OpenClaw Playbook: From Zero to Fully Automated AI Agent

Summary: OpenClaw is a local-first, open-source AI agent platform that runs on your own hardware. This playbook covers installation, configuration, and workflow automation from zero to fully operational — no cloud required.

OpenClaw: The Complete Playbook

From Zero to Fully Automated AI Agent — On Your Own Machine, On Your Own Terms

1. Introduction and Big Picture

What Is OpenClaw?

OpenClaw is a local-first, open-source AI agent platform that runs on your own machine — a laptop, a home server, or a VPS — and connects directly to the messaging apps you already use: WhatsApp, Telegram, Slack, Discord, Signal, iMessage, Microsoft Teams, and more.

It is not a chatbot you visit on a website. It is not a SaaS product that holds your data on someone else's servers. OpenClaw is a daemon — a long-running background process that lives on your hardware, uses your files, calls APIs on your behalf, runs commands, sends messages, and automates workflows. You install it once, configure it to your needs, and it works for you around the clock.

The core philosophy is simple: your assistant, your machine, your rules.

That means:

Why OpenClaw Exists

Most AI assistants today follow the same pattern: you go to a website, type a prompt, read a response, copy-paste it somewhere useful, and repeat. They are reactive and siloed. They cannot act on your behalf, access your local files, trigger workflows, or coordinate across the systems you actually work in.

OpenClaw was built to close that gap. It turns an LLM from a passive text generator into an autonomous agent that can:

All while keeping you in control of permissions, approvals, and data flow.

For founders, operators, engineers, and power users who run on multiple tools and channels, OpenClaw becomes an always-on operating layer that sits between your LLMs and the rest of your stack.

Flagship Use Cases

OpenClaw is not a single-purpose tool. The most common deployments fall into these categories:

Personal productivity and founder OS. A single generalist agent that handles your inbox triage, drafts replies, summarizes long documents, manages your calendar, tracks tasks, and delivers daily briefings — all through a Telegram or WhatsApp conversation on your phone.

Outbound and sales automation. Agents that ingest lead lists, enrich contacts with research, generate personalized outreach emails, manage sending sequences, and triage replies — with human approval gates for anything that goes out.

Research and intelligence. Agents that monitor news, build prospect dossiers, compile market briefings, and deliver structured summaries on a schedule or on demand.

DevOps and SRE copilot. Agents hooked into your logs, metrics, and alerting systems that can scan for anomalies, draft incident reports, suggest root causes, and run remediation scripts with your approval.

Content production. Agents that turn bullet points into blog posts, repurpose long-form content into social threads and email campaigns, and schedule publishing across channels.

Monitoring and alerting. Agents that watch health checks, metrics dashboards, and error logs, then send smart alerts and can trigger automated remediation with guardrails.

Smart home and personal concierge. Agents that coordinate home automation APIs, manage reminders, track habits, and serve as a general life assistant through your preferred chat app.

Who This Guide Is For

This playbook is written for anyone who wants to go from zero knowledge of OpenClaw to running production-grade AI agents on their own infrastructure.

2. Core Concepts and Mental Models

Before you install anything, you need to understand the architecture. OpenClaw is built around a small set of core primitives that combine into flexible, powerful agent systems.

The Gateway

The Gateway is the central process — the daemon that runs in the background on your machine. Think of it as a traffic controller. It receives events from multiple sources (incoming chat messages, scheduled cron jobs, webhooks, CLI commands), routes them to the right agent, and sends responses back through the appropriate channel.

When you "run OpenClaw," you are running the Gateway. It stays alive, listens for inputs, and dispatches work.

Agents

An agent is a reasoning loop. It receives an input (a message, a trigger event, a scheduled task), thinks about what to do using an LLM, decides which tools to call, executes those tool calls, observes the results, and continues the loop until the task is complete or it needs to wait for more input.

Each agent has its own configuration:

An agent is not a one-shot prompt-response. It is a persistent process with state, memory, and the ability to take real actions in your system.

Tools

Tools are the agent's hands. Each tool is a structured function with defined inputs, outputs, and permissions. When the agent decides it needs to read a file, it calls the fs.read tool. When it needs to run a shell command, it calls exec. When it needs to send a Telegram message, it calls message.send.

Core built-in tools include:

Each tool can be enabled or disabled per agent, per workspace, and per channel. High-risk tools like exec and browser can require explicit user approval before execution.

Skills

Skills are the agent's playbooks. While tools define what the agent can do, skills teach the agent how and when to do it.

