Find a file
zhangxy-zju d75c13aae0
fix(cli): run ACP Agent tool calls concurrently (#2516) (#3463)
* fix(cli): run ACP Agent tool calls concurrently (#2516)

When the model returns multiple Agent tool calls in a single turn, the
ACP Session previously executed them sequentially in a plain for-loop,
multiplying latency by the number of sub-agents spawned.

Mirror the partition logic in coreToolScheduler.partitionToolCalls:
consecutive Agent calls form a parallel batch (safe because sub-agents
have no shared mutable state); any other tool forms its own sequential
batch so the model's implicit ordering is preserved. Response-part
ordering still matches the original functionCalls order.

Add a focused test that uses controllable deferred executes to prove
both Agent calls start before either resolves, and that the fed-back
functionResponse ordering is stable regardless of resolution order.

* Address PR #3463 review: bound concurrency + robust test timing

Two issues raised by the /review bot:

1. The raw Promise.all fan-out bypassed the bounded-concurrency guard
   that coreToolScheduler applies via QWEN_CODE_MAX_TOOL_CONCURRENCY.
   Replaced with an inline runBounded helper that mirrors core's
   runConcurrently (Promise.race on a bounded executing set, default
   cap 10), keeping in-order result collection.

2. The concurrency test used a 10-iteration microtask yield loop before
   asserting both execute() spies had been invoked. That's fragile —
   runTool's pre-execute path (build → getDefaultPermission →
   evaluatePermissionRules → permission branch → PreToolUseHook) has
   more await boundaries than 10 ticks guarantees, and the CI run
   reported call-a still at 0 invocations at the assertion point.

   Reworked the test to wait on an explicit `called` deferred that
   resolves *inside* the execute() mock body. Under sequential
   behaviour only one `called` would ever fire → `Promise.all([called-a,
   called-b])` deadlocks → vitest's per-test timeout surfaces the
   regression. Under the fix both fire before either result resolves.

* fix(acp): degrade gracefully when AgentTool invocation has no eventEmitter

The concurrency test for #2516 timed out on CI with "Test timed out in
5000ms" after the `await Promise.all([called-a, called-b])` rewrite in
the previous review-fix commit. The 5000ms wait was the symptom; the
root cause is that neither `execute()` was ever being called.

runTool's AgentTool branch was guarded with `'eventEmitter' in invocation`,
which is a *key-presence* check. The test mock provides
`{ eventEmitter: undefined, ... }` — the key exists (value undefined),
the branch is entered, and `SubAgentTracker.setup` immediately throws
inside `eventEmitter.on(...)`. The try/catch in runTool swallows the
throw and returns an error response, so `invocation.execute()` never
runs, `called[id].resolve()` never fires, and the test deadlocks.

The earlier review commit (4519c5f9c) interpreted the CI symptom as
"10 microtask yields aren't enough" and rewrote the assertion around a
deferred `Promise.all`. But the old test's `toHaveBeenCalledTimes(1)`
failure with 0 invocations was already the same bug — execute was never
called. The new formulation just converted the visible failure from an
assertion mismatch into a timeout.

