LocalClaw
Local-first AI agent optimized for open-source models. Privacy-focused with offline capability. Run AI completely on your own hardware.
Local-First Privacy-Focused AI Agent
LocalClaw is designed for users who prioritize privacy and want to use open-source AI models locally without cloud dependencies. Everything runs on your own hardware - your data never leaves your machine.
Philosophy: "Your data, your hardware, your control" - Complete privacy and offline capability.---
Why LocalClaw?
Complete Privacy
In an era where data privacy is increasingly concerning, LocalClaw takes a different approach:
Cloud-based AI Agents:
Your Data ?Internet ?Cloud Server ?AI Processing ?Response
⚠️ Data leaves your control
LocalClaw:
Your Data ?Local AI ?Response
?Data never leaves your machineWhat This Means:
- ?No data sent to cloud providers
- ?No third-party access to your conversations
- ?No data retention policies to worry about
- ?Full compliance with privacy regulations
Offline Capability
LocalClaw works without internet:
┌─────────────────────────────────────?? LocalClaw Offline Mode ?├─────────────────────────────────────?? ?? ?No internet required ?? ?Works on air-gapped systems ?? ?No cloud service dependencies ?? ?Perfect for remote locations ?? ?└─────────────────────────────────────?```
**Use Cases**:
- Remote work locations
- Secure facilities
- Travel without reliable internet
- Cost savings on data plans
### No API Costs
Run free open-source models:
| Model | Quality | VRAM Required |
|-------|---------|---------------|
| **Llama 3 8B** | Good | 6GB |
| **Llama 3 70B** | Excellent | 40GB |
| **Mistral 7B** | Good | 6GB |
| **Qwen 14B** | Very Good | 12GB |
| **Phi-3 Mini** | Decent | 4GB |
**Cost Comparison**:
| Solution | Monthly Cost (heavy use) |
|----------|-------------------------|
| Cloud AI (GPT-4) | `$100-500+` |
| Cloud AI (Claude) | `$50-200+` |
| **LocalClaw** | **`$0`** (electricity only) |
---
## Key Features
$#1.
Local-First Design
**Everything Runs Locally**:
- AI model inference
- Data storage
- Configuration
- Logs and history
**Benefits**:Privacy: Your data stays on your machine Speed: No network latency Cost: No API fees Control: Full control over everything Reliability: Works without internet
$#2.
Open-Source Model Support
**Supported Model Formats**:
- GGUF (GGML Unified Format)
- GGML (older format)
- ONNX (Open Neural Network Exchange)
**Where to Get Models**:
- Hugging Face (https://huggingface.co)
- The Bloke's quantized models
- Official model repositories
**Popular Models**:
| Model | Size | Quality | Best For |
|-------|------|---------|----------|
| Llama 3 8B | 4.7GB | Good | General use |
| Mistral 7B | 4.1GB | Good | Fast responses |
| Qwen 14B | 9GB | Very Good | Multilingual |
| Phi-3 Mini | 2.3GB | Decent | Low-end hardware |
$#3.
Offline Capability
**What Works Offline**:
- ?All AI inference
- ?Conversation history
- ?File operations
- ?Local automations
**What Requires Internet**:
- ⚠️ Model downloads (one-time)
- ⚠️ Model updates
- ⚠️ Web search features (if enabled)
$#4.
Privacy Features
**Privacy by Design**:┌─────────────────────────────────────?? LocalClaw Privacy Stack ?├─────────────────────────────────────?? ?? 🔒 Local Processing ?? 🔒 Encrypted Storage (optional) ?? 🔒 No Telemetry ?? 🔒 No Analytics ?? 🔒 No Data Collection ?? ?└─────────────────────────────────────?```
Installation
Prerequisites
| Requirement | Details |
|---|---|
| RAM | 8GB minimum, 16GB recommended |
| Storage | 20GB for models + data |
| GPU | Optional (speeds up inference) |
| OS | Windows 10+, macOS 12+, Linux |
Method 1: Quick Start (Recommended)
Step 1: Install
git clone https://github.com/sunkencity999/localclaw
cd localclaw
npm installStep 2: Download a Model
# Download Llama 3 8B GGUF model
# (~5GB, one-time download)
npm run download-model llama-3-8bStep 3: Configure
# config.yaml
model:
type: local
path: ./models/llama-3-8b.gguf
context_length: 4096
gpu_layers: 35 # Set to 0 for CPU-onlyStep 4: Run
npm startMethod 2: Docker
docker run -d --name localclaw \
-p 8080:8080 \
-v ./models:/app/models \
-v ./data:/app/data \
--gpus all \
localclaw/latestMethod 3: Ollama Integration
If you already use Ollama:
# LocalClaw can use existing Ollama models
# Just configure:
model:
type: ollama
ollama_url: http://localhost:11434
model: llama3Configuration
Basic Configuration
# config.yaml
# Model settings
model:
type: local
path: ./models/llama-3-8b.gguf
# Model parameters
context_length: 4096
temperature: 0.7
max_tokens: 2048
# GPU acceleration
gpu_layers: 35 # -1 for all layers on GPU
# Storage settings
storage:
data_path: ./data
encryption: false # Enable for encrypted storage
# Performance settings
performance:
threads: 8 # CPU threads for inference
batch_size: 512GPU Acceleration
NVIDIA GPU:
gpu:
enabled: true
layers: 35 # Number of layers on GPU
memory: 8GB # VRAM allocationApple Silicon (M1/M2/M3):
gpu:
enabled: true
metal: true # Use Metal APICPU Only (no GPU):
gpu:
enabled: false
threads: 8 # Use more CPU threadsUse Cases
Privacy-Critical Applications
Scenario: Handle sensitive data (legal, medical, financial)
Why LocalClaw:
Sensitive Data ?LocalClaw ?Processing ?Response
? └── Never leaves your machine
?HIPAA compliant
