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.

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.

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?

Build profitable AI agents with accurate memory. When information changes, your agent should update its understanding automatically. Reliable outputs reduce rework and improve adoption across every deployment. Build AI agents people trust and continue using. memory.atomicstrata.ai/?utm_…

We achieved 2.1k GitHub stars for LLM Wiki Compiler! This milestone belongs to everyone who has explored the project, shared feedback and contributed along the way. We're grateful to have an awesome dev community building with us! Disclaimer: no LLMs were harmed in the making of this repo

Keep your organization's operating knowledge current. When someone corrects a process, the agent updates the relevant guidance and retrieves the latest version when another teammate needs to act. Turn every correction into better operations. memory.atomicstrata.ai/?utm_…

Scale your AI products across more deployments. Developers should be able to support private context without rebuilding the product for every environment. One architecture can serve multiple deployments while keeping each workspace isolated and reliable. Increase your profits without multiplying technical complexity. memory.atomicstrata.ai/?utm_…

What does enterprise AI need before it can be trusted with real work? Our founder @jbruce shares what Atomic Strata is building: governed AI context memory that enterprises can configure, inspect, and correct. Watch our showcase with Imagination in Action @imaginationxyz @johnkwerner

Organizations ships SaaS way faster without burning runway on context. Your coding agent now knows every codebase update, so it avoids repeatedly building the same context. Keep your SaaS worry-free and generate revenue back to you without a hassle. memory.atomicstrata.ai/?utm_…

We help big organizations close deals faster without paying for a CRM. Work with an agent that remembers every lead touchpoint so you close deals faster without paying for a CRM that nobody updates Turn every follow-up into revenue. memory.atomicstrata.ai/?utm_…

Run 10 client accounts without the 10x overhead from session spending. These agents keeps every account’s memory from decay, helping your team deliver consistent output across accounts at a fraction of the usual overhead. Put your next 10 accounts on one agent. memory.atomicstrata.ai/?utm_…

Make your agent memory portable so you can switch anytime. Atomic Memory Cloud works through HTTP, TypeScript, Python, CLI, MCP, and framework adapters, so teams can start hosted while keeping a self-host path open. memory.atomicstrata.ai/?utm_…

Find out why your agents hallucinate. Developers need the full memory receipt in one place before they can fix production behavior. Debugging memory is easy with Atomic Memory Cloud. memory.atomicstrata.ai/?utm_…

An enterprise agent should remember where the last conversation ended. Continuity makes every interaction more dependable. Power your agents with Atomic Memory Cloud. memory.atomicstrata.ai/?utm_…

Work with an AI knowledge compiler that understands your whole project. LLM-Wiki Compiler v1.3.0 adds recursive source discovery, literal path exclusions, project-specific compile instructions, and four viewer themes for your wiki. Check the full summary → github.com/atomicstrata/llm-…

Building with Claude, GPT-6, or anything else? no problem. This memory layer doesn't lock you into a model.

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_…

The harness often matters more than the model. In the @ycombinator Paper Club session, experts showed harnesses combining persistent memory, usable tools, strict rules, and feedback loops. Claude Opus reportedly scored around 30% on its own, then reached 95.5% with Seth Karten’s Prime Agent harness and even 100% in some cases. The result is a clear signal that surrounding systems can turn shaky reasoning into reliable output. The next lever is giving agents configurable memory. This lets the harness keep context and reuse prior decisions, making longer workflows more reliable without retraining. Longer workflows hold together when memory can carry context forward.

The debate started with the benchmark gap. ARC Prize said Astra scored 62.7% on its own harness, while OpenAI’s setup returned 99.9%. Meaning this number came from a harness, not the model. The benchmark creators emphasize that this score doesn't mean AGI has been reached. Instead, it highlights how much optimization depends on the orchestration layer surrounding the core weights.

The big debate happening in AI is: are we finally at AGI? @OpenAI's GPT-6 Astra pushed that question back into the feed after the company said it had entered the AGI era.

