Configurable
Compose models, embeddings, storage, and policy. Built around your stack, not ours.
Atomic Strata builds open, self-hosted context infrastructure.
AtomicMemory gives developers persistent, inspectable, correctable AI memory.
Compose models, embeddings, storage, and policy. Built around your stack, not ours.
Persist across sessions, agents, and teams. Context that doesn't disappear at end of chat.
Read every retrieval. Version every claim. State you can audit, not guess at.
Scopes, permissions, audit trails, correction workflows. First-class, not bolted on.
An open source context memory engine for AI applications and agents. Run it locally, self-host it, swap providers, inspect context state, and build context memory into your own applications, agents, and workflows.
import { AtomicMemory } from "@atomicstrata/memory"; const mem = new AtomicMemory({ scope: "workspace:acme", providers: { embedding, store, model }, }); // retrieve, with full trace const ctx = await mem.retrieve(query, { trace: true });
Deploy where your data lives. No vendor lock-in, no shared multi-tenant memory pool.
Plain JSON over HTTP. Works alongside any model, agent runtime, or orchestration framework.
First-class clients for the languages most AI applications are already written in.
Swap models, embeddings, and storage. Compose around your existing infrastructure.
Layered identity model — context is never broadly shared by accident.
Read what's there, why it's there, and what's about to be retrieved next.
Update, version, and explicitly retire claims. Memory that can be wrong, and then right.
Traces and metrics live alongside the data path — not as a separate product.
Atomic Strata is building context infrastructure for a world where AI systems operate across models, applications, agents, teams, and organizations.
Bring AtomicMemory into AI applications, agents, assistants, and workflows. Stay close to the metal: HTTP, SDKs, your storage, your models.
One context layer across users, projects, and agentic systems — instead of N fragmented memories trapped inside each tool.
We're building governed context for organizations that need it private, inspectable, correctable, and audit-grade from day one.
AtomicMemory is designed around clear boundaries, replaceable components, and inspectable context flow. Teams can compose the layer around their own models, storage, embedding providers, applications, and governance requirements.
Atomic Strata works with technical teams that need context memory to be private, inspectable, correctable, and audit-grade from the beginning — across AI applications, internal agents, and secure deployments.
We work closely with a small number of technical teams shaping the configurable context layer. If governed AI context is on your roadmap, we’d like to hear about it.
Start with AtomicMemory, explore the docs, or talk to us about governed context memory for teams and enterprises.
