Open-source multi-account manager for Claude CLI enables profile switching

What claude-multi-account does
This open-source CLI tool solves the limitation of Claude CLI storing everything in a single ~/.claude/ directory, which locks users to one account at a time. It provides unlimited isolated profiles with shared settings.
Key features
- Isolated profiles — each account gets its own config directory
- Shared MCP servers & settings — define once, auto-applied to all accounts
- Global CLAUDE.md — instructions and skills that work across every account
- Plugins & marketplace — manage plugins globally or per-account
- Direct launch — run
claude-workorclaude-personaldirectly from terminal - Local backup/restore — timestamped archives with one click
- Cloud backup/restore — sync all profiles to cloud, restore on any machine
- Export/import — transfer profiles between machines as a single token
- Auto dependency detection — installs missing deps on first run
Installation options
Multiple installation methods are available:
npm install -g @ghackk/multi-claudepip install multi-claudebrew install ghackk/tap/multi-claudescoop install multi-claude- One-liner:
curl -fsSL https://raw.githubusercontent.com/ghackk/claude-multi-account/master/install.sh | bash
The tool works on Windows, Linux, macOS, and Termux.
Who it's for
Developers who need to switch between work and personal Claude accounts without logging out each time.
📖 Read the full source: r/ClaudeAI
👀 See Also

Be brief beats caveman plugin in Claude Code compression benchmark
A 24-prompt benchmark shows Claude Code's caveman compression plugin produces the same token counts and quality as simply prepending 'be brief.' — but the plugin's consistent output shape and safety escape rules offer structural advantages.

claude-real-video: Free Tool to Make Claude Watch Videos with Perception Layer
claude-real-video adds a local perception layer so Claude sees camera moves, pacing, gestures, and voice emotion. Demo watches NVIDIA GTC 2026 keynote and narrates screen action. Free version available.

Testing δ-Mem on Apple Silicon: MLX Implementation and Benchmarks
δ-mem paper implemented via mlx on a MacMini 64GB shows mixed but promising local benchmarks, with gains in realistic replay scenarios.

Building a $6.4k Local LLM Server: TCO Breakdown vs API Costs
A developer shares a detailed total cost of ownership for a 4x MI100 local server running llama.cpp, compared to API equivalents including OpenAI and Z.AI coding plans.