Skip to main content

Lattice AI — local-first Digital Brain that keeps your knowledge durable across any AI model.

Project description

Lattice AI

Lattice AI is a local-first Digital Brain that keeps your knowledge durable across any AI model.

Lattice AI는 모델이 바뀌어도 내 지식과 맥락을 보존하는 로컬 우선 AI 브레인입니다.

Your model is the voice you use today. Your Brain is the asset you keep. Lattice AI preserves conversations, documents, decisions, project context, relationships, and workflows on your computer by default. Cloud models, model downloads, update checks, and other external communication happen only after explicit consent.

It is not a ChatGPT clone, a model launcher, a graph database, or a note app. It is a private AI memory layer wrapped in a Living Brain experience.

PyPI Version npm Version VS Code Marketplace Version Open VSX Version CI Status License

Why You Need It

You need Lattice AI when:

  • you ask different AI models about the same project and lose the context each time;
  • your decisions are scattered across chats, notes, PDFs, folders, and tools;
  • you want to switch models without rebuilding memory from zero;
  • you want your AI Brain to stay on your computer by default;
  • you want backup, restore, inspect, and export paths for your Brain.

이런 사람에게 필요합니다:

  • 매번 AI를 바꿀 때마다 프로젝트 맥락을 다시 설명하는 사람
  • 문서, 대화, 결정, 파일이 여기저기 흩어져 있는 사람
  • 내 지식을 특정 AI 서비스 안에 묶어두고 싶지 않은 사람
  • 로컬에 저장되는 개인 AI 브레인을 원하는 사람

What You Can Do

  • Chat with a Brain that remembers useful context instead of treating every session as disposable.
  • Add documents, local folders, notes, screenshots, and conversations with source-aware memory.
  • See recent memories, older memories, topics, relationships, and the full knowledge graph when you want deeper structure.
  • Create consent-first Brain automation drafts for memory digests, project reviews, and follow-up suggestions before any schedule is enabled.
  • Use a recommended local model without learning model internals first.
  • Keep advanced controls, audit logs, roles, and retention in a separate Admin surface.
  • Export or back up your Brain as an encrypted .latticebrain archive.

One-Minute Flow

  1. Launch the app and wake the Brain.
  2. Create or open a local profile.
  3. Let Lattice explain what this computer can run.
  4. Start with the recommended model as the Brain's voice, or skip and choose later.
  5. Talk to your Brain.
  6. Use the memory rings to move from current context to the full knowledge graph.
  7. Back up, inspect, export, or restore the Brain when you need ownership actions.

Living Brain Flow

1. Wake Brain

The first screen makes the Brain the product. It explains the three-step path: confirm owner, check the computer, choose the Brain voice.

2. Login

Choose the owner of the Brain. The profile is not a SaaS account by default; it is the local identity for the knowledge you keep.

Login

3. Environment Analysis

See what kind of local AI experience this computer can support before choosing a model.

Environment Analysis

4. Recommended Models

Start with a short list: safest recommendation, faster model, stronger model. Advanced details stay available without overwhelming first-time users.

Recommended Models

5. Install And Load

Download and load only after consent. Lattice explains model size, local execution, and network use before work starts.

Install and Load

6. Brain Chat

Talk normally. Useful decisions and context become memory, then appear later as topics, relationships, graph structure, and the concentric memory rings around the Brain.

Brain Chat Home

7. Review Center

Automation results are staged for review before they become durable decisions. Snooze, unsnooze, run now, approve, and dismiss actions stay explicit.

Review Center

Brain Depths

The user travels inward from everyday memory to deeper structure:

Level User name What the user gets
Level 1 Now memory The living Brain presence and current conversation context
Level 2 Older memory Durable memories with source-aware recall
Level 3 Topics Recurring themes across chats and documents
Level 4 Relationships How decisions, people, files, and ideas connect
Level 5 Full knowledge graph Nodes, edges, search, and focused detail for advanced exploration

Walkthrough:

v7.6.0 Living Brain walkthrough

Screenshot index and capture notes: output/release/v7.6.0/SCREENSHOT_INDEX.md

Install

Run from Python:

pip install ltcai
LTCAI

Run from npm:

npm install -g ltcai
ltcai

Open the local app:

http://127.0.0.1:4825/app

Apple Silicon local model extras:

pip install "ltcai[local]"

