The way to Construct a QwenPaw Agent Workspace with Customized Expertise, Mannequin Suppliers, Console Entry, and Streaming API Testing
if not (WORKING_DIR / “config.json”).exists(): run(qwenpaw_cmd(“init”, “–defaults”), examine=False) else: print(“QwenPaw working listing already initialized:”, WORKING_DIR) provider_candidates = [ { “env”: “OPENAI_API_KEY”, “provider_id”: “openai”, “name”: “OpenAI”, “base_url”: “https://api.openai.com/v1”, “model”: os.environ.get(“QWENPAW_MODEL”, “gpt-4o-mini”), “chat_model”: “OpenAIChatModel”, “prefix”: “sk-“, }, { “env”: “OPENROUTER_API_KEY”, “provider_id”: “openrouter”, “name”: “OpenRouter”, “base_url”: “https://openrouter.ai/api/v1”, “model”: os.environ.get(“QWENPAW_MODEL”, “openai/gpt-4o-mini”), “chat_model”: “OpenAIChatModel”, “prefix”: “sk-or-“, }, { “env”: “DASHSCOPE_API_KEY”,…
