QwenLM/qwen-code

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

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Summary Information

Updated 12 minutes ago
Added to GitGenius on February 18th, 2026
Created on June 26th, 2025
Open Issues & Pull Requests: 1,747 (-3)
GitHub issues: Enabled
Number of forks: 3,172
Total Stargazers: 28,369 (+2)
Total Subscribers: 150 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 2.7 hours
Mean response time: 8.4 days
90th percentile: 10.6 days
Tracked items: 5,123

How this project is maintained

About 7% of issues opened in the past year have never received a reply. 48% of open issues come from outside the core team, a mix of external reports and the maintainers' own roadmap. 22% of tracked open issues have had no activity in three months. 73% of issues opened in the past year have been closed, leaving a working backlog. Three people close 62% of everything that gets resolved.

Charts & Analytics

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Issue Activity (beta)

Open issues: 1,408
New in 7 days: 228
Closed in 7 days: 87
Avg open age: 59 days
Stale 30+ days: 786
Stale 90+ days: 249

Recent activity

Opened in 7 days: 198
Closed in 7 days: 81
Comments in 7 days: 268
Events in 7 days: 774

Top labels

  • type/bug (3,075)
  • priority/P2 (1,530)
  • type/feature-request (1,200)
  • status/needs-triage (1,188)
  • category/core (870)
  • priority/P3 (675)
  • status/ready-for-agent (543)
  • category/ui (412)

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Detailed Description

Qwen Code is an open-source AI coding agent that runs in your terminal.

The tool addresses the need for an agentic coding assistant by combining multiple capabilities into a single framework. It operates as an autonomous agent with built-in memory, skill discovery, and support for sub-agents and agent teams. The system uses the Model Context Protocol (MCP) to extend functionality dynamically without requiring manual setup. Rather than locking users into a single model provider, it supports multiple API backends including OpenAI, Anthropic, Gemini, and Qwen, plus local models through Ollama and vLLM, allowing runtime switching between providers.

Developers should choose this tool if they want an agentic coding assistant that avoids vendor lock-in through its open-source framework and model flexibility. It suits teams and individuals who prefer terminal-based workflows but need access to IDE plugins, desktop applications, or integration with messaging platforms like Telegram, DingTalk, WeChat, and Feishu. The multi-protocol support makes it particularly valuable for organizations with heterogeneous model deployments or those wanting to experiment with different LLM providers without rewriting their setup.

The project receives issue reports from both core maintainers and external users, indicating adoption beyond the immediate team without creating an unsustainable support burden. Maintainers typically respond to new issues and pull requests within a day. Work in the issue tracker centers on bug fixes, feature requests, and core functionality improvements.