A skill is a folder containing a SKILL.md file — a markdown document with YAML frontmatter (name, description, trigger conditions) and detailed instructions that guide the agent through a specific workflow. Skills can also include helper scripts, config templates, and reference data.

Skills are modular and composable. You can install community skills from registries like ClawHub, write your own, or customize existing ones.

Channels

Channels are the messaging integrations through which you and others interact with your agents. OpenClaw supports:

Triggers

Triggers define how workflows start:

Memory

Memory gives agents persistence across sessions. OpenClaw uses a combination of:

Memory is local. It lives on your machine in structured files and lightweight databases.

Local-First Architecture

Here is what runs where:

If you want to keep everything fully local, you can run local LLMs (via Ollama, llama.cpp, or similar) and avoid cloud model APIs entirely.

3. Install: Zero to First Response

System Requirements

OpenClaw runs on anything that can sustain a Node.js process:

Recommended environments:

Installation Methods

One-liner script (recommended):

curl -fsSL https://get.openclaw.io | bash

npm / Node.js install:

npm install -g openclaw

Docker:

docker pull openclaw/openclaw:latest
docker run -d --name openclaw \
  -v ~/openclaw-data:/data \
  -p 3000:3000 \
  openclaw/openclaw:latest

First-Time Setup

After installation, run the onboard wizard:

openclaw onboard

This interactive flow walks you through:

1. Creating your workspace directory

2. Selecting and configuring your first AI model provider

3. Setting up your first messaging channel (Telegram is the easiest to start with)

4. Running a test message to verify everything works end to end

Verifying the Install

openclaw status          # Check daemon status, connected channels, and model availability
openclaw channels list   # List configured channels and their connection state
openclaw models list     # Check available models
openclaw logs --tail 50  # View recent log activity

4. First-Time Configuration

Config File Anatomy

OpenClaw stores its configuration in config.yaml inside your workspace. The main sections:

Configuring AI Model Providers

Supported providers:

Model selection strategy — use a tiered approach:

Tier · Use Case · Recommended Models

Router / classifier · Intent classification, simple Q&A · GPT-4o-mini, Claude Haiku

General reasoning · Most agent tasks · GPT-4o, Claude Sonnet

Complex reasoning · Deep analysis, high-stakes decisions · o1, o3, Claude Opus

Bulk / batch · High-volume processing · Cheapest model that meets quality bar

Security-First Defaults

OpenClaw's default configuration is deliberately restrictive:

The recommended progression:

1. Start read-only. Observe agent behavior in logs.

2. Enable write access to its workspace.

3. Enable curated API access for specific services.

4. Enable shell with approval (agent shows command, waits for your "yes").

5. Enable autonomous execution only for well-tested, low-risk workflows.

Managing Secrets

5. Channels and Messaging Apps

Supported Channels

Channel · Best For · Setup Method

Telegram · Personal use, getting started · Bot token via BotFather

WhatsApp · Personal + business communication · QR code / pairing code

Slack · Team agents, internal tools · OAuth app

Discord · Community-facing, developer teams · Bot application

Signal · Privacy-focused personal use · Linked device

iMessage · macOS users · System integration

Microsoft Teams · Enterprise environments · App registration

Feishu / Lark · ByteDance platform teams · App registration

Setting Up Your First Channel (Telegram)

1. Open Telegram, search for @BotFather, create a new bot, copy the token

2. Add the channel: openclaw channels add telegram

3. Follow the prompts to enter your bot token

4. Verify: openclaw channels list

5. Send a test message and check logs

Multi-Channel Routing

A single Gateway can serve multiple channels simultaneously:

Sessions are kept separate per user per channel — context from your WhatsApp conversation does not leak into your Slack conversations.

6. Agents, Souls, and Personalities

Designing the Soul / Persona

A well-designed soul covers:

Role definition: What the agent is and is not. "You are a senior growth operations assistant for a B2B SaaS company."

Tone and communication style: Concise or verbose, formal or casual, direct or diplomatic.

Hard boundaries: What the agent must never do. "Never run shell commands without showing them first. Never send emails without explicit approval."

Escalation rules: When to ask for human input. "If uncertain about a decision affecting money, customers, or public content, ask before acting."

Output format preferences: Default to markdown, use tables for comparisons, keep summaries under 200 words.

Example soul prompt:

You are Ops, a senior operations assistant for a B2B SaaS startup.

Role:
- Research prospects and companies using web tools.
- Draft personalized outreach emails.
- Summarize long documents and meeting notes.
- Manage task lists and follow-ups.

