Shaojin Wen 35daec35ea fix(verify-pr): keep the verdict line a plain paragraph and check the comment footer (#16002)
* fix(verify-pr): keep the verdict line a plain paragraph and check the comment footer

Three verification rounds on PR #15984 shipped their opening verdict as a
single Markdown heading: the round title and the whole ~950-character
verdict paragraph shared one `#` line, which GitHub renders as one giant
H1 — a wall of bold text instead of a readable lead. All three comments
also went out without the attribution footer the publish contract
requires.

The skill only said "verdict line first" without pinning the Markdown
shape, and the consistency checker only checked that the verdict word
and head OID appear in the first ten lines, so both defects passed every
gate.

- The skill now pins the verdict line to a plain paragraph (a round
  title may lead it as plain text, never as a heading), with the
  measured #15984 example and a template.
- Local rounds assemble the exact posted body — report content plus the
  resolved footer — into comment.md, post that file, and read the
  comment back against it byte for byte; report.md stays footer-free by
  contract.
- The checker warns when the lead line is a heading carrying the
  verdict or an over-long heading (GFM indents of up to three spaces
  included), and when comment.md lacks a well-formed attribution
  footer. Warnings, not errors: a cosmetic defect must not make the CI
  publisher refuse an otherwise consistent verdict.
- Pin tests reproduce the shipped shape from the real round-3 comment,
  and the workflow meta-test pins the new skill text.

* fix(verify-pr): accept CRLF line endings in the comment-body checks

Review of #16002 reproduced a Windows-authored comment.md failing the
anchored footer regex: splitting only on \n left a stray \r on the
footer line, so a valid attribution was reported missing and the local
publish gate sent the artifact back for unnecessary repair. GitHub
renders CRLF and LF identically.

Normalize CRLF (and lone CR) to LF before the line-based checks on
report.md and comment.md, and pin LF/CRLF parity: the same valid footer
passes under both endings, a missing footer and a heading lead still
warn under CRLF, and the reviewer's CLI probe is covered end to end.
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2026-10-29 15:14:52 +00:00
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npm version License Node.js Version Downloads

QwenLM%2Fqwen-code | Trendshift

The open-source AI coding agent for your terminal, editor, desktop, browser, and chat.

中文 | Deutsch | français | 日本語 | Русский | Português (Brasil) | 한국어

Qwen Code in the terminal, desktop app, browser, editor, and chat, with VS Code, Zed, JetBrains, Telegram, DingTalk, WeChat, and Feishu integrations

Why Qwen Code?

  • Agentic out of the box — Auto-Memory, Auto-Skills, SubAgents, Agent Teams, and MCP. Dynamic workflows, zero setup.
  • Open-source, inside and out — The framework and the Qwen models are open-source. They evolve together. No vendor lock-in.
  • Multi-protocol — Supports OpenAI, Anthropic, Gemini, and Qwen APIs. Any third-party provider or local model (Ollama / vLLM). Switch at runtime.
  • Beyond the terminal — IDE plugins, Desktop app, Web UI, SDKs, and chat integrations (Telegram / DingTalk / WeChat / Feishu).

Tip

Qwen Code is actively iterating on itself — using its own agent and models to file issues, submit PRs, review code, and run tests. Powered by the community, driven by AI.

Installation

Linux / macOS:

curl -fsSL https://qwen-code-assets.oss-cn-hangzhou.aliyuncs.com/installation/install-qwen-standalone.sh | bash

Windows:

irm https://qwen-code-assets.oss-cn-hangzhou.aliyuncs.com/installation/install-qwen-standalone.ps1 | iex

Restart your terminal after installation to ensure environment variables take effect.

NPM / Homebrew

NPM (requires Node.js 22+):

npm install -g @qwen-code/qwen-code@latest

Homebrew (macOS / Linux):

brew install qwen-code

Quick Start

Open a terminal in your project and start Qwen Code:

cd /path/to/your-project
qwen

Inside the session, run /auth to configure your provider and API key. Then try:

Explain this repository and show me where to start.

See the Authentication Guide and Settings Reference for detailed setup.

