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Give AI agents durable memory on storage your team controls

#ai-agents #memory+4

MinIO AIStor Memory is a new enterprise storage layer for agent memory, workspaces, and secrets that is worth evaluating before production agent stacks sprawl across separate databases and vaults. MinIO AIStor Memory is useful for teams moving AI agents from experiments into governed production workflows. It treats agent memory, workspaces, and secrets as a managed storage problem instead of scattering them across transcripts, vector stores, object buckets, metadata databases, and ad hoc vault wiring. What it is AIStor Memory is a MinIO product layer for durable agent memory. MinIO says it captures agent interactions, organizes them into structured memory, and retrieves relevant knowledge for later runs while keeping the data on enterprise-controlled infrastructure. Who it helps It is most relevant for platform, AI infrastructure, and security teams that need agents to resume work, share organizational context, and preserve provenance without handing long-term memory to a hosted black box. How to evaluate it Start with the product page and press release, then map it against your current agent stack: where memory lives, where work-in-progress files live, where secrets are accessed, and which audit controls already exist. Compare it with your current object store, vector database, secrets manager, and sandbox runtime. Limits and risks This is an enterprise product, not a drop-in open-source library. Validate pricing, deployment model, identity controls, retention rules, and how secrets are separated from retrievable memory. Agent memory can also preserve bad decisions, prompt-injection artifacts, or stale assumptions if governance is weak. Sources AIStor Memory product page MinIO announcement

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Capn Hook gives coding agents local memory that expires when files change

#coding-agents #codex+3

A practical local CLI for Claude Code and Codex users who keep paying the same search cost across agent sessions. Capn Hook is a local memory layer for coding agents. It lets an agent chart hard-won codebase discoveries as small question-to-file entries, then ask that local chart before repeating the same repository search in a later session. The useful part is its stale-answer model: every charted answer is tied to backing file hashes. If a referenced file changes or disappears, Capn Hook prunes the entry before it can be used again. That makes it a better fit for fast-moving codebases than a static notes file or a giant persistent context dump. Why it is worth a bookmark: it integrates with Claude Code and Codex through session-start hooks, stores local markdown entries under .capn/, can run semantic recall through QMD, and offers a deterministic BM25 path with capn init --no-embedding. The README also publishes an eval claim across 60 real developer questions, but treat that as vendor evidence until you test it on your own repository. Use it if your agents repeatedly rediscover routing, billing, auth, deployment, or test-layout facts. Skip it if your team does not want agent hooks modifying local project setup, or if you cannot review what gets saved under .capn/.

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Give AI agents durable memory on storage your team controls

Capn Hook gives coding agents local memory that expires when files change

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