Lattice AI
Your model is the voice you use today. Your Brain is the asset you keep.
모델은 갈아타도, 내 지식은 내 컴퓨터에 남는 로컬 우선 AI 브레인.
Chat, files, folders, notes, and web pages all flow into one durable knowledge graph on your computer. Any model — local MLX or cloud — can speak with that memory. Nothing leaves your machine without explicit consent.
대화·파일·폴더·웹페이지가 전부 내 컴퓨터 안의 지식 그래프로 쌓이고, 어떤 모델이든 그 기억을 이어받아 대화합니다.
What You Can Do
Chat with a Brain that remembers — every conversation grows durable, source-linked memory  |
See how knowledge connects — a real relationship graph, not a file list  |
Capture anything — files, whole folders, notes, screenshots, web pages  |
Automate with review — agent changes become proposals you approve first  |
Pick a model in one click — recommended local models for your hardware  |
Stay in control — audit, roles, retention in a separate admin surface  |
Watch a file become memory — three named steps, not a pipeline diagram  |
Say how much it may do alone — one dial in plain words; dangerous actions stay blocked either way  |
Why Lattice AI
- Own your memory — knowledge lives in a local SQLite Brain you can back up,
export, inspect, and restore (
.latticebrainencrypted archive). - Model-independent — switch between local MLX models and cloud models without rebuilding context from zero.
- Honest by design — the Brain tells you when retrieval context is limited, when captured pages extracted poorly, and when the vector index is catching up.
- Safe automation — automations are consent-first drafts; edits to existing content always pass through a reviewable proposal with a diff.
매번 AI에게 프로젝트 맥락을 다시 설명하고 있다면, 지식이 여러 서비스에 흩어져 있다면, 그 기억을 특정 회사가 아니라 내가 소유하고 싶다면 — Lattice AI가 그 브레인입니다.
Quick Start
pip install ltcai # or: npm install -g ltcai
LTCAI # then open http://127.0.0.1:4825/app
Apple Silicon local models: pip install "ltcai[local]". Desktop app (Tauri)
ships as a dmg on each GitHub Release.
First-run flow — wake the Brain, pick the owner, load a recommended model:
 |
 |
 |
Screenshot index and capture notes: output/release/v11.1.0/SCREENSHOT_INDEX.md
Current Release
The current release is 11.1.0 — Product Intelligence:
The v9–v11.0 line hardened the foundation — proposal-first trust, honest signals, a 100% line-and-branch test floor. 11.1.0 builds the intelligence layer on top of it: the Brain gets fast at scale, notices things on its own, remembers pictures and recordings, learns who you are, and connects to the tools you already use.
- Fast at scale. A pluggable vector-index layer (brute-force default,
int8 quantized and HNSW opt-in via the
hnswextra) plus a durable background embed queue. Measured on Apple Silicon: hybrid search p50 at 10k vectors went from 299 ms to 10.1 ms, and stays at 43.9 ms at 50k (recall@10 0.987) — the plan's <50 ms target met at 5× the target corpus. Approximate results sayapprox: true; quantized's honest verdict (no RAM win here, ~2.2× slower) is printed, not hidden. - Alive, not just searchable. Contradiction detection now files
review-queue proposals with plain-language resolutions; approving one
stamps the temporal model (
valid_from/valid_to/superseded_by, withas_of(timestamp)slicing). Event-driven synthesis proposes parent concepts, missing links and a proactive Brain Brief after every 25th ingest — every write goes through the proposal path, asserted by tests. - Pictures and recordings are memories. Behind
allow_multimodal(default off, off ⇒ byte-identical): images become first-classImagenodes with OCR text, real captions only when a vision model produced one (the caption-fabricating stub was deleted), separate image vectors with late fusion, and inline thumbnails in the Evidence panel that never bypass the local-file approval gate. Recordings are first-classAudionodes with honest transcription degradation. - It knows you — transparently. A Self-Model subgraph (Self / Preference / Decision / Habit / Relationship) built only from proposals you approve, injected into answer context under a strict token budget, fully listable and deletable. Agents can propose whole-folder reorganizations — structurally incapable of proposing deletions.
- Connected, selectively. An approval-gated Obsidian vault bridge
(wikilinks become edges, idempotent re-runs) and a signed, encrypted
subgraph-share prototype where received knowledge arrives as proposals —
off by default behind
LATTICEAI_BRAIN_NETWORK.
