MemoryAtlas

A git-based differential memory backend: markdown files store the current 'now' state of knowledge while Git's commit graph preserves how facts evolved over time. A git-native retrieval agent explores the repository via sandboxed shell commands (`grep`, `git log`, `git diff`, `git blame`) to build targeted context — no vector database, no embeddings, no BM25, just git and an LLM. Ships as a small FastAPI service and is also importable as a Python library.

Storage
Markdown files in a git repo; each user gets an isolated orphan branch (`user/{user_id}`) checked out into a per-user worktree, so users share no history without needing per-user repos. Current-state files are the query surface; history lives in git commits, loaded on demand. Storage backend is local disk by default (a hard requirement of the shell-based retrieval agent); an optional GitHub backup backend can mirror user branches offsite.
Retrieval
A multi-turn LLM retrieval agent with a single `run(command=...)` tool that shells out to read `index.md` and probe git history (grep/git log/git diff/git blame), then outputs a structured retrieval plan (file sections, diffs, commit logs) resolved into context. Optimized for a compact current-state surface with on-demand temporal deep-dives.
Self-host
Self-host: trivial
License
MIT (declared in README; no LICENSE file committed to the repo)
Pricing
Open-source (MIT per README); self-hosted, no paid tier for the library. I/O-bound — runs on a 1-vCPU VPS. One-click Coolify/Docker Compose deploy. · Free / OSS
GitHub stars
891
Last release
Last commit
2026-06-26
First catalogued
2026-06-30

Strengths

  • No vector DB, no embeddings, no BM25 — just git + an LLM, so nothing to babysit and memory stays human-readable
  • Differential/temporal intelligence: 'how has this fact changed?' answered from git diffs/logs without scanning full history
  • Current-state focus keeps the query surface (and token cost) lean; historical depth is on-demand
  • Per-user orphan-branch isolation without per-user repos
  • Production-proven: powers Annabelle, a persistent-memory assistant across thousands of WhatsApp/Messenger conversations
  • Self-hostable with zero external dependencies (local storage backend); optional GitHub mirror

Watch out

  • No LICENSE file is committed — the README declares MIT (badge + a 'License' section), but until the file lands the code is technically all-rights-reserved by default; confirm before embedding or redistributing
  • No tagged GitHub releases; the README self-labels v0.5.0 / 'production'
  • Retrieval requires a real local directory (the agent shells out to grep/git) — the storage backend cannot be a pure object store
  • Per its own roadmap, the indexing strategy is memory-intensive and an entity can become an over-loaded catch-all; retrieval quality depends on the LLM agent

Best for

  • Builders of long-horizon conversational or personal-assistant agents who want human-readable, git-versioned memory without a vector store
  • Teams that value auditability and 'smart forgetting' via git history over embedding-based recall

How it integrates

Benchmark results

No sourced results yet.

Sources

Last verified 2026-06-30 · updated by discover-frameworks