# WebAgent
**Repository Path**: mirrors/WebAgent
## Basic Information
- **Project Name**: WebAgent
- **Description**: WebAgent 是自主搜索 AI Agent 项目，包含两部分： WebDancer：端到端智能体训练框架，旨在增强基于网络的 AI 智能体的多步骤信息搜索能力 WebWalke
- **Primary Language**: Python
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: https://www.oschina.net/p/webagent
- **GVP Project**: No
## Statistics
- **Stars**: 3
- **Forks**: 0
- **Created**: 2025-05-30
- **Last Updated**: 2025-10-18
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
[](https://huggingface.co/Alibaba-NLP/Tongyi-DeepResearch-30B-A3B)
[](https://github.com/Alibaba-NLP/DeepResearch)
[](https://tongyi-agent.github.io/blog/introducing-tongyi-deep-research/)
🤗 HuggingFace ｜
ModelScope | 💬 WeChat(微信)
👏 Welcome to try Tongyi DeepResearch via our **[ Modelscope online demo](https://www.modelscope.cn/studios/jialongwu/Tongyi-DeepResearch)** or **[🤗 Huggingface online demo](https://huggingface.co/spaces/Alibaba-NLP/Tongyi-DeepResearch)** or 
**[bailian service](https://bailian.console.aliyun.com/?spm=a2ty02.31808181.d_app-market.1.6c4974a1tFmoFc&tab=app#/app/app-market/deep-search/)**!
> [!NOTE]
> This demo is for quick exploration only. Response times may vary or fail intermittently due to model latency and tool QPS limits. For a stable experience we recommend local deployment; for a production-ready service, visit [bailian](https://bailian.console.aliyun.com/?spm=a2ty02.31808181.d_app-market.1.6c4974a1tFmoFc&tab=app#/app/app-market/deep-search/) and follow the guided setup.
# Introduction
We present **Tongyi DeepResearch**, an agentic large language model featuring 30.5 billion total parameters, with only 3.3 billion activated per token. Developed by Tongyi Lab, the model is specifically designed for **long-horizon, deep information-seeking** tasks. Tongyi DeepResearch demonstrates state-of-the-art performance across a range of agentic search benchmarks, including Humanity's Last Exam, BrowserComp, BrowserComp-ZH, WebWalkerQA,xbench-DeepSearch, FRAMES and SimpleQA.
> Tongyi DeepResearch builds upon our previous work on the [WebAgent](./WebAgent/) project.
More details can be found in our 📰 Tech Blog.
## Features
- ⚙️ **Fully automated synthetic data generation pipeline**: We design a highly scalable data synthesis pipeline, which is fully automatic and empowers agentic pre-training, supervised fine-tuning, and reinforcement learning.
- 🔄 **Large-scale continual pre-training on agentic data**: Leveraging diverse, high-quality agentic interaction data to extend model capabilities, maintain freshness, and strengthen reasoning performance.
- 🔁 **End-to-end reinforcement learning**: We employ a strictly on-policy RL approach based on a customized Group Relative Policy Optimization framework, with token-level policy gradients, leave-one-out advantage estimation, and selective filtering of negative samples to stabilize training in a non‑stationary environment.
- 🤖 **Agent Inference Paradigm Compatibility**: At inference, Tongyi DeepResearch is compatible with two inference paradigms: ReAct, for rigorously evaluating the model's core intrinsic abilities, and an IterResearch-based 'Heavy' mode, which uses a test-time scaling strategy to unlock the model's maximum performance ceiling.
