Stop re-explaining your project to every new AI session.
One local memory for Claude Code, Codex, and every MCP agent — shared across sessions, tools, and projects. Zero dependencies. Nothing leaves your machine.
Install · How it works · Numbers · Compare · FAQ
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Every new chat, your AI forgets. You re-explain the project, the decisions, the gotchas — every time, in every tool. Yggdrasil is a tiny always-on memory that any agent plugs into. Open a new session, in any project, with any AI, and it already knows what you decided, what broke, and what's still open.
$ cd ~/projects/checkout-api && claude # a brand-new session
🌳 Yggdrasil (injected automatically at session start)
• [project_status] payments refactor: idempotency keys added; open: e2e tests
• [lesson] webhook 401 → signing secret rotated; update env + redeploy
> "have I solved a flaky websocket reconnect anywhere before?"
🌳 recall → found in project `realtime-dash`:
refresh the token *before* opening the socket, then retry with capped backoff.
No "let me remind you what we did yesterday." It's just there.
Two commands, inside Claude Code (the plugin launches via uv):
/plugin marketplace add VonderVuflya/Yggdrasil
/plugin install yggdrasil
The engine lazy-starts on first use and generates its own local token — no API key, no cloud, nothing to configure. Codex and Cursor use the same flow.
All other channels — CLI daemon, Homebrew, npm, Claude Desktop, from source…
| Host / tool | Command |
|---|---|
| uvx (recommended CLI) | uvx --from yggdrasil-memory ygg install |
| npm / npx | npx yggdrasil-memory install |
| pipx | pipx install yggdrasil-memory && ygg install |
| pip | pip install yggdrasil-memory && ygg install |
| Homebrew (macOS) | brew install VonderVuflya/tap/yggdrasil && ygg install |
| Claude Desktop (app) | drag the .mcpb from the latest release onto Settings → Extensions, paste your token (ygg token) — the desktop app then shares the same memory as your CLI agents (guide) |
| from source | uvx --from git+https://github.com/VonderVuflya/yggdrasil.git ygg install |
ygg install is a one-time guided setup: it installs an always-on background service, registers the MCP tools with Claude Code and Codex, and — if your hardware allows — recommends optional local models (or pick none to stay zero-config).
There is also a yggdrasil-memory skill for any Claude surface: MCP connects the tools, the skill teaches the agent when to use them. Use both for the best behavior.
Try it with nothing installed and a throwaway DB: uvx --from yggdrasil-memory ygg serve --reset --db /tmp/ygg.sqlite.
Then just work: ask your agent "recall what we decided about this project", tell it "remember this decision" — next session it's already there. Verify the install any time with ygg doctor.
Already have history? Seed memory from your existing Claude Code + Codex transcripts, Obsidian vaults, and CLAUDE.md repos — distilled locally:
ygg seed --dry-run # see what it would import; drop the flag to distill for realLeaving another memory tool? ygg import --from mcp-memory --path memory.json pulls its whole store into Yggdrasil (deduped, secret-guarded) — then you can delete it.
- 🧠 Persistent — decisions, lessons, and project status survive across sessions.
- 🔌 One brain, every tool — Claude Code, Codex, and any MCP host share the same memory.
- 🌐 Cross-project recall — "this looks like what you did in project B — reuse it?"
- 🧹 Curated, not captured — your agent saves the few things that matter; governance dedupes and archives, never deletes.
- 🌱 Self-maintaining (opt-in) — a small local model consolidates memory in the background. Zero API tokens.
- 🪪 One identity everywhere — an optional name and persona every agent picks up, so Claude Code and Codex feel like the same assistant.
- 🔒 100% local — your memory lives on your machine. No cloud, no account, no telemetry.
Yggdrasil is memory + tools — the intelligence is your LLM. It just makes sure the right memory is in front of the right agent at the right moment.
- 🛎️ Always-on daemon — a tiny local service (~21 MB RAM) your agents reach over MCP tools (
ygg_search,ygg_recall,ygg_remember…). - 🪝 Hooks — session start auto-injects identity, project status, and open follow-ups (~300 tokens); an optional per-prompt hook auto-recalls memory relevant to each request.
- 📌 Ranking — pinned and frequently-recalled memories surface first.
- 🧹 Governance — duplicates and conflicts are queued for review; changes are non-destructive (archive, never delete).
- 📓 Obsidian — every memory doubles as a plain-Markdown note you can read, edit, and grep.