Switch the guard to a truthy check against `invocation.eventEmitter`.
Semantics for real AgentTool are unchanged — `agent.ts:392` declares
`readonly eventEmitter: AgentEventEmitter = new AgentEventEmitter()`,
so production always enters the branch. The only new behavior is that
incomplete invocations (or test mocks) skip SubAgentTracker setup
cleanly instead of crashing. `subAgentCleanupFunctions` stays `[]`,
so the cleanup forEach at the success/error paths is a no-op.
2026-04-24 15:22:45 +08:00
.github fix(i18n): sync mismatched keys between en.js and zh.js (#3534) 2026-04-24 00:38:32 +08:00
.husky Sync upstream Gemini-CLI v0.8.2 (#838) 2025-10-23 09:27:04 +08:00
.qwen feat(web-search): remove built-in web_search tool, replace with MCP-based approach (#3502) 2026-04-24 11:29:02 +08:00
.vscode Merge branch 'main' into feat/sandbox-config-improvements 2026-03-06 14:38:39 +08:00
docs feat(web-search): remove built-in web_search tool, replace with MCP-based approach (#3502) 2026-04-24 11:29:02 +08:00
docs-site feat: update docs 2025-12-15 09:47:03 +08:00
eslint-rules pre-release commit 2025-07-22 23:26:01 +08:00
integration-tests feat(web-search): remove built-in web_search tool, replace with MCP-based approach (#3502) 2026-04-24 11:29:02 +08:00
packages fix(cli): run ACP Agent tool calls concurrently (#2516) (#3463) 2026-04-24 15:22:45 +08:00
scripts fix(i18n): sync mismatched keys between en.js and zh.js (#3534) 2026-04-24 00:38:32 +08:00
.dockerignore fix(cli): skip stdin read for ACP mode 2026-03-27 11:47:01 +00:00
.editorconfig pre-release commit 2025-07-22 23:26:01 +08:00
.gitattributes pre-release commit 2025-07-22 23:26:01 +08:00
.gitignore feat: add bugfix workflow, test-engineer agent, and debugging skills 2026-04-04 18:30:09 +08:00
.npmrc chore: remove google registry 2025-08-08 20:45:54 +08:00
.nvmrc chore: Expand node version test matrix (#2700) 2025-07-21 16:33:54 -07:00
.prettierignore Merge branch 'main' into feat/add-vscode-settings-json-schema 2026-03-03 11:21:57 +08:00
.prettierrc.json pre-release commit 2025-07-22 23:26:01 +08:00
.yamllint.yml Sync upstream Gemini-CLI v0.8.2 (#838) 2025-10-23 09:27:04 +08:00
AGENTS.md feat: add bugfix workflow, test-engineer agent, and debugging skills 2026-04-04 18:30:09 +08:00
CONTRIBUTING.md docs: add Screenshots/Video Demo section to PR template 2026-03-20 16:59:53 +08:00
Dockerfile refactor: Extract web-templates package and unify build/pack workflow 2026-02-26 21:02:46 +08:00
esbuild.config.js feat: add wasm build config (#2985) 2026-04-09 14:21:00 +08:00
eslint.config.js fix(editor): detect Zed.app on macOS when CLI is not in PATH (#3303) 2026-04-21 17:06:58 +08:00
LICENSE Sync upstream Gemini-CLI v0.8.2 (#838) 2025-10-23 09:27:04 +08:00
Makefile feat: update docs 2025-12-22 21:11:33 +08:00
package-lock.json chore: bump version to 0.15.1 (#3541) 2026-04-23 11:06:07 +08:00
package.json chore: bump version to 0.15.1 (#3541) 2026-04-23 11:06:07 +08:00
README.md docs: update authentication methods to reflect OAuth discontinuation (#3325) 2026-04-17 15:34:18 +08:00
SECURITY.md fix: update security vulnerability reporting channel 2026-02-24 14:22:47 +08:00
tsconfig.json # 🚀 Sync Gemini CLI v0.2.1 - Major Feature Update (#483) 2025-09-01 14:48:55 +08:00
vitest.config.ts test(channels): add comprehensive test suites for channel adapters 2026-03-27 15:26:39 +00:00

npm version License Node.js Version Downloads

QwenLM%2Fqwen-code | Trendshift

An open-source AI agent that lives in your terminal.

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

🎉 News

  • 2026-04-15: Qwen OAuth free tier has been discontinued. To continue using Qwen Code, switch to Alibaba Cloud Coding Plan, OpenRouter, Fireworks AI, or bring your own API key. Run qwen auth to configure.

  • 2026-04-13: Qwen OAuth free tier policy update: daily quota adjusted to 100 requests/day (from 1,000).

  • 2026-04-02: Qwen3.6-Plus is now live! Get an API key from Alibaba Cloud ModelStudio to access it through the OpenAI-compatible API.

  • 2026-02-16: Qwen3.5-Plus is now live!

Why Qwen Code?

Qwen Code is an open-source AI agent for the terminal, optimized for Qwen series models. It helps you understand large codebases, automate tedious work, and ship faster.

  • Multi-protocol, flexible providers: use OpenAI / Anthropic / Gemini-compatible APIs, Alibaba Cloud Coding Plan, OpenRouter, Fireworks AI, or bring your own API key.
  • Open-source, co-evolving: both the framework and the Qwen3-Coder model are open-source—and they ship and evolve together.
  • Agentic workflow, feature-rich: rich built-in tools (Skills, SubAgents) for a full agentic workflow and a Claude Code-like experience.
  • Terminal-first, IDE-friendly: built for developers who live in the command line, with optional integration for VS Code, Zed, and JetBrains IDEs.

