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OpenClaw Installation Setup provides the end-to-end technical deployment blueprints, infrastructure sizing calculators, and OS-specific dependency matrices required to establish stable, hardened OpenClaw runtime environments.

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Foundational Guides
Architecture & Protocols
Topic Directory
Frequently Asked Questions

Foundational Pillars (Start Here)

Authoritative implementation blueprints and core configurations verified by our technical engineering team.

Hardware Sizing

OpenClaw System Requirements: What You Actually Need

Realistic hardware benchmarks comparing RAM, CPU, VRAM, and disk speed requirements across cloud and local inference tiers.

Core Installation

OpenClaw Install: A Beginner’s Error-Free Guide

Step-by-step installation instructions eliminating dependency conflicts, Python path traps, and missing library errors.

Hardware Benchmark

OpenClaw Mac Mini: Run Local Agents for Under $100

Empirical evaluation of low-cost Apple Silicon hardware for running 24/7 dedicated background agent daemons.

Infrastructure Engineering: Hardware Sizing & Multi-Platform Environments

Deploying OpenClaw requires aligning hardware resources with target operational profiles. For agents utilizing cloud-hosted model APIs (Claude 3.7 Sonnet, GPT-4o), compute requirements remain modest: the host system primarily handles asynchronous I/O, process management, and local vector indexing. In contrast, local inference setups demand high memory bandwidth and dedicated unified memory or VRAM to prevent severe token generation bottlenecks.

Across all operating systems (macOS, Linux, Windows), establishing a clean virtual environment or containerized sandbox is mandatory. Isolating OpenClaw dependencies prevents system Python corruption and ensures repeatable deployments across enterprise server fleets.

Platform TierMinimum CPU / MemoryInference ModalityRecommended Deployment Target
Cloud API Client1 vCPU / 1 GB RAMRemote HTTPS (Anthropic/OpenAI)$5 VPS (Hostinger, Hetzner, DigitalOcean)
Local Hybrid Stack4 Cores / 8 GB RAMRemote LLM + Local Vector DBUsed Mac Mini (M1/M2) or local desktop
Full Local Autonomous8 Cores / 16-32 GB Unified RAMLocal Quantized LLMs (7B-14B via Ollama)Mac Mini M4 (24GB+) or RTX 3060/4060 GPU
Enterprise Cluster16+ vCPU / 64 GB RAMMulti-Agent Swarm with concurrent workersDedicated bare-metal server with Docker Swarm

Curated Topic Directory & Implementation Guides

Explore specific technical tutorials, integration setups, and architectural deep-dives.

Platform-Specific Deployments

Hardening, Security & System Sizing

Frequently Asked Questions

Direct technical answers to common architecture, performance, and deployment questions.

Which operating system is best for hosting OpenClaw?

Linux (Ubuntu Server 22.04/24.04 LTS or Debian 12) is the industry standard for headless cloud servers due to minimal overhead and robust systemd daemon support. For desktop local development, macOS with Apple Silicon provides the best local unified memory performance.

Can I run OpenClaw on a Raspberry Pi?

Yes, for cloud-backed agents. A Raspberry Pi 4 or 5 with 4 GB of RAM runs OpenClaw comfortably when dispatching tasks to external LLM APIs like Claude or OpenAI.

What Python version is required for OpenClaw?

OpenClaw requires Python 3.10, 3.11, or 3.12. Avoid outdated Python 3.8 or experimental pre-release versions to maintain library stability.

How do I ensure OpenClaw restarts automatically if the server reboots?

On Linux, configure a systemd service unit. On macOS, use `launchd`. On Docker, configure the container with `restart: unless-stopped`.

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Every article is written from hands-on testing — covering details the official documentation skips.

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Getting Started

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Everything you need to get OpenClaw up and running, for every platform and use case.

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Models

LLM providers, local model setup and comparisons to find the right model for your workflow.

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Installation & Setup

Step-by-step install guides for Mac, Windows, Linux and Docker — including errors the docs skip.

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