---
title: deep-research
canonical_url: "https://www.modelscope.cn/studios/rust17/deep-research"
md_url: "https://www.modelscope.cn/studios/rust17/deep-research.md"
repository: rust17/deep-research
last_updated: 2026-04-02
sdk_type: docker
downloads: 0
stars: 0
---

# deep-research

> deep-research - rust17 在 ModelScope 创建的在线 Demo。An autonomous research assistant powered by LLMs, designed to perform deep information gathering, multi-step reasoning, and structured report synthesis.

rust17/deep-research 是 ModelScope 魔搭社区上的在线可交互 Demo（创空间），基于 docker 构建。

- **Repository**: rust17/deep-research
- **SDK**: docker
- **Downloads**: 0
- **Stars**: 0
- **Last updated**: 2026-04-02

Source: https://www.modelscope.cn/studios/rust17/deep-research

---

# Deep Research Agent

An autonomous research assistant powered by LLMs, designed to perform deep information gathering, multi-step reasoning, and structured report synthesis.

[**🌐 Try it Online**](https://modelscope.cn/studios/rust17/deep-research)

## 🌟 Key Features

- **Multi-Agent Orchestration**: A specialized `Orchestrator` decomposes goals into sub-tasks, delegated to focused `SubAgents` for deep investigation.
- **Robust ReAct Framework**: Built on a unified `BaseReActAgent` abstraction, ensuring consistent reasoning, acting, and context management across all agent roles.
- **Real-time Event Streaming**: Powered by a custom `StreamHandler`, providing live feedback on Thoughts, Actions, and tool outputs.
- **Powerful Toolset**:
  - **Smart Search**: Integrated with DuckDuckGo for up-to-date web discovery.
  - **High-Precision Scraping**: Uses `Trafilatura` and `Playwright` for clean content extraction and JavaScript rendering support.
  - **Multi-Format Parsing**: Built-in support for PDF analysis and Markdown conversion via `MarkItDown`.
- **Interactive UI**: A modern Streamlit-based dashboard for visualizing the research process and managing loops.

## 🛠️ Technical Stack

- **Core**: Python 3.12+
- **Agent Framework**: Custom ReAct orchestration with OpenAI-compatible API integration.
- **UI**: Streamlit
- **Package Management**: [uv](https://github.com/astral-sh/uv)

## 🚀 Quick Start

### 1. Installation

Ensure you have [uv](https://github.com/astral-sh/uv) installed, then run:

```bash
# Clone the repository
git clone <repo-url>
cd deep-research

# Install dependencies and setup virtual environment
uv sync
```

### 2. Configuration

Create a `.env` file in the project root:

```env
OPENAI_API_KEY=your_api_key_here
OPENAI_BASE_URL=https://api.openai.com/v1
MODEL_NAME=gpt-4o  # Or your preferred model
```

### 3. Usage

Start the interactive research interface:

```bash
uv run dr
```

## 📂 Project Structure

- `src/deep_research/`
  - `agents/`: Core agent roles (`Orchestrator`, `SubAgent`, `Validator`) inheriting from a shared `BaseAgent`.
  - `core/`: Infrastructure layer including `LLMClient`, `Log`, and `StreamHandler`.
  - `tools/`: Extensible tool modules and the `ToolRegistry` manager.
  - `prompts/`: Role-specific system instructions and templates.
  - `models.py`: Shared data models for events and pulses.
  - `config.py`: Global configuration and environment management.
  - `gui.py`: Streamlit frontend implementation.

## 🔗 Reference

- [MiroThinker](https://github.com/MiroMindAI/MiroThinker)

## 📜 License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.