base on Tongyi DeepResearch, the Leading Open-source DeepResearch Agent <div align="center"> <picture> <img src="./assets/logo.png" width="100%"> </picture> </div> <hr> <div align="center" style="line-height: 1;"> [](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/) </div> <p align="center"> 🤗 <a href="https://huggingface.co/Alibaba-NLP/Tongyi-DeepResearch-30B-A3B" target="_blank">HuggingFace</a> ｜ <img src="./assets/tongyi.png" width="14px" style="display:inline;"> <a href="https://modelscope.cn/models/iic/Tongyi-DeepResearch-30B-A3B" target="_blank">ModelScope</a> <p align="center"> <a href="https://trendshift.io/repositories/14217" target="_blank"><img src="https://trendshift.io/api/badge/repositories/14217" alt="Alibaba-NLP%2FWebAgent | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a> # Introduction We present <img src="./assets/tongyi.png" width="14px" style="display:inline;"> **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 <img src="./assets/tongyi.png" width="14px" style="display:inline;"> [WebAgent](./WebAgent/) project. More details can be found in our 📰&nbsp;<a href="https://tongyi-agent.github.io/blog/introducing-tongyi-deep-research/">Tech Blog</a>. <p align="center"> <img width="100%" src="./assets/performance.png"> </p> ## 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)<br> [🤖 ModelScope](https://modelscope.cn/models/iic/Tongyi-DeepResearch-30B-A3B) | 30B-A3B | 128K | # News [2025/09/17]🔥 We have released **Tongyi-DeepResearch-30B-A3B**. # Deep Research Benchmark Results <p align="center"> <img width="100%" src="./assets/benchmark.png"> </p> ## 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. Prepare Evaluation Data - Create a folder named `eval_data/` in the project root. - Place your QA file in **JSONL** format inside this directory, e.g. `eval_data/example.jsonl`. - Each line must be a JSON object that includes **both** of the following keys: ```json {"question": "...","answer": "..."} ``` - A sample file is provided in the `eval_data` folder for reference. - If you plan to use the *file parser* tool, **prepend the file name to the `question` field** and place the referenced file inside the `eval_data/file_corpus/` directory. ### 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` - path to the evaluation set, e.g. `example`. * `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. ## 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 <p align="center"> <img width="100%" src="./assets/family.png"> </p> 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)<br> [2] [WebDancer: Towards Autonomous Information Seeking Agency](https://arxiv.org/pdf/2505.22648)<br> [3] [WebSailor: Navigating Super-human Reasoning for Web Agent](https://arxiv.org/pdf/2507.02592)<br> [4] [WebShaper: Agentically Data Synthesizing via Information-Seeking Formalization](https://arxiv.org/pdf/2507.15061)<br> [5] [WebWatcher: Breaking New Frontier of Vision-Language Deep Research Agent](https://arxiv.org/pdf/2508.05748)<br> [6] [WebResearcher: Unleashing unbounded reasoning capability in Long-Horizon Agents](https://arxiv.org/pdf/2509.13309)<br> [7] [ReSum: Unlocking Long-Horizon Search Intelligence via Context Summarization](https://arxiv.org/pdf/2509.13313)<br> [8] [WebWeaver: Structuring Web-Scale Evidence with Dynamic Outlines for Open-Ended Deep Research](https://arxiv.org/pdf/2509.13312)<br> [9] [WebSailor-V2: Bridging the Chasm to Proprietary Agents via Synthetic Data and Scalable Reinforcement Learning](https://arxiv.org/pdf/2509.13305)<br> [10] [Scaling Agents via Continual Pre-training](https://arxiv.org/pdf/2509.13310)<br> [11] [Towards General Agentic Intelligence via Environment Scaling](https://arxiv.org/pdf/2509.13311) ## 🌟 Misc <div align="center"> [](https://www.star-history.com/#Alibaba-NLP/DeepResearch&Date) </div> ## 🚩 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 ```bibtex @misc{tongyidr, author={Tongyi DeepResearch Team}, title={Tongyi-DeepResearch}, year={2025}, howpublished={\url{https://github.com/Alibaba-NLP/DeepResearch}} } ```", Assign "at most 3 tags" to the expected json: {"id":"14895","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"