Shaojin Wenandverify 4a5c487681 test(managed-agent): gate the expired-cursor resync answer (#16076)
* test(managed-agent): gate the expired-cursor resync answer

FG9e now advances the Session stream's replay floor past a lagging
client and requires one `agent.session.resync_required` frame on both
the public and WebShell streams, with no event id and the floor and
snapshot cursors it must reload through. The same cursor on the plain
events route answers `409 cursor_expired`, and the floor itself stays
readable.

This closes the expired-cursor cell the FG8 tracker (#14163) deferred
and #15750 carried. The case measures 21.3 s locally; the Hosted fork
budget and Verify step move with it.

* test(managed-agent): wait for projection coverage before advancing the floor

Awaitility treats a lambda that returns without failing as satisfied,
so the coverage guard must assert rather than return. Without this a
poll that lands before the lead's Turn is projected would skip the
advance and dereference a null window.

* docs(managed-agent): correct the resync case's tracker references

FG9e now carries the third case, so the relay-hold sentence must name
only the two resume cases. The FG8 record no longer predicts a slice-2
landing and points at the FG9e delivery instead.

* ci(managed-agent): record the resync case's measured range

The 60 s fork-budget entry was written from one 21.3 s run; the final
audit runs measured 21–23 s, so state the range the budget covers.

* docs(managed-agent): fix the stale step-ceiling header and pin note

The Hosted job's opening parenthetical still summed 291 while the tail,
the Verify step and the job ceiling had moved to 292/202/325. Also say
the replay-floor flag is a default pin, not something currently off.

---------

Co-authored-by: verify <verify@local>
2026-11-02 00:28:04 +08:00
2025-07-22 23:26:01 +08:00
2025-08-08 20:45:54 +08:00
2025-07-22 23:26:01 +08:00
2026-11-01 12:40:25 +08:00
2025-10-23 09:27:04 +08:00
2025-12-22 21:11:33 +08:00
2026-11-01 12:40:25 +08:00

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())

Daemon Mode

Run Qwen Code as a long-lived service when you want browser, editor, SDK, and automation clients to share agent sessions without each starting a separate process. The daemon provides the built-in Web Shell and an HTTP + SSE API, supports multiple workspaces, and lets clients reconnect to active sessions.

Start it in the foreground and open the Web Shell:

qwen serve --open

The default address is http://127.0.0.1:4170. To run and manage it in the background instead:

qwen daemon start
qwen daemon status
qwen daemon logs -f
qwen daemon stop

By default, the daemon listens on loopback without bearer authentication; any local process that can reach it can use the full API, including code execution. On shared or untrusted hosts, set QWEN_SERVER_TOKEN and use --require-auth. See the Daemon guide for workspaces, authentication, remote access, and the API.

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%