llm-wiki-memory

Install

Paste this one-liner into your AI coding agent — it covers both a fresh install and an update:

Set up llm-wiki-memory in this project: fetch https://raw.githubusercontent.com/ctxr-dev/llm-wiki-memory/main/AI-INSTALL-PROMPT.md and follow it EXACTLY (it covers fresh install and update; if already installed, the same file is local at @.llm-wiki-memory/src/AI-INSTALL-PROMPT.md).

Or run it yourself — macOS / Linux:

git clone https://github.com/ctxr-dev/llm-wiki-memory ./.llm-wiki-memory/src
./.llm-wiki-memory/src/bootstrap.sh                    # add --commit-memory to git-track the wiki (you commit it)
./.llm-wiki-memory/src/bootstrap.sh --schedule hourly  # recommended: hourly cron / launchd

The --schedule hourly job is recommended, not cosmetic: it is what runs the gradual embedding warm in the background. Without it (and without a running webapp daemon) nothing warms ahead of time — recall still self-heals, but each cold note is embedded inside the first search that touches it.

Windows (PowerShell — the native installer, same flags): bootstrap.ps1 / -CommitMemory / -Schedule hourly.

The bootstrap is idempotent — re-running preserves your .env and rule files.

Full install guide — what bootstrap does (8 steps), Windows prerequisites, updating, non-Claude client registration, and shared team-wiki setup → docs/install.md.

What you get

Session to session, your assistant carries your context forward on its own, you stay in control of what gets saved, and everything lives on your machine. How each moment happens: Automatic = no action from you · Agent-led = in its normal flow · Asks first = saves only on your explicit yes · Background = offline housekeeping.

When you…	What you get	How
Open a session	It opens already knowing where you left off — a short briefing with your recent notes, in-progress plans + checkbox progress, and the wiki leaves matching your git branch.	Automatic
Start a real task	Before working it recalls lessons from similar past work and applies them silently (applied lesson: <title>), so old mistakes don’t repeat.	Agent-led
Say “remember this”	Saved as a plain-Markdown leaf in your local wiki (e.g. knowledge/infra/decision/…md), versioned in git and shared with every AI tool on your machine — not a scratchpad that vanishes.	Agent-led
Correct it / “save that as a lesson”	It proposes one lesson at a time and saves nothing until you say yes. One approval covers one lesson.	Asks first
Approve a plan	Captured as a tracked <slug>.plan.md with checkboxes and status, so progress survives across sessions.	Automatic
End or compact a session	The conversation is distilled into dated daily/ notes; a later step folds those into durable knowledge and lessons.	Automatic
Enable the optional schedule	Offline, it merges near-duplicate notes and archives stale ones — never a hard delete, always reversible. Off by default.	Background

The Automatic rows are hooks in Claude Code; every other MCP client (Cursor, Codex, Claude Desktop) does the same steps by following the rules bundled at install, and gets the same MCP tools. The “asks first” consent holds on every client.

Why a wiki instead of RAG

RAG memory stacks are powerful but heavy: a vector database, a container, an embedding service, ongoing ops. For small and medium projects that overhead is rarely worth it, yet you still want the agent to remember everything and improve across sessions. llm-wiki-memory gives you that loop with a local hosted wiki as the substrate: every category is a nested tree of plain-Markdown leaves (never a flat pile), git history and validation come free, the tree stays human-readable, and recall runs on local embeddings — nothing leaves your machine.

Highlights

Everything lives in a local .llm-wiki-memory/ folder — no vector DB, no container, no cloud.

Every memory is a markdown leaf with full git history; your project repo is never touched — unless you install a shared team wiki.

Self-improvement lessons save only with your explicit consent, one approval per lesson. → write-gate.md

Long transcripts are chunked and distilled in pieces; a failed chunk is stashed and retried with no data loss. → capture.md

An opt-in offline pass dedupes near-identical notes and refreshes stale ones — reversible, never a hard delete. → consolidate.md

Every save is compared to the corpus first, using the same calibrated thresholds: a near-certain duplicate is refused and names the existing leaf, a near neighbour is reported, everything else saves silently. No pre-save search for the agent to run, and it fails open if the embedder is down.

