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.

[Latest release] [PyPI] [Glama quality score] [Benchmarks] [AGPL-3.0] [alpha]

Install · How it works · Numbers · Compare · FAQ

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[Yggdrasil — a brand-new session already knows your project, and recalls a fix from another project]

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.

opencode mcp list        # -> ✓ yggdrasil connected

// ~/.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" }    }  }}

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.

# local llama.cpp — no key neededygg config set embed_backend openaiygg config set embed_url http://127.0.0.1:8080/v1ygg config set embed_model bge-small-en-v1.5ygg redeploy
# OpenRouter — free embeddings, no GPU neededygg config set embed_backend openaiygg config set embed_url https://openrouter.ai/api/v1ygg config set embed_model nvidia/llama-nemotron-embed-vl-1b-v2:freeygg config set embed_api_key sk-or-...    # or export YGG_EMBED_API_KEYygg redeploy

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

🔗 One memory across your machines

Work on a laptop and a beefier box? Link them once and they stop being two brains.

# on the laptop$ ygg link🔗 pairing code   valid 5 minutes · single use
    ygg://192.168.3.152:42070/a4e0df489737#FOucjgJ1FrUB…
# on the other machine — paste it$ ygg link ygg://192.168.3.152:42070/a4e0df489737#FOucjgJ1FrUB…  ✓ linked with laptop   https://192.168.3.152:42070  ✓ laptop: fetch 84 · push 12 · delete 0 · tombstones 0

That's it. From then on a memory written on one machine is on the other in about a second, ygg delete removes it from both, and either machine keeps working with its own local copy when the other is asleep — reads never wait for the network. When a machine comes back, the two compare fingerprints and repair whatever they missed, without you running anything.

There is no polling. Yggdrasil is called a handful of times a day, so writes push as they happen and a sync is triggered by reads; idle machines send nothing.

What this is not: a cloud. There is no relay, no account, no server of ours in the path. The machines talk to each other directly over your own network, over TLS with each other's certificate pinned, using a key pair that exists only on those two machines. The pairing code itself names the issuing machine's certificate, so even the very first connection is verified — something else on the network answering in its place gets refused before the code's secret is sent. Nothing binds a network port until you run ygg link — install Yggdrasil and it stays on loopback, exactly as before.

ygg link --list          # what's linked, and whether writes are actually arrivingygg link --sync          # reconcile now, don't wait for a readygg unlink <name>        # revoke that machine's key here

📊 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):

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.

Corpus size is part of the number. On a real 4,799-memory store the default multilingual embedder falls to the lexical baseline (recall@1 0.55); bge-m3 holds 0.775 there. If your store grows into the thousands, pick bge-m3 — details in BENCHMARKS.md.

🆚 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.

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.

{ "name": "Jarvis", "persona": "concise, proactive, dry wit", "user_facts": ["prefers TypeScript", "ships small PRs"] }

❓ FAQ

🚦 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.

Shipped recently: 🔗 machine-to-machine memory (ygg link) · relation graph (ygg relate: solves / supersedes / contradicts).

Next: 🛰️ cross-surface (one memory across CLI, web, and phone — the HTTP facade is in, see docs/cross-surface.md) · 🐧 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.