🤗 HuggingFace ｜ ModelScope | 💬 WeChat(微信) | 📰 Blog | 📑 Paper

👏 Welcome to try Tongyi DeepResearch via our Modelscope online demo or 🤗 Huggingface online demo or bailian service!

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 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, BrowseComp, BrowseComp-ZH, WebWalkerQA,xbench-DeepSearch, FRAMES and SimpleQA.

Tongyi DeepResearch builds upon our previous work on the 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 🤖 ModelScope	30B-A3B	128K

News

[2025/09/20]🚀 Tongyi-DeepResearch-30B-A3B is now on OpenRouter! Follow the Quick-start 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 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.

# Example with Conda
conda create -n react_infer_env python=3.10.0
conda activate react_infer_env

2. Installation

Install the required dependencies:

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:

# 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 for web search and Google Scholar
JINA_API_KEYS: Get your key from Jina.ai for web page reading
API_KEY/API_BASE: OpenAI-compatible API for page summarization from OpenAI or MiniMax
MINIMAX_API_KEY: (Optional) Get your key from MiniMax — can be used as the summary model (see MiniMax Integration)
DASHSCOPE_API_KEY: Get your key from Dashscope for file parsing
SANDBOX_FUSION_ENDPOINT: Python interpreter sandbox endpoints (see 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:

{"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:

[
  { "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": "(Uploaded 1 file: ['report.pdf'])\n\nWhat 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 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. You can run the inference without any GPUs.

You need to modify the following in the file 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.

7. Using MiniMax as the Summary Model

MiniMax provides OpenAI-compatible LLM APIs that can be used as the summary model for page summarization. MiniMax models (e.g., MiniMax-M2.7, MiniMax-M2.7-highspeed) offer 204K context length and enhanced reasoning and coding capabilities, which are well-suited for long document summarization tasks.

Option A: Set MiniMax as the summary model directly

In your .env file:

API_KEY=your_minimax_api_key
API_BASE=https://api.minimax.io/v1
SUMMARY_MODEL_NAME=MiniMax-M2.7

Option B: Use MINIMAX_API_KEY as a fallback

If API_KEY is not set, the system will automatically fall back to using MINIMAX_API_KEY with MiniMax's API endpoint:

MINIMAX_API_KEY=your_minimax_api_key

Using MiniMax in the qwen-agent framework

MiniMax is also registered as an LLM provider in the qwen-agent framework. You can use it by specifying model_type: 'minimax' or by using a model name containing "minimax":

from qwen_agent.llm import get_chat_model

# Auto-detected by model name
llm = get_chat_model({'model': 'MiniMax-M2.7'})

# Or explicitly specify the provider
llm = get_chat_model({
    'model': 'MiniMax-M2.7',
    'model_type': 'minimax',
    'generate_cfg': {
        'temperature': 0.7,
        'max_tokens': 4096,
    }
})

Benchmark Evaluation

We provide benchmark evaluation scripts for various datasets. Please refer to the evaluation scripts directory for more details.

FAQ

Please refer to the FAQ 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 (ACL 2025)
[2] WebDancer: Towards Autonomous Information Seeking Agency (NeurIPS 2025)
[3] WebSailor: Navigating Super-human Reasoning for Web Agent
[4] WebShaper: Agentically Data Synthesizing via Information-Seeking Formalization
[5] WebWatcher: Breaking New Frontier of Vision-Language Deep Research Agent
[6] WebResearcher: Unleashing unbounded reasoning capability in Long-Horizon Agents
[7] ReSum: Unlocking Long-Horizon Search Intelligence via Context Summarization
[8] WebWeaver: Structuring Web-Scale Evidence with Dynamic Outlines for Open-Ended Deep Research
[9] WebSailor-V2: Bridging the Chasm to Proprietary Agents via Synthetic Data and Scalable Reinforcement Learning
[10] Scaling Agents via Continual Pre-training
[11] Towards General Agentic Intelligence via Environment Scaling
[12] AgentFold: Long-Horizon Web Agents with Proactive Context Management
[13] WebLeaper: Empowering Efficient, Info-Rich Seeking for Web Agents
[14] BrowseConf: Confidence-Guided Test-Time Scaling for Web Agents
[15] Repurposing Synthetic Data for Fine-grained Search Agent Supervision
[16] ParallelMuse: Agentic Parallel Thinking for Deep Information Seeking
[17] AgentFrontier: Expanding the Capability Frontier of LLM Agents with ZPD-Guided Data Synthesis
[18] Nested Browser-Use Learning for Agentic Information Seeking

🌟 Misc

🚩 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：[email protected]

Contact Information

For communications, please contact Yong Jiang ([email protected]).

Citation

@article{tongyidr,
  title={Tongyi DeepResearch Technical Report},
  author={Team, Tongyi DeepResearch and Li, Baixuan and Zhang, Bo and Zhang, Dingchu and Huang, Fei and Li, Guangyu and Chen, Guoxin and Yin, Huifeng and Wu, Jialong and Zhou, Jingren and others},
  journal={arXiv preprint arXiv:2510.24701},
  year={2025}
}

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Introduction

Features

Model Download

News

Deep Research Benchmark Results

Quick Start

1. Environment Setup

2. Installation

3. Environment Configuration and Prepare Evaluation Data

Environment Configuration

Prepare Evaluation Data

Supported File Formats:

File References for Document Processing:

File Organization:

4. Configure the Inference Script

5. Run the Inference Script

6. You can use OpenRouter's API to call our model

7. Using MiniMax as the Summary Model

Benchmark Evaluation

FAQ

Deep Research Agent Family

🌟 Misc

🚩 Talent Recruitment

Contact Information

Citation

FilesExpand file tree

README.md

Latest commit

History

README.md

File metadata and controls

Introduction

Features

Model Download

News

Deep Research Benchmark Results

Quick Start

1. Environment Setup

2. Installation

3. Environment Configuration and Prepare Evaluation Data

Environment Configuration

Prepare Evaluation Data

Supported File Formats:

File References for Document Processing:

File Organization:

4. Configure the Inference Script

5. Run the Inference Script

6. You can use OpenRouter's API to call our model

7. Using MiniMax as the Summary Model

Benchmark Evaluation

FAQ

Deep Research Agent Family

🌟 Misc

🚩 Talent Recruitment

Contact Information

Citation