Communication:
- Be concise and direct. No filler.
- Use bullet points for lists, tables for comparisons.
- Default to action: suggest next steps, not just analysis.

Boundaries:
- Never send emails or messages without explicit approval.
- Never execute shell commands without showing them first.
- Never commit or push code without confirmation.
- If a task requires spending money or accessing financial systems, stop and ask.

Escalation:
- If uncertain about factual claims, say so and offer to research.
- If a request involves legal, financial, or public-facing content, flag it for review.

Generalist vs. Specialists

One generalist agent is the right starting point. It handles everything through a single persona and accumulates context about your work over time — the "Founder OS" pattern.

Multiple specialist agents make sense when:

Common specialists: Inbox agent, Research agent, DevOps agent, Content agent, Outbound agent.

7. Tools and Skills

Tools Reference

Tool · What It Does · Risk Level

fs.read · Read files from workspace · Low

fs.write · Create or overwrite files · Medium

fs.edit · Edit specific sections of files · Medium

fs.list · List directory contents · Low

apply_patch · Apply structured patches to files · Medium

exec · Run shell commands · High

web.search · Search the web · Low

web.fetch · Fetch a URL's content · Low

browser · Automate browser sessions · High

http · Make HTTP API requests · Medium

message.send · Send messages through channels · Medium

cron · Schedule tasks · Medium

gateway · Inter-agent communication · Medium

Skills Structure

A skill folder:

skills/
  research/
    SKILL.md          # Instructions with YAML frontmatter
    templates/        # Output templates, schemas
    scripts/          # Helper scripts if needed

A SKILL.md file:

---
name: research
description: "Conduct structured research on companies, people, or topics."
triggers:
  - keyword: "research"
  - keyword: "dossier"
  - intent: "find information about"
tools:
  - web.search
  - web.fetch
  - fs.write
---

# Research Skill

When the user asks you to research a topic, company, or person:

1. Start with a web search to identify key sources.
2. Fetch the most relevant pages and extract structured information.
3. Organize findings into a structured report with sections:
   - Overview
   - Key facts
   - Recent news / developments
   - Relevant links
   - Suggested next actions
4. Save the report to the workspace as `research/[topic]-[date].md`.
5. Summarize the key findings in your chat response.

Discovering and Installing Skills

openclaw skills list                    # List available skills
openclaw skills search "outbound"       # Search by category
openclaw skills install outbound-email  # Install a skill
openclaw skills installed               # Check installed skills

Enablement Strategy

Layer · When · Skills

Foundation · Day 1 · Summarize, memory, web search, file management

Productivity · Week 1 · Email drafting, research, content writing, task management

Automation · Week 2+ · Outbound sequences, DevOps monitoring, browser automation

Advanced · Month 2+ · Shell execution, CI/CD, multi-agent orchestration

8. Memory, Workspace, and Data

Workspace Structure

~/openclaw/
  config.yaml           # Main configuration
  agents/               # Agent definitions
  skills/               # Installed and custom skills
  memory/               # Long-term memory files
  workspace/            # Active working files
    docs/               # Reference documents
    projects/           # Project-specific workspaces
    output/             # Generated content
  logs/                 # Daemon and workflow logs
  .env                  # Secrets (excluded from git)

Everything is file-based and inspectable.

Memory Operations

openclaw memory clear --session   # Clear session memory
openclaw memory clear --all       # Nuclear option
openclaw memory export            # Export memory data

Backup workspace:

cp -r ~/openclaw ~/openclaw-backup-$(date +%Y%m%d)
# Or use git for version control
cd ~/openclaw && git add -A && git commit -m "backup $(date)"

Teaching OpenClaw About Your Business

1. Drop documents into the workspace. Place PDFs, markdown files, text files, and spreadsheets in workspace/docs/.

2. Create SOPs as skill files. Convert standard operating procedures into skills.

3. Use pinned notes for preferences. Pin recurring instructions in the agent's config or memory.

4. Feed FAQs and product sheets. For support or sales agents, the FAQ document is their most valuable resource.

9. Using OpenClaw as a Chat Assistant

Entry Points

CLI / TUI:

openclaw chat

Messaging apps: Once a channel is configured, interact with your agent exactly as you would with any contact — no special app, no browser tab, no login.

