yggdrasil-memory 0.12.0

One shared, durable memory for all your AI coding agents — MCP, local-first, zero-dependency.

🌳 Yggdrasil

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.

🚀 Install

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 every agent host it finds — Claude Code, Codex, OpenCode — and, if your hardware allows, recommends optional local models (or pick none to stay zero-config).

OpenCode — nothing to configure

Install OpenCode first, then run ygg install (or ygg redeploy if Yggdrasil is already set up) — the entry is written for you and merged into any existing opencode.json. Confirm with:

opencode mcp list        # -> ✓ yggdrasil connected

Installed OpenCode after Yggdrasil? Just re-run ygg install.

If you'd rather write it by hand, note that OpenCode's schema differs from Claude's in four places at once — servers live under mcp (not mcpServers), type is required, command is one array (not command + args), and env is environment (not env), so the Claude snippet won't port:

// ~/.config/opencode/opencode.json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "yggdrasil": {
      "type": "local",
      "command": ["/path/to/python3", "~/.yggdrasil/scripts/ygg_mcp_server.py"],
      "enabled": true,
      "environment": { "YGG_ENGINE_URL": "http://127.0.0.1:42069" }
    }
  }
}

No token goes in the config — the engine reads the 0600 ~/.yggdrasil/token itself. Run ygg doctor if the tools don't show up.

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 real

Leaving 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.

Why

• 🧠 Persistent — decisions, lessons, and project status survive across sessions.
• 🔌 One brain, every tool — Claude Code, Codex, OpenCode, 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.

🧠 How it works

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.

🎛️ Memory tiers — zero-config by default

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 = 0.94, 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 (768d)
mxbai-embed-large	670 MB	English, high quality (1024d)
paraphrase-multilingual	~560 MB	multilingual (EN/RU + 50 langs, 768d)
bge-m3	1.2 GB	multilingual, top quality (heavier)

Embedding backend — Ollama by default. To use an OpenAI-compatible /v1/embeddings server instead (llama.cpp's llama-server --embeddings, OpenRouter, LM Studio, vLLM), set embed_backend:

# local llama.cpp — no key needed
ygg config set embed_backend openai
ygg config set embed_url http://127.0.0.1:8080/v1
ygg config set embed_model bge-small-en-v1.5
ygg redeploy

# OpenRouter — free embeddings, no GPU needed
ygg config set embed_backend openai
ygg config set embed_url https://openrouter.ai/api/v1
ygg config set embed_model nvidia/llama-nemotron-embed-vl-1b-v2:free
ygg config set embed_api_key sk-or-...    # or export YGG_EMBED_API_KEY
ygg redeploy

The key is stored in ~/.yggdrasil/embed_api_key (0600) rather than config.json, and reaches the daemon as a file path — so it never shows up in ps, the launchd plist or the systemd unit. ygg config list masks it.

Check it took with ygg doctor — dense should name your model:

✓ dense    active (nvidia/llama-nemotron-embed-vl-1b-v2:free)

OpenRouter: two settings that will bite you

1. Use an inference key, not a provisioning key. Keys from openrouter.ai/settings/provisioning-keys can only mint other keys — embedding calls with one return a baffling 401 User not found. Create a normal key at openrouter.ai/settings/keys instead.

Note that GET /api/v1/models answers 200 OK for any key, valid or not — it ignores auth entirely, so it can't tell you whether your key works. Check GET /api/v1/key instead: it returns is_provisioning_key, and fails outright on a bad key.

2. Privacy settings silently hide most models. If a model 404s with All providers have been ignored, the model is fine — your account is filtering out every provider that serves it. Fix it at openrouter.ai/settings/privacy. That filter is also why openai/text-embedding-3-* can come back 403 on a provider's terms of service.

Browse what's actually available at openrouter.ai/models?output_modalities=embeddings (26 models at the time of writing). Useful ones:

Model	Price / 1M tokens
nvidia/llama-nemotron-embed-vl-1b-v2:free	$0
perplexity/pplx-embed-v1-0.6b	$0.004
intfloat/multilingual-e5-large	$0.01 — multilingual
google/gemini-embedding-2	$0.20

Staying local still wins on quality and privacy: on the 232-memory / 110-query corpus, local paraphrase-multilingual scores recall@1 0.964 vs 0.946 for the free hosted model — and your memories never leave the machine. Reach for a hosted backend when the box can't run Ollama, not to chase accuracy.

Bigger vectors do not buy accuracy here — on the same corpus mxbai-embed-large (1024d) scores 0.809 and nomic-embed-text (768d) 0.818, a difference their confidence intervals swallow whole. What actually moves the number is whether the model handles your languages: both are English-only and collapse to 0.40–0.45 on cross-language queries, where the multilingual default holds 0.95.

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.

📊 The numbers

Measured by eval/ygg_eval.py — 232 memories, 110 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 ~11)	0.94	1.00	0.76
Whole store (no filter, pool 232)	0.72	0.87	0.69

Within a project — the path you use — the right memory is #1 for 0.94 of queries and in the top 3 every time (recall@3 = 1.00). Searching the whole store with no filter is harder (recall@1 0.72, recall@3 0.87 across all 232). Zero-dep lexical mode already solves keyword and code-identifier queries (1.00); the local model adds meaning and cross-language (crosslingual 0.25 → 0.95). The full breakdown in BENCHMARKS.md has 95% CIs, pool sizes, and per-class scores — rerun it in a minute: python3 eval/ygg_eval.py --report.

🆚 Yggdrasil vs the rest

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)	✅	⚠️ auto-notes	❌ captures everything	⚠️	⚠️ free-form notes
One memory across tools	✅	❌ vendor-siloed	✅	✅	✅
Cross-project recall ("solved this in project B")	✅	❌ repo-scoped	⚠️	⚠️	⚠️
100% local by default	✅	✅	⚠️ cloud sync add-on	❌ 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	⚠️ optional Chroma	⚠️ needs API key or Docker stack	❌
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.

🧰 Commands

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.

❓ FAQ

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).

🚦 Status & roadmap

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.

🤝 Contributing

Issues and PRs welcome. Run scripts/run_gates.sh and python3 -m unittest discover -s tests before submitting — all gates must stay green.

📜 License

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.