?GDPR compliant
?Attorney-client privilege maintainedExamples:
- Legal document analysis
- Medical record summarization
- Financial data processing
- Confidential business analysis
Offline Environments
Scenario: Work without reliable internet
Setup:
1. Download models while online
2. Copy to offline machine
3. Run LocalClaw completely offlineUse Cases:
- Remote research stations
- Maritime vessels
- Rural locations
- Secure facilities
Air-Gapped Systems
Scenario: Maximum security isolation
Implementation:
┌─────────────────────────────────────?? Air-Gapped System ?? ?? ┌─────────────? ?? ?LocalClaw ? ?? ?+ Local AI ? No network ?? ? Model ? connection ?? └─────────────? ?? ?? Data enters via USB only ?└─────────────────────────────────────?```
---
## System Requirements
| Component | Minimum | Recommended |
|-----------|---------|-------------|
| **CPU** | 4 cores | 8+ cores |
| **Memory** | 8GB RAM | 16-32GB RAM |
| **Storage** | 20GB SSD | 100GB+ SSD |
| **GPU** | Optional | 8GB+ VRAM (NVIDIA/AMD) |
| **OS** | Windows 10, macOS 12, Linux | Latest |
### Performance Expectations
| Hardware | Tokens/second |
|----------|---------------|
| **M3 Max** | 30-50 tok/s |
| **RTX 4090** | 40-60 tok/s |
| **RTX 3060** | 20-30 tok/s |
| **CPU Only (8 core)** | 5-10 tok/s |
| **CPU Only (4 core)** | 2-5 tok/s |
---
## Comparison with Alternatives
| Feature | LocalClaw | OpenClaw | Cloud AI |
|---------|-----------|----------|----------|
| **Privacy** | ⭐⭐⭐⭐?Complete | ⭐⭐?Good | ?Data leaves |
| **Offline** | ?Full | ⚠️ Limited | ?No |
| **Cost** | `$0` API | `$` API | `$$` API |
| **Speed** | Medium | Fast | Fastest |
| **Model Quality** | Good | Best | Best |
| **Hardware** | 8GB+ RAM | 2GB RAM | Any |
---
## Pros & Cons
### Advantages
| Advantage | Explanation |
|-----------|-------------|
| **Complete Privacy** | Data never leaves your machine |
| **Offline Capable** | Works without internet |
| **No API Costs** | Free open-source models |
| **Full Control** | You control everything |
| **Compliance** | HIPAA, GDPR friendly |
| **No Rate Limits** | Use as much as you want |
### Limitations
| Limitation | Explanation |
|------------|-------------|
| **Hardware Requirements** | Needs 8GB+ RAM |
| **Slower Than Cloud** | Local inference is slower |
| **Model Quality** | Open-source models less capable |
| **Model Management** | You manage model downloads |
| **Storage** | Models take significant space |
---
## Pricing
**LocalClaw Software**: Completely FREE (MIT License)
**Costs**:
- **Software**: Free
- **Models**: Free (open-source)
- **Electricity**: ~`$5-20`/month depending on usage
- **Hardware**: Your existing computer or one-time purchase
**Savings vs Cloud AI**:Cloud AI (heavy use): $100-500/month
LocalClaw: $0/month (after hardware)
Break-even: 1-6 months
---
## Community and Support
- **GitHub**: https://github.com/sunkencity999/localclaw
- **Issues**: https://github.com/sunkencity999/localclaw/issues
- **Discussions**: https://github.com/sunkencity999/localclaw/discussions
---
---
## Quick Start Guide
Get LocalClaw up and running quickly.
### Step 1: Install
```bash
git clone https://github.com/sunkencity999/localclaw.git
cd localclaw
npm install
npx localclaw setupStep 2: Configure
Set your AI model and API key in the configuration.
Step 3: Connect and Go
Link your messaging platform and start using your AI agent.
Full documentation: https://github.com/sunkencity999/localclaw#readme
Source code: https://github.com/sunkencity999/localclaw
FAQ
Is LocalClaw free to use?
Yes, LocalClaw is free and open source (MIT license). You only pay for AI model API costs if using external models.
What are the system requirements for LocalClaw?
LocalClaw requires 8GB RAM of RAM minimum. Runtime: Node.js. It runs on Windows, macOS, and Linux.
Can I self-host LocalClaw?
Yes. LocalClaw is open source (MIT) and can be self-hosted on your own hardware. Clone the repository from GitHub and follow the installation guide.
How does LocalClaw compare to OpenClaw?
LocalClaw offers a different approach compared to OpenClaw. While OpenClaw provides the largest ecosystem with 13,729+ skills and maximum flexibility, LocalClaw focuses on lightweight. Choose LocalClaw if you prioritize its specific features; choose OpenClaw for the broadest compatibility and community support.
Is LocalClaw suitable for beginners?
LocalClaw requires some technical knowledge to set up (Node.js). If you are a beginner, consider starting with QClaw (one-click install) or MaxClaw (cloud-based, no setup) first, then graduate to LocalClaw as you gain experience.
License
MIT License - Free for personal and commercial use.
Summary
LocalClaw is a local-first privacy-focused AI agent offering:
- Complete Privacy -- Data never leaves your machine
- Offline Capable -- Works without internet
- No API Costs -- Free open-source models
- Full Control -- You control everything
- Compliance -- HIPAA, GDPR friendly
Best For:
- Privacy-conscious users
- Offline environments
- Sensitive data handling
- Cost-conscious heavy users
- Air-gapped systems
Not Recommended For:
- Users with low-end hardware (less than 8GB RAM)
- Those wanting fastest responses
- Users needing best model quality
- People uncomfortable with model management