# Model Download
You can directly download the model by following the links below.
| Model | Download Links | Model Size | Context Length |
| :-----------------: | :-----------------------------------------: | :----------: | :--------------: |
| Tongyi-DeepResearch-30B-A3B | [🤗 HuggingFace](https://huggingface.co/Alibaba-NLP/Tongyi-DeepResearch-30B-A3B)
[🤖 ModelScope](https://modelscope.cn/models/iic/Tongyi-DeepResearch-30B-A3B) | 30B-A3B | 128K |
# News
[2025/09/20]🚀 Tongyi-DeepResearch-30B-A3B is now on [OpenRouter](https://openrouter.ai/alibaba/tongyi-deepresearch-30b-a3b)! Follow the [Quick-start](https://github.com/Alibaba-NLP/DeepResearch?tab=readme-ov-file#6-you-can-use-openrouters-api-to-call-our-model) guide.
[2025/09/17]🔥 We have released **Tongyi-DeepResearch-30B-A3B**.
# Deep Research Benchmark Results
## Quick Start
This guide provides instructions for setting up the environment and running inference scripts located in the [inference](./inference/) folder.
### 1. Environment Setup
- Recommended Python version: **3.10.0** (using other versions may cause dependency issues).
- It is strongly advised to create an isolated environment using `conda` or `virtualenv`.
```bash
# Example with Conda
conda create -n react_infer_env python=3.10.0
conda activate react_infer_env
```
### 2. Installation
Install the required dependencies:
```bash
pip install -r requirements.txt
```
### 3. Environment Configuration and Prepare Evaluation Data
#### Environment Configuration
Configure your API keys and settings by copying the example environment file:
```bash
# Copy the example environment file
cp .env.example .env
```
Edit the `.env` file and provide your actual API keys and configuration values:
- **SERPER_KEY_ID**: Get your key from [Serper.dev](https://serper.dev/) for web search and Google Scholar
- **JINA_API_KEYS**: Get your key from [Jina.ai](https://jina.ai/) for web page reading
- **API_KEY/API_BASE**: OpenAI-compatible API for page summarization from [OpenAI](https://platform.openai.com/)
- **DASHSCOPE_API_KEY**: Get your key from [Dashscope](https://dashscope.aliyun.com/) for file parsing
- **SANDBOX_FUSION_ENDPOINT**: Python interpreter sandbox endpoints (see [SandboxFusion](https://github.com/bytedance/SandboxFusion))
- **MODEL_PATH**: Path to your model weights
- **DATASET**: Name of your evaluation dataset
- **OUTPUT_PATH**: Directory for saving results
> **Note**: The `.env` file is gitignored, so your secrets will not be committed to the repository.
#### Prepare Evaluation Data
The system supports two input file formats: **JSON** and **JSONL**.
#### Supported File Formats:
**Option 1: JSONL Format (recommended)**
- Create your data file with `.jsonl` extension (e.g., `my_questions.jsonl`)
- Each line must be a valid JSON object with `question` and `answer` keys:
```json
{"question": "What is the capital of France?", "answer": "Paris"}
{"question": "Explain quantum computing", "answer": ""}
```
**Option 2: JSON Format**
- Create your data file with `.json` extension (e.g., `my_questions.json`)
- File must contain a JSON array of objects, each with `question` and `answer` keys:
```json
[
{"question": "What is the capital of France?", "answer": "Paris"},
{"question": "Explain quantum computing", "answer": ""}
]
```
**Important Note:** The `answer` field contains the **ground truth/reference answer** used for evaluation. The system generates its own responses to the questions, and these reference answers are used to automatically judge the quality of the generated responses during benchmark evaluation.
#### File References for Document Processing:
- If using the *file parser* tool, **prepend the filename to the `question` field**
- Place referenced files in `eval_data/file_corpus/` directory
- Example: `{"question": "report.pdf What are the key findings?", "answer": "..."}`
#### File Organization:
```
project_root/
├── eval_data/
│ ├── my_questions.jsonl # Your evaluation data
│ └── file_corpus/ # Referenced documents
│ ├── report.pdf
│ └── data.xlsx
```
### 4. Configure the Inference Script
- Open `run_react_infer.sh` and modify the following variables as instructed in the comments:
* `MODEL_PATH` - path to the local or remote model weights.
