100% local your machine no cloud no signup

Native Control Center for OpenClaw

Install LocalClaw once. Control your local models, agents, channels and scheduled OpenClaw work from a native macOS dashboard.

Installer $49. No signup. No prompts collected. Runs on your machine.

LocalClaw mascot, orange robot crab
LocalClaw v1.0
LocalClaw logo
LocalClaw
Version 1.0.140 (build 285)
Local LLM
Online
Gateway
openai/gpt-5.4-mini
Healthy
System
CPU 28% · RAM 31.2/32 GB
299.2K
Tokens used
6 requests · 30 days
1 active
Channels
22 available · 1 account
OpenClaw readiness
Ready
5/5 checks passing
Gateway Chat Model Channel Skill
Next best actions
Open OpenClaw Chat
Everything essential is ready.
System Load
CPU38%
RAM31.3 / 32.0 GB
Swap1.85 / 3.00 GB
0 MB
OpenClaw
0 MB
LM Studio
0 MB
Node
Recent activity
Managed skills: /Users/redsun/.openclaw/skills
Workspace: /Users/redsun/.openclaw/workspace
Channel inventory refreshed.
Working now
ChatReady
Channels1 connected · 22 available
Skills18 active · 58 installed
Modelsopenai/gpt-5.4-mini
Automation6 active · 7 jobs
Operations
OpenClaw Chat
Connect channels
Manage models
Add skills

// THE APP

Everything to run OpenClaw locally, in one place

A native macOS control center for the local AI stack you already own: models, agents, channels, schedules and system health.

Control Center

See gateway health, CPU/RAM pressure, tokens used and OpenClaw version before anything breaks.

Models

Switch between Local LLM, Cloud LLM and OAuth LLM without hand-editing config files.

Agents

Create autonomous OpenClaw agents, assign models and keep their local workspace visible.

Cron Jobs

Schedule recurring local tasks and see what will run, when it runs and which agent owns it.

Kanban

Turn local automation into visible work: backlog, ready, doing, review and done.

Chat · Skills · Channels

Use beta dev tools, channel connections and local skills from the same dashboard.

// LOCAL FIT ENGINE

Benchmarks are useful. Fit is what makes local AI work.

LocalClaw ranks models by practical local fit: your memory headroom, context target, use case, quantization and install path. Every recommendation explains why it was picked.

Run the fit check →
01

Hardware fit first

RAM, VRAM, OS and model size decide whether a model feels instant or painful.

Apple M4 · 16 GB → laptop-safe picks

02

Context has a cost

Long context increases KV cache memory, so LocalClaw warns before a model becomes slow.

32K context → extra headroom check

03

Use-case weighting

Coding, chat, reasoning, vision and speed priorities change the shortlist transparently.

Coding → code benchmark + model tags

04

Installability matters

The best model is the one you can actually load locally with the right quantization.

Q5_K_M / Q4_K_M shown upfront

LocalClaw crab logo

LocalClaw

Download it like an app

  • Guided macOS setup for OpenClaw
  • Managed local models and runtime checks
  • Agents, channels, skills and schedules in one UI
  • 100% local: your machine, your data

Manual setup

Hours in the terminal

  • Install dependencies by hand
  • Edit config files and CLI flags
  • Track models, agents and channels separately
  • Debug local runtime issues after the fact

// CATALOGUE

A small sample of what LocalClaw tracks

Frequently Asked Questions

What is LM Studio?

LM Studio is a free desktop application that lets you run Large Language Models (LLMs) locally on your computer. No internet needed, no data sent anywhere. It provides a chat interface similar to ChatGPT but everything runs on YOUR hardware.

What is quantization (Q4, Q5, Q8)?

Quantization is a compression technique that reduces model size while preserving most of the quality. Think of it like JPEG compression for images. Q4 = more compressed (smaller, slightly lower quality), Q8 = less compressed (larger, nearly original quality). Q5_K_M is the sweet spot for most users.

How much RAM do I need to run a local AI model?

Rule of thumb: the model file size plus 2-3 GB for the system. A 5 GB model needs at least 8 GB RAM. On macOS with Apple Silicon, the unified memory makes things more efficient. On Windows/Linux with a GPU, VRAM helps offload the model.

Apple Silicon vs NVIDIA GPU for local AI?

Apple Silicon (M1-M4) uses unified memory, meaning your entire RAM is available for the model. This is incredibly efficient. NVIDIA GPUs are faster for inference but limited by VRAM (typically 8-24 GB). Both are great choices.

Is my data private when using LocalClaw?

Yes. The model finder runs in your browser and does not collect prompts, hardware specs, or selected models. When using LM Studio with recommended models, inference runs locally on your machine with no cloud API calls.

What are the best local AI models in 2026?

For 8 GB RAM: Qwen 3.5 4B or Gemma 4 E4B. For 16 GB: Gemma 4 12B, Qwen 3.5 9B, GLM 4.6 Air 12B or Mistral Small 3.2 24B (tight). For 32 GB+: Gemma 4 31B, Qwen 3 Next 80B/3B MoE or Qwen 3 Coder 30B. For reasoning: Kimi K2 Thinking, DeepSeek V3.2 Exp, or Hermes 4 70B. For coding: MiniMax M2 and Qwen 3 Coder. For vision: Qwen 3 VL 32B or Gemma 4 multimodal.

What is OpenClaw?

OpenClaw is the open-source, self-hosted AI assistant at the heart of the LocalClaw ecosystem. It connects to your local models running in LM Studio or Ollama and provides a unified chat interface on desktop, web, and CLI. It's 100% private — no telemetry, no cloud, no API keys required.

What is free and what is paid?

The LocalClaw web model finder is free: use it to choose the right LLM/TTS model for your hardware. The LocalClaw beta app is the optional native macOS app that simplifies setup, handles activation, supports updates and helps manage your local AI stack. No terminal-first workflow. The current beta lifetime offer is $49 one-time, no subscription, no recurring fees. Your license is valid forever. See pricing →