Qwen-Coder is a dedicated code model trained on 5.5 trillion tokens across 92 programming languages. Cursor and Windsurf use it as the open-source base for their internal coding models. In this challenge you build a local code review agent using the Qwen-Agent framework - a production-grade open-source Python framework maintained by the Qwen team for building agents with function calling, tool use, and MCP support.

Everything runs locally on your machine via Ollama. No API key. No account. No data leaves your environment. Qwen Chat guides you through the build one step at a time. When you are done you have a working agent you can point at any Python file and get a structured code review back.

Before opening Qwen Chat, run these three commands in your terminal:

pip install qwen-agent

curl -fsSL https://ollama.ai/install.sh | sh

ollama pull qwen2.5-coder:7b

The Ollama pull downloads Qwen-Coder to your machine. It runs about 4GB. Once it is done you have a local inference server ready at localhost:11434 that the Qwen-Agent framework can call directly. No further setup needed.

Open chat.qwen.ai in a browser tab. Paste this prompt to begin:

I want to build a local code review agent using the Qwen-Agent Python framework connected to Qwen-Coder running via Ollama at localhost:11434. The agent should accept a file path as input, read the file, and return a structured code review covering: code quality issues, potential bugs, readability problems, and specific improvement suggestions. Ask me one question at a time to guide me through building this. Start by asking me what programming language I want to review first.

A working local code review agent: a single Python file using the Qwen-Agent framework, connected to Qwen-Coder via Ollama, that accepts any Python file path and returns a structured review with flagged issues and specific suggestions. Run it from your terminal. Point it at your own projects. Keep it.

The agent runs entirely on your machine. Your code never leaves your environment. Qwen-Coder does the analysis. Qwen-Agent handles the tool loop. You built it in one session guided by Qwen Chat.

Qwen is Alibaba's open-weight AI model family - use Qwen Chat to guide you through building a working local code review agent using the Qwen-Agent framework and Qwen-Coder running entirely on your own machine.

Build a Local Code Review Agent With Qwen-Coder

Airbnb CEO Brian Chesky said publicly that Airbnb relies heavily on Qwen to power its AI-driven customer service agent. His exact words: "It is very good. It is also fast and cheap." Airbnb tested ChatGPT for the same role and found its integration abilities were not ready. Qwen was.

Airbnb uses 13 different AI models across its platform. Qwen is the one the CEO named by name. The deciding factors were native multilingual support, speed, and cost. Airbnb fine-tuned it on 500 million reviews and over a million support interactions. The agent you built in this challenge is a prototype of exactly what Airbnb runs in production.

Cursor and Windsurf - two of the most widely used AI coding tools among developers - both use Qwen-Coder as the open-source base for their internal models. The same model you just pulled via Ollama is what their teams chose when they needed a foundation for production coding assistance.

That choice was not made because Qwen-Coder was free. It was made because it performed. Apache 2.0 licensing meant they could deploy it commercially without usage fees or permission requests. The agent you built today runs on the same foundation.

Our open-weight models now account for over 45% of all OpenRouter API traffic, up from under 2% a year ago. Qwen is the largest contributor to that shift. Among developers choosing models for coding workloads specifically, Qwen-Coder and its derivatives account for roughly half of all coding tokens on OpenRouter.

This is not a niche or experimental choice. It is becoming the default infrastructure for AI-powered developer tools. The agent you built today puts you on the same foundation.

Airbnb, Cursor, and Windsurf all use Qwen in production. See how real production adoption looks like and why enterprises are choosing it.

Real Production Proof

Qwen Code is our open-source AI agent for the terminal, optimized for the Qwen model family. It understands large codebases, automates tedious work, and ships faster - with optional integration for VS Code, Zed, and JetBrains IDEs. Install it with one command and run it in any project folder. It is the next step after the agent you just built in this challenge.

We publish an SDK that gives you programmable access to the same engine that drives the Qwen Code CLI. If the agent you built in this challenge gave you ideas for something bigger - a custom tool, an automated workflow, a team-wide code quality system - the SDK is where to take it. Install with npm install @qwen-code/sdk.

Qwen Code is an open-source terminal agent built on Qwen-Coder - install it, point it at a codebase, and let it understand, automate, and ship.

Qwen Code: AI Agent for Your Terminal

Qwen runs global online hackathons for developers and AI engineers — agent-focused tracks with cash prizes and Qwen Cloud compute credits. Past events have included $70,000+ prize pools across five tracks covering memory agents, agentic coding, video generation, autonomous research, and edge AI.

Check for upcoming hackathons and competitions at
🔗 https://qwencloud-hackathon.devpost.com

We are looking for developers to represent Qwen in their communities - running meetups, creating technical content, and hosting workshops. Selected Ambassadors receive $100 in monthly cloud credits and direct access to the Qwen product and model teams. 

Apply at 
🔗 https://qwen.ai/ambassador

We are hiring engineers and researchers across AI infrastructure, model development, and applied AI. 

If building production agent systems is the direction you want to go professionally, open roles are listed at   
🔗 https://careers.alibabacloud.com/en/home?lang=en

Qwen is one of the most downloaded open source model families in the world - over 1 billion downloads, Apache 2.0 licensed, and built for the community to use, fine-tune, and build on. 

Whether you want to contribute, participate in events, or connect with the community, there are many ways to get involved.

Qwen

Build a Local Code Review Agent With Qwen-Coder

Real Production Proof

Qwen Code: AI Agent for Your Terminal

Opportunities to Learn and Work With Us