Memoir – Claude Code 플러그인을 사용하는 AI 에이전트 메모리용 Git

Git for AI Memory Hierarchical Memory with Git-Like Version Control 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. Project page: memoir-ai.dev — full overview, demos, and roadmap. 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. - 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. pip install memoir-ai The distribution name on PyPI is memoir-ai . The Python import isimport memoir and the CLI ismemoir . Inside a Claude Code session, run: /plugin marketplace add zhangfengcdt/memoir /plugin install memoir@memoir No manual pip install needed if you have uv on PATH — the plugin auto-resolves to uvx --from memoir-ai memoir when the bare memoir binary isn't installed. Don't have uv ? Install it once with the standard one-liner: curl -LsSf https://astral.sh/uv/install.sh | sh That's enough — the plugin handles the rest. It registers hooks for session start, user-prompt-submit, and stop, so your project gets automatic context injection and auto-captured memories. See the Claude Code plugin guide for the full slash-command and hook reference. Memoir's CLI defaults to Anthropic claude-haiku-4-5 as of v0.1.6 — set your key first: export ANTHROPIC_API_KEY="sk-..." Then create a store and round-trip a memory: # 1. Create a memoir store memoir new my-memoir-store cd my-memoir-store # 2. Store with an explicit path (offline, no LLM call) memoir remember "Sarah prefers tabs and 2-space indents" -p preferences.coding.style # 3. Store with auto-classification (LLM picks the path; needs API key) memoir remember "I work in Pacific time" # 4. Read back by path (offline) memoir get preferences.coding.style # 5. Semantic search (LLM-backed) memoir recall "what does Sarah prefer?" # 6. Open the visual explorer (auto-opens in your browser) memoir ui Curious what the UI looks like before installing? Browse the UI Gallery. Prefer a different model? memoir recall "..." --model gpt-4o-mini (needs OPENAI_API_KEY ), or set MEMOIR_LLM_MODEL in your shell. Resolution order: --model flag → MEMOIR_LLM_MODEL → claude-haiku-4-5 . See the full Quickstart for the Python API and version-control workflow. Full docs live at zhangfengcdt.github.io/memoir: - Quickstart — five-minute tour of the core loop. - CLI Reference — every command, flag, and exit code. - UI — the visual explorer (Tree / Graph / Timeline / Places + /stats ). - Claude Code — plugin install, slash commands, hooks, lifecycle. - Architecture — taxonomy, classifier, store, search. - API Reference — Python SDK. - Examples — context branching, memory debugging, reproducible testing. Memoir is alpha and contributions are very welcome — especially from people building coding agents, since that's the audience we're optimizing for. Good first paths in: - Pick an issue from the issue tracker or open one describing a gap. - Fork the repo, branch off main , and runmake ci before opening a PR (lint, tests, docs build must be green). - Bug reports with a minimal reproducer and benchmark / taxonomy proposals for coding-agent use cases are particularly appreciated. Apache License 2.0 — see LICENSE.