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Qwen Agent

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Basic Information

Qwen-Agent is a developer-focused framework for building LLM-powered applications and agents based on the Qwen family of models. It provides components and patterns for instruction following, tool usage, multi-step planning, and memory integration, and ships example apps including a Browser Assistant, Code Interpreter and Custom Assistant. The project is used as the backend of Qwen Chat and supports deployment against hosted model services or self-hosted OpenAI-compatible endpoints. It targets developers who want modular building blocks for agents, offering both low-level primitives and higher-level agent implementations to compose conversational, tool-enabled, and retrieval-augmented workflows. The repo includes documentation, examples, and optional GUI support for rapid demo deployment.

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10726
App URL
https://github.com/QwenLM/Qwen-Agent
Github Repository
https://github.com/QwenLM/Qwen-Agent/blob/main/README.md

App Details

Features
The repository exposes atomic components (LLM classes inheriting from BaseChatModel, tools inheriting from BaseTool) and higher-level agents (Agent, Assistant). It supports function calling and parallel function calls with configurable templates and parsing behavior, and provides built-in tools like a code interpreter and RAG integrations. MCP integration and cookbooks enable external memory and filesystem servers including sqlite. There is Gradio-based GUI support, example scripts for Qwen3, QwQ, Qwen2.5-Math and vLLM/Ollama model services, demos for tool-call workflows, and options to pass LLM generation parameters including a use_raw_api mode. The repo also documents deployment and environment prerequisites.
Use Cases
Qwen-Agent accelerates development of tool-enabled conversational systems by supplying reusable components, example agents, and demo code so teams can prototype and deploy LLM applications quickly. It simplifies integrating external model services, configuring generation parameters, and adding custom tools or file-reading capabilities. Built-in support for function calling and parallel tool invocation helps implement complex multi-step behaviors and automation flows. RAG examples and a fast long-document QA solution enable scaling to very large contexts. MCP support provides standardized memory and storage server patterns. A Gradio WebUI option lets developers expose interactive demos without building custom frontends. The README notes the code interpreter executes locally and is not sandboxed.

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