Agents should get better with experience. We teamed up with @tolokaAI to measure that. Toloka evaluated xmemory on closed datasets. No peeking at the data. No tuning to the benchmark. Just learning from failures and measuring what improves. 📈 The results: 26% lower token costs when xmemory replaced document search as the policy store. And adding lessons from past runs lifted scores by 4.8 percentage points on held-out tasks. 💡 It means with memory, a mid-size open-weight model scored 0.75 – beating Sonnet 5 at 0.68 and GPT-5.6 Terra at 0.69 without memory. This experiment is just the beginning of what memory can unlock. We see even bigger gains on longer-horizon tasks, where every lesson has more chances to pay off. Read our blog post below!

The right model for the right step. That’s how we build memory at xmemory – benchmarking for quality, cost, and latency. We tested @typesafeai's Jev as a judge of extracted data. It recovered 81% of the accuracy gap between a fast and a slow extractor while sending only 38% of records back for another pass. The catch? It worked when judging individual fields. Asked to judge a whole record, it did no better than a coin flip. Link in the first comment!

We’re excited to announce @xmemory_ai’s partnership with @temporalio! We have deep respect for Temporal’s leadership in reliable workflow execution, and we’re glad to work closely together as developers build the next generation of long-running agents and agentic teams. The integration brings together durable execution and shared, structured memory. Temporal helps workflows recover from interruptions and continue their work. xmemory lets developers define the facts, relationships, and domain rules agents rely on – keeping that knowledge available across runs, sessions, and teammates. Together, we’re helping developers build agents that keep working – and teams that build on what each agent learns. Learn more in our blog post via the link in the first comment! #Temporal #xmemory #AgenticAI

66.88%. 80.1%. 85%. 90.79%. 93%. 100%. These are all SOTA scores on agentic memory benchmarks. None of them tell you whether the system will work in production. The deeper problem isn't the data — it's that we often misunderstand what these numbers actually measure. In our recent white paper we open-sourced datasets that target specific memory functions. Today we published a follow-up that explains why we think the well-known agentic memory benchmarks (LoCoMo, LongMemEval) miss the mark for production systems, and what we measure instead. We're in a field that is measuring itself against itself. The real question isn't 'are we beating last week's leaderboard?' — it's 'are we building something that makes people's work meaningfully better?' That's harder to measure. It's also the only thing that matters. (Link in the first comment) Let's sit down and talk about your real use cases!

We’re excited to share that our team at @xmemory_ai has published our main white paper. The core idea is simple: without focus, AI systems try to "remember" everything, and that is where they fail. Schema is the best way to teach them focus. We compared xmemory against major open-source and commercial RAG and hybrid RAG systems, markdown-file memory approaches ("agentic memory"), and memory implementations in customer-facing frontier AI apps. Instead of testing whether systems can recall similar text, we evaluated whether they can store, update, deduplicate, and retrieve facts and relationships reliably. xmemory achieved 97% accuracy, compared with 87% for the strongest competitor. Our core also outperforms the latest frontier models’ one-shot APIs in extraction tasks, showing that the harness matters just as much as model quality. We’ve also open-sourced our measurement datasets and a toolkit to generate new ones synthetically. Huge thanks to the dream team of engineers at xmemory who keep pushing this vision into production. If your company is seeing memory systems fail in real workflows, we’d love to talk.

Why are we forcing agents to adapt to software stacks that were never designed for them? A lot of what people call "agent infrastructure" today looks like a temporary patchwork - prompts, wrappers, glue code, and brittle orchestration compensating for systems that cannot express meaning clearly enough on their own. We think that is backwards. Agents should handle intent and reasoning. Systems should handle semantics, structure, and ambiguity. If that shift happens, it could change the software stack more than most people expect. Read our second blog post via the link in the first comment.

Thrilled to see @xmemory_ai featured in @BessemerVP’s latest AI Infrastructure Roadmap for 2026. We’re one of the companies highlighted in Memory, Context, and State Management, alongside seven other strong builders. What stands out even more than the mention is the broader shift it signals: memory is becoming core infrastructure for production AI. As AI moves from one-off interactions to persistent, real-world workflows, the systems that win will be the ones that can retain context, manage state, and stay grounded over time. Proud of what the our team is building, and excited for what comes next. #AIInfrastructure #Memory #ContextManagement #AgenticAI #EnterpriseAI

Today we’re sharing our first blog post: “Schema as the Core of Reliability.” Our view is simple: AI memory breaks down when facts are stored as unstructured text and reconstructed later. That approach can work for thematic recall, but it becomes fragile when systems need to answer exact questions, maintain state, support workflows, and drive automation. In the piece, we explore why: - Search can recover context, but memory must support facts - Graph RAG is a meaningful step forward, but not the endpoint - Reliable memory needs structure - types, relations, constraints, deduplication, provenance - Schema is not decoration - it is the contract that makes memory observable, enforceable, and dependable If AI systems are going to do more than sound coherent - if they are going to make decisions, trigger workflows, and operate over long horizons - memory has to become a governed system of facts. That is where reliability starts. Read the full post - link in the first reply.