Lattice AI v3 local-first AI workspace platform with knowledge graph, vector index, hybrid search, agents, and workspace modes
Project description
Lattice AI
Lattice AI v3 — Local-First AI Workspace Platform.
Work across Personal and Organization workspaces with Knowledge Graph, Vector Index, Hybrid Search, Native Chat, agents, files, models, and Basic / Advanced / Admin modes.
Install
Install the local workspace:
pip install ltcai
Add Apple Silicon local model support:
pip install "ltcai[local]"
Install the npm CLI:
npm install -g ltcai
Install the coding extension:
Quick Start
Start the workspace:
LTCAI
Then open:
http://127.0.0.1:4825/app
Development checkout:
npm install
npm run dev
Useful validation commands:
npm run check:python
npm run test:unit
npm run build
What Is Lattice AI?
Lattice AI v3 is a local-first AI workspace platform for people and teams who want their files, models, graph context, retrieval, and agent workflows in one place.
- Primary app shell:
/appis the default product experience with Native Chat, Knowledge Graph, Hybrid Search, Files, Pipeline, Agents, Models, My Computer, Settings, and Admin areas. Legacy/chatremains available as a rollback/debug path. - Local-first AI Workspace: work starts on your machine, with local data and workspace state by default.
- AI Pipeline Platform: plan, execute, review, retry, and replay work across local models, cloud models, tools, files, and generated artifacts.
- Knowledge Graph Platform: documents, images, screenshots, notes, conversations, and decisions become linked entities, relationships, evidence, and reusable context.
- Multi-Agent Workflow Platform: agents hand off structured context, review work, retry with reasons, and keep timelines inspectable.
- Personal / Organization Workspace: move between personal work and team workspaces with role-aware views and Basic / Advanced / Admin modes.
- Vector Index and Hybrid Search: local vector rows are derived from the Knowledge Graph and fused with keyword and graph signals.
- Local Model Management: choose current multimodal local models with source disclosure, hardware-aware recommendations, and cloud fallback options.
- SSO for teams: organization workspaces can be paired with Okta or Microsoft Entra ID patterns for team access.
Why Lattice AI?
Most AI tools split your work across a chat window, a model picker, loose files, and disconnected automations. Lattice AI keeps those parts together:
- files and conversations become graph context;
- graph context feeds pipelines and coding actions;
- model cards disclose country, company, run mode, internet usage, and model identity;
- personal and organization workspaces keep team workflows separate from local work;
- multi-agent workflows leave behind replayable plans, reviews, retries, and outcomes.
v3.0.0 Highlights
Lattice AI v3.0.0 makes /app the primary workspace shell and ships the v3
backend retrieval stack together with the native frontend.
- Native v3 Chat lives inside
/app#/chatand streams through the realPOST /chatbackend while showing friendly setup guidance when no model is loaded. - Knowledge Graph, Vector Index, and Hybrid Search are first-class retrieval surfaces. Hybrid results show keyword, local vector, and graph scores.
- Personal and Organization workspaces, plus Basic / Advanced / Admin modes, are built into the shell.
- Legacy
/chatremains reachable for rollback and debugging. - The default embedding signal is
lattice-local-hash-v1, a deterministic local fallback. It is not described as a production semantic embedding model; future providers may include Ollama, MLX, OpenAI-compatible endpoints, and other local embedding runtimes.
Screenshots
Workspace
Knowledge Graph
AI Pipeline
Admin Dashboard
Mobile Responsive
Knowledge Graph Flow
files / documents / images / screenshots / conversations / decisions
-> multimodal understanding
-> entity and relationship extraction
-> evidence and artifact storage
-> Knowledge Graph update
-> AI pipeline context
-> coding actions / analysis / documents / team workflows
The graph keeps useful workspace context available even when you change models.
v3 Backend Retrieval
The v3 backend adds a local-first retrieval stack that combines the Knowledge Graph, a SQLite vector index, and hybrid result fusion. It preserves existing graph data while adding derived vector rows that can be rebuilt at any time.
