Memoir

Memoir is a high-performance semantic memory system for AI agents that brings Git-like version control to AI memory management. It replaces opaque vector databases with transparent, versioned, cryptographically secure memory storage using hierarchical semantic paths.

Why agents need versioned memory

AI memory is a Global Variable anti-pattern. Every production agent hits the same three walls: context contamination, token rent, and memory drift. Memoir brings version control to your agent's mind.

Your agent doesn't respect your git state. Context contamination happens every time you git checkout. Without branch-aware memory, your agent tries to apply experimental refactor patterns to stable production hotfixes.

You're paying "token rent" on a flat file. Using CLAUDE.md or MEMORY.md as a global store is a cache-killer. Every minor memory update invalidates your entire prefix cache, forcing you to pay full price to re-process your entire conversation.

Your agent's memory is code without version control. Today's AI memory — CLAUDE.md, vector stores, scratchpads — is treated like an append-only blob. One bad session poisons every future retrieval. Without memoir blame or memoir checkout, there's no way to audit who taught the agent a rule or revert a hallucination without wiping the whole store.

Key features

• Git-like Versioning — Branch, commit, merge, and rollback memories with cryptographic integrity.
• Semantic Paths — Replace UUID keys with meaningful paths like profile.professional.skills.python.
• O(log n) Lookups — Fast hierarchical search instead of expensive vector operations.
• Memory Aggregation — Automatic consolidation of related memories at semantic locations.
• Clean Architecture — Proper separation of storage, classification, and search layers.
• Multiple Search Engines — Choose between fast keyword-based or intelligent LLM-powered search.