Skip to main content

​
Installation

First build takes a few minutes while Rust compiles dependencies. Subsequent builds are incremental and much faster.

​
Prerequisites

​
Linux (Debian/Ubuntu)

​
macOS

​
Windows

Install Visual Studio Build Tools with the “Desktop development with C++” workload and the Windows 10/11 SDK.

​
Clone and build

The first build will take a few minutes while Rust compiles all dependencies. Subsequent builds are incremental.

​
Production build

The built app will be in src-tauri/target/release/bundle/ — platform-specific installer (.dmg, .deb, .msi, etc.).

​
Verify

After launching, OpenPawz opens to the Today dashboard. Go to Settings → Providers to configure your first AI provider, then create agents and start chatting. Ollama powers three systems in OpenPawz: low-cost tool execution, semantic memory, and offline chat. We strongly recommend installing it for zero-cost local inference, but these roles can also be filled by cloud models from any provider.
Pawz auto-detects Ollama on localhost:11434, auto-starts it if needed, and auto-pulls the embedding model on first launch.

​
Worker Model Setup

The worker model (worker-qwen) is what makes tool execution free. When your cloud AI agent (Gemini, Claude, GPT) needs to run a tool via the MCP Bridge, it delegates to the local worker instead of making additional API calls. To set up the worker:
  1. Go to Settings → Advanced → Ollama
  2. Click Setup Worker Agent
  3. This creates a custom worker-qwen model with the right system prompt
Or from the command line:
The one-click setup in Settings creates the optimized worker-qwen variant automatically.
Without Ollama, tool calls go through your paid AI provider. With Ollama, they’re free. See the MCP Bridge guide for the full Architect/Worker architecture.