How to Set Up and Use OpenClaw: Exploring Local AI Agents

Today, I spent some time exploring and testing OpenClaw, an amazing open-source AI agent platform running locally on my Linux system.

OpenClaw is designed to function as an autonomous personal assistant and a local gateway process. It connects Large Language Models (LLMs) to various interfaces and runs workflows using agentic loops, custom tools (skills), and persistent memory.

If you are looking to get started, I highly recommend watching this comprehensive YouTube tutorial: How to Set Up and Use OpenClaw (ClawdBot / MoltBot).

Here is a summary of what I explored, how OpenClaw organizes its agent workspaces, and my key takeaways from building and experimenting with local AI agents.

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🚀 Setting Up OpenClaw Locally

Getting OpenClaw up and running on a Linux system is remarkably straightforward. You can install it using their official onboarding script:

curl -fsSL https://openclaw.ai/install.sh | bash

Alternatively, if you prefer a manual installation to customize the setup:

1. Clone the repository:

   git clone https://github.com/openclaw/openclaw.git
   cd openclaw
   

2. Install dependencies and build the UI:

   pnpm install && pnpm build && pnpm ui:build
   

3. Initialize the daemon:

   pnpm link --global
   openclaw onboard --install-daemon
   

Running with Local LLMs

While OpenClaw supports cloud-based models (like Claude, GPT, or Gemini APIs), one of its biggest strengths is running completely locally for maximum privacy. To power your local agents, you can host models using Ollama:

1. Install Ollama:

   curl -fsSL https://ollama.com/install.sh | sh
   

2. Run a model (e.g., Llama 3 or Gemma 2):

   ollama run llama3
   

3. Configure OpenClaw: Point the agent settings to your Ollama local API endpoint (typically http://localhost:11434).

In my test setup, I configured my local agent—named SKDev Bot—using the Gemini 2.5 Flash - Minimal model to test chat workflows, local context management, and workspace file read/write operations.

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📂 Understanding the Agent Workspace

The beauty of OpenClaw is that your agent’s personality, identity, and memory are not hidden away in a database. Instead, they are represented as standard Markdown files inside a local workspace directory (e.g., /home/username/.openclaw/workspace).

Here is how OpenClaw structures its files to manage context and execute tasks:

OpenClaw local dashboard running chat workflows with workspace files.

• SOUL.md (Personality Core): This file defines the core drive, values, tone, and behavioral guidelines for the agent. It dictates how the agent responds (e.g., direct, polite, humorous).
• IDENTITY.md (Persona Details): Contains the external appearance, avatar/emoji settings, and display name (like “SKDev Bot”) so the interface knows how to represent the bot.
• USER.md (User Context): Stores information about you—such as your timezone, language, and personal preferences—which the agent reads to personalize its actions.
• TOOLS.md (Skills & Actions): Lists the specific tools or API integrations the agent can call to perform actions (like system commands, file edits, or web searches).
• HEARTBEAT.md (Scheduled Tasks): Configures proactive behaviors. Instead of just replying to messages, the agent uses the heartbeat to periodically execute cron jobs (e.g., checking calendar updates or email alerts).
• MEMORY.md (Persistent Memory): Where the agent saves long-term facts, key-value data, and context across multiple chat sessions. (If this is missing, the agent will reset its context between sessions).
• AGENTS.md (Sub-agents configuration): Sets rules on how sub-agents are spawned and how they coordinate with each other.

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✨ What I Explored

During today’s session, I focused on testing the following areas:

1. Local Agent Initialization: Running OpenClaw locally and setting up the web dashboard at http://127.0.0.1:18789.
2. Chat Workflows: Starting new sessions, testing response latencies, and verifying how the model converts time zones (e.g., converting UTC to Indian Standard Time (IST) for my location).
3. Workspace File Management: Understanding how the agent dynamically reads from and writes to the workspace Markdown files to keep track of its context.
4. Autonomous Execution: Investigating how the agent manages long context windows (indicated by the context usage meter in the dashboard UI).

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🏁 Key Takeaway

Every project teaches something new. Building and experimenting with autonomous AI tools like OpenClaw is helping me better understand the future of intelligent applications.

By running agents locally and managing their context via editable, human-readable markdown files, we get full transparency and control over what our AI assistant knows and does. The shift from basic chatbots to agentic, “always-on” systems with hands will change how we interact with technology.

If you are interested in trying this out, definitely check out the official repository or watch the video guide to build your own personal assistant!