New · Agent memory SDK

Your agents forget everything.
We fix that.

Vector DBs are recall — that's a search engine, not a brain. A brain has structure, time, and the ability to answer: what did I know when I decided that? That's a temporal graph. That's Graphiquity.

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“Your agent made the wrong call yesterday. Can you replay exactly what it knew at 3:47pm when it decided?

Vector DBs say no. Graphiquity says AT RECORDED '2026-04-13T15:47:00Z'.”

Five pillars of agent memory

Built into the database itself — not bolted on as a layer above embeddings.

Persistent

Outlives context windows, process restarts, and multi-instance scaling. State lives in the graph, not in the agent process.

Queryable

Real graph structure: entities, facts, sessions, contradictions. Query with Cypher. Not just “k-nearest neighbor on a blob.”

Temporal

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.

Auditable

Tamper-evident hash chain over every memory mutation. Stamped _actor=agent:<id>. WORM mode optional. The audit trail comes for free.

Shared

Multi-agent collaboration through cross-graph queries. One agent's findings inform another's. Federated by design, isolated by default.

Projectable

Agent memory can be projected into structured JSON documents. Define a template once, and episodic graph memory materializes into documents with full bitemporal replay.

Three methods. That's the SDK.

npm install @graphiquity/memory — then record, recall, and time-travel.

// Connect to a memory graph (one per agent, scoped API key) const mem = new Memory({ endpoint, apiKey, graph: 'agent-memory-support-bot' }); // Start a session — the agent's "current conversation" const { sessionId } = await mem.startSession({ agentId: 'support-bot', userId }); // Record an event — message, tool call, observation, anything await mem.record({ sessionId, type: 'UserMessage', text, entities }); // Recall — hybrid: text + graph hops + (optional) time travel const ctx = await mem.recall({ query, hops: 2, limit: 10 }); // Time travel — what did the agent know at this exact moment? const past = await mem.recall({ query, asOf: '2026-04-13T15:47:00Z' }); // Reflect — turn episodic memory into a structured document const doc = await mem.reflect({ sessionId, template: 'case-summary' }); // Pairs with document templates: graph memory in, JSON document out.

Vs. the vector DBs

Zep, Mem0, Redis-vector, pgvector. They're search engines. We're a brain.

Capability Graphiquity 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

Reference agents, built on the SDK

We dogfood the SDK. These are real agents shipping in the platform.

Shipped

Schema Advisor

Nightly agent that audits a graph's schema, indexes, and query patterns. Suggests reversible improvements with cost/benefit math.

  • 5 analyzers: missing indexes, unused labels, vector candidates, oversized indexes, schema drift
  • Memory-driven: only reports what changed since the last run
  • Priority tunes itself from your apply/ignore feedback
Shipped

Schema Applier

Auto-approve agent. Watches the Advisor's suggestions, applies the reversible ones automatically per your policy.

  • Per-kind policy: always / staging-only / notify / none
  • Endpoint-constrained — cannot emit arbitrary Cypher
  • Dry-run mode + rate limits
Shipped

Investigation Copilot

Interactive agent for fraud, breach, and claims investigation. Read-only by protocol, with full case memory accumulating across sessions.

  • Eight tools — query, explain, describe_graph, sample_data, recall_prior_cases, save_finding, propose_write, conclude
  • Hard caps: 20 tool calls / 15 queries / 5s timeout per investigation
  • Proposes Cypher for human approval — never writes directly

Built secure from day one

One graph per agent. Scoped API keys. Short TTLs. Hash-chained audit. The blast radius of a compromised agent is one memory graph.

One graph per agent

Each agent instance gets its own memory graph. Cross-agent reads require explicit key scope.

API key scoping

Keys carry auth.graphs, auth.tenantId, apiKeyRole. Engine enforces at every call.

Per-key rate limits

Independent query/mutate buckets per key. Runaway loops hit 429 with Retry-After.

TTL on every key

Default 7 days for agent keys. Rotated by orchestrator. Leaked keys expire.

Actor attribution

Every mutation stamped _actor=agent:<id>. Filter the access log by actor for replay.

Reversibility gate

Auto-apply endpoints accept only reversible operations. Append-only history covers the rest.

Give your agents a brain.

Free to start. Ship in an afternoon. Replay forever.

npm install @graphiquity/memory