Installation

Linux / macOS

bash -c "$(curl -fsSL https://qwen-code-assets.oss-cn-hangzhou.aliyuncs.com/installation/install-qwen.sh)"

Windows (Run as Administrator)

Works in both Command Prompt and PowerShell:

powershell -Command "Invoke-WebRequest 'https://qwen-code-assets.oss-cn-hangzhou.aliyuncs.com/installation/install-qwen.bat' -OutFile (Join-Path $env:TEMP 'install-qwen.bat'); & (Join-Path $env:TEMP 'install-qwen.bat')"

Note

: It's recommended to restart your terminal after installation to ensure environment variables take effect.

Manual Installation

Prerequisites

Make sure you have Node.js 20 or later installed. Download it from nodejs.org.

NPM

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

Homebrew (macOS, Linux)

brew install qwen-code

Quick Start

# Start Qwen Code (interactive)
qwen

# Then, in the session:
/help
/auth

On first use, you'll be prompted to sign in. You can run /auth anytime to switch authentication methods.

Example prompts:

What does this project do?
Explain the codebase structure.
Help me refactor this function.
Generate unit tests for this module.
Click to watch a demo video

🦞 Use Qwen Code for Coding Tasks in Claw

Copy the prompt below and paste it 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.

Authentication

Qwen Code supports the following authentication methods:

  • API Key (recommended): use an API key from Alibaba Cloud Model Studio (Beijing / intl) or any supported provider (OpenAI, Anthropic, Google GenAI, and other compatible endpoints).
  • Coding Plan: subscribe to the Alibaba Cloud Coding Plan (Beijing / intl) for a fixed monthly fee with higher quotas.

⚠️ Qwen OAuth was discontinued on April 15, 2026. If you were previously using Qwen OAuth, please switch to one of the methods above. Run qwen and then /auth to reconfigure.

Use an API key to connect to Alibaba Cloud Model Studio or any supported provider. Supports multiple protocols:

  • OpenAI-compatible: Alibaba Cloud ModelStudio, ModelScope, OpenAI, OpenRouter, and other OpenAI-compatible providers
  • Anthropic: Claude models
  • Google GenAI: Gemini models

The recommended way to configure models and providers is by editing ~/.qwen/settings.json (create it if it doesn't exist). This file lets you define all available models, API keys, and default settings in one place.

Quick Setup in 3 Steps

Step 1: Create or edit ~/.qwen/settings.json

Here is a complete example:

{
  "modelProviders": {
    "openai": [
      {
        "id": "qwen3.6-plus",
        "name": "qwen3.6-plus",
        "baseUrl": "https://dashscope.aliyuncs.com/compatible-mode/v1",
        "description": "Qwen3-Coder via Dashscope",
        "envKey": "DASHSCOPE_API_KEY"
      }
    ]
  },
  "env": {
    "DASHSCOPE_API_KEY": "sk-xxxxxxxxxxxxx"
  },
  "security": {
    "auth": {
      "selectedType": "openai"
    }
  },
  "model": {
    "name": "qwen3.6-plus"
  }
}

Step 2: Understand each field

Field What it does
modelProviders Declares which models are available and how to connect to them. Keys like openai, anthropic, gemini represent the API protocol.
modelProviders[].id The model ID sent to the API (e.g. qwen3.6-plus, gpt-4o).
modelProviders[].envKey The name of the environment variable that holds your API key.
modelProviders[].baseUrl The API endpoint URL (required for non-default endpoints).
env A fallback place to store API keys (lowest priority; prefer .env files or export for sensitive keys).
security.auth.selectedType The protocol to use on startup (openai, anthropic, gemini, vertex-ai).
model.name The default model to use when Qwen Code starts.

Step 3: Start Qwen Code — your configuration takes effect automatically:

qwen

Use the /model command at any time to switch between all configured models.