See the terminal interface

Qwen Code terminal interface

How to Use Qwen Code

Choose the interface that fits your workflow:

Interface Get started
Terminal Run qwen in your project — see Quick Start.
Desktop Download Qwen Code Desktop for macOS, Windows, and Linux.
Web Run qwen serve --open to open the Web UI in your browser. (experimental)
Editor Set up VS Code, Zed, or JetBrains.
Chat Connect Telegram, DingTalk, WeChat, or Feishu, then run qwen channel start.

For automation and custom integrations:

SDK example (Python)
import asyncio

from qwen_code_sdk import is_sdk_result_message, query


async def main() -> None:
    result = query(
        "Summarize the repository layout.",
        {
            "cwd": "/path/to/project",
            "path_to_qwen_executable": "qwen",
        },
    )

    async for message in result:
        if is_sdk_result_message(message):
            print(message["result"])


asyncio.run(main())

Capabilities

If you know Claude Code, you already know Qwen Code — and then some. We've put significant effort into bringing Qwen Code to feature parity with Claude Code, improving both breadth and reliability across the board.

Feature Qwen Code Claude Code
SubAgents, Agent Teams, Dynamic Workflows ✓ ✓
Auto-Memory, Auto-Skills, Hooks ✓ ✓
Built-in Skills (/review, /batch, /loop, /bugfix…) ✓ ✓
MCP, Plan Mode, LSP Integration ✓ ✓
Auto Mode, Sandbox, Git Worktrees ✓ ✓
Computer Use (desktop automation) ✓ ✓
IDE Plugins (VS Code / JetBrains / Zed) ✓ ✓
SDK ✓ ✓
Headless Mode, Session Management ✓ ✓
Open-source — model and framework ✓ —
Multi-protocol (OpenAI / Anthropic / Gemini / Qwen + any provider) ✓ —
Agent Arena (multi-model head-to-head on same task) ✓ —
Daemon Mode — qwen serve (multi-client shared agent) ✓ —
IM Channels (Telegram / DingTalk / WeChat / Feishu) ✓ —

Qwen Code Evaluation

On SWE-bench Verified, seven Qwen Code versions scored 76.40%–77.80% on average using the same Qwen 3.7 Max model (500 tasks, three trials per version).

Average SWE-bench Verified scores by Qwen Code version: 0.0.14, 77.80%; 0.1.0, 76.73%; 0.5.0, 76.60%; 0.10.0, 76.47%; 0.15.0, 77.67%; 0.19.0, 76.40%; 0.22.0, 77.33%.

Full results and evaluation configuration →

Ecosystem

  • Qwen Code Desktop — Official desktop app for macOS, Windows, and Linux

  • AionUi — A modern GUI for command-line AI tools including Qwen Code

  • Gemini CLI Desktop — A cross-platform desktop/web/mobile UI for Qwen Code

  • qwen-audio-agent — Realtime full-duplex voice frontend for Qwen Code over ACP — talk hands-free with barge-in and a local wake word

  • 🦞 Qwen Code Claw — Let other agents (Claude, Codex, etc.) delegate coding tasks to Qwen Code via ACP. Paste this prompt into your agent:

Use Qwen Code as a coding agent. First, fetch and read this skill reference:
https://raw.githubusercontent.com/QwenLM/qwen-code/refs/heads/main/.qwen/skills/qwen-code-claw/SKILL.md
Then follow the instructions inside to install, authenticate, and use Qwen Code via acpx for all coding tasks.
  • Aliyun Model Studio CLI — Official CLI for Aliyun's AI platform (bailian-cli). Extends Qwen Code with image/video generation, knowledge retrieval, app orchestration, and model deployment

Privacy

See the Terms of Service and Privacy Notice, including Chrome extension and Browser Use data handling.

Contributing

Contributions are welcome! See CONTRIBUTING.md for guidelines.

Acknowledgments

This project was originally based on Google Gemini CLI v0.8.2. We gratefully acknowledge the Gemini CLI team's excellent work. Starting from Qwen Code v0.1, we stopped syncing with upstream and began independent development as a multi-protocol, multi-platform agent framework with deep integrations for Qwen models and beyond.

Languages
TypeScript 76.5%
Java 11.5%
JavaScript 5.4%
Rust 5%
Python 0.5%
Other 0.9%