* refactor(core): rewrite compression prompt to 9-section claude-code-style format
Replaces the <state_snapshot> XML template with a numbered 9-section
structure that mandates verbatim preservation of user messages, including
the historical chronological list (section 6). The new format is
designed to pair with post-compact file/image restoration (separate work)
so the agent can resume long single-turn tasks without losing intent.
* refactor(core): align compaction trigger string with new 9-section prompt
The user-turn trigger injected after the system prompt still said
'generate the <state_snapshot>' from the old XML prompt era. Updated to
'produce the 9-section summary' to match Task 1's new prompt format.
Also tightens the prompt test to assert the specific user-message
verbatim mandate (not just the word 'verbatim' anywhere) so a future
regression that drops the mandate won't silently pass.
* feat(core): add postCompactAttachments module with file path extractor
extractRecentFilePaths walks history newest-first and returns the top N
unique file paths touched by read_file/write_file/edit/replace tool calls.
Pure function, no side effects, no state cache — readiness for the next
compaction-rewrite tasks.
* refactor(core): simplify extractRecentFilePaths internals
Three small cleanups from code review:
- Map<string, number> -> Set<string> (the index value was never read)
- Guard against maxFiles <= 0 explicitly (avoids returning 1 result
when caller passes 0 as a 'disable' sentinel)
- Document 'replace' as a legacy alias for 'edit' so a future cleanup
pass does not delete it as apparent dead code
Adds one test covering the maxFiles=0 path.
* feat(core): add image extractor with source-tool metadata
extractRecentImages walks history newest-first, collects up to N image
inlineData parts, and attributes each one to the model+functionCall that
preceded it (when one exists). Returns chronological order so callers
can render a meaningful 'last visual state ends here' strip.
* feat(core): add size-adaptive file reader for post-compact restore
readFileSizeAdaptive reads a file and returns one of: embed (full content
for files ≤ maxTokens × 4 chars), reference (path-only for large files),
missing (deleted since last touch), or binary (non-text content). The
embed/reference distinction mirrors claude-code's compact_file_reference
vs file attachment behavior, but without introducing new message types.
* refactor(core): harden readFileSizeAdaptive size accounting
Three corrections from code review:
- Import CHARS_PER_TOKEN from tokenEstimation.ts (canonical) instead of
redeclaring locally, preventing silent drift between modules.
- Compare decoded character length, not raw byte length, against the
cap. Otherwise a 10k-char Chinese file would be ~30k bytes and would
be mis-classified as 'reference' despite fitting the budget.
- Rename FileReadResult -> FileEmbedResult to avoid a name collision
with the unrelated FileReadResult interface in fileUtils.ts.
Adds a CJK-text test that catches the byte/char regression.
* feat(core): add file restoration block composer
buildFileRestorationBlocks reads each candidate file, classifies it as
embed/reference/missing/binary, and emits one consolidated reference
block (path-only list) plus one user message per embedded small file.
Total embed size is capped at POST_COMPACT_TOKEN_BUDGET; over-budget
files downgrade to reference.
* test(core): make budget test actually exercise the downgrade path
The previous version of this test wrote 3 files totalling 9k chars
against a 200k char budget. The assertions trivially passed regardless
of whether the budget check existed in the implementation.
The new version writes 11 files of 20k chars (each at the per-file cap)
so the budget is exhausted by the 10th and the 11th must downgrade
from embed to reference. Asserts both: file 11 appears in the reference
block, and file 11's content does NOT appear in any embed block.
* feat(core): add image restoration block composer
buildImageRestorationBlock emits a single user message whose first part
is a metadata header (turn index + source tool name + args per image),
followed by the inlineData parts themselves. Handles user-paste images
(no source tool) by labeling them as 'user-provided'.
* feat(core): add composePostCompactHistory orchestrator
Assembles the full post-compact history in order:
summary → model ack → file references → file embeds → image block.
Each section is built by the per-concern extractors and builders added
in previous tasks. This is the single integration point that
chatCompressionService.compress() will call once the wire-up task lands.
