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Temporal Graph Database for Developers

A graph database that
remembers everything.

Every mutation is automatically versioned. Query any point in time with Cypher. Built-in MCP server lets AI agents explore, query, and mutate your graph directly. Append-only storage with immutable snapshots. Zero configuration.

Start Building — Free See it in action → Read the Docs
Quickstart bash
curl -X POST https://api.graphiquity.com/query \
  -H "Authorization: Bearer <token>" \
  -d '{
    "graph": "my-graph",
    "cypher": "MATCH (p:Person) RETURN p LIMIT 10",
    "atTime": "2026-02-15T00:00:00Z"
  }'
Versions retained
0
Data ever overwritten
<4s
Write-to-read

Undo Any Mistake

Accidentally deleted 10,000 nodes? Bad migration? Query your graph before the mistake happened and restore exactly what you need. No backups to find, no recovery scripts to write, no downtime.

Audit-Ready by Default

Regulators ask "what did the data say on March 15th?" — you answer with a single query. Every change is tracked, timestamped, and immutable. Auditability features that support SOC 2, HIPAA, and GDPR programs.

Debug with a Timestamp

Production bug at 3am? Query the graph at the exact moment it happened. See the state your code saw. No log correlation, no guessing. Reproduce any issue deterministically.

Full Version History

Every node and relationship keeps its complete history — who changed what, when, and what the previous values were. Compare any two points in time. Diff your graph like you diff your code.

Four Ways to Query

Cypher, SQL, GraphQL, or REST Document API — pick the language your team already knows. Every interface hits the same storage layer. Use Cypher for graph traversals, SQL for analysts, GraphQL for front-end developers, and the Document API for simple CRUD.

10x Simpler than Neo4j

Neo4j requires JVM tuning, heap sizing, cluster management, and a dedicated ops team. Graphiquity is a managed service with a single API endpoint. Sign up, create a graph, start querying in under 60 seconds.

High-Performance Bulk Import

Load millions of nodes and edges via S3-based fast import. Append-only writes, automatic index creation, per-operation result tracking. No size limits on payloads.

Explore Visually

Built-in graph explorer with force-directed layout, node expansion, and property inspection. See your data as a network, not rows in a table. No third-party tools required.

Built for AI Agents

Native MCP server lets Claude, GPT, or any agent explore schemas, run Cypher, mutate data, and traverse history. Agents get structured graph context, not flat text. Your knowledge graph becomes the agent's long-term memory.

See your data as a graph

Nodes, relationships, and properties rendered as an interactive network. Explore connections, trace paths, and understand your data topology at a glance.

Graph Explorer
WORKS_AT WORKS_AT CONTRIBUTES_TO MANAGES CONTRIBUTES_TO FUNDED_BY HAS_RELEASE Alice :Person Bob :Person Acme Corp :Company GraphQL API :Project Startup Fund :Investor v2.1 :Release

Write Cypher. Get JSON.

No proprietary DSL. Use the industry-standard Cypher query language to create, traverse, and mutate your graph. Results come back as structured JSON over HTTPS.

Query Console
acme-graph ▶ Run
MATCH (p:Person)-[r:WORKS_AT]->(c:Company)
WHERE c.name = "Acme Corp"
RETURN p.name, r.since, c.industry
3 rows completed in 12ms
p.name r.since c.industry
Alice 2023-01 Technology
Bob 2024-06 Technology
Carol 2025-03 Technology

Versioned graph history, by default.

Most graph databases overwrite on mutation. Graphiquity keeps every version of every node and relationship. Pass atTime in your query request to get a consistent snapshot at any point in the past.

POST /query
{
  "graph": "acme",
  "cypher": "MATCH (p:Person) RETURN p.role, p.team",
  "atTime": "2026-02-15T00:00:00Z"
}
Version History — Alice
Create v1 Jan 15, 2026
name: "Alice", role: "Engineer", team: "Platform"
Update v2 Mar 1, 2026
role: "Engineer" → "Senior Engineer"
Update v3 Jun 10, 2026
role: "Senior Engineer" → "Tech Lead", team: "Platform" → "Architecture"
Why Temporal Matters

Your data has a history. Use it.

Traditional graph databases give you a snapshot — the current state. Graphiquity gives you the entire timeline. That unlocks capabilities that aren't possible anywhere else.

Regulatory Compliance "Show me the access control graph on January 12th." One query. No custom audit tables. No ETL pipelines.
Incident Response Reconstruct the exact system state at the moment of failure. No log correlation. Deterministic replay.
Change Intelligence Track how relationships evolve over time. Who reported to whom last quarter? Which services were connected before the outage?
Safe Migrations Restructure your graph with confidence. If anything goes wrong, the previous state is one timestamp away. No backup rituals.
ML Training Data Generate labeled datasets from any point in time. Train models on historical graph snapshots without maintaining separate data copies.
Dispute Resolution Prove exactly what the data said at a specific moment. Immutable, timestamped, and cryptographically verifiable.

How It Works

Three steps. No infrastructure.

Send a query, get results. Every component is fully managed, scales independently, and requires zero configuration from you.

HTTPS Endpoint
Authenticated
Rate-limited
Query Engine
Cypher parser
& executor
Storage
Append-only engine
Temporal versioning
JWT + API Key Auth
Tenant Isolation
Automatic Backups
Point-in-Time Recovery

Deployment

Managed cloud or your own infrastructure.

Graphiquity runs as a managed service or deploys into your own AWS account as a single unit — query engine, disk storage, and API. Your VPC, your security controls, your compliance boundary.

Get Started

FAQ

Frequently asked questions

How is data stored?
Data is stored in an append-only disk engine with immutable versioned snapshots. Each record is a versioned document with automatic temporal metadata. The engine uses delta-aware compaction, CSR acceleration for graph traversals, and sidecar indexes for O(1) lookups. Daily EBS snapshots provide point-in-time recovery.
How do graph traversals work over time?
When you pass an atTime parameter, every node and relationship lookup resolves to the version that was active at that timestamp. Adjacency traversals, label scans, and pattern matching all operate on the resolved temporal snapshot. The query engine resolves time first, then traverses.
What's the consistency model?
Writes are strongly consistent on the writer (WAL-backed, single-process sequential). Reads are eventually consistent — typically 2-4 seconds after a write. Temporal queries (atTime) return deterministic results — the same query with the same timestamp always returns the same data.
What's the largest practical graph?
The storage engine scales horizontally with no practical upper limit on total data. Query performance depends on traversal depth and result set size, not total graph size. Typical queries return in 10–100ms. For very large traversals, contact us about performance tuning.
What import/export formats are supported?
Export returns JSON arrays of nodes and edges with all properties and temporal metadata. Import accepts the same format. You can also use Cypher CREATE statements for programmatic data loading. CSV and GraphML import are on the roadmap.
How is data secured?
Encryption at rest (AES-256) and in transit (TLS 1.2+). Per-tenant isolation via partition-key scoping. Paid plans get fully isolated storage. Authentication via JWT (Cognito) or API keys. Enterprise deployments run in your own VPC with your own security controls.
What are typical latencies?
Indexed lookups: under 10ms. Multi-hop traversals: 50–500ms depending on depth and graph size. Bulk import via S3: millions of operations per minute. The engine uses CSR acceleration, persistent query caches, and incremental snapshot reloads for consistent low-latency reads.