memoir-ai 0.1.7

High-performance semantic memory system integrating LangMem with ProllyTree

Memoir

Git for AI Memory

Hierarchical Memory with Git-Like Version Control

Memoir is a hierarchical memory system for AI agents, with Git-like version control built in. Instead of storing facts as opaque embeddings and guessing at relevance through vector similarity, Memoir classifies every fact into a semantic taxonomy path like preferences.coding.style or workflow.coding.testing. Retrieval becomes explainable (you see which path was chosen and why) and efficient (O(log n) tree lookup — no vector index, no embedding model to host, no re-rank stage). Memoir is framework-agnostic — usable from any agent runtime via its CLI, Python SDK, or MCP server — and sits alongside any static prompt or instruction file your agent already uses, carrying the evolving layer (decisions, debugging trails, accumulated preferences, branch-specific context) that a static file can't version or query.

Why Memoir for Coding Agents

• Hierarchical paths, not embeddings. Every fact lands at a named path in a 3-level taxonomy (~200 paths in v1.1.0). No vector DB to operate, no similarity threshold to tune — lookup is a tree walk and results trace directly back to the path that matched.
• Explainable retrieval. Unlike semantic/vector search, you can always answer why a memory was surfaced: the agent picked path preferences.coding.style and read the value there. Good for debugging, auditability, and reproducibility.
• Efficient at scale. Taxonomy classification is O(log n), not O(n). No embedding inference on the hot path, no vector index to warm, no re-ranker. The CLI returns typical recalls in hundreds of milliseconds.
• Complements static prompts. System prompts and instruction files are great for invariants. Memoir handles the evolving layer — per-session decisions, debugging history, branch-specific context — so the prompt stays lean and the history stays queryable.
• Branches for experiments. Try a refactor direction or a new coding style on experiment/*, keep it if it works, discard it if it doesn't — same workflow you already trust from git.
• Taxonomy designed for coding workflows. v1.1.0 maps to how coding agents actually think — workflow.coding, debugging, knowledge, preferences.tools — not generic prose buckets.
• Framework-agnostic, agent-native ergonomics. Works with Claude Code, LangGraph, custom runtimes, or anything that can shell out. --json on every CLI command, stable exit codes, KV-cache-friendly output shapes, and an MCP server for any MCP-compatible client.

Install from PyPI

pip install memoir-ai

The distribution name on PyPI is memoir-ai. The Python import is import memoir and the CLI is memoir.

Install for Claude Code

Inside a Claude Code session, run:

/plugin marketplace add zhangfengcdt/memoir
/plugin install memoir@memoir

The plugin registers hooks for session start, user-prompt-submit, and stop, so your project gets automatic context injection and auto-captured memories. Each project gets its own store under ~/.memoir/memoir_<hash>/ (override with MEMOIR_STORE). See the Claude Code plugin guide for the full slash-command and hook reference.

Quick look

export MEMOIR_STORE=/tmp/my_store
memoir new "$MEMOIR_STORE"
memoir remember "prefer pytest over unittest, parametrize aggressively"
memoir recall "what's my testing setup?"

That's the core loop — auto-classified on the way in, semantically retrieved on the way out.

Documentation

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.

Contributing

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 run make 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.

License

Apache License 2.0 — see LICENSE.