* feat(core)!: rewrite compress() to claude-code-style full-history model
Replaces the split-point + tail-preservation model with full-history
compression + composePostCompactHistory. The entire curated history is
sent to the summary side-query, and the post-compact history is
assembled by the new composer (summary + ack + file restores + image
restore).
BREAKING: the previously-exported findCompressSplitPoint,
splitPointRetainingTrailingPairs, COMPRESSION_PRESERVE_THRESHOLD, and
TOOL_ROUND_RETAIN_COUNT will be removed in the next commit. Tests that
exercise them remain failing temporarily.
* chore(core): remove obsolete split-point compression infrastructure
Deletes findCompressSplitPoint, splitPointRetainingTrailingPairs,
COMPRESSION_PRESERVE_THRESHOLD, MIN_COMPRESSION_FRACTION, and
TOOL_ROUND_RETAIN_COUNT, plus the tests that exercised them. The new
behavior is covered by composePostCompactHistory and its unit tests.
Also cleans up:
- Stale orphan-strip comment in compress() that described the deleted
manual-trigger orphan-funcCall handling.
- TEST_ONLY.COMPRESSION_PRESERVE_THRESHOLD hatch in client.ts.
- Docstring references in config.ts and compactionInputSlimming.ts.
* test(core): add single-turn computer-use compaction regression
Reproduces the scenario the rewrite targets: one user prompt kicks off
many screenshot tool calls. Asserts that (a) the user prompt is carried
into the summary verbatim and (b) the 3 most recent screenshots are
restored as an image block with source-tool metadata. This is the canary
test for the computer-use UX claim made in the design discussion.
* docs(core): remove stale "split point" references in tokenEstimation comments
Aligns the docstrings with the new compose-based compression flow. The
"split point" and "splitter" concepts no longer exist after the rewrite.
* fix(core): iterate parts reverse so parallel tool calls keep the last N
Real-session E2E surfaced a bug: a model that issues N parallel ReadFile
calls puts all N functionCall parts in ONE model+fc content. The
extractor's outer history walk is newest-first, but the inner parts
walk was forward — so for a 6-parallel batch hitting the cap of 5,
the FIRST 5 parts won and the actually-most-recent (last-listed) file
was dropped.
Fix: walk parts in reverse within each content. Applied symmetrically
to extractRecentImages (same shape, even rarer trigger).
Adds a regression test that hits a 6-parallel batch.
* fix(core): code-review fixes — fence escape, path sanitize, alias removal
- CommonMark-safe fence in file embed blocks. The old 3-backtick fence
closed prematurely when a file's content contained a triple-backtick
run (Markdown, CLAUDE.md, JSDoc with code examples) — leaking the
remainder as unfenced text. Now uses a fence one longer than the
longest backtick run in the content.
- Strip control characters (\r, \n, \t) from file paths before
rendering into attachment markdown. Paths come from model-controlled
history; a \n could inject markdown structure. The actual path stays
intact for tool calls — only the displayed string is sanitized.
- Remove the historyForCompression alias for curatedHistory in
compress(). The alias was added as a comment anchor during the
rewrite but didn't carry semantic information.
* refactor(core): rewrite compression prompt to <state_snapshot> XML with 9 claude-aligned sections
Replaces the 9-section numbered-text prompt with qwen-code's original
<state_snapshot> XML envelope, but with the 9 inner section tags
content-aligned to claude-code:
<primary_request_and_intent>
<key_technical_concepts>
<files_and_code_sections>
<errors_and_fixes>
<problem_solving>
<all_user_messages>
<pending_tasks>
<current_work>
<next_step>
Also:
- <scratchpad> -> <analysis>, stripped by postProcessSummary (saves
~600-800 tokens of CoT noise per compaction).
- "Resume directly..." trailer moved out of the prompt body and into
postProcessSummary (no longer re-generated by the model every
compaction; lives once in code with our own wording).
- Section 6 verbatim-policed mandate relaxed to "chronological, include
short messages like 'ok' / 'continue'" — matches claude-code intent
without forcing the model to literally copy long user messages.