Prompting Basics

Persistent Instructions

For instructions you want the agent to follow in every conversation:

Everyday Use Patterns

10. Automation and Workflow Design

The Automation Mindset

Best candidates for automation:

The 5-Stage Flow

Every OpenClaw workflow follows a debuggable pattern:

1. Trigger: Something starts the workflow

2. Collect: Gather the inputs needed

3. Decide: Use AI reasoning to determine what to do

4. Act: Execute the decision

5. Observe: Log the outcome, handle errors, notify or report

Example: Daily Founder Digest

Trigger: Cron, every day at 7:30 AM

Collect:

Decide:

Act:

Observe: Log digest contents, any errors, send status

Error Handling

11. Recommended Starter Agents and Playbooks

Inbox and Communication Triage Agent

Problem: 30–60 minutes a day reading, prioritizing, and replying to messages.

Setup: Email API access (read, draft, label), classification skill, Telegram or WhatsApp for digests and draft approval.

Workflow:

1. Scan inbox for new messages periodically

2. Classify: urgent → action-needed → FYI → archive

3. For urgent/action-needed, draft reply and send to chat for review

4. You approve, edit, or reject. Approved drafts send automatically.

Guardrails: Never auto-send without approval. Flag messages mentioning money, contracts, or legal matters.

Research and Briefing Agent

Problem: Structured research takes hours when done manually.

Setup: Web search, web fetch, browser, file write. Triggered by message or CLI.

Workflow:

1. Receive research request

2. Execute structured research plan: search, fetch, find news, check data

3. Organize into dossier: overview, leadership, funding, recent developments, talking points

4. Save to workspace, deliver summary through chat

Guardrails: Cite all sources. Flag uncertain or conflicting information.

Outbound / Sales Agent

Problem: Personalizing outreach at scale is slow and inconsistent when done manually.

Setup: File read/write for lead lists, web search/fetch for enrichment, HTTP for CRM and ESP APIs.

Workflow:

1. Ingest a lead list (CSV or CRM pull)

2. Enrich each lead with company research, recent news, role context

3. Generate personalized email from enrichment data and template

4. Queue drafts for human batch approval

5. Send approved emails through ESP or SMTP

6. Monitor replies and classify (interested / not interested / objection / OOO)

Guardrails: All emails require batch approval. Enrichment data logged for auditability.

Content Production Agent

Problem: Turning ideas and raw material into polished, multi-format content takes disproportionate time.

Setup: File read/write, web search, HTTP for publishing APIs.

Workflow:

1. Receive content brief (topic, audience, format, key points)

2. Research topic for supporting data and current relevance

3. Draft primary piece (blog post, article, report)

4. Generate derivatives: Twitter/X thread, LinkedIn post, email newsletter, short-form summary, and optionally an audio script for ElevenLabs voice narration

5. Present all versions for review, then publish on approval

DevOps / SRE Copilot

Problem: Infrastructure generates logs and alerts faster than you can process them.

Setup: Exec (with approval for prod commands), HTTP for metrics APIs, file read/write.

Workflow:

1. Cron-triggered health checks query metrics and log endpoints

2. On warning or critical: send structured alert with context

3. On incident declaration: pull relevant logs, suggest root causes, draft incident report

4. For known issues: suggest (or execute with approval) remediation steps

5. Post-incident: generate post-mortem draft

Guardrails: Production commands always require approval. Remediation runs dry-run first.

Smart Home / Life Concierge

Setup: HTTP for home automation APIs (Home Assistant, IFTTT), calendar integration, task tools.

Examples:

12. Best Practices for Prompt and Workflow Design

Writing Stable Agent Prompts

Good prompts are:

Multi-Step Flows vs. Mega-Prompts

Break complex work into sequential steps, each with its own clear prompt, tool set, and success criteria:

1. Research the company → structured dossier

2. Using dossier, draft personalized email → draft

3. Review draft (human or AI) → approve or request changes

4. Send approved email → log result

5. Schedule follow-up → set reminder

Each step can be tested independently and uses only the tools it needs.

Schemas and Templates

When you need structured output, provide a schema:

{
  "company_name": "",
  "founded": "",
  "hq_location": "",
  "funding_total": "",
  "key_people": [{"name": "", "role": ""}],
  "recent_news": [{"headline": "", "date": "", "url": ""}],
  "summary": ""
}

Schemas make outputs parseable by downstream workflows and reduce hallucinated formatting.

Guardrails and Approval Flows

For any action that is irreversible or high-stakes, implement approval gates:

13. Advanced Multi-Agent Patterns

Hub-and-Spoke Architecture

A single orchestrator receives all user requests, classifies intent, delegates to the appropriate specialist agent, and aggregates results.