Transformer embeddings rank queries on-device (default onnx-community/embeddinggemma-300m-ONNX); nothing leaves your machine. → embeddings.md

Every atom carries an apply-strength — P0 (guardrail) / P1 (default) / P2 (contextual). Relevance ranks first; priority only breaks near-ties.

Paste one prompt or run one script. Idempotent.

A leaf can carry a real diagram, not a screenshot: 29 diagram types render to inline SVG that themes with the page and self-checks its own geometry. → diagrams-examples.md

_{Every diagram type, drawn by the engine itself — browse the live gallery (full screen, dark mode) or read the examples page. The renderer refuses a diagram whose labels collide, whose runs cut through unrelated boxes, or whose content falls outside the frame.}

Works with your agent

MCP client	Hooks (Claude Code only)	MCP tools	Write-gate
Claude Code	✅ session-start / pre·post-compact / session-end / exit-plan-mode / pre-tool-use	✅	instructions + hook + server
Cursor · Codex · Claude Desktop · any MCP client	✗	✅	instructions + server

Hook-driven auto-capture is Claude Code only; every other client gets the same MCP tools and the same discipline. Every tool takes a required scopes (the directories in play) and every write a target (which wiki to write into) — see docs/mcp-tools.md. The LLM provider used for capture / compile / consolidate is set in .env, independent of the client — claude / codex / cursor-agent CLIs, anthropic / openai / openai-compatible (ollama, vLLM, lm-studio, litellm) APIs, or mock. See docs/configuration.md.

Documentation

Guide	What’s in it
How it works	The write / read / offline flows, with diagrams.
MCP tools	The tool surface, required scopes + target, read-only CLI, cloud-sync caveat.
Memory write-gate	The three-layer read-freely / write-gated model + audit ledger.
Capture pipeline	Chunked, recoverable distillation; audit frontmatter; redistill.
Consolidate	The opt-in offline refinement pass — every pass, verdict, and knob.
Embeddings	On-device ranking, the vector cache, model choice.
Configuration	.env + settings.yaml reference; embedding-model guide.
Manual commands	The cli.mjs surface + architecture responsibility matrix.
Private brain & shared team wikis	The scope chain, shared install/adoption, ranking, team caveats.
Architecture	Per-concern split (this package vs the underlying engine).
Install (full)	Bootstrap steps, clients, shared setup, updating.

Testing

npm test           # unit suite
npm run test:e2e   # full lifecycle against the real skill-llm-wiki CLI (LLM stubbed)

1961 tests (1797 unit + 164 e2e). The e2e suite builds a wiki from scratch in a temp directory and asserts genesis, daily capture, lesson / knowledge / plan / investigation absorption, compile promotion + dedup, recall, tree-growth integrity, and idempotency against the real skill-llm-wiki CLI with mocked LLM responses.

Requirements

Node 22.18 or newer (for import.meta.main), and git. No Docker, no Python. The embedding model downloads on first run (~219 MB across 5 files: 197 MB of q4 weights plus a 20 MB tokenizer), then runs fully offline. cli.mjs init prefetches it so the wait lands in setup rather than your first recall (set embed.backend: lexical in settings.yaml to skip it entirely).

Model (embed.model in settings.yaml)	Dim	Window	Download	License
onnx-community/embeddinggemma-300m-ONNX — default	768	2048	~219 MB (q4 weights + tokenizer)	Gemma Terms of Use
Xenova/bge-large-en-v1.5 — previous default	1024	512	~340 MB (q8)	MIT
Xenova/bge-base-en-v1.5	768	512	~110 MB	MIT
Xenova/bge-small-en-v1.5	384	512	~35 MB	MIT

A model change re-embeds the vector caches automatically — see docs/embeddings.md.

License

MIT