Advanced Local AI: Building Digital Employees with Ollama + OpenClaw

2025 was called the "Year of Local Large Models," and we've gotten used to running Llama 3 or DeepSeek with Ollama to chat and ask about code. But by 2026, simple "conversation" no longer satisfies the appetites of tech enthusiasts.

We want Agents—not just capable of speaking, but truly able to work for us.

Today let's talk about the most hardcore combination in the local AI space right now: Ollama (reasoning engine) + OpenClaw (autonomous execution framework). Under this architecture, AI is no longer just a text generator in a chat box, but a "digital employee" that can operate browsers, read and write files, and run code.

Step 1: Build the Reasoning Foundation (Ollama)

Any Agent needs a smart "brain," and in a local environment, Ollama remains the most robust choice.

If you haven't installed it yet, just go to ollama.ai to download the appropriate version. Once installed, we typically open a terminal and enter commands to download models.

Recommended Models

For Agent applications, choose models that support Tool Calling:

# General reasoning model
ollama pull llama3.3

# Code-specialized model
ollama pull qwen2.5-coder:32b

# Strong reasoning model
ollama pull deepseek-r1:32b

# Lightweight option
ollama pull gpt-oss:20b

But this actually brings a small annoyance: terminal downloading is a "black box."

When you want to try different models (like comparing Qwen 2.5 and Llama 3 effects), or when model files are very large (tens of GB), looking at the monotonous progress bar in the terminal makes it difficult to intuitively manage these behemoths. Moreover, once you have many models installed, deciding which to delete and how much video memory each occupies becomes a headache.

Add a Visual Panel to Ollama: OllaMan

To solve this problem and also make subsequent model scheduling more relaxed, I recommend using it in conjunction with OllaMan for this step.

It can directly read your local Ollama service and provide an App Store-like graphical interface. You can visually browse the online model library on it, click on images to download, and see clear download rates and progress in real time.

More importantly, before handing the model to the Agent, you can first test the model's reasoning ability in OllaMan's conversation interface. After all, if a model can't even handle basic conversation logically, there's no need to waste time configuring it into the Agent.

Once the model environment is ready, the foundation is solid. Now for the main event.

Step 2: Deploy the Execution Hub (OpenClaw)

OpenClaw is currently one of the best local Agent frameworks in terms of experience. Its core capability lies in execution—it has system-level permissions, can execute Shell commands, read and write files, and even control browsers.

Prerequisites

Before installing OpenClaw, make sure your system meets the following requirements:

• Node.js 22 or higher

You can check your Node version with:

node --version

One-Click Installation (Recommended)

OpenClaw officially provides the most convenient one-click installer script, which automatically handles Node.js detection, CLI installation, and the onboarding wizard:

macOS / Linux / WSL2

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

Windows (PowerShell)

iwr -useb https://openclaw.ai/install.ps1 | iex

If you only want to install the CLI without running the onboarding wizard:

# macOS / Linux / WSL2
curl -fsSL https://openclaw.ai/install.sh | bash -s -- --no-onboard

Other Installation Methods

If you already have Node.js 22+ installed, you can also install manually:

npm Installation

npm install -g openclaw@latest
openclaw onboard --install-daemon

pnpm Installation

pnpm add -g openclaw@latest
pnpm approve-builds -g
openclaw onboard --install-daemon

macOS Application

If you're on macOS, you can also download the OpenClaw.app desktop application:

1. Download the latest .dmg file from OpenClaw Releases
2. Install and launch the app
3. Complete system permissions setup (TCC prompts)

Configuring Ollama Integration

After installation, you need to connect OpenClaw with your Ollama service.

1. Enable Ollama API Key

OpenClaw requires an API Key to identify the Ollama service (any value works; Ollama itself doesn't need a real key):

# Set environment variable
export OLLAMA_API_KEY="ollama-local"

# Or via OpenClaw config command
openclaw config set models.providers.ollama.apiKey "ollama-local"

2. Verify Ollama Service

Ensure Ollama is running:

# Check if Ollama is running
curl http://localhost:11434/api/tags

# Start Ollama service if not running
ollama serve

3. Run Configuration Wizard

OpenClaw provides an interactive configuration wizard that automatically detects your Ollama models:

openclaw onboard

The wizard will automatically:

• Scan your local Ollama service (http://127.0.0.1:11434)
• Discover all models that support tool calling
• Configure default model settings

4. Manual Configuration (Optional)

If you want to manually specify models, edit the config file ~/.openclaw/openclaw.json:

{
  "agents": {
    "defaults": {
      "model": {
        "primary": "ollama/llama3.3",
        "fallbacks": ["ollama/qwen2.5-coder:32b"]
      }
    }
  }
}

5. Verify Configuration

Check if OpenClaw has successfully recognized your Ollama models:

# List all models recognized by OpenClaw
openclaw models list

# List installed Ollama models
ollama list

Start the Gateway

Once configured, start the OpenClaw Gateway:

openclaw gateway

The Gateway runs on ws://127.0.0.1:18789 by default. It's OpenClaw's core service, responsible for coordinating model calls and skill execution.

Step 3: Practical Tips and Scenarios

Environment setup is just the beginning. OpenClaw's true power lies in its rich Skills ecosystem.

Scenario 1: Automated Code Review

OpenClaw can directly read your local project files. You can give it commands like:

During this process:

1. OpenClaw calls file system skills to traverse directories.
2. Ollama (Llama 3) reads the code and performs logical reasoning.
3. OpenClaw organizes the reasoning results and writes them to a new file.

This is far more efficient than copying code segments to ChatGPT, and the data never leaves your local machine.

Scenario 2: Remote Commander (IM Integration)

OpenClaw supports integration with chat platforms like Slack, Discord, and Telegram. This means you can turn your home computer into a server that's always on standby.

Usage Example: After configuring the Telegram bot integration, when you're out and about, you just need to send a message on your phone: "Hey Claw, help me check the remaining disk space on my home NAS. If it's below 10%, send me an alert."

OpenClaw will run the Shell command df -h on your home computer, analyze the results, and send the report back to your phone.

Summary

By using Ollama to provide intelligence, OllaMan to manage model assets, and OpenClaw to execute specific tasks, we've built a complete local AI productivity loop.

The biggest charm of this combination is: completely private, completely free, completely under your control.

If you're tired of just chatting, try installing it on your computer and see how your workflow can evolve with the help of this AI assistant.