's schema, indexes, and query patterns. Suggests reversible improvements with cost/benefit math.
Auto-approve agent. Watches the Advisor's suggestions, applies the reversible ones automatically per your policy.
always / staging-only / notify / noneInteractive agent for fraud, breach, and claims investigation. Read-only by protocol, with full case memory accumulating across sessions.
query, explain, describe_graph, sample_data, recall_prior_cases, save_finding, propose_write, concludeOne graph per agent. Scoped API keys. Short TTLs. Hash-chained audit. The blast radius of a compromised agent is one memory graph.
Each agent instance gets its own memory graph. Cross-agent reads require explicit key scope.
Keys carry auth.graphs, auth.tenantId, apiKeyRole. Engine enforces at every call.
Independent query/mutate buckets per key. Runaway loops hit 429 with Retry-After.
Default 7 days for agent keys. Rotated by orchestrator. Leaked keys expire.
Every mutation stamped _actor=agent:<id>. Filter the access log by actor for replay.
Auto-apply endpoints accept only reversible operations. Append-only history covers the rest.
Free to start. Ship in an afternoon. Replay forever.