Architecture At A Glance

  • Product category: local-first Digital Brain.
  • Core capability: private AI memory layer for conversations, documents, decisions, relationships, workflows, and project context.
  • UX metaphor: Living Brain.
  • Desktop shell: Tauri 2 starts a localhost sidecar.
  • Frontend: React, TypeScript, Vite, TanStack Query, Zustand, Cytoscape.js, React Flow, and generated OpenAPI types.
  • Backend: FastAPI on localhost is the UI source of truth.
  • Brain Core: independent lattice_brain package for graph, memory, context, conversations, ingestion, runtime, workflow, storage, and portability.
  • Storage: SQLite default; PostgreSQL/pgvector is optional scale mode.
  • Portability: encrypted .latticebrain archives plus backup, restore, inspect, verify, import dry-run, and confirmed restore/import flows.
  • Trust boundary: local-first by default; cloud calls, downloads, Telegram, Brain Network, Docker/Postgres setup, and update checks are opt-in.
  • Admin separation: normal Brain use stays separate from users, audit logs, policies, security events, retention, and index rebuilds.

See ARCHITECTURE.md for the current architecture.

Local Development

npm install
npm run dev

Main validation set:

npm run check:python
node scripts/run_python.mjs -m ruff check .
npm run lint
npm run typecheck
npm run test:unit
npm run test:integration
npm run test:visual
npm run desktop:tauri:check
npm run docs:check-links

See docs/DEVELOPMENT.md for developer workflow details.

Current Release

The current release is 7.6.0 Brain-Centered UX & Architecture Closure:

  • First-run onboarding now starts with a dedicated Wake Brain surface before owner/profile setup, so the first impression is the local Brain rather than a generic account or model wizard.
  • Brain Home now includes concentric memory rings around the Living Brain plus direct depth controls, letting users move from Now to Memory, Topics, Relationships, and the full graph.
  • Architecture review closure is machine-checkable through the 7.6 readiness contract covering AgentRuntime, ToolRegistry, central Config, server decomposition, Knowledge Graph hardening, and Brain UX.
  • The two local review files (review.md and ux-brain-simplification-review.md) are incorporated into this release line: architecture gaps are covered by explicit boundaries/tests, and UX gaps are closed by the Brain-first wake flow plus ring-based progressive disclosure.

Expected artifacts for 7.6.0 release must use exact filenames:

  • dist/ltcai-7.6.0-py3-none-any.whl
  • dist/ltcai-7.6.0.tar.gz
  • ltcai-7.6.0.tgz
  • dist/ltcai-7.6.0.vsix
  • src-tauri/target/release/bundle/dmg/Lattice AI_7.6.0_aarch64.dmg

Do not upload dist/*. Package registry publishing remains owner-run.

See docs/ROADMAP_RECOMMENDATIONS.md for the strategic roadmap slices applied through 7.6.0 and the follow-up tracks.

Known Limitations

  • External package registries are owner-published and can lag behind GitHub.
  • PostgreSQL/pgvector is optional scale mode. SQLite is the default local Brain.
  • 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/workflow simulation without a loaded LLM is deterministic and does not call a model; it is labeled as LLM-free/model-free rather than presented as autonomous model success.