More Examples
Coding Plan (Alibaba Cloud ModelStudio) — fixed monthly fee, higher quotas
{
  "modelProviders": {
    "openai": [
      {
        "id": "qwen3.6-plus",
        "name": "qwen3.6-plus (Coding Plan)",
        "baseUrl": "https://coding.dashscope.aliyuncs.com/v1",
        "description": "qwen3.6-plus from ModelStudio Coding Plan",
        "envKey": "BAILIAN_CODING_PLAN_API_KEY"
      },
      {
        "id": "qwen3.5-plus",
        "name": "qwen3.5-plus (Coding Plan)",
        "baseUrl": "https://coding.dashscope.aliyuncs.com/v1",
        "description": "qwen3.5-plus with thinking enabled from ModelStudio Coding Plan",
        "envKey": "BAILIAN_CODING_PLAN_API_KEY",
        "generationConfig": {
          "extra_body": {
            "enable_thinking": true
          }
        }
      },
      {
        "id": "glm-4.7",
        "name": "glm-4.7 (Coding Plan)",
        "baseUrl": "https://coding.dashscope.aliyuncs.com/v1",
        "description": "glm-4.7 with thinking enabled from ModelStudio Coding Plan",
        "envKey": "BAILIAN_CODING_PLAN_API_KEY",
        "generationConfig": {
          "extra_body": {
            "enable_thinking": true
          }
        }
      },
      {
        "id": "kimi-k2.5",
        "name": "kimi-k2.5 (Coding Plan)",
        "baseUrl": "https://coding.dashscope.aliyuncs.com/v1",
        "description": "kimi-k2.5 with thinking enabled from ModelStudio Coding Plan",
        "envKey": "BAILIAN_CODING_PLAN_API_KEY",
        "generationConfig": {
          "extra_body": {
            "enable_thinking": true
          }
        }
      }
    ]
  },
  "env": {
    "BAILIAN_CODING_PLAN_API_KEY": "sk-xxxxxxxxxxxxx"
  },
  "security": {
    "auth": {
      "selectedType": "openai"
    }
  },
  "model": {
    "name": "qwen3.6-plus"
  }
}

Subscribe to the Coding Plan and get your API key at Alibaba Cloud ModelStudio(Beijing) or Alibaba Cloud ModelStudio(intl).

Multiple providers (OpenAI + Anthropic + Gemini)
{
  "modelProviders": {
    "openai": [
      {
        "id": "gpt-4o",
        "name": "GPT-4o",
        "envKey": "OPENAI_API_KEY",
        "baseUrl": "https://api.openai.com/v1"
      }
    ],
    "anthropic": [
      {
        "id": "claude-sonnet-4-20250514",
        "name": "Claude Sonnet 4",
        "envKey": "ANTHROPIC_API_KEY"
      }
    ],
    "gemini": [
      {
        "id": "gemini-2.5-pro",
        "name": "Gemini 2.5 Pro",
        "envKey": "GEMINI_API_KEY"
      }
    ]
  },
  "env": {
    "OPENAI_API_KEY": "sk-xxxxxxxxxxxxx",
    "ANTHROPIC_API_KEY": "sk-ant-xxxxxxxxxxxxx",
    "GEMINI_API_KEY": "AIzaxxxxxxxxxxxxx"
  },
  "security": {
    "auth": {
      "selectedType": "openai"
    }
  },
  "model": {
    "name": "gpt-4o"
  }
}
Enable thinking mode (for supported models like qwen3.5-plus)
{
  "modelProviders": {
    "openai": [
      {
        "id": "qwen3.5-plus",
        "name": "qwen3.5-plus (thinking)",
        "envKey": "DASHSCOPE_API_KEY",
        "baseUrl": "https://dashscope.aliyuncs.com/compatible-mode/v1",
        "generationConfig": {
          "extra_body": {
            "enable_thinking": true
          }
        }
      }
    ]
  },
  "env": {
    "DASHSCOPE_API_KEY": "sk-xxxxxxxxxxxxx"
  },
  "security": {
    "auth": {
      "selectedType": "openai"
    }
  },
  "model": {
    "name": "qwen3.5-plus"
  }
}

Tip: You can also set API keys via export in your shell or .env files, which take higher priority than settings.jsonenv. See the authentication guide for full details.

Security note: Never commit API keys to version control. The ~/.qwen/settings.json file is in your home directory and should stay private.

