Atomic Strata builds configurable context memory infrastructure for AI applications, agents, teams, and enterprises. Our open source project, AtomicMemory, gives developers a self-hosted context memory engine for persistent, inspectable, and correctable AI context.
AI products are moving beyond isolated prompts and disposable chat histories. Agents need durable context. Teams need shared context memory.
Context memory is no longer a feature. It is tunable production infrastructure.
Compose models, embeddings, storage, and governance to fit the system you're building — not the one we anticipated.
Every retrieval is traceable. Every mutation is versioned. Context state is something you can read, not something you guess at.
Scopes, permissions, audit trails, and correction workflows are first-class — not afterthoughts bolted onto a vector index.
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 is working with technical teams exploring persistent context across AI applications, internal agents, enterprise workflows, and secure deployment environments.
We are especially interested in teams that need context memory to be private, inspectable, correctable, and governed from the beginning.
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.
Become a design partnerEssays and technical notes on context memory, AI infrastructure, governed agents, and the systems layer emerging around intelligent software.
Read the blogAI systems are becoming stateful. As applications and agents move beyond isolated prompts, context memory becomes a core production layer: something to inspect, correct, govern, and compose.
Start with AtomicMemory, explore the docs, or talk to us about governed context memory for teams and enterprises.
