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* feat(web-shell): render the compression outcome from structured meta The compression commands stream their outcome as English sentences over ACP, which a host that owns its own UI language cannot render. They now carry the outcome as machine-readable `_meta` next to the sentence, the transcript record and replay pass it through, and the web shell renders it in the user's language with the token counts formatted for that language. A compression that finds nothing to strip reports a terminal no-op phase rather than reusing the pending one: the payload-driven row has to state the outcome, and a pending row is dropped once the turn ends. The invocation note rides its own `_meta` key rather than sharing the result's. The daemon UI reducer folds a turn's text frames into one block and spreads `_meta` key by key, so a shared key would be overwritten by the frame that follows. Nothing else reads the new keys and no frame gains the discrete-message marker, so a host that renders text as-is sees the same sentences it saw before. Opening a session whose last action was a compression also reconciles the composer's context counters once. They are seeded from the last usage frame in the replay, and a compression emits none, so they described the context it replaced; a read is only written back while the session is live and its model and context window have settled, which is what the reconcile waits for. * docs(design): describe the structured compression outcome The design records why the outcome travels as metadata beside the sentence, why the invocation note needs its own key, why the no-op is a terminal phase, and how the composer counters reconcile on an attach that ends on a compression. It also keeps the compatibility analysis for other clients, which is a constraint of this change rather than a follow-up.
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
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
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.