Qwen Code (formerly Gemini CLI) is an open-source, multi-platform AI coding assistant designed to operate in the terminal, web shell, desktop (Tauri), VS Code, JetBrains, and chat channels (DingTalk, WeChat, Slack, etc.). The architecture is a monorepo with ~30+ packages centered around a core library (`packages/core`) that provides agent runtime, tool orchestration, memory, MCP support, and telemetry. The primary entry points are the CLI (`packages/cli` with an Ink-based TUI), the web shell (`packages/web-shell`), and a daemon server (`packages/cli/src/serve/`) that exposes REST/SSE endpoints for session management, file editing, and sandboxed shell execution. The system supports multiple LLM providers (OpenAI, Anthropic, Gemini, DeepSeek, etc.) and features sub-agents, goal-oriented workflows, approval modes, browser automation (CUA driver in Rust), and a Java managed-agent server for enterprise deployments. Data flows are user-to-agent: user input enters via a client (CLI, web, IDE), is processed by the core agent loop, which invokes tools (shell, file system, LSP, MCP servers) and streams responses back. External dependencies include Node.js v22+, pnpm, and platform-specific native binaries (Rust CUA driver, Electron desktop). The codebase is actively maintained with daily commits, extensive integration tests, and CI/CD pipelines. While it is a software application, it includes telemetry modules and a few Python scripts for benchmark analysis, but no research-grade data pipelines or ML training code.

The QwenLM/Qwen3 repository provides resources for the Qwen3 family of large language models, including model cards, documentation, and usage examples. It serves as an application-focused hub for inference, deployment, training, and evaluation of Qwen3 models. The repository contains extensive multilingual documentation, inference demos (web and CLI), deployment guides for various frameworks (vLLM, SGLang, etc.), training examples (using Axolotl, LLaMA-Factory, etc.), quantization techniques, and evaluation harnesses (notably for ARC-AGI). While the core model weights are hosted externally, this repo enables end-to-end usage through well-organized examples and scripts.

QwenLM/Qwen-AgentWorld is a research benchmark evaluation toolkit for Language World Models (LWMs). The repository contains the AgentWorldBench evaluation infrastructure: a standalone CLI script (`eval/eval.py`) that orchestrates a two-stage evaluation pipeline — first calling an OpenAI-compatible inference endpoint to generate environment predictions from a world model, then using an LLM judge (e.g., GPT) to score the predictions across five dimensions (format, factuality, consistency, realism, quality). The code covers seven agent domains (MCP, Search, Terminal, SWE, Android, Web, OS) through per-domain prompt templates and task configurations. The repository is not a model serving system or training framework; it is a reproducible evaluation harness published alongside the Qwen-AgentWorld model weights and benchmark dataset (hosted separately on Hugging Face). The core value is in the robust parsing utilities for extracting structured output from LLM responses, including sophisticated handling of think/response tag removal and multi-strategy JSON extraction from judge outputs.

This toolkit provides a command-line interface for transcribing long audio and video files using the Qwen-ASR API. It overcomes the API's 3-minute duration limit by splitting audio into chunks based on voice activity detection (VAD) and processing them in parallel. The toolkit includes reusable components for audio loading, VAD-based segmentation, and API interaction with retry logic and text post-processing. The main workflow involves loading media, applying VAD to detect speech segments, splitting audio into chunks respecting a target duration, transcribing chunks in parallel via the API, and combining results with optional SRT subtitle generation.