.@OpenAI just announced GPT-6 Astra, with Sam Altman calling it their best model for computer use, professional work, science, coding and cybersecurity. If models can now operate software across serious tasks, enterprises need to ask a new question: "which workflows are ready for agents to own first?" Without clear boundaries, agents can turn a simple workflow into a permission, compliance and accountability problem. That moves AI adoption from chat productivity into process design, where teams define the task, the review gate and the failure path. Is your stack scoped tightly enough for an agent to act?

The AI stack is becoming multi-model by default. AWS’s enterprise agentic AI guidance argues that teams need patterns that preserve flexibility as models, frameworks, and providers keep changing.

Our founder @jbruce shares how context portability changes his AI workflow. Watch to learn about his full stack and how Atomic Strata lets him switch between AI tools without losing the same project state. 🎙️: The Block Media Podcast

The risky AI app may be the one an employee built to save time. Shadow AI happens when unsanctioned assistants, agents, and low-code workflows start handling company data outside approved security controls.

The harder AI governance problem isn't only jailbreaks. It is what happens when employees start building their own internal AI apps, each touching different systems, roles, and data boundaries. 🎙️: The Block Media Podcast

AI agents need transaction-grade context before they can safely act. Recent research on agentic transactions argues that agents need reliable execution, consistent outcomes, safe concurrency, and durable state once they trigger side effects.

Should AI agents be allowed to transact without data governance? Our founder explains why agentic systems need governed data layers before they can safely interact with financial infrastructure, smart contracts, or private enterprise context. 🎙️: The Block Media Podcast

Would switching AI providers break the context your agents depend on? Gartner highlights that ecosystem lock-in and proprietary interoperability are major hidden risks for enterprises rushing to scale Generative AI.

AI lock-in is bigger than picking a model. Our founder, @jbruce explains why enterprises get stuck when one provider owns the model. If one vendor controls inference, context, memory, and data access, switching later can mean rebuilding the parts your agents depend on. 🎙️: The Block Media Podcast

"Build a specialist once, use it forever" only works if the memory doesn't rot. A boundary for your bots only helps if you can see the context that sits inside it and correct it when needed. A scope you can see into is a scope you can trust. Set up an "Orchestrator" Bot that designs its own specialist team on the fly, built to handle any request that comes its way and pair it with Atomic Memory. github.com/atomicstrata/atom…

Your AI agent keeps repeating the same mistake, and you don't know why. That is the cost of memory you can't inspect. AI Memory needs 3 things to be trustworthy: the right scope, the right retrieval, and a trail you can follow. Here is how each one works: 1) Scope. This keeps memory in the right place. A scope is the boundary a memory belongs to. 2) Retrieval. This brings back only what helps. It searches the relevant scope for context that answers the current request. A small, relevant starting point for the next model call. 3) Provenance. This keeps memory accountable. The trail back to where a memory came from and how it changed. Atomic Memory runs all three. Your application chooses the scope, Atomic Memory records the memory and its lineage, and Retrieval then returns only the useful context for the next model call. Build a context layer you can inspect and correct. github.com/atomicstrata/atom…

Feed knowledge once and your AI remembers forever. Point any Obsidian vault → llm-wiki-compiler → auto-generated MCP server The AI agent will query your wiki live without RAG pipelines or vector DB setup. The payoff here is that everything in your context window is persisted into Atomic Memory. Build a durable understanding of your wiki over time. github.com/atomicstrata/llm-…

Everyone has built their own version of LLM-wiki. But only one ships a health score, citation precision, typed lifecycles, and CI-gateable eval. The benchmark says it all. Test it for yourself: github.com/atomicstrata/llm-…

Context is becoming the most valuable data in AI. Great discussion today at Malaysia Blockchain Week on why the next bottleneck for AI isn’t access to more data—it’s access to reliable, reusable context. As agents become more autonomous, context needs to be portable, configurable, and verifiable. That’s what we’re building at Atomic Strata: the Configurable AI Memory Layer. github.com/atomicstrata

llm-wiki's template AutoSci tracks 12 entity types across 5 research workflows and Newsroom runs a full editorial pipeline on the same engine. This is the best demo of what configurable profiles unlock and the fact that both ship out of the box means you do not need to rebuild from scratch. Atomic Strata's llm-wiki helps you achieve that. github.com/atomicstrata/llm-…

