qwen-agent-docs/website/content/en/guide/get_started/features.md
Qwen-Agent is a powerful and flexible framework for building intelligent LLM-powered applications. Key features include:
Unified Agent Interface
High-level Agent base class with ready-to-use implementations (e.g., Assistant, FnCallAgent) for rapid development.
Advanced Tool Calling Native support for parallel, multi-step, and multi-turn function/tool calls with automatic parsing and execution.
RAG Efficient document QA over 1M+ tokens using hybrid RAG and agent-based decomposition—outperforming native long-context models in benchmarks.
Built-in Tools Includes versatile tools out of the box:
code_interpreter: Execute Python codeweb_search and web_extractor: Perform web searches and extract page contentimage_search: Perform image searches with imageimage_zoom_in_tool: Zoom in on a specific region of an image by cropping it based on a bounding boxMCP (Model Context Protocol) Integration Seamlessly connect to external tools and services (e.g., github, filesystem, SQLite) via the open MCP standard.
Custom Tool Support
Easily define and register your own tools using the @register_tool decorator and BaseTool interface.
Multi-Model Compatibility Supports Qwen3, Qwen3-VL, Qwen3-Omni, Qwen3-Coder, QwQ, Qwen2.5 series, and other Qwen models via:
Built-in Tool Call Parser Adapted to Qwen's tool call template, qwen-agent can still use the model's tool calling capability normally when the model service does not support tool call parser. It also supports the use of the tool call parser that comes with the model service.
Context Management Automatically manage the long text of the agent to ensure that it does not exceed the maximum length of the model while ensuring the effectiveness of the agent.
Web GUI with Gradio
One-line launch of interactive web demos: WebUI(agent).run(). Built with Gradio 5.
Rich Agent Applications Reference implementations for:
Streaming & Interactive Output Full support for streaming responses with real-time token-by-token display.
Extensible Architecture Modular design: swap LLMs, tools, memory, and agent planning strategies independently for custom agentic AI.