Frequently Asked Questions

Everything You Need to Know About OpenClaw

This page answers the most common questions about OpenClaw — from first-time installation to multi-agent automation, model selection, and staying current with security updates. Jump to any category below or scroll through all sections.

Getting Started   Installation & Setup   Models & LLM Providers   Agents & Automation   Security & Updates

Getting Started with OpenClaw

New to OpenClaw? These questions cover what it is, who it’s for, and how to take your first steps.

OpenClaw is an open-source AI agent framework that lets you build, deploy, and automate workflows powered by large language models. It connects to cloud LLM providers (Anthropic, OpenAI, DeepSeek, and others) and to locally-run models, so you can choose exactly what runs where. Agents built in OpenClaw can interact with external services, process data, trigger events, and chain multi-step tasks — all without manual intervention once configured.
Basic familiarity with the command line makes setup significantly smoother, but OpenClaw is designed to be accessible beyond traditional developers. Many of its workflows are configured through YAML files and a web interface rather than code. That said, if you want to build custom integrations or write agent logic from scratch, Python knowledge will be useful. The guides on this site are written to be clear for people at both ends of that spectrum.
OpenClaw itself is free and open source. What you pay for — if anything — depends on which LLM provider you connect it to. Cloud providers like Anthropic and OpenAI charge per token. If you run OpenClaw with a local model through Ollama or LM Studio, you can operate it entirely for free with no API costs. See the Local Model Setup guide for details.
Start with the Getting Started guide, which covers what OpenClaw is, how it’s structured, and the fastest path to a working install. From there, follow the installation guide for your operating system: Mac, Windows, or Linux.

Installation & Setup

Common questions about getting OpenClaw installed and running for the first time.

OpenClaw runs on macOS, Linux, and Windows. The experience is smoothest on macOS and Linux — particularly Ubuntu and Debian-based distros. Windows setup works but has more edge cases, especially around WSL2 and path handling. Docker is the most consistent cross-platform option if you want to avoid OS-specific issues. Guides for all four environments are available: Mac, Windows, Linux, Docker.
The install script can fail without a clear error message for several reasons — most commonly a missing system dependency, a Python version conflict, or a permissions issue on the target directory. The install.sh error guide covers the five most common failures with step-by-step fixes for each. Running the script with bash -x install.sh will print every command as it executes, which usually makes the failure point obvious.
OpenClaw requires Python 3.10 or later. Python 3.12 is the most tested version as of current releases. Running it under Python 3.9 or below will produce import errors that don’t always clearly indicate the version mismatch as the cause. If you have multiple Python versions installed, make sure the one on your PATH is 3.10+. On macOS, Homebrew users should verify with python3 --version after installation.
Yes — Docker is actually the recommended setup for anyone who wants a clean, reproducible environment or who has had trouble with native installation. The Docker setup guide walks through the full process including volume mounts, port configuration, and how to persist your agent configs between container restarts.
This error appears when OpenClaw’s agent runner loses its connection to the control interface after a restart or network change. It’s not a sign of a broken install — it just means the pairing token has expired or the service restarted in a state where it’s waiting for a new handshake. The fix typically takes under two minutes. See the dedicated guide: Fix It in 3 Steps.

Models & LLM Providers

Questions about connecting OpenClaw to cloud and local language models.

OpenClaw supports any provider that exposes an OpenAI-compatible API, plus several with native integrations. Well-tested providers include Anthropic (Claude), OpenAI (GPT-4o and later), DeepSeek, Mistral, and Groq. For local models, it works with Ollama, LM Studio, and any server that exposes a compatible endpoint. The LLM Providers page lists all supported options with connection details.
You need an Anthropic API key and the correct base URL in your OpenClaw config file. The Anthropic integration guide covers the exact config block, which model strings to use, and how to handle the most common authentication errors that occur during first-time setup.
It depends on the workflow. Claude Sonnet performs well across general-purpose agent tasks and has a large context window that suits multi-step pipelines. DeepSeek is a strong cost-cutter for high-volume or less complex tasks. For fully offline setups, Mistral 7B and Llama 3 via Ollama are the most stable choices. The model comparison guide benchmarks these across five real agent workflows.
Yes. Pair OpenClaw with a locally running model server like Ollama and it will operate entirely offline with no outbound API calls. You still need an internet connection for initial package installation, but once set up the full agent runtime can run air-gapped. The Local Model Setup guide covers the complete configuration.
The most effective approaches are: choosing a smaller, faster model for tasks that don’t need high reasoning (DeepSeek V2 is a strong option here), setting tight max_token limits per agent step, caching responses for repeated lookups, and batching non-time-sensitive tasks. The cost reduction guide walks through each technique with configuration examples.

Agents & Automation

How to build workflows, connect integrations, and automate tasks with OpenClaw agents.

Practical use cases covered on this site include: reading, drafting and sending emails via Gmail; tracking GitHub releases and triggering deployments; monitoring stock prices and sending alerts; building a crypto portfolio tracker; proxying agents through a Cloudflare tunnel for public access; tracking GOG game libraries; and connecting to external event systems via webhooks. The Integrations page lists all supported channel types.
Gmail integration uses OAuth2 credentials configured in your agent’s channel settings. The Gmail integration guide walks through credential setup, scopes required, and how to structure an agent that can read, classify, draft replies, and send — with example configs for each action.
Hooks are OpenClaw’s system for triggering agent workflows from external events — webhooks, scheduled timers, file changes, or API calls from other systems. They let you start an agent run from outside the OpenClaw interface entirely. The Hooks guide covers all trigger types with configuration examples.
Yes — multi-agent workflows are one of OpenClaw’s core strengths. You can chain agents sequentially, run them in parallel, or have one agent spawn sub-agents for specific subtasks. The comparison guide OpenClaw vs MoltBot covers how OpenClaw handles multi-agent orchestration in detail.

Security & Updates

Staying current with OpenClaw releases and keeping your instance secure.

OpenClaw releases updates through its GitHub repository. The GitHub releases guide covers how to track new versions, what to check in changelogs before upgrading, and how to set up auto-deployment so your instance updates without manual intervention. The Version Updates page tracks recent changes with plain-English summaries.
OpenClaw has had several publicly disclosed vulnerabilities, including CVE-2026-25253. The security vulnerabilities guide covers the four most critical risks with specific patching instructions for each. If you’re running a public-facing instance, reviewing this guide is strongly recommended.
The Version Updates section breaks down each release in plain language — what changed, what broke, and what you need to do. The changelog breakdown guide covers the seven most impactful recent updates in detail.
The Community Resources page aggregates useful fixes from Reddit, Discord, and GitHub Issues. The dedicated article 8 Community Fixes You Won’t Find in the Docs compiles the most widely applicable solutions from community channels.
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