How to Build a QwenPaw Agent Workspace with Custom Skills, Model Providers, Console Access, and Streaming API Testing

In this tutorial, we implement a QwenPaw workflow that provides a practical environment for building and testing an agent-powered assistant. We install and initialize QwenPaw, configure its working directory, set up authentication, connect optional model providers via Colab secrets, and create a structured workspace with custom skills and local knowledge files. We also launch the QwenPaw Console via a Colab-accessible URL, expose it through an optional Cloudflare tunnel, and test the streaming chat API programmatically, enabling us to use QwenPaw both as an interactive assistant and as an API-driven agent framework.

We start by importing all required Python modules and setting up the main directories for the QwenPaw Colab workspace. We configure environment variables for authentication, logging, working paths, and secure access to the QwenPaw Console. We also define helper functions to run shell commands, check ports, stop old app processes, and read API keys from Colab secrets or environment variables.

We initialize the QwenPaw working directory and prepare the base configuration file for the default agent. We define multiple model-provider options, such as OpenAI, OpenRouter, DashScope, DeepSeek, and Gemini, so the setup can adapt to whichever API key we provide. We then update the QwenPaw configuration with the default agent profile, workspace path, API settings, and timezone.

We create the default QwenPaw agent configuration with console access, memory support, streaming output, and guarded tool execution. We automatically configure the selected model provider when a supported API key is available in Colab secrets or environment variables. We save the active model and agent settings so QwenPaw can use the configured provider during chat and API-based interactions.

We create a custom research_brief skill inside the QwenPaw workspace to guide the agent toward structured research outputs. We add demo knowledge files that explain the Colab setup and provide the agent with a local workspace context to inspect. We then print the prepared workspace paths and run QwenPaw commands to verify the daemon, available models, and registered skills.

We stop any previous QwenPaw app process and launch a fresh QwenPaw Console server on the configured Colab port. We wait until the server becomes available, then print the login credentials, the local URL, the log path, and the Colab proxy URL. We also optionally start a Cloudflare tunnel so the QwenPaw Console can be accessed through a temporary public link.

We define a streaming API client that sends messages to QwenPaw through the /api/console/chat endpoint. We test the configured agent by asking it to read the local workspace notes and summarize the Colab setup, including advanced experimental ideas. We finish by printing the final access details, workspace path, log location, tunnel URL if available, and a command to stop the running server.