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.
  2. Create or open a local profile.
  3. Let Lattice explain what this computer can run.
  4. Start with the recommended model, or skip and choose later.
  5. Talk to your Brain.
  6. Watch memories, topics, relationships, and graph structure emerge from real use.
  7. Back up, inspect, export, or restore the Brain when you need ownership actions.

Living Brain Flow

1. 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

2. Environment Analysis

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

Environment Analysis

3. Recommended Models

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

Recommended Models

4. Install And Load

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

Install and Load

5. Brain Chat

Talk normally. Useful decisions and context become memory, then appear later as topics, relationships, and graph structure.

Brain Chat Home

6. 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:

v6.3.1 Living Brain walkthrough

Screenshot index and capture notes: output/release/v6.3.1/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 Preparation

The current development target is 6.4.0 Digital Brain Quality Hardening:

  • lattice_brain.quality adds a non-destructive Brain quality layer for embedding fallback labelling, drift/re-index planning, BM25 lexical scoring, hybrid fusion, reranker fallback contracts, memory candidate quality, graph-edge confidence/evidence scoring, structured context guardrails, and retrieval benchmark metrics.
  • Graph/search API reads now carry workspace scope through graph, node, neighborhood, relationship, keyword, vector, graph, and hybrid search paths.
  • Memory Manager prune, compact, and clear operations are scoped to the caller's workspace and owner boundary; unscoped graph clearing from Memory Manager is blocked until a workspace-safe graph delete path exists.
  • Digital Brain quality documentation now records the 6.4.0 baseline, risk register, validation checklist, and intentionally deferred work.
  • The release remains local-first: no automatic web/email/calendar ingestion, no package publishing, no production deployment, and no external reranker or embedding API use without explicit opt-in.

Expected artifacts for 6.4.0 release must use exact filenames:

  • dist/ltcai-6.4.0-py3-none-any.whl
  • dist/ltcai-6.4.0.tar.gz
  • ltcai-6.4.0.tgz
  • dist/ltcai-6.4.0.vsix
  • src-tauri/target/release/bundle/dmg/Lattice AI_6.4.0_aarch64.dmg

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

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
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-6.4.0.tar.gz (2.4 MB view details)

Uploaded Source

Built Distribution

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

ltcai-6.4.0-py3-none-any.whl (2.3 MB view details)

Uploaded Python 3

File details

Details for the file ltcai-6.4.0.tar.gz.

File metadata

  • Download URL: ltcai-6.4.0.tar.gz
  • Upload date:
  • Size: 2.4 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.14.5

File hashes

Hashes for ltcai-6.4.0.tar.gz
Algorithm Hash digest
SHA256 f4ce31de2b388750fa01ceba216829f0f7f8f13af0e41c01198d4d1faa462257
MD5 126d9eb7cf37ddf1ec22c5f9f5e43bf0
BLAKE2b-256 3eea7ec2c25b1f99f5c7442c13de716a118a98e1f0ec40ec2a32f1b77a86f0f3

See more details on using hashes here.

File details

Details for the file ltcai-6.4.0-py3-none-any.whl.

File metadata

  • Download URL: ltcai-6.4.0-py3-none-any.whl
  • Upload date:
  • Size: 2.3 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.14.5

File hashes

Hashes for ltcai-6.4.0-py3-none-any.whl
Algorithm Hash digest
SHA256 8564610a4ae3a2984a013af3de15eeeceb7fab94e624b9dc846a34a841ef457a
MD5 592fa14ee3424ffb5ec23184f82a6ff1
BLAKE2b-256 ddc2c97ec75aeed68b114b2459fb3f09aede6ce781e6e5d73b4d6ec480c8baf3

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