Qwen Agent开源框架，用于构建 Qwen 兼容的代理机器人

4.8 (5)

审阅者 Daniel Nikulshyn·更新 2026年7月

概览

Qwen Agent 是一个面向开发者的框架，用于构建基于 LLM 的应用程序，充分利用阿里巴巴 Qwen 模型在指令跟随、规划和工具使用方面的能力。它提供了助理、多智能体系统和工作流的构建块，支持调用函数、执行代码以及处理文档。该框架包含用于检索增强生成、浏览器和文件交互以及处理扩展上下文的工具，适用于研究助理、数据分析机器人和自定义聊天应用等任务。它以 Python 库的形式在 GitHub 上分发，并可与 Qwen 的 API 以及本地托管模型集成。

主要功能

函数调用和工具集成
多代理交叉控制工具
长文档和 RAG 支持
代码解释器和浏览器工具
与 Qwen API 和本地部署兼容
Python SDK 附带示例助理

价格

模型: Freemium
分类: AI Agents
评分: 4.8 / 5 (5)

使用场景

集成外部工具

构建 Qwen 代理机器人，通过 API 和接口将其集成到 Qwen 框架中，释放外部工具和服务的强大功能。

长上下文推理应用

开发 Qwen 代理机器人，利用长上下文推理能力提供决策和复杂数据和场景中了解更多细微差别。

优点 & 缺点

优点

开源并完全自定义
天然支持 Qwen 工具调用和长上下文
包含成熟的代理和 RAG 组件
兼容云端和自主部署的 Qwen 模型

缺点

主要针对 Qwen 模型进行优化
需要 Python 和开发者专业知识
文档偏向中文用户
生态系统规模小于 LangChain 或 LlamaIndex

评测

4.8

5 个评分的平均值。

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Mei-Ling Wong

May 20, 2026

Skeptical, then convinced

I went in skeptical — most tools in this space overpromise. It actually delivers on function calling and tool integration, and open source and freely customizable caught me off guard. still, I'd recommend giving it a real trial.

Liam O’Connor

Feb 23, 2026

Years in this space

I've evaluated a lot of these over the years. What stands out here is long-document and RAG support — handled better than most — and works with both cloud and self-hosted Qwen models. Worth the time if this is your use case.

Marcus Bell

Feb 11, 2026

Compared a few options

Evaluated this against two competitors. Where it wins: python SDK with example assistants and works with both cloud and self-hosted Qwen models. Where it lags: primarily optimized for Qwen models. On balance the feature set — especially python SDK with example assistants — justifies the 4 stars for our use case.

Sofia Lindqvist

Dec 6, 2025

Use it every day

Honestly didn't expect to like it this much. Code interpreter and browser tools is exactly what I needed, and native support for Qwen's tool calling and long context. but I reach for it almost every day now and it just clicks.

Victor Nguyen

Oct 26, 2025

Years in this space

I've evaluated a lot of these over the years. What stands out here is compatible with Qwen API and local deployments — handled better than most — and works with both cloud and self-hosted Qwen models. Worth the time if this is your use case.