安装

添加到你的 Claude Code skills 目录：

# Add to your Claude Code skills
git clone https://github.com/eclaire-labs/eclaire

安全报告

已验证

上次扫描：—

{
  "status": "PASSED",
  "issues": []
}

README.md

ECLAIRE

Name: eclaire
Author: eclaire-labs

Privacy-focused AI assistant for your data

_{Click to watch on YouTube}

Features • Installation • Selecting Models • Architecture • Development • Contributing • Docs • API

⚠️ 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:

Tailscale or other private networks/VPNs

Cloudflare Tunnels

A reverse proxy with authentication

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 APIs
Background Workers: Job processing and scheduling (runs unified with backend by default)
Data Layer: PostgreSQL (recommended) or SQLite for persistence; database or Redis for job queue
AI Services: Local LLM backends (llama.cpp, MLX, LM Studio, etc.) for inference; Docling for document processing
External Integrations: GitHub and Reddit APIs for bookmark fetching

Roadmap

Support for more data sources and integrations
More robust full text indexing and search
Better extensibility and plugin system
Improved AI capabilities and model support
Evals for models and content pipelines
More hardening and security
Top requests from the community

Development

For contributors who want to build from source.

Additional Prerequisites

Beyond Docker and an LLM server, you'll need:

Node.js ≥ 24 with corepack enabled
pnpm (managed via corepack)

Document/image processing tools:

macOS:

brew install --cask libreoffice
brew install poppler graphicsmagick imagemagick ghostscript libheif

Ubuntu/Debian:

sudo apt-get install libreoffice poppler-utils graphicsmagick imagemagick ghostscript libheif-examples

Setup

git clone https://github.com/eclaire-labs/eclaire.git
cd eclaire
corepack enable
pnpm setup:dev
pnpm dev

Access the application:

Frontend: http://localhost:3000
Backend: http://localhost:3001/health

Building Docker Locally

To build and test custom Docker images:

./scripts/build.sh
docker compose -f compose.yaml -f compose.dev.yaml -f compose.local.yaml up -d

Contributing

We 💙 contributions! Please read the Contributing Guide.

Security

See SECURITY.md for our policy.

Telemetry

There should be no telemetry in the Eclaire code although 3rd party dependencies may have. If you find an instance where that is the case, let us know.

Community & Support

Issues: GitHub Issues

常见问题

What is eclaire?⌄

eclaire is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by eclaire-labs. Local-first, open-source AI assistant for your data. Unify tasks, notes, docs, photos, and bookmarks. Private, self-hosted, and extensible via APIs. It has 898 GitHub stars.

Is eclaire safe to use?⌄

Yes. eclaire passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.

How do I install eclaire?⌄

Clone the repository with "git clone https://github.com/eclaire-labs/eclaire" and add it to your Claude Code skills directory (see the Installation section above).

What programming language is eclaire written in?⌄

eclaire is primarily written in TypeScript. It is open-source under eclaire-labs on GitHub, so you can review or fork the full source.

Are there alternatives to eclaire?⌄

Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh eclaire against similar tools.

eclaire

安装

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