Local Model Setup (Ollama / vLLM)

You can also run models locally — no API key or cloud account needed. This is not an authentication method; instead, configure your local model endpoint in ~/.qwen/settings.json using the modelProviders field.

Ollama setup
  1. Install Ollama from ollama.com
  2. Pull a model: ollama pull qwen3:32b
  3. Configure ~/.qwen/settings.json:
{
  "modelProviders": {
    "openai": [
      {
        "id": "qwen3:32b",
        "name": "Qwen3 32B (Ollama)",
        "baseUrl": "http://localhost:11434/v1",
        "description": "Qwen3 32B running locally via Ollama"
      }
    ]
  },
  "security": {
    "auth": {
      "selectedType": "openai"
    }
  },
  "model": {
    "name": "qwen3:32b"
  }
}
vLLM setup
  1. Install vLLM: pip install vllm
  2. Start the server: vllm serve Qwen/Qwen3-32B
  3. Configure ~/.qwen/settings.json:
{
  "modelProviders": {
    "openai": [
      {
        "id": "Qwen/Qwen3-32B",
        "name": "Qwen3 32B (vLLM)",
        "baseUrl": "http://localhost:8000/v1",
        "description": "Qwen3 32B running locally via vLLM"
      }
    ]
  },
  "security": {
    "auth": {
      "selectedType": "openai"
    }
  },
  "model": {
    "name": "Qwen/Qwen3-32B"
  }
}

Usage

As an open-source terminal agent, you can use Qwen Code in four primary ways:

  1. Interactive mode (terminal UI)
  2. Headless mode (scripts, CI)
  3. IDE integration (VS Code, Zed)
  4. TypeScript SDK

Interactive mode

cd your-project/
qwen

Run qwen in your project folder to launch the interactive terminal UI. Use @ to reference local files (for example @src/main.ts).

Headless mode

cd your-project/
qwen -p "your question"

Use -p to run Qwen Code without the interactive UI—ideal for scripts, automation, and CI/CD. Learn more: Headless mode.

IDE integration

Use Qwen Code inside your editor (VS Code, Zed, and JetBrains IDEs):

TypeScript SDK

Build on top of Qwen Code with the TypeScript SDK:

Commands & Shortcuts

Session Commands

  • /help - Display available commands
  • /clear - Clear conversation history
  • /compress - Compress history to save tokens
  • /stats - Show current session information
  • /bug - Submit a bug report
  • /exit or /quit - Exit Qwen Code

Keyboard Shortcuts

  • Ctrl+C - Cancel current operation
  • Ctrl+D - Exit (on empty line)
  • Up/Down - Navigate command history

Learn more about Commands

Tip: In YOLO mode (--yolo), vision switching happens automatically without prompts when images are detected. Learn more about Approval Mode

Configuration

Qwen Code can be configured via settings.json, environment variables, and CLI flags.

File Scope Description
~/.qwen/settings.json User (global) Applies to all your Qwen Code sessions. Recommended for modelProviders and env.
.qwen/settings.json Project Applies only when running Qwen Code in this project. Overrides user settings.

The most commonly used top-level fields in settings.json:

Field Description
modelProviders Define available models per protocol (openai, anthropic, gemini, vertex-ai).
env Fallback environment variables (e.g. API keys). Lower priority than shell export and .env files.
security.auth.selectedType The protocol to use on startup (e.g. openai).
model.name The default model to use when Qwen Code starts.

See the Authentication section above for complete settings.json examples, and the settings reference for all available options.

Benchmark Results

Terminal-Bench Performance

Agent Model Accuracy
Qwen Code Qwen3-Coder-480A35 37.5%
Qwen Code Qwen3-Coder-30BA3B 31.3%

Ecosystem

Looking for a graphical interface?

  • 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

Troubleshooting

If you encounter issues, check the troubleshooting guide.

Common issues:

  • Qwen OAuth free tier was discontinued on 2026-04-15: Qwen OAuth is no longer available. Run qwen/auth and switch to API Key or Coding Plan. See the Authentication section above for setup instructions.

To report a bug from within the CLI, run /bug and include a short title and repro steps.

Connect with Us

Acknowledgments

This project is based on Google Gemini CLI. We acknowledge and appreciate the excellent work of the Gemini CLI team. Our main contribution focuses on parser-level adaptations to better support Qwen-Coder models.