Three follow-ups to #11686, from its sandboxed verification report and the audits of this change. The plan-recorded wall is elapsed time and the next reverse-audit round is priced at the costliest admission-to-admission span, so a pause is charged twice: it spends the wall and, while it is the longest gap, prices the next round. A new Review Deadline section in the user docs gives the ceilings: under the default reserve a review can pause after its first audit pair (rounds 1 and 2 launch together) for at most about 3h20m / 5h20m / 7h20m on the 8h / 12h / 16h walls, (wall - 80m) / 2 on any wall of 4h or more, less by half of any time before the pair launched. It also says a stopped review composes and reports what it proved with the verdict capped, that running /review again follows the incremental-review rules rather than continuing the audit, and that setting the wall through the environment applies to every review in the session. DESIGN.md states the same rule, including the gate's price floors. The soft-deadline paragraph under Headless runs and the --deadline help point to the section, and the help now names what the environment epoch wins over. Tests drive the real gate to pin the ceilings, the later-launch discount and the costliest-span case. capture-local keeps #11686's one-line error contract (a usage error exits 2, anything else 1) and now prints the error's stack after that line under --debug, so an internal fault can be located without turning operator errors back into crash banners. Only the flag does this; debug variables in the environment do not. The handler also rules --out before the tree is captured, as fetch-diff and issue-context do: a directory --out used to run the whole capture and then fail with EISDIR, and a blank one wrote the plan to a whitespace-named file. The shared assertWritableOutPath now names the mistake for a non-string --out: "--out must be given once, as a file path" for a repeated flag (an array), "--out must name a file path" for --no-out, instead of `out.trim is not a function`. fetch-diff and issue-context pass --out through instead of String()-ing it, which wrote a repeated flag to one comma path and --no-out to a file named `false`; both now exit 2 before fetching.
The open-source AI coding agent for your terminal, editor, desktop, browser, and chat.
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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.
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:
- Headless — Run
qwen -p "..."in scripts, CI/CD, or batch jobs. - SDKs — Build with TypeScript, Python, or Java.
- Daemon — Run
qwen serveto connect clients over HTTP + SSE (ACP). (experimental) Daemon guide.
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
Evaluation Configuration
| Configuration | Value |
|---|---|
| Dataset | princeton-nlp/SWE-bench_Verified, 500 cases |
| Runs | 3 trials per version, 1,500 jobs per version; 7 Qwen Code versions |
| Model | Qwen 3.7 Max |
| Sampling | temperature=1, top_p=0.95 |
| Thinking / Reasoning | interleaved_thinking=true, thinking_type=adaptive, reasoning_effort=high |
| Context / Output Limits | context_1m=true, model_context_length=1,000,000, max_tokens=64,000, model_max_tokens=65,535 |
| Harness Limits | max_iterations=500, runtime_timeout_sec=7,200, agent_timeout_multiplier=3.0 |
Scores by Qwen Code Version
| Version | Avg Score | Pass Rate | pass@3 | pass^3 |
|---|---|---|---|---|
| 0.0.14 | 77.80% | 84.6% | 84.6% | 69.2% |
| 0.1.0 | 76.73% | 84.6% | 84.6% | 68.0% |
| 0.5.0 | 76.60% | 83.0% | 83.0% | 68.4% |
| 0.10.0 | 76.47% | 84.4% | 84.4% | 67.4% |
| 0.15.0 | 77.67% | 84.6% | 84.6% | 68.6% |
| 0.19.0 | 76.40% | 85.0% | 85.0% | 65.8% |
| 0.22.0 | 77.33% | 84.4% | 84.4% | 68.0% |
Avg Score Trend
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 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
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.