Release History

Version Theme
7.6.0 Brain-Centered UX & Architecture Closure: Wake Brain first-run surface, concentric memory rings with direct depth controls, and machine-checkable closure of the two architecture/UX review files
7.5.0 Runtime Debt Burn-down & Release Risk Cleanup: API consumers get normalized contract views, retrieval quality uses a 250+ record corpus fixture, stale artifact risk is removed, npm audit is clean, and Tauri is updated past the old block warning
7.4.0 Runtime Contract Convergence & Corpus Retrieval: agent/workflow/audit/realtime records share the agent-run-contract/v1 family, and retrieval quality gates run against a real corpus-scale fixture
7.3.0 Runtime Contract & Retrieval Quality: shared agent-run contract across runtimes plus deterministic hybrid recall/ranking regression gates
7.2.0 Runtime Trust Baseline: agent run preview/readiness, simulation-mode guardrails, live ToolRegistry manifest/diagnostics, and tests for dispatch/governance/catalog drift
7.1.0 Brain Usability Completion: clearer first-run onboarding, ingestion progress/emergence, richer graph controls, inline answer proof, workspace/profile/admin discovery, empty/error/consent feedback, and VS Code sync status
7.0.0 Brain Productization Loop: first-screen ingestion for files/folders/notes/web, answer-level memory proof and source citations, model-continuity demo flow, five-minute first-run loop, and recall/KG quality eval in CI
6.7.0 Brain IA Cleanup: reachable rich pages, separated product routes vs compatibility aliases, shared Brain shell navigation, and lazy-loaded rich pages
6.6.0 Brain Proof Runtime: backend-owned Brain proof API, model-continuity wiring, first-screen proof that saved context can be recalled across model changes, and direct Brain Home document upload
6.5.0 Brain Experience Readiness: Brain readiness signal, depth progress rail, source-aware memory-save feedback, and visual coverage for the first-memory loop
6.4.0 Digital Brain Quality Hardening: workspace-scoped graph/search/memory reads and mutations, Brain quality primitives, structured context guardrails, and retrieval benchmark coverage
6.3.1 Access Runtime / i18n Follow-up: app-factory access-control extraction, focused access runtime tests, and Capture/Review Center i18n coverage
6.3.0 Product Hardening Completion: Brain archive/provenance/ingestion UX polish, Review Center Run Now contract hardening, local model runtime status, app-factory review wiring, shim smoke, i18n guard, and exact release artifacts
6.2.0 Product Decomposition / Release Smoke Automation: App and ProductFlow feature extraction, legacy root shim shrink, model download consent UX, typed router contexts, localized smoke tests, and wheel/npm/static/Tauri release smoke
6.1.0 Product Hardening / Digital Brain Completion: local Brain entry without a loaded model, Brain memory/backup loop, Review Center semantics, ToolRegistry authorization boundary, and root CLI/runtime compatibility
6.0.0 Product Reset / Review Center Completion: Snoozed filter, Unsnooze, OpenAPI-derived Review typing, Review feature extraction, v6 docs and scorecard
5.6.0 Brain Automation Review Center: workspace-scoped automation review inbox, source-aware provenance, guarded approve/dismiss/snooze/run_now actions, and Act Review tab
5.5.0 Release Coordination: synchronized package/runtime/static versions and release docs for the 5.5.0 line while preserving v5.4.0 Brain Automation Scheduler behavior
5.4.0 Brain Automation Scheduler: consent-first recipe drafts (Daily/Weekly/Follow-up), TriggerService with dedup/LATTICE_TZ/degraded, runtime graph cleanup, E2E scenarios
5.3.0 Product Clarity and Runtime Cleanup: user-first README/onboarding, unified Digital Brain identity, legacy compatibility map, and app factory runtime seams
5.2.0 User-Focused Model Transformation: structured model capability registry, HF verification transparency, model recommendation UX, and workspace-scoped marketplace state
5.1.0 Product Trust & Clarity Release: clarifies the private AI memory-layer promise, hardens CSP/secret/auto-read/download gates, adds trust/privacy docs, and refreshes evidence
5.0.0 Multilingual Brain Foundation Release: adds persisted Korean/English language choice across first-run onboarding, Brain home, graph exploration, and Admin Console
4.7.2 Intuitive Brain UX Release: safer login, one-click recommended setup, direct Brain views, memory-save feedback, and exact artifacts
4.7.0 Admin Separation Release: adds the separate Admin Console for users/logs/security/Brain operations
4.6.1 Living Brain Release Refresh: publishable version bump after v4.6.0 PyPI immutability
4.6.0 Living Brain Experience: made Brain plus conversation the home product
4.5.1 Product Reimagining RC: reset shell, navigation, onboarding, and visual system
4.5.0 Product Experience Recovery RC: restored first-run setup, workspace/model onboarding, and graph discoverability

Documentation

License

MIT. See LICENSE.

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

ltcai-7.6.0.tar.gz (3.0 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

ltcai-7.6.0-py3-none-any.whl (2.9 MB view details)

Uploaded Python 3

File details

Details for the file ltcai-7.6.0.tar.gz.

File metadata

  • Download URL: ltcai-7.6.0.tar.gz
  • Upload date:
  • Size: 3.0 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.14.5

File hashes

Hashes for ltcai-7.6.0.tar.gz
Algorithm Hash digest
SHA256 485a9d934e66563854fbee4820189356fb810d3b315ce9ce674e2c6e219f5b2c
MD5 13f522d936b214064ea3479563d69e1c
BLAKE2b-256 77dc919009c3dab82f337bad3c7d75f5d2497364d2f4b9c9eed97f1d9511b4f4

See more details on using hashes here.

File details

Details for the file ltcai-7.6.0-py3-none-any.whl.

File metadata

  • Download URL: ltcai-7.6.0-py3-none-any.whl
  • Upload date:
  • Size: 2.9 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.14.5

File hashes

Hashes for ltcai-7.6.0-py3-none-any.whl
Algorithm Hash digest
SHA256 3aa59240ac3443cea5028970a47a1daa70a77f63ab36f389c4d44b80a182b85b
MD5 a1129f0b0e4a9698e7826ce853c2e857
BLAKE2b-256 34185c7a574f01ae207c3e8833f5c44d76d4d04aeae055075196a14b35d4ce02

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page