We just open-sourced AtomicMemory. The AI memory industry has a black-box problem. AtomicMemory is a configurable open-source SDK self-hosted Core engine for memory your AI can inspect, correct, swap, and run on your own infrastructure. Apache 2.0. HTTP-first. Docker quickstart. github.com/atomicstrata

This memory engine is built to be swapped. Not lock you in. Memory providers will have you rewriting parts of your code tied to them to switch them out. This is what we built to avoid with Atomic Memory. The SDK is designed so that if you ever want to swap out the memory backend, you only touch the configuration. Your application code stays exactly as you wrote it. Start building without the lock-in. ⬇️ github.com/atomicstrata/atom…

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.

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…

Our panel is stacked 👇 🔹 Blake Perez, PhD — @BTSE_Enterprise 🔹 Foo Wui Ngiap — @getfailsafe (Co-founder) 🔹 JT Song — @0G_labs (APAC Head) 🔹 Mark Rydon — @AethirCloud (CSO & Co-founder) 🎤 MOD: Aileen — @AtomicStrata #AIAgents #AlgoTrading #NewAIParadigm

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.

AI agents forget everything between sessions. @AtomicStrata built the open-source memory layer that fixes it. 1,300 GitHub stars since launch. @jbruce got inbound from JPMorgan's head of agentic within days. Watch their pitch: youtu.be/nVOKw_Z0-Yo?t=3193

We at Atomic Strata are excited to be pitching at the Stanford Blockchain Acceleartor Demo Day today 9-10:30a pt. Join here luma.com/x3dqhaw0

How can your agent last 50 days from now and keep your workflow smooth? The Answer: Consistent Inspectable Memory. Our AUDN CRUD framework allows for inspectable, time-based alteration of your memory to keep it up to date when current memory layers only return a summary with zero provenance. What you need is an auditable record for every fact your agent ingests. Add / Update / Supersede / Clarify / Delete / No-Op You see the decision it makes, the source, and the trust score. An open source for a memory layer you can verify 👇 github.com/atomicstrata/atom…

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…

Codex OAuth support is now live and set as the default in the Codex plugin quickstart. Skip the manual API key setup. Authenticate once and your coding sessions are persistent, portable, and fully under your control. github.com/atomicstrata/atom…

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