MinIO debuts AIStor Memory, the long-term memory AI agents need to scale safely

UPDATED 23:12 EDT / JULY 29 2026 BIG DATA MinIO debuts AIStor Memory, the long-term memory AI agents need to scale safely by Mike Wheatley Object storage software company MinIO Inc. says it has cracked the persistent memory problem for artificial intelligence agents with the launch of a new offering called AIStor Memory. Whereas conventional chatbots like Claude and ChatGPT can generally get away with much more limited context because they usually only need to respond to a few prompts, AI agents often carry out much longer, multistep tasks that can span multiple sessions. This means that having persistent memory is a much more pressing concern. AI agents perform tasks such as creating documents, drafting analyses and answering complex questions that could previously only be done by humans. The resulting outputs, according to MinIO, are basically organizational knowledge – a record of what a business knows – and believes that controlling these are critical to scaling AI agents. AIStor Memory is MinIO’s attempt to enable this. In a blog post, the company explained that it treats agent memory as a native data type that lives alongside standard objects and tables, allowing AI agents to retain context across multiple sessions more easily. This means they can quickly pick up where they left off on unfinished tasks and operate on enterprise data with existing governance controls. According to MinIO, the resulting knowledge the agents generate will remain on infrastructure controlled by customers themselves. Co-founder and co-Chief Executive AB Periasamy said all of the knowledge generated by AI agents should live on enterprise-controlled infrastructure. “AIStor Memory brings long-term memory, persistent workspaces and secrets together on a single enterprise-controlled foundation,” he explained. “A single agent’s memory becomes the shared substrate for the entire organization.” At present, enterprises face an uphill struggle to give agents a persistent memory. They’re required to combine their object storage resources with metadata databases, vector stores, secrets managers, governance tools and synchronization pipelines, and that is a major drain on information technology team’s resources. With AIStor Memory, the company is offering an integrated alternative that can be mounted onto existing sandboxes, which also plays nicely with existing AI infrastructure, tools and frameworks without any modifications required. The new offering is designed to support long-running multistep agentic workflows, where the work performed by AI agents needs to be able to survive interruptions and where sensitive data must remain fully governed. The company said AIStor Memory can enable specific agentic use cases such as software engineering agents working on large codebases, deep research and analysis that spans days, human-in-the-loop workflows that must be paused and resumed later, and enterprise AI systems that rely on regulated data. MinIO explained that AIStor Memory is designed to sit beneath the KV-cache layer served by its own MemKV service, where it can preserve the complete state of an agent’s work, rather than only the context used during the inference process. This enables it to support “infinite context,” with persistent memory scaling based on the available capacity rather than a model’s context window, without the data having to be summarized, truncated or otherwise minimized. AIStory Memory is tightly integrated with existing infrastructure, which means there’s no need to set up a separate database, vector store, metadata tier or synchronization pipeline, and it employs techniques such as erasure coding, bitrot protection, encryption, co