Example flow:

1. User: "Research Acme Corp and draft an outreach email."

2. Orchestrator parses intent: research + email drafting

3. Delegates to research agent → structured dossier returned

4. Passes dossier to writer agent → draft returned

5. Orchestrator presents final output to user

Advantages: Each agent is focused. Permissions are scoped. Failures are isolated.

Swarm / Peer-to-Peer

Multiple agents communicate via internal messages without a central orchestrator.

Example: Content pipeline

Example: Outbound Pod

14. Integrations and External Systems

Common Integration Categories

Using Generic API Tools

For any system with a REST or GraphQL API:

1. Configure the API endpoint and authentication headers

2. Define the operations the agent is allowed to call

3. Create a skill that teaches the agent the API's structure and response formats

Browser Automation

For systems without APIs (or with insufficient APIs):

Treat as a fallback — always prefer API integrations where available.

Webhooks: Reacting to External Events

Configure external systems to POST to your OpenClaw webhook endpoint. Workflow automation platforms like n8n can serve as the middleware layer, routing events from dozens of services into your agent:

15. Security, Privacy, and Governance

Threat Model

Threat · Description

Prompt injection · Malicious content in emails/pages tricks agent into unintended actions

Config errors · Overly permissive tool access, leaked API keys

Agent misbehavior · Agent misinterprets request and takes destructive action

Key compromise · API keys leaked through logs, config files, or agent outputs

Unauthorized access · Wrong person gains ability to send agent commands

Permissions Model

Secrets Management

Logging and Audit Trails

OpenClaw logs all activity: incoming messages, tool calls, tool results, outbound actions, errors, decisions.

Best practices:

16. Performance, Reliability, and Cost Control

Model Selection for Cost Efficiency

A single agent can use different models for different operations. For a full breakdown of these providers, see our guide to choosing the right LLM.

Infrastructure Choices

Setup · Best For · Uptime · Cost

Laptop · Development, trying out · When open · Free

Home server / Pi · Personal always-on use · ~95%+ · $50–150 one-time

VPS (Hetzner, DO) · Production, solo · 99.9% · $5–20/month

K8s cluster · Teams, high-volume · 99.99% · $50+/month

For most solo users and small teams, a $5–10/month VPS is the recommended production setup.

Cost Controls

17. Debugging and Troubleshooting

Reading Logs

openclaw logs --tail              # Follow live logs
openclaw logs --level error       # Filter by level
openclaw logs --agent founder-os  # Filter by agent
openclaw logs --since "2h"        # Filter by time range

Common Installation Issues

Issue · Fix

Node.js version mismatch · Requires Node.js 18+. Check with node --version.

Port conflicts · Check with lsof -i :3000 for your configured port

Firewall blocking webhooks · Open incoming connections on the webhook port

Missing env variables · Recreate .env with all required API keys

Channel auth failures · Re-run channel setup flow — tokens expire

Diagnosing Agent Misbehavior

1. Check logs for the specific interaction

2. Identify failure type: wrong tool, hallucination, prompt misinterpretation, or tool failure

3. Update the soul or skill to address the root cause

4. Test: replay the same input and verify behavior is corrected

Workflow Validation

openclaw validate          # Validate workflow configuration
openclaw triggers list     # List active triggers and status

18. From Power User to Builder

Writing Custom Skills

mkdir skills/my-custom-skill

Create SKILL.md:

---
name: weekly-metrics
description: "Compile and deliver a weekly metrics report."
triggers:
  - cron: "0 9 * * 1"
  - keyword: "weekly metrics"
tools:
  - http
  - fs.write
  - message.send
---

# Weekly Metrics Report

When triggered:
1. Fetch KPIs from analytics API
2. Fetch revenue data from billing API
3. Compare this week vs last week
4. Generate report: headline metric changes, top 3 wins, top 3 concerns, recommended actions
5. Save to workspace/reports/weekly-[date].md
6. Send summary to #metrics channel on Slack

Reload: openclaw skills reload

Version-Controlling Your Setup

cd ~/openclaw
git init
echo ".env" >> .gitignore
echo "logs/" >> .gitignore
git add -A
git commit -m "initial setup"

Track config, skills, agent definitions, and workflow configurations in git. Use branches for experiments.

