eclaire

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:

1. Download configuration files
2. Generate secrets automatically
3. 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