Run AI CoWork Locally with OpenClaw & Ollama (Complete Setup)

Quick Overview

The video provides a complete setup guide for running an AI coworker locally using OpenClaw, which acts as a gateway, integrated with Ollama for model execution, allowing users to control their computer via messaging apps like Slack while keeping data private.

Key Points: The AI assistant, originally named Clawdbot, has been officially renamed to OpenClaw. OpenClaw installation requires running followed by . OpenClaw integrates with Ollama to run models locally, ensuring user data remains 100% private. The video demonstrates setting up Slack integration by configuring OAuth & Permissions, enabling Socket Mode, and subscribing to necessary events in the Slack API Console. Ollama must be installed first, and models like are downloaded locally using . The configuration process for web UI access involves running and selecting local gateway execution. The final result demonstrates controlling the local computer (e.g., listing directory contents) via commands sent through the OpenClaw web UI or directly via Slack.

Context: This tutorial details the process of setting up OpenClaw, an AI personal assistant that runs locally on the user's device, to manage tasks and bridge various messaging services (like WhatsApp, Telegram, and Slack) to AI coding agents via a centralized gateway. The system utilizes Ollama to run large language models (LLMs) locally, emphasizing data privacy by keeping all processing on the user's machine rather than in the cloud. The video walks through installation, Ollama setup, and the specific configuration steps required for Slack integration.

Detailed Analysis

The video guides the viewer through setting up OpenClaw, an AI assistant that integrates with messaging apps and runs local LLMs via Ollama. The presenter first explains the naming journey, noting the project rebranded from Clawdbot to OpenClaw. Installation starts by installing OpenClaw globally via npm: , followed by running the onboarding wizard: . A key configuration step involves setting the context length in Ollama settings to at least 64k tokens for better performance. Next, the presenter details integrating with Ollama by running (noting that the old alias is deprecated). After downloading a model locally using , the user accesses the OpenClaw web UI (http://127.0.0.1:18789/). The video then transitions to setting up Slack integration using the documentation provided by Praison AI. This involves creating a Slack app, configuring OAuth & Permissions with specific scopes ( , , , , ), enabling Socket Mode with scope, subscribing to necessary events ( , ), and finally reinstalling the app. The demonstration concludes by showing that commands sent via the Slack interface (like listing files in a directory) are successfully executed by the locally running OpenClaw gateway.