E2E (qwen3.6-plus, 6 substantial .ts files + thorough analysis):
raw history 6508 -> summary 1513 (after strip ~947), 38% history
compression. Overall context 24642 -> 20647 reported (-16%), with
another ~664 tokens actually saved by the post-strip but not
reflected in the conservative token-math heuristic.
* docs(core): code-review polish on XML prompt rewrite
Four small follow-ups from review of
|
||
|---|---|---|
| .github | ||
| .husky | ||
| .qwen | ||
| .vscode | ||
| docs | ||
| docs-site | ||
| eslint-rules | ||
| integration-tests | ||
| packages | ||
| scripts | ||
| .dockerignore | ||
| .editorconfig | ||
| .gitattributes | ||
| .gitignore | ||
| .npmrc | ||
| .nvmrc | ||
| .prettierignore | ||
| .prettierrc.json | ||
| .yamllint.yml | ||
| AGENTS.md | ||
| CONTRIBUTING.md | ||
| Dockerfile | ||
| esbuild.config.js | ||
| eslint.config.js | ||
| LICENSE | ||
| Makefile | ||
| package-lock.json | ||
| package.json | ||
| README.md | ||
| SECURITY.md | ||
| tsconfig.json | ||
| vitest.config.ts | ||
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 authto 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
Quick Install (Recommended)
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 22 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
qwenand then/authto reconfigure.
API Key (recommended)
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
exportin your shell or.envfiles, which take higher priority thansettings.json→env. See the authentication guide for full details.
Security note: Never commit API keys to version control. The
~/.qwen/settings.jsonfile 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.
Set generationConfig.contextWindowSize inside the matching provider entry
and adjust it to the context length configured on your local server.
Ollama setup
- Install Ollama from ollama.com
- Pull a model:
ollama pull qwen3:32b - 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",
"generationConfig": {
"contextWindowSize": 131072
}
}
]
},
"security": {
"auth": {
"selectedType": "openai"
}
},
"model": {
"name": "qwen3:32b"
}
}
vLLM setup
- Install vLLM:
pip install vllm - Start the server:
vllm serve Qwen/Qwen3-32B - 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",
"generationConfig": {
"contextWindowSize": 131072
}
}
]
},
"security": {
"auth": {
"selectedType": "openai"
}
},
"model": {
"name": "Qwen/Qwen3-32B"
}
}
Usage
As an open-source terminal agent, you can use Qwen Code in five primary ways:
- Interactive mode (terminal UI)
- Headless mode (scripts, CI)
- IDE integration (VS Code, Zed)
- SDKs (TypeScript, Python, Java)
- Daemon mode —
qwen serveexposes ACP over HTTP+SSE so multiple clients share one agent (experimental)
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):
Daemon mode (qwen serve, experimental)
cd your-project/
qwen serve
# → qwen serve listening on http://127.0.0.1:4170 (mode=http-bridge)
Run Qwen Code as a local HTTP daemon so IDE plugins, web UIs, CI scripts and custom CLIs all share one agent session over HTTP+SSE — instead of each spawning their own subprocess. Loopback bind has no auth by default (set QWEN_SERVER_TOKEN to enable bearer auth even on loopback); remote binds (--hostname 0.0.0.0) require a token — boot refuses without one. See:
SDKs
Build on top of Qwen Code with the available SDKs:
- TypeScript: Use the Qwen Code SDK
- Python: Use the Python SDK
- Java: Use the Java SDK
Python SDK example:
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())
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/exitor/quit- Exit Qwen Code
Keyboard Shortcuts
Ctrl+C- Cancel current operationCtrl+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.jsonexamples, 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. Runqwen→/authand 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
- Discord: https://discord.gg/RN7tqZCeDK
- Dingtalk: https://qr.dingtalk.com/action/joingroup?code=v1,k1,+FX6Gf/ZDlTahTIRi8AEQhIaBlqykA0j+eBKKdhLeAE=&_dt_no_comment=1&origin=1
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.