Embedding status: the current default is lattice-local-hash-v1, a
deterministic local fallback embedder for indexing and tests. It provides a
stable vector signal without downloads or cloud calls; it is not a production
semantic embedding model. Future provider support may include Ollama, MLX,
OpenAI-compatible providers, and other local embedding runtimes.
Core API contracts:
POST /api/search/hybridGET /api/search/keyword?q=...GET /api/search/vector?q=...GET /api/graphGET /api/graph/node?node_id=...GET /api/graph/relationshipGET /api/index/statusPOST /api/index/rebuild
See docs/V3_BACKEND_ARCHITECTURE.md for the storage model, search model, migration behavior, and API response shape.
Local Model Policy
Lattice AI recommends current-generation multimodal models for local use and keeps local model choices explicit.
| Family | Default role | Example recommendation |
|---|---|---|
| Gemma 4 | Default Google multimodal family | mlx-community/gemma-4-12b-it-4bit |
| Gemma 4 large | Higher-quality local multimodal work | mlx-community/gemma-4-31b-it-4bit |
| Qwen3-VL | Smaller, balanced multimodal options | mlx-community/Qwen3-VL-4B-Instruct-4bit |
| Llama 4 | Meta multimodal option | mlx-community/Llama-4-Scout-17B-16E-Instruct-4bit |
Every recommended model card shows maker country, maker company, run mode, internet requirement, and model name. See MODEL_POLICY.md.
Architecture
Personal / Organization Workspace
-> files, chats, screenshots, model choices, workflow events
-> Knowledge Graph
-> AI Pipeline
-> Multi-Agent Workflow
-> coding actions, documents, analysis, team handoffs
Core areas:
- FastAPI local workspace app
- Knowledge Graph storage and graph APIs
- AI pipeline and workflow designer
- Multi-agent handoff, review, retry, and replay records
- Local model management and model recommendation catalog
- VS Code / Cursor / VSCodium extension surface
- Personal and organization workspace boundaries
Documentation
- ARCHITECTURE.md — workspace, graph, pipeline, and model-management overview
- docs/architecture.md — full architecture reference
- PROJECT_PRINCIPLES.md — product principles
- AI_PHILOSOPHY.md — how AI is used in the workspace
- MODEL_POLICY.md — local model recommendation policy
- KNOWLEDGE_GRAPH.md — graph model and behavior
- docs/MULTI_AGENT_RUNTIME.md — multi-agent workflow runtime
- docs/WORKFLOW_DESIGNER.md — AI pipeline designer
- docs/REALTIME_COLLABORATION.md — realtime workspace events
- docs/ENTERPRISE.md — organization workspaces and SSO
- docs/PLUGIN_SDK.md — plugin SDK
- RELEASE_NOTES.md and docs/CHANGELOG.md
Release history
| Version | Theme |
|---|---|
| 3.0.1 | Release-blocker remediation — provider-backed embeddings (Hash/MLX/Ollama/OpenAI/Custom), unified AgentRuntime boundary, every v3 surface connected or clearly unavailable |
| 3.0.0 | v3 local-first AI workspace platform — /app, Native Chat, Knowledge Graph, Vector Index, Hybrid Search, workspace modes |
| 2.2.7 | Visual system stabilization — cohesive dark/light screens, crisp chat composer, dark graph canvas, Workspace OS polish |
| 2.2.6 | Token-native CSS foundation |
| 2.2.5 | Release hygiene hotfix — dark overlays, modal stack, cache-busting, favicon, and Telegram log masking |
| 2.2.4 | Chat dark-mode completion |
| 2.2.3 | Frontend stability and UX fixes |
| 2.2.2 | Frontend QA stabilization — mobile nav, admin actions, overflow fixes, and expanded visual tests |
| 2.2.1 | Frontend and UX overhaul for responsive workspace, themes, graph UX, admin reflow, and file attachment |
| 2.2.0 | Multimodal-first Knowledge Graph and local model source disclosure |
| 2.1.0 | Multi-agent workflow maturity |
| 2.0.0 | AI pipeline, workflow, and plugin platform foundation |
| 1.7.0 | Graph and collaboration |
| 1.6.0 | Product experience deepening |
License
MIT
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