Yggdrasil

🌳 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…

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 add two independent tiers:

The runtime only *computes* vectors and runs the background model — every memory and every vector stays in the same local SQLite.

ygg install scans for Ollama, LM Studio and llama.cpp, offers to start whichever is installed but idle, and then shows one menu per job with green for models you already have and red for models it would download. Pick a row, and it writes embed_backend, embed_url and distill_url for you — including the part nobody guesses right, that embed_url wants the /v1 base while distill_url wants the host root. A runtime on another machine is one URL: paste it and the dialect is detected. ygg recommend shows the same scan plus the full catalog without changing anything.

Full model menu

Embeddings (semantic search):

Embedding backend — Ollama by default. ygg install sets all of this for you once you pick a runtime; the manual route below is for scripted setups and for changing one thing later. 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)

Wiring LM Studio or OpenRouter by hand? The gotchas (two URL shapes, model ids, JIT loading, inference vs provisioning keys, privacy filters) are in docs/model-backends.md.

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):

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