19. Team and Organization Playbook

Multi-User Patterns

Workspaces and Namespaces

workspaces/
  personal-damien/    # Personal agent workspace
  sales/              # Sales team workspace
  engineering/        # Engineering workspace
  support/            # Customer support workspace

Team-Oriented Agent Examples

Governance for Organizations

20. End-to-End Blueprints

Blueprint: Founder OS

Goal: Always-on assistant acting as the founder's operating system.

Architecture: One generalist agent with broad skills (inbox, research, content, task management), connected to WhatsApp and Slack.

Workflows:

Blueprint: Outbound Engine

Goal: Multi-agent pipeline from raw lead data to personalized, quality-controlled outbound email.

Data flow: CSV upload → Data agent cleans → Research agent enriches → Writer agent drafts → QA agent reviews → Human batch-approves → Sender dispatches → Reply agent monitors

Metrics: Lead-to-send conversion rate, personalization quality score, reply rates, time from lead ingestion to first send.

Blueprint: DevOps Co-Pilot

Architecture:

Integrations: Prometheus/Grafana for metrics, ELK/Loki for logs, PagerDuty for alerting, GitHub for deploys, Slack for communication.

Metrics: MTTD, MTTA, MTTR, false alert rate, post-mortem completion rate.

Blueprint: Knowledge Hub

Goal: Q&A system that makes company knowledge accessible through natural conversation.

Architecture: Q&A agents backed by indexed documents, routed by team or topic.

Key skills: Document indexing, semantic search, source citation, honest "I don't know" detection.

Metrics: Question answer rate, source accuracy, user satisfaction, knowledge base coverage gaps identified.

21. Command Cheat Sheet

# Installation
curl -fsSL https://get.openclaw.io | bash
openclaw onboard

# Daemon management
openclaw start                    # Start the Gateway
openclaw stop                     # Stop the Gateway
openclaw restart                  # Restart the Gateway
openclaw status                   # Check daemon, channels, agents

# Channels
openclaw channels add <platform>  # Add a new channel
openclaw channels list            # List all channels and status
openclaw channels remove <id>     # Remove a channel

# Models
openclaw models list              # List configured models
openclaw models add <provider>    # Add a model provider
openclaw models test              # Test model connectivity

# Skills
openclaw skills list              # List available skills
openclaw skills installed         # List installed skills
openclaw skills install <slug>    # Install a skill
openclaw skills reload            # Reload all skills

# Memory and workspace
openclaw memory clear --session   # Clear session memory
openclaw memory clear --all       # Clear all memory
openclaw memory export            # Export memory data

# Workflows and triggers
openclaw triggers list            # List active triggers
openclaw run <workflow>           # Manually trigger a workflow
openclaw validate                 # Validate configuration

# Logs and debugging
openclaw logs --tail              # Follow live logs
openclaw logs --level error       # Filter by log level
openclaw logs --agent <name>      # Filter by agent
openclaw logs --since "2h"        # Filter by time

# Chat
openclaw chat                     # Open CLI chat session

22. Progress Checklist

Beginner (day 1–3):

Intermediate (week 1–2):

Advanced (week 3–4):

Expert (month 2+):

OpenClaw's architecture gained enterprise-level validation in March 2026 when Meta acquired Moltbook — a social network for AI agents built on the OpenClaw framework. Meta now deploys OpenClaw-based agents internally at scale. For the full picture of how this plays out inside a 70,000-person company, see our analysis of Zuckerberg's CEO agent and Meta's agentic strategy.

This playbook is published on hokai.io — the AI tool directory that helps you find, compare, and stack the right tools for your workflow.

Frequently Asked Questions

What is OpenClaw?

OpenClaw is an open-source, local-first AI agent platform that lets you design, run, and automate multi-step AI workflows entirely on your own machine. It keeps your data private and requires no cloud subscription.

How do I get started with OpenClaw?

Install via npm by running npm install -g openclaw, or download the standalone binary from the GitHub releases page. Then run openclaw init to set up your first workspace.

What can OpenClaw automate?

OpenClaw automates document processing, data extraction, API orchestration, code review pipelines, scheduled summarization jobs, and any multi-step workflow that chains AI calls with tool use � all without leaving your local environment.

What does local-first mean for OpenClaw?

Local-first means your agent configs, data, and LLM API calls are processed on your own hardware. No workflow state is sent to a third-party server. You can run OpenClaw offline and retain full control over credentials and outputs.

What are the main use cases for OpenClaw?

Common use cases include automated research assistants, nightly report generators, codebase analysis bots, data enrichment pipelines, and personal productivity agents that run on a schedule � all self-hosted on your own machine.