matrixark.ai
Context infrastructure for vertical AI products.
MatrixArk is the production context-management platform behind the open-source TemporalStore. Agents send raw queries, tool results, documents, and answers to one context boundary; MatrixArk handles extraction, time-aware storage, freshness, replay, and token-budgeted context-pack assembly, so every prompt carries the right information at the right moment.
POST /context/pack
{
"query": "Can we approve another GPU batch?",
"scope": "acme/infra/procurement",
"token_budget": 1800
}
→ ContextPack {
fresh: ["latest approval, open commitments, recent spend"],
blocked: ["superseded budget memo (stale)"],
cites: ["temporalstore://event/9f3a"],
replay: "pack_2026-08-10T14:02Z"
}The platform
One context boundary, not another prompt template.
Production agents do not need a bigger prompt; they need live context infrastructure. MatrixArk gives vertical AI teams a single API to call with raw material and get back a prompt-ready pack.
Vertical AI apps send whatever they have: raw user questions, tool traces, documents, source references, final answers, and lightweight scope hints. MatrixArk does the rest. It extracts entities, event types, timestamps, validity windows, and permissions; routes each record to the right store; keeps recent sequences and aggregates fresh; and, at request time, compiles a bounded, replayable context pack that fits a token budget.
The engine underneath is TemporalStore, the open-source time-aware serving core. MatrixArk keeps the customer API simple while the engine handles validated time windows, returned-result limits, dirty-summary markers, and replayable audits. Enterprise storage backends attach only when scale or consistency demand it.
What one request touches
A single call flows through six stages. Ingest and extract turn raw events into serving state; compile writes bounded records; retrieve, rank, and pack assemble the prompt and learn from the outcome.
Take the raw material
Messages, tool traces, docs, approvals, source refs, and final answers arrive through one context API.
Find the context
Entities, event type, timestamps, validity, permissions, and prompt relevance are pulled from each event.
Write bounded records
Scope hashes, context nodes, timestamped events, secondary indexes, and dirty summaries land in the store.
Read within limits
Intent becomes time windows, filters, and candidate nodes; bounded reads return fresh facts and stale blockers.
Score for the prompt
Candidates are ranked by freshness, authority, permission, and relevance against the request-time budget.
Return, then remember
A token-budgeted, replayable pack is returned; accepted answers, corrections, and outcomes are written back.
Architecture
From the app to the tier, one context path.
The vertical AI app talks only to the MatrixArk context boundary. Below it, the open-source TemporalStore core serves time-aware reads and writes, backed by tiered memory and, where needed, enterprise storage.
The app never touches storage directly. TemporalStore serves the common case from tiered memory and SSD; MatrixObject, MatrixKV, and MatrixDB join only when disaggregated scale or transactional truth is required. Learn more in TemporalStore.
Compare
Where MatrixArk sits in the context landscape.
Memory frameworks and context databases proved the need. MatrixArk turns those patterns into one production serving boundary: durable temporal state, request-time freshness, and replayable prompt inputs.
| System | Approach | Temporal & replay | One service | Retrieval |
|---|---|---|---|---|
| MatrixArk / TemporalStore | Context boundary over a time-aware serving engine | Native time windows, validity, and replayable context packs | One API; extraction, routing, ranking, and packing built in | Filter-first traversal, optional vector recall |
| OpenViking / VikingMem | Hierarchical event/entity memory with L0/L1/L2 layers | Temporal compression; file-walk at prompt time | Memory layer; serving and storage assembled separately | Hierarchy traversal plus embeddings |
| Mem0 | Developer add/search memory APIs | Scoped memories; limited replay | Library plus a vector store | Configurable vector retrieval |
| Zep / Graphiti | Temporal knowledge graph memory | Bi-temporal graph; strong history | Graph service plus app-side assembly | Hybrid semantic, keyword, and graph |
| MemOS / Memori | Memory operating layer for agents | Memory lifecycle; replay varies | Framework over external stores | Pluggable retrievers |
What that means in practice
The frameworks above are strong at what they do: Mem0 makes memory a two-call API, Zep and Graphiti model history as a temporal graph, MemOS and Memori give agents a memory lifecycle, and OpenViking makes hierarchy intuitive. MatrixArk keeps the easy API shape and adds the parts production teams end up building themselves: bounded time-aware reads, request-time freshness, stale-memory blocking, permission-aware ranking, and a context pack you can replay for evals and audits.
The trade-off is deliberate. MatrixArk is infrastructure, not a drop-in library, so it rewards teams shipping durable agents over quick prototypes. Because the serving core is open source, you can adopt TemporalStore first and add enterprise backends later without changing the context API.
Benchmark headline
On a shared harness against OpenViking, TemporalStore matches or beats it: 42% vs 34.7% overall answer quality, 38% vs 16% on LongMemEval, and a 44% tie on LOCOMO — with a fair reader and the same embeddings on both sides.
Read the full method and numbers in the TemporalStore memory benchmark and the OpenViking product-parity guide.
Enterprise & operations
Run it managed, private, or on-prem — all in service of context.
MatrixArk runs as a managed service on AWS, GCP, or Azure, or in your private cloud and on-prem environments for strict data, latency, or compliance needs. The context boundary, the TemporalStore serving core, and the storage tier are operated to five-nines availability with replay and audit built in.
Enterprise storage backends attach to the same context path when scale or consistency demands it:
- MatrixObject Enterprise — disaggregated shared storage that separates compute from durable context, serving many datanodes from one namespace. See MatrixObject.
- MatrixKV Enterprise — low-volume transactional metadata for ownership, leases, approvals, and trusted control state. See MatrixKV.
- MatrixDB Enterprise — high-throughput KV for hot sessions, profile state, and cache metadata at scale. See MatrixDB.
None of these is required to start. The open-source TemporalStore core covers the common case; enterprise backends are an operational upgrade, not a rewrite.
Contact
Bring MatrixArk to your context layer.
Talk to us about production LLM context management: extraction, time-aware serving, freshness, replay, and enterprise storage for your agents.
Prefer the open-source path? Start with TemporalStore at temporalstore.ai and reach out when you need the managed platform.