Thank you for your contributions! The llm-wiki-compiler grew thanks to these builders who loved using it and found ways to improve it at the same time. The repo is at a completely different level than where it started. Kudos to all the builders who helped shape the repo.

llm-wiki v1.1 now makes wiki templates shareable. Set rules for your wiki that define what pages exist, how they connect, and who reviews what. The new release lets you package those rules as a template and share it for anyone to install. Try the new template system: github.com/atomicstrata/llm-…

Two templates ship out of the box: AutoSci and Newsroom Plus batched embeddings, eval health reports with graph connectivity metrics, and import/export that respects every gate.

- Users define the entities, fields, relations, and workflows. - llm-wiki enforces the rules, generates the pages, and tracks the state. All backed by the same validator that powers the CLI, SDK, MCP, and export.

llm-wiki v1.0: From Compiler to Programmable Wiki Renditions of @karpathy's idea is mostly compiled sources into a knowledge base. v1.0 changes the question from "what does it compile" to "what do you want to build?" Introducing llm-wiki-compiler CLP 🧠 github.com/atomicstrata/llm-…

Get early access for agent memory that survives the session. SDK, CLI and Python install paths are ready for builders who want persistent memory across real agent workflows. Now live for teams building with Claude Code, Cursor, Codex and MCP ⬇️ lp.atomicstrata.ai/early-acc…

Multi-agent loops need a shared second brain. In a multi-agent workflow, every agent creates state that has to survive beyond a single run. That brain has to remember decisions, source evidence, corrections, permissions, and the history behind changed claims. When memory breaks, each loop keeps working from partial context, stale assumptions, and scattered session history. AtomicMemory treats memory as a live system that can be inspected, corrected, revised, and trusted as the workflow evolves. The loop gets better when memory can change with it. Make agent memory revision-native. github.com/atomicstrata

Agent memory without grounding just becomes context pollution. Most memory stacks are append-only. No provenance or way to correct false beliefs, just more text crammed into your context window. Memory needs source tracking and update semantics so you can track each fact's source and edit what your agent believes and why. Don't build on a black box. github.com/atomicstrata/atom…

Loop engineering is a lot more risky than most builders think. Anthropic's playbook for designing systems alarms comprehension rot as one of the things loop engineering can't solve on its own. When assumptions compound across sessions with no correction mechanism, the quality of the original prompt stops mattering. That is the gap loop engineering creates that prompting alone cannot fix.

The safest bet in infrastructure is the one you can walk away from. Most memory tools bundle API and engine together so switching means rewriting your app. A typed SDK with a provider boundary makes backends a config value instead of a migration project. Build for the interface and treat backends as swappable ✅ github.com/atomicstrata/atom…

Three quiet failure points break almost every knowledge base: 1) Generated content goes wrong because the model can misread a source or connect ideas that were never actually related. 2) An imported content goes wrong because another tool's extraction mistakes come along with it. 3) A page correct in March can be wrong by June if the source underneath it changed. Most tools trust their own output directory by default, which means all three problems ship straight to the agent unchecked. Run LLMwiki on your own sources and see the trust signals in action. github.com/atomicstrata/llm-…

There's an underlying problem in AI memory growing stale over time and a bigger context window can't fix this. Agents are designed to be intelligent but it keeps getting dumber about the same queries you make. Last week, it found the right sources and wrote a correct answer. This week, almost the same query but it ignored your previous correction and update, and repeated the same retrieval as last time. AI's knowledge doesn't compound by default when you talk to it. We built LLM-wiki so that your agent isn't limited to a simple search & summarize.

