Atomic Strata builds high-performance, configurable context for AI applications, agents, and enterprise workflows.
Santa Monica, CA
Joined May 2026
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Let your system run autonomously.
Give your agents memory they can use across sessions, and your team a way to see what was stored, retrieved, corrected, and why.
Configurable memory for AI agents, now in Public Beta.
memory.atomicstrata.ai/?utm_…
Banks, funds and fintechs are on stage at @aiDotEngineer NYC this week, talking about agents in production.
One question we'd add to every deployment checklist:
When the agent is wrong, can you show what it remembered, where that came from, and who corrected it?
@Google’s new @GeminiApp agent keeps four kinds of memory: session, semantic, procedural and episodic.
Memory is now the headline feature of workplace agents.
The next question every team will ask: can we see what it remembers, correct it, and take it with us?
That's the layer we build: configurable agent memory your team can see into. What was stored, retrieved, corrected, and why.
memory.atomicstrata.ai/?utm_…
1/ Most RAG setups read your documents at question time.
LLM Wiki Compiler reads them first, and compiles them into a wiki your LLM can use. It works in two phases:
2/ Phase 1: concept extraction.
It scans each changed source, pulls out its concepts, and finishes extraction across the whole corpus before writing a single page. Shared concepts merge consistently, and extraction failures surface before they touch retrieval.
3/ Phase 2: typed wiki pages.
Each page combines synthesized prose with source citations and links to related concepts. Scattered references become connected knowledge.
4/ Every later query starts from that structure instead of raw chunks.
Open source: github.com/atomicstrata/llm-…
How it works: llmwiki.atomicstrata.ai/conc…
Quick question: where does your agent's memory live today?
100%In the prompt
0%Our own vector DB
0%A memory API
0%It doesn't remember
2 votes • Final resultsVast Data's CTO says agent memory belongs in storage. For the KV cache that makes agents fast, agreed.
But there's a second layer: what your agent believes about users, projects and decisions.
That one needs to be visible. What was stored, retrieved, corrected, and why.
Atomic Memory Cloud gives your team that view, hosted or self-hosted with the same SDK.
Now in public beta: memory.atomicstrata.ai/?utm_…
Add durable memory to your AI agent in a few commands.
The Atomic Memory Cloud OS QuickStart takes you from local setup to your first stored and retrieved preference with Docker, an OpenAI API key, and three CLI steps.
Start building with a configurable AI memory: docs.atomicstrata.ai/open-so…
Imagine a @DBSbank AI assistant that goes beyond generic banking answers.
Instead, it uses your approved financial context to give more personalized guidance on savings, cards, and travel benefits without making you explain your goals every time.
Atomic Memory Cloud can help you achieve that.
memory.atomicstrata.ai/?utm_…
Cut your token costs by up to 90% with agent memory.
Ever notice your agents reload the same files and repeat decisions? That's because context stays trapped in the transcript of every previous task. Each repeated file and decision adds more tokens for the model to process, which increases the cost of completing the task.
So how does agent memory reduce that cost?
Having an external memory system lets an agent extract durable information from each conversation and store it separately, rather than keeping everything in its active context.
That information is indexed by meaning and task relevance. So the agent can then retrieve the specific facts it needs when a later task requires them.
This gives the model a smaller, task-specific context to process, and doesn't require the agent to read the entire transcript again.
Are you ready to reduce repeated context and lower token costs?
Check out Atomic Memory Cloud:
memory.atomicstrata.ai/?utm_…
Here’s what @OpenAI announced at DevDay:
1. ChatGPT is becoming a workspace through ChatGPT Space, which brings projects, goals, context, and previous work together.
2. GPT-6.1 Sol improves coding, computer use, and agentic tasks while costing less than Astra.
3. Astra Ultrafast brings faster performance to ChatGPT Work, Codex, and the API.
4. Dots are persistent Astra-powered agents with cloud computers, connected apps, configurable permissions, and ongoing task execution.
5. Codex is gaining parallel agents, cloud environments, mobile access, voice, worktrees, and automatic code reviews.
6. The Agents API is adding deeper computer use, multi-agent orchestration, and stronger tool handling.
7. Pages and Slides now support live dashboards, charts, connected data, and collaborative presentations inside ChatGPT.
8. Plugins are becoming interactive apps with sidebars, custom viewers, and event triggers.
9. ChatGPT is expanding into Slack and Teams, where it can investigate questions, prepare updates, and use connected tools.
10. Meeting agents can record discussions, save notes, extract decisions, and turn outcomes into follow-up work.
11. OpenAI introduced Sign in with ChatGPT, giving users a new way to access connected applications.
12. A new $500 monthly Pro tier offers higher limits and exclusive Astra Ultrafast access in Work and Codex.
AI agents are moving closer to completing meaningful work across tools, environments, and teams. Reliable, portable context will become essential as these systems become more persistent.
Atomic Strata retweeted
My skills work across coding agents. I wanted my preferences to travel with me too.
With @AtomicStrata Memory, @cursor_ai can pick up the stack preferences @opencode learned about me.
Here’s what that looks like 👇
The most valuable part of your AI is its memory.
In this clip, Atomic Strata co-founder @Aileentech explains why owning your AI data matters for keeping that context portable and useful over time.
Is your AI context portable, or is it tied to a single platform?
Claude Sonnet 5.5 is impressive.
This new release costs up to 30% less than Sonnet 5, while matching Opus 5.5 on knowledge benchmarks. It's capable of achieving results while using fewer tokens, making it more efficient for real-world projects.
It all began with LLM Wiki Compiler, which turned scattered documents into a structured wiki and gave agents the knowledge they need to work with.
As we built it, we saw that agents needed more than structured knowledge because they also needed to remember decisions and trace past interactions,
which led us to build Atomic Memory Cloud and bring the same self-hosted and configurable approach to the memory agents rely on.