All of it lands with the floor intact: 6,261 tests, 100.00% of 37,590 statements and 10,658 branches, verified on macOS 3.14, a fresh-resolve python 3.11 environment, and a clean linux python:3.14 container.
Release notes: RELEASE.md · Full history: docs/CHANGELOG.md
Expected artifacts for 11.1.0 release must use exact filenames:
dist/ltcai-11.1.0-py3-none-any.whldist/ltcai-11.1.0.tar.gzltcai-11.1.0.tgzdist/ltcai-11.1.0.vsixsrc-tauri/target/release/bundle/dmg/Lattice AI_11.1.0_aarch64.dmg
Do not use wildcard artifact uploads. Package registry publishing remains owner-run.
Architecture At A Glance
FastAPI on localhost is the source of truth; the React/Vite frontend and the
Tauri desktop shell sit on top; the independent lattice_brain package owns
the graph, memory, ingestion, and portability. Local-first by default — cloud
calls, downloads, Telegram, and update checks are opt-in.
See ARCHITECTURE.md for details and
docs/DEVELOPMENT.md for the developer workflow
(npm install && npm run dev, validation via npm run lint,
npm run test:unit, npm run test:visual).
Known Limitations
- External package registries are owner-published and can lag behind GitHub.
- PostgreSQL/pgvector is optional scale/migration tooling. SQLite remains the live local Brain store in 10.3.0.
- Docker, model downloads, cloud model calls, Telegram, Brain Network, and update checks require explicit user action.
- Conversation does not fabricate answers when no model is loaded. Agent and workflow simulation without a loaded LLM is deterministic and LLM-free (it does not call a model) — labeled as such, never presented as autonomous model success.
- Some backend-generated messages (for example the Postgres DSN notice) are produced server-side in English and are shown as-is; server-side i18n is not part of 10.3.0.
Release History
| Version | Theme |
|---|---|
| 11.1.0 | Product Intelligence |
| 11.0.1 | Both Branches |
| 11.0.0 | Full Measure |
| 10.10.0 | Quiet Station |
| 10.9.0 | Never Blocks |
| 10.8.0 | Within Reach |
| 10.7.0 | Plain Surface |
| 10.6.4 | Loud Limits |
| 10.6.3 | Loud Limits |
| 10.6.2 | Ask First |
| 10.6.1 | First Things |
| 10.6.0 | Promoted Panels |
| 10.5.0 | Everyday Words |
| 10.4.0 | Named Ground |
| 10.3.0 | Measured Ground |
| 10.2.0 | Load-Bearing Fixes |
| 10.1.1 | Reachable Boundary |
| 10.1.0 | Hybrid Brain |
| 10.0.1 | One Source of Truth |
| 10.0.0 | Plain Language |
| 9.9.9 | Lean Shell |
| 9.9.8 | Autonomy Dial |
| 9.9.7 | No Gaps Left |
| 9.9.6 | Same Brain Everywhere |
| 9.9.5 | Closed Gaps |
| 9.9.4 | Durable Loops |
| 9.9.3 | Closed Loops |
| 9.9.2 | Artifact Trust |
| 9.9.1 | Clean Foundations |
| 9.9.0 | Fail-Closed Trust |
| 9.8.0 | Honest Knowledge Pipeline |
| 9.7.0 | Proactive Hybrid Brain |
| 9.6.0 | Trusted Agent Loop |
| 9.5.0 | Command Center |
| 9.4.0 | Question-Driven Everyday Automation |
| 9.3.0 | Proactive Brain Intelligence |
| 9.2.0 | Model-Agnostic File Generation |
| 9.1.0 | Code Review Completion & Fail-Closed Runtime |
| 9.0.0 | Code Review Closure & Runtime Cleanup |
Per-release details: RELEASE_NOTES.md
Documentation
- docs/WHY_LATTICE.md — product philosophy
- docs/TRUST_MODEL.md — local-first trust model
- PRIVACY.md — privacy and external communication policy
- FEATURE_STATUS.md — feature status and limitations
- SECURITY.md — security posture
- RELEASE.md — release guide and notes
License
MIT. See LICENSE.
1 maintainer
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