The problem is not just what your agent forgets. Wrong claims silently get stored all the time, poisoning what your agent believes. Atomic Memory asks before it commits. See what AUDN catches before it reaches your memory storage. github.com/atomicstrata/atom…

Google has built the ultimate blueprint for an AI agent's digital brain. llm-wiki-compiler just became one of the first tools to support @Google's Open Knowledge Format, a standard for exchanging compiled knowledge between systems. Interoperability is a principle we build around at Atomic Strata. Our tools are designed to work with the broader ecosystem, not be the only thing you need. Say goodbye to lock-in; this is a step toward AI memory that belongs to you. github.com/atomicstrata/llm-…

Here's the correction tool you need when your agent's memory fails. Inspect your Agent's memory storage through CRUD. Developers can find the exact claim that's causing a problem → see the trust score it carried → directly fix what's wrong. This is intentional for cases where builders need to manually insert a fact they know is correct without going through the write gate.

designing loops that prompt your agents, remember what worked, and amend when the source of truth changes.

Switch from OpenAI to Anthropic or any open-source models without losing your agent’s memory. 🧠 Memory layers are often coupled to the model they were built around, so if you swap your LLM, you have no choice but to start over. The way to switch providers without losing a single memory is to store it independently. This is made possible through our open-source Atomic Memory. Your agent's memory, claim history, lineage, and trust scores all carry over untouched. github.com/atomicstrata/atom…

Think of Grammarly, but for your agent's memory. Right now agents don't have checks or flags when you feed it information. Atomic Memory shows you findings to review before anything hits your storage, so your agent's retrievals are kept clean and most accurate. Open source, with benchmarks leading at retrieval accuracy for lower cost than alternatives. Give it a run and let us know your feedback 🤝 github.com/atomicstrata/atom…

Andrej Karpathy's pattern is to compile knowledge once and grow smarter for every query you make, which is what we built with LLM-Wiki Compiler. We extended that idea to AI agent memory. Once you've fed information to your agent, it's tracked as claims with evidence and lineage. When something changes, only what needs to change gets revised. Nothing gets silently replaced without a record of what it used to believe. The repo is open and we are actively building. Come contribute! ⬇️ github.com/atomicstrata/atom…

At GTC Taipei, Jensen Huang introduced @nvidia's next-generation AI computing platform, Vera Rubin, which is designed to power the next wave of massive "agentic" AI workloads. Jensen spoke about memory being the hardest part in all this and how processing all different data and its relationships is incredibly complicated. The fix we have for the software-level is simple in principle: before any fact enters storage, a decision has to be made on it. Whether to add, update, delete what it contradicts, or skip information at the write layer. So by the time an agent queries memory it only retrieves trusted and non-contradicting facts. That decision layer is what AUDN does inside Atomic Memory. NVIDIA is revolutionizing the infrastructure for agentic memory. Atomic Memory follows by solving what's worth keeping there.

Run a Smarter Memory Layer at a Lower Cost for your AI Agents Your agents inject their entire memory file into every prompt, whether it is relevant or not. Hermes native memory includes the full MEMORY.md every turn. OpenClaw carries full cross-channel context on every query. That is a fixed token cost you pay regardless of whether any of what it retrieves is useful. Atomic Memory sits underneath both Hermes and OpenClaw and changes how memory gets injected. Retrieving only the facts the current query actually needs. Benchmarks show it does this at a lower cost per query than tools with comparable retrieval accuracy, which enables precise context injection without the token overhead.

This is @_HermesAgent backed by Atomic Memory in a real work setup. Every decision, update, and correction your team makes gets organized and stays inspectable across sessions. Atomic Memory improves your Hermes agent by replacing the 2.2KB native memory cap with unbounded, per-turn memory that resolves contradictions before anything hits storage. The memory layer your team actually needs. github.com/atomicstrata/atom…

Our benchmark is out for v66! Atomic Memory delivers top-tier memory performance in each reported category while costing significantly less to run in real applications. The case for switching makes itself 😉 github.com/atomicstrata/atom…

Every memory write in AtomicMemory goes through a decision before anything touches storage. Add | Update | Delete | Supersede | Clarify | No-Op it's called AUDN and it's the reason AtomicMemory doesn't turn into a junk drawer over time. 🧵

Discord Community is now OPEN 👾 This is where memory layer discussion sparks, feedback shapes what we ship next, and a place to connect with builders who care about the agent memory layer as much as you do. Welcome to builders, contributors, and early supporters. discord.gg/wvfdVpbzZ6