In this guide, we will establish a QwenPaw workflow that serves as a robust platform for developing and evaluating an agent-based assistant. The process begins with the installation and initialization of QwenPaw, followed by configuring the working directory and setting up authentication protocols. We will also connect to optional model providers using Colab secrets and create a well-structured workspace that includes custom skills and local knowledge files. Furthermore, we will access the QwenPaw Console through a URL compatible with Colab, and optionally expose it via a Cloudflare tunnel. This setup allows us to utilize QwenPaw both as an interactive assistant and as a framework driven by APIs, enhancing our testing capabilities for the streaming chat API programmatically.
Creating a QwenPaw Agent Workspace with Tailored Skills and API Testing
This guide walks you through setting up a QwenPaw workspace, enabling custom skills, and testing APIs effectively.
