⚠️ Important Notices
[!IMPORTANT]
Pre-release / Development Status
Eclaire is currently in pre-release and under active development.
Expect frequent updates, breaking changes, and evolving APIs/configuration.
If you deploy it, please backup your data regularly and review release notes carefully before upgrading.
[!WARNING]
Security Warning
Do NOT expose Eclaire directly to the public internet.
This project is designed to be self-hosted with privacy and security in mind, but it is not hardened for direct exposure.
We strongly recommend placing it behind additional security layers such as:
Description
Eclaire is a local-first, open-source AI that organizes, answers, and automates across tasks, notes, documents, photos, bookmarks and more.
There are are lot of existing frameworks and libraries enabling various AI capabilities; few deliver a complete product allowing users to get things done. Eclaire assembles proven building blocks into a cohesive, privacy-preserving solution you can run yourself.
With AI gaining rapid adoption, there is a growing need for alternatives to closed ecosystems and hosted models, especially for personal, private, or otherwise sensitive data.
- Self-hosted - runs entirely on your hardware with local models and data storage
- Unified data - one place where AI can see and connect everything
- AI-powered - content understanding, search, classification, OCR, and automation
- Open source - transparent, extensible, and community-driven
What's New in v0.6.0
- Unified deployment: frontend + backend + workers can run in a single container
- Simplified Self-Hosting - new one-command
setup.sh flow, plus a streamlined compose.yaml
- Better AI Support - New vision models (including Qwen3-VL), llama.cpp router, improved MLX support.
- Modern Frontend - Migrated from Next.js to Vite + TanStack Router
- SQLite Support: Full SQLite database support alongside Postgres for simpler workloads
- Database Queue Mode: Postgres or SQLite for job processing with no external dependencies
- New Admin CLI - Manage your instance from the command line
See the CHANGELOG for full details.
Features
- Cross-platform: macOS, Linux and Windows.
- Private by default: By default all AI models run locally, all data is stored locally.
- Unified data: Manage across tasks, notes, documents, photos, bookmarks and more.
- AI conversations: chat with context from your content; see sources for answers; supports streaming and thinking tokens.
- AI tool calling: The assistant has tools to search data, open content, resolve tasks, add comments, create notes, and more
- Flexible deployment: Run as a single unified container or separate services. SQLite or Postgres. Database queue or Redis. (See Architecture section below.)
- Full API: OpenAI-compatible REST endpoints with session tokens or API keys. API Docs
- Model backends: works with llama.cpp, vLLM, mlx-lm/mlx-vlm, LM Studio, Ollama, and more via the standard OpenAI-compatible API. (See Selecting Models.)
- Model support: text and vision models from Qwen, Gemma, DeepSeek, Mistral, Kimi, and others. (See Selecting Models.)
- Storage: all assets (uploaded or generated) live in Postgres or file/object storage.
- Integrations: Telegram (more channels coming).
- Documents: PDF, DOC/DOCX, PPT/PPTX, XLS/XLSX, ODT/ODP/ODS, MD, TXT, RTF, Pages, Numbers, Keynote, HTML, CSV, and more.
- Photos/Images: JPG/JPEG, PNG, SVG, WebP, HEIC/HEIF, AVIF, GIF, BMP, TIFF, and more.
- Tasks: track user tasks or assign tasks for the AI assistant to complete; the assistant add comments to tasks or write to separate docs.
- Notes: plain text or Markdown format. Links to other assets.
- Bookmarks: Fetches bookmarks and creates PDF, Readable and LLM friendly versions. Special handling for Github and Reddit APIs and metadata.
- Organization: Tags, pin, flag, due dates, etc. across all asset types.
- Hardware acceleration: takes advantage of Apple MLX, NVIDIA CUDA, and other platform-specific optimizations.
- Mobile & PWA: installable PWA; iOS & Apple Watch via Shortcuts; Android via Tasker/MacroDroid.
Sample use cases
- Dictate notes using Apple Watch (or other smartwatch).
- Save bookmarks to read later; generate clean “readable” and PDF versions.
- Create readable and PDF versions of websites
- Extract text from photos and document images (OCR).
- Bulk-convert photos from HEIC to JPG.
- Analyze, categorize, and search documents and photos with AI.
- Create LLM-friendly text/Markdown versions of documents and bookmarks.
- Save interesting content (web pages, photos, documents) from phone, tablet, or desktop.
- Ask AI to find or summarize information across your data.
- Schedule automations (e.g., “Every Monday morning, summarize my tasks for the week.”).
- Chat with AI from web, mobile, Telegram, and other channels.
- Process sensitive information (bank, health, etc.) privately on local models.
- De-clutter your desktop by bulk-uploading and letting AI sort and tag.
- Migrate data from Google/Apple and other vendors into an open, self-hosted platform under your control.
Screenshots
Installation
Prerequisites
- Docker and Docker Compose
- A local LLM server - llama.cpp recommended
Quick Start
mkdir eclaire && cd eclaire
curl -fsSL https://raw.githubusercontent.com/eclaire-labs/eclaire/main/setup.sh | sh
The script will:
- Download configuration files
- Generate secrets automatically
- Initialize the database (PostgreSQL)
After setup completes:
# 1. Start your LLM servers (in separate terminals)
# Models download automatically on first run if not already cached
llama-server -hf unsloth/Qwen3-14B-GGUF:Q4_K_XL --ctx-size 16384 --port 11500
llama-server -hf unsloth/gemma-3-4b-it-qat-GGUF:Q4_K_XL --ctx-size 16384 --port 11501
# 2. Start Eclaire
docker compose up -d
Complete the setup wizard — open http://localhost:3000 in your browser, or run ./eclaire onboard for CLI setup. The wizard guides you through admin account creation, AI provider configuration, and model selection.
Configuration
Configuration lives in two places:
.env - secrets, database settings, ports- Admin UI / CLI - LLM providers, models, and active selection (stored in DB)
Stopping
docker compose down
Selecting Models
Eclaire uses AI models for two purposes:
- Backend: Powers the chat assistant (requires good tool calling)
- Workers: Processes documents and images (requires vision capability)
Apple Silicon: Mac users can leverage MLX for optimized local inference. See the configuration guide for details.
Use the CLI to manage models:
./eclaire model list
See AI Model Configuration for detailed setup and model recommendations.
Architecture
Eclaire follows a modular architecture with clear separation between the frontend, backend API, background workers, and data layers.
📋 View detailed architecture diagram →
Key Components
- Frontend: Vite web application with React 19, TanStack Router, and Radix UI
- Backend API: Node.js/Hono server with REST AP