* `DATASET` - full path to your evaluation file, e.g. `eval_data/my_questions.jsonl` or `/path/to/my_questions.json`.
* `OUTPUT_PATH` - path for saving the prediction results, e.g. `./outputs`.
- Depending on the tools you enable (retrieval, calculator, web search, etc.), provide the required `API_KEY`, `BASE_URL`, or other credentials. Each key is explained inline in the bash script.
### 5. Run the Inference Script
```bash
bash run_react_infer.sh
```
---
With these steps, you can fully prepare the environment, configure the dataset, and run the model. For more details, consult the inline comments in each script or open an issue.
### 6. You can use OpenRouter's API to call our model
Tongyi-DeepResearch-30B-A3B is now available at [OpenRouter](https://openrouter.ai/alibaba/tongyi-deepresearch-30b-a3b). You can run the inference without any GPUs.
You need to modify the following in the file [inference/react_agent.py](https://github.com/Alibaba-NLP/DeepResearch/blob/main/inference/react_agent.py):
- In the call_server function: Set the API key and URL to your OpenRouter account’s API and URL.
- Change the model name to alibaba/tongyi-deepresearch-30b-a3b.
- Adjust the content concatenation way as described in the comments on lines **88–90.**
## Benchmark Evaluation
We provide benchmark evaluation scripts for various datasets. Please refer to the [evaluation scripts](./evaluation/) directory for more details.
## Deep Research Agent Family
Tongyi DeepResearch also has an extensive deep research agent family. You can find more information in the following paper:
[1] [WebWalker: Benchmarking LLMs in Web Traversal](https://arxiv.org/pdf/2501.07572) (ACL 2025)
[2] [WebDancer: Towards Autonomous Information Seeking Agency](https://arxiv.org/pdf/2505.22648) (NeurIPS 2025)
[3] [WebSailor: Navigating Super-human Reasoning for Web Agent](https://arxiv.org/pdf/2507.02592)
[4] [WebShaper: Agentically Data Synthesizing via Information-Seeking Formalization](https://arxiv.org/pdf/2507.15061)
[5] [WebWatcher: Breaking New Frontier of Vision-Language Deep Research Agent](https://arxiv.org/pdf/2508.05748)
[6] [WebResearcher: Unleashing unbounded reasoning capability in Long-Horizon Agents](https://arxiv.org/pdf/2509.13309)
[7] [ReSum: Unlocking Long-Horizon Search Intelligence via Context Summarization](https://arxiv.org/pdf/2509.13313)
[8] [WebWeaver: Structuring Web-Scale Evidence with Dynamic Outlines for Open-Ended Deep Research](https://arxiv.org/pdf/2509.13312)
[9] [WebSailor-V2: Bridging the Chasm to Proprietary Agents via Synthetic Data and Scalable Reinforcement Learning](https://arxiv.org/pdf/2509.13305)
[10] [Scaling Agents via Continual Pre-training](https://arxiv.org/pdf/2509.13310)
[11] [Towards General Agentic Intelligence via Environment Scaling](https://arxiv.org/pdf/2509.13311)
## 🌟 Misc
[](https://www.star-history.com/#Alibaba-NLP/DeepResearch&Date)
## 🚩 Talent Recruitment
🔥🔥🔥 We are hiring! Research intern positions are open (based in Hangzhou、Beijing、Shanghai)
📚 **Research Area**：Web Agent, Search Agent, Agent RL, MultiAgent RL, Agentic RAG
☎️ **Contact**：[yongjiang.jy@alibaba-inc.com]()
## Contact Information
For communications, please contact Yong Jiang (yongjiang.jy@alibaba-inc.com).
## Citation
```bibtex
@misc{tongyidr,
author={Tongyi DeepResearch Team},
title={Tongyi DeepResearch: A New Era of Open-Source AI Researchers},
year={2025},
howpublished={\url{https://github.com/Alibaba-NLP/DeepResearch}}
}
``` 
