memgit — git for AI memory

Your AI assistants forget everything when the session ends. memgit fixes that.

Version-controlled, cross-AI context that persists, diffs, rolls back, and syncs like code. Switch from Claude to Cursor to ChatGPT mid-project — your context is already there.

Why not claude.md? Why not mem-search?

You've probably already tried both. Here's why they hit a ceiling:

Proof — context costs you can measure

Run this on your own store to see the actual numbers (measured where possible; estimates labeled):

$ memgit stats

  Total memories:   108   (41 feedback · 23 user · 19 project · 12 reference · 8 convention · 5 lesson)
  Priority:          3 critical · 67 medium · 38 low

  Context footprint  (measured where possible; estimates labeled)

  Surface                                             Tokens
  Full store (every memory as context)                12,840
  Resume digest  (measured render)                       540
  Recall block  (estimate: top-3 rules ≈ chars/4)        ~60

  per-session injected ≈ 600 tokens (estimate) vs 12,840 tokens if the full store were loaded

Why such a big difference? claude.md loads all context every session. memgit injects a bounded resume digest plus BM25-matched recall — only what is relevant to this session, not everything you've ever recorded. The digest is measured by actually rendering it, and the store total is the real corpus size; nothing here is a simulated benchmark.

The git analogy is literal

memgit's data model maps exactly to git:

This is not metaphorical — memgit uses a content-addressed object store (SHA-256 blobs) identical to git's architecture. Every memory has a stable SHA. Identical content has identical SHAs. Old state is always recoverable.

The store IS a git repo

Every memory is a readable .toon file under memories/. Push your entire memory set to GitHub with standard git:

memgit git init --remote git@github.com:yourteam/ai-memory.git
memgit git push

Teammates pull and start with your AI's learned rules from session 1:

git clone git@github.com:yourteam/ai-memory.git ~/.claude/memgit-store
memgit setup all

You can grep, git blame, and git diff your memories just like code:

grep -rl "database" ~/.claude/memgit-store/memories/
git log --follow memories/no-db-mock.toon
git diff HEAD~7 memories/

Install

Mac / Linux:

pip install memgit

Mac (Homebrew):

brew tap code4161/tap && brew install memgit

Windows:

choco install memgit
# or
pip install memgit

(The Chocolatey package is live on community.chocolatey.org; newly pushed versions can take a few days to clear moderation — pip install memgit always has the latest.)

Any AI tool config (no Python needed — npx auto-installs on first run):

{ "mcpServers": { "memgit": { "command": "npx", "args": ["-y", "memgit-mcp"] } } }

Quickstart (3 minutes)

# 1. Install and initialize
pip install memgit
memgit init               # auto-detects the best location, finds your existing
                          # Claude Code memories, and offers to import them

# 2. Register with your AI tools (interactive picker)
memgit setup

# 3. See your token savings
memgit stats

init walks you through it — no paths to hunt down. (Importing later is one command with no arguments: memgit sync auto-finds ~/.claude/projects/*/memory.)

Restart your AI tool — it now searches your memory store at the start of every session.

Adopting memgit mid-project

Memory tools have a cold-start problem: install one halfway through a project and it knows nothing — there's no initial point, and context only trickles in from future sessions. memgit solves this with a one-time seeding pass:

cd your-project
memgit onboard          # mines the repo, prints the bootstrap brief

onboard first extracts a repo digest deterministically — git history (recent commit subjects, hot files/directories by churn, authors, branch, tags), detected stack from manifests, and the docs worth reading — using bounded, read-only probes that stay near-instant even on huge repositories. The brief then tells your AI agent exactly what to do with it: read only the listed files (no tree crawling), extract 10–20 durable facts (purpose, architecture, conventions, current state, gotchas), save each as a typed memory, and checkpoint the seed set. Paste it into a session — or don't: if the AI searches memory in a project that has none, the MCP server itself replies with the bootstrap instructions instead of a bare "no results."

Memories are project-scoped, filter-by-default (v0.7.0): each carries the workspace it belongs to, and searches, recall injections, and the resume digest (recent memories, checkpoints, depth hints) are filtered to the current project's family plus explicitly-global memories — another project's content never leaks in. Widen deliberately with memgit search --all-projects / all_projects: true (every hit then carries its project label), or hard-filter one project with --project. A memory with no project is explicitly global (applies everywhere): save one with memgit add --global or project: "". A save whose project cannot be determined is never silently global — it's quarantined under _unknown (visible in list as [?project], flagged by lint, surfaced nowhere) until you relabel it with memgit doctor --relabel.

Resume where you left off

Ask an AI "can we proceed on the pending tasks?" in a fresh session and it will guess from whatever file happens to be open. memgit resume replaces the guess with the record:

memgit resume            # last checkpoints, work in flight, recent + critical memories
memgit resume --plain    # plain text, for piping into an AI context
memgit resume --json     # for tooling

Wire it into Claude Code so memory becomes automatic — no tool call, no judgment required:

memgit setup hooks       # installs all five hooks (~/.claude/settings.json)

Why hooks and not just good tool descriptions? We measured it: across 166 real sessions, hook-injected context was delivered in 100% of them while voluntary memory-tool calls happened in 6%. What a hook enforces happens.

The resume digest is deliberately bounded (~350 tokens measured on a 500-memory store): rules are clipped, the critical list is capped, and full text is one get_memory call away.

Scale to 10,000+ sessions

After months of use, your checkpoint history grows. Squash compresses it, gc reclaims the disk:

memgit squash --keep-last 100    # keep last 100 checkpoints, squash everything older
memgit squash --older-than 30    # squash everything older than 30 days
memgit squash --dry-run          # preview first

memgit gc                        # delete unreachable objects, trim reflogs
memgit gc --dry-run              # preview
memgit gc --squash-keep 200      # compact history, then sweep

Read the full README on GitHub →