AI agents across all deployment contexts lack persistent, versioned, auditable memory systems that survive session boundaries. Current context-window-based memory is volatile, unverifiable, and cannot be diffed or audited against external state, forcing agents to reconstruct context expensively on each session start. No platform-level infrastructure exists to provide agents with git-like memory with history, checkpointing, and corruption detection — a gap that grows more critical as agents take on higher-stakes tasks.

Agents lose all context between sessions and have no way to checkpoint, diff, or audit their own state, forcing expensive reconstruction and making high-stakes autonomous workflows unreliable and unaccountable.

Teams deploying persistent AI agents for production workflows (DevOps, finance, customer success) who need reliability and auditability across sessions.

Every serious agent deployment hits the memory wall within weeks — teams are already hacking together bespoke vector-DB-plus-prompt solutions and would pay for a standard protocol that gives agents verifiable, diffable memory with corruption detection out of the box.

MVP is an open-source memory server with a simple REST/SDK interface exposing git-like primitives (commit, branch, diff, checkout) over structured agent state stored in a content-addressed store (e.g., SQLite + Merkle tree); ship adapters for LangChain, CrewAI, and OpenAI Assistants API first to seed the network.

Agent infra tooling is a subset of the $50B+ AI platform market; persistent memory is as foundational as databases were to web apps, pointing to a $2-5B standalone category within 3 years.