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* perf(memory): move the main session's auto-memory guidance into a bundled skill The main session's `# auto memory` system-prompt section was 16,140 of 35,792 characters on every request, while the main model writes memories on only a few turns. The session now gets a resident policy (directories and routing, one line per type, what not to save, the two save steps with the frontmatter template and category names, deduplication, reading and verifying memories) plus a pointer to a new bundled `auto-memory` skill that carries the full type guidance with examples, the metadata rules, team memory and the persistence section. The route reuses resolveBundledReferenceRoute: pointer, pointer via tool_search, the full policy where no skill can be loaded, and the resident policy alone when the user disabled the skill. The policy is rebuilt once the tool registry exists, since the session-start memory refresh runs before it. The skill is listed only with managed auto-memory on. The extraction, Dream, /remember and import agents keep the full constants; buildManagedAutoMemoryPrompt output is unchanged. A two-way anchor test pins every constant line into SKILL.md and keeps the moved text out of the resident policy. Default sessions send 4,572 instead of 15,868 characters for this section (about 2.8k fewer tokens per request). The bundled listing grows by 233 characters; its budget moves from 6,800 to 7,050. Refs #16291 * fix(memory): resolve the auto-memory route per reader and keep the daemon catalog working The daemon's skills catalog builds its SkillManager on a Config shim without isSkillEnabled. The bundled auto-memory skill is in core's bundled directory, so the conditional-skill check called it, threw, and returned an uninitialized catalog. The shim now answers it from the workspace's managed auto-memory setting. The main-session policy was built once on the session Config, so every derived Config inherited its pointer: a subagent without the Skill tool (no skill manager, #12424) was told to load a skill it cannot reach. The policy now keeps its inputs and resolves the route in getAutoMemoryPrompt for the Config that reads it, built once per route. That also replaces the post-registry rebuild and follows Skill tool reachability between refreshes. - List auto-memory only under legacy recall; by-name lookup uses the same conditional set as the listing. - Put back two index rules the resident policy had lost: MEMORY.md has no frontmatter, and lines past the index limit are truncated. - Remove MemoryManager.buildAutoMemoryPrompt, which had no callers. - Fix the getAutoMemoryPrompt docblock, read SKILL.md once in its test, and update the design doc and measured lengths. Refs #16291 * fix(memory): load the auto-memory reference without an approval prompt Private memory writes are auto-allowed, but the Skill tool asks by default, so in the default approval mode every memory save began with a confirmation to load the reference. The Skill tool now allows the call when the name resolves to the bundled `auto-memory` (read-only text, no allowedTools, no hooks); a same-named project, user or extension skill still asks. Also share the memory tier lines between the full and resident policies, and name the skill through one constant in `skills/types.ts`, used by the skill manager, the Skill tool and the daemon's skills catalog.
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.
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. - Computer Use — Set up native MCP tools or Node REPL + SDK in Qwen Code, Claude Code, or Codex.
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).
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.