Out of the box, Yggdrasil runs on SQLite + FTS5 with zero dependencies — instant keyword search, no models, nothing to download. Optional local models via Ollama add two independent tiers:
| Tier | You add | You gain |
|---|---|---|
| 0 · default | nothing — SQLite + FTS5 | keyword search, zero deps, instant — recall@1 = 0.77 |
| 1 · semantic | an embedding model (all-minilm 45 MB · paraphrase-multilingual ~560 MB) |
search by meaning, across languages — recall@1 = 0.93, recall@3 1.00 |
| 2 · self-maintaining | a small LLM (qwen2.5:1.5b ~1 GB) |
background dedupe/merge of memory (propose-only) |
Ollama only computes vectors and runs the background model — every memory and every vector stays in the same local SQLite. ygg install detects your hardware and recommends a fit (ygg recommend shows the full catalog).
Full model menu
Embeddings (semantic search):
| Model | Size | Good for |
|---|---|---|
all-minilm |
45 MB | English, tiny & fast |
nomic-embed-text |
274 MB | English, better quality |
paraphrase-multilingual |
~560 MB | multilingual (EN/RU + 50 langs) |
bge-m3 |
1.2 GB | multilingual, top quality (heavier) |
Background consolidation (small LLM):
| Model | Size | Good for |
|---|---|---|
qwen2.5:0.5b |
~400 MB | tiny, fast on CPU |
qwen2.5:1.5b |
~1 GB | best CPU default |
llama3.2:3b |
~2 GB | better quality, slower on CPU |
The engine itself is swappable — any service meeting the MemoryBackend contract is a drop-in (YGG_ENGINE_URL); see docs/backend-boundary.md.
Measured by eval/ygg_eval.py — 35 labelled queries, ranking weights tuned on the dev split only, so holdout is the unbiased number (recall@1, with the paraphrase-multilingual model):
| Search view | holdout recall@1 | recall@3 | zero-dep lexical |
|---|---|---|---|
| Within a project (the real path, pool ~6) | 0.93 | 1.00 | 0.77 |
| Whole store (no filter, pool 35) | 0.80 | 1.00 | 0.77 |
recall@3 = 1.00 in both views — with the local model the right memory is always in the top 3, even searching the entire store; it's #1 0.93 of the time within a project. Zero-dep lexical mode already solves keyword and code-identifier queries (1.00). Small corpus (n=35), so the full breakdown in BENCHMARKS.md shows 95% CIs, pool sizes, and per-class scores — and you can rerun it in a minute: python3 eval/ygg_eval.py --report.
Everyone else either auto-captures transcripts or sells you a cloud. Yggdrasil's bet: keep the few things that matter, curated and de-duped, in plain rows you own — and share them across every tool and project.
| Yggdrasil | Built-in memory (Claude Code · Codex) | claude-mem | mem0 / OpenMemory | basic-memory | |
|---|---|---|---|---|---|
| Curated decisions / lessons / status (not transcripts) | ✅ | ❌ captures everything | |||
| One memory across tools | ✅ | ❌ vendor-siloed | ✅ | ✅ | ✅ |
| Cross-project recall ("solved this in project B") | ✅ | ❌ repo-scoped | |||
| 100% local by default | ✅ | ✅ | ❌ hosted-first | ✅ | |
| Zero dependencies (stdlib + SQLite) | ✅ | — | ❌ Node + Bun + worker daemon | ❌ Docker + Qdrant + LLM key | ❌ |
| Works with no LLM & no API key | ✅ | ✅ | ❌ AI-compresses | ❌ | ✅ |
| Semantic search, fully local | ✅ opt-in Ollama | ❌ grep-only | ❌ | ||
| Plain Markdown you own (Obsidian-ready) | ✅ | ✅ | ❌ | ❌ | ✅ |
Closest neighbor — claude-mem: capture-everything memory that records and AI-compresses every session (Node 20+ and Bun, a persistent worker daemon; Chroma optional). Yggdrasil is the opposite bet: a small, high-signal store instead of a growing firehose. mem0 is an SDK plus a hosted platform for building apps that remember their users — even self-hosted it needs an LLM API key. Built-in memories are genuinely useful — and structurally siloed: one vendor, one repo, one machine, literal grep. Yggdrasil is the layer above them (and ygg seed can bootstrap itself from those same transcripts). Different layer entirely: context-mode (live context window) and Context7 (fresh library docs) — both pair fine with Yggdrasil.
Agents see six MCP tools: ygg_health, ygg_bootstrap, ygg_search, ygg_recall, ygg_remember, ygg_materialize — auto-registered by the plugin or ygg install.
Full ygg CLI reference
Memory ops
| Command | What it does |
|---|---|
ygg recall --query "…" |
Cross-project search — "have I done this anywhere?" |
ygg search --project P --query "…" |
Project-scoped search (--type, --tag, --limit, --json) |
ygg remember --project P --type lesson --content "…" |
Save a durable memory (secret-guarded, deduped) |
ygg bootstrap --project P |
Pull a project's memory before starting work |
ygg pin --id ID · ygg unpin --id ID |
Pin a memory so it reliably surfaces |
ygg relate --from A --rel solves --to B · ygg relations --id ID |
Link memories (solves/supersedes/contradicts) · see why a memory exists / what replaced it |
ygg supersede --id OLD --by NEW |
Archive an outdated memory — --by records what replaced it |
ygg materialize --id ID --project P |
Export one memory to an Obsidian note |
ygg export-native --project P |
Write a curated digest into AGENTS.md/MEMORY.md — feed Claude Code & Codex's native memory |
ygg import --from TOOL --path P |
Migrate another memory tool's store into Yggdrasil (mcp-memory, basic-memory; --dry-run first) |
ygg review [--apply] |
Work the governance queue — consolidate duplicates, flag stale/conflicting memories (archive-only, reversible) |
ygg delete --id ID · ygg reset … |
Hard-delete one memory · bulk-undo a bad seed (confirms first) |
Cold start
| Command | What it does |
|---|---|
ygg seed |
Distill Claude Code + Codex transcripts, Obsidian vaults, CLAUDE.md repos — incremental, deduped, fully local |
ygg seed --dry-run · --force |
Discover + estimate only · re-distill everything |
ygg seed --schedule 03:30 |
Nightly auto-distill (launchd) — memory keeps itself fresh; off / status |
ygg sync --repo <your-git-repo> |
Sync memory across machines through your own git repo — plain JSON files, no cloud in the loop |
ygg distill --source PATH |
Distill one dir/file into lessons |
ygg reindex |
Backfill missing embeddings (restores dense recall) |
Service & setup
| Command | What it does |
|---|---|
ygg install · ygg doctor · ygg update |
Guided setup · diagnose with actionable fixes · upgrade |
ygg config |
Show/set persistent settings (list · get · set · unset) |
ygg status · start · stop · restart · logs |
Manage the always-on daemon |
ygg hooks · unhooks · register |
SessionStart hook on/off · (re)register MCP |
ygg recommend · token · uninstall |
Model catalog · print auth token · remove everything |
Give it a personality — edit ~/.yggdrasil/identity.json:
{ "name": "Jarvis", "persona": "concise, proactive, dry wit", "user_facts": ["prefers TypeScript", "ships small PRs"] }Heavy seeding, weak laptop? Point distillation at any box on your LAN — a desktop with Ollama, LM Studio, llama.cpp, even an iPhone running a local-LLM server app: ygg config set distill_url http://<box>:11434. Yggdrasil auto-detects the API dialect (Ollama or OpenAI-compatible); your data still never leaves your network — details in docs/ygg-cli.md.
Claude Code already has built-in memory — why Yggdrasil?
Built-in memories are per-vendor, per-repo, per-machine, and retrieved by literal text match. Yggdrasil is the layer above: the same memory in Claude Code, Codex, and any MCP host, recall across projects, optional semantic search — still 100% local. It bridges them both ways: ygg seed distills your existing native memory + transcripts into the shared brain, and ygg export-native writes a curated digest back into AGENTS.md/MEMORY.md — so even a fresh clone or a tool without Yggdrasil still gets your curated memory.
Does it send my code or memory to the cloud?
No. The engine, the database, and the optional models all run locally. No account, no telemetry. The only outbound call is a version check against PyPI.
Does it automatically remember everything?
No — by design. Retrieval is automatic; writing is deliberate (the agent calls ygg_remember for durable lessons). Capture-everything pollutes memory and burns tokens, so we don't. The optional background model consolidates what's already saved (propose-only).
Do I need a GPU or an API key?
No. The default is pure lexical search — zero dependencies, instant. Semantic search is opt-in and uses a local model via Ollama. The installer recommends one that fits your hardware.
How heavy is it, and what does it cost in tokens?
The engine idles at ~21 MB RAM (lexical default) with ~0% CPU; disk is tens of KB per memory. Session start injects ~300 tokens; each tool call returns a small snippet. All heavy work (indexing, embeddings, consolidation) runs off-LLM on your machine.
Can I edit or delete memories by hand?
Yes. Memories materialize to Markdown notes in an Obsidian vault — read, edit, or remove them like any file. The engine never hard-deletes; it archives (reversible).
Alpha. The happy path and the governance loop are gate-tested (scripts/run_gates.sh); not yet hardened for multi-user or production use. macOS today; Linux/Windows service installers are built and in final on-device testing.
Next: 🛰️ cross-surface sync (one memory across CLI, web, and phone) · 🔗 relation graph (SOLVES / SUPERSEDES / CONTRADICTS) · 🐧 Linux/Windows GA.
Issues and PRs welcome. Run scripts/run_gates.sh and python3 -m unittest discover -s tests before submitting — all gates must stay green.
GNU AGPL v3.0 — see LICENSE. Free and open source: use, modify, self-host, redistribute. If you modify it or offer it as a network service, you must release your source under the same license.