[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-2543":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":23,"hasPages":25,"topics":26,"createdAt":10,"pushedAt":10,"updatedAt":36,"readmeContent":37,"aiSummary":38,"trendingCount":16,"starSnapshotCount":16,"syncStatus":39,"lastSyncTime":40,"discoverSource":41},2543,"DeepResearch","Alibaba-NLP\u002FDeepResearch","Alibaba-NLP","Tongyi Deep Research, the Leading Open-source Deep Research Agent","https:\u002F\u002Ftongyi-agent.github.io\u002Fblog\u002Fintroducing-tongyi-deep-research\u002F",null,"Python",19359,1481,127,81,0,9,126,517,52,44.51,"Apache License 2.0",false,"main",true,[27,28,29,30,31,32,33,34,35],"agent","alibaba","artificial-intelligence","deep-research","deepresearch","information-seeking","llm","tongyi","web-agent","2026-06-12 02:00:42","\u003Cdiv align=\"center\">\n  \u003Cpicture>\n      \u003Cimg src=\".\u002Fassets\u002Flogo.png\" width=\"100%\">\n  \u003C\u002Fpicture>\n\u003C\u002Fdiv>\n\n\u003Chr>\n\n\u003Cdiv align=\"center\" style=\"line-height: 1;\">\n\n[![MODELS](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FModels-5EDDD2?style=for-the-badge&logo=huggingface&logoColor=ffffff&labelColor)](https:\u002F\u002Fhuggingface.co\u002FAlibaba-NLP\u002FTongyi-DeepResearch-30B-A3B)\n[![GITHUB](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGithub-24292F?style=for-the-badge&logo=github&logoColor=white)](https:\u002F\u002Fgithub.com\u002FAlibaba-NLP\u002FDeepResearch)\n[![Blog](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FBlog-4285F4?style=for-the-badge&logo=google-chrome&logoColor=white)](https:\u002F\u002Ftongyi-agent.github.io\u002Fblog\u002Fintroducing-tongyi-deep-research\u002F)\n[![Paper](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FPaper-red?style=for-the-badge&logo=arxiv&logoColor=white)](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2510.24701)\n\n\u003C\u002Fdiv>\n\u003Cp align=\"center\">\n\u003Cp align=\"center\">\n🤗 \u003Ca href=\"https:\u002F\u002Fhuggingface.co\u002FAlibaba-NLP\u002FTongyi-DeepResearch-30B-A3B\" target=\"_blank\">HuggingFace\u003C\u002Fa> ｜\n\u003Cimg src=\".\u002Fassets\u002Ftongyi.png\" width=\"14px\" style=\"display:inline;\"> \u003Ca href=\"https:\u002F\u002Fmodelscope.cn\u002Fmodels\u002Fiic\u002FTongyi-DeepResearch-30B-A3B\" target=\"_blank\">ModelScope\u003C\u002Fa> | 💬 \u003Ca href=\".\u002Fassets\u002Fwechat_new.jpg\">WeChat(微信)\u003C\u002Fa> | 📰 \u003Ca href=\"https:\u002F\u002Ftongyi-agent.github.io\u002Fblog\u002Fintroducing-tongyi-deep-research\u002F\">Blog\u003C\u002Fa> | 📑 \u003Ca href=\"https:\u002F\u002Farxiv.org\u002Fpdf\u002F2510.24701\">Paper\u003C\u002Fa>\n\n\u003Cp align=\"center\">\n\u003Ca href=\"https:\u002F\u002Ftrendshift.io\u002Frepositories\u002F14895\" target=\"_blank\">\u003Cimg src=\"https:\u002F\u002Ftrendshift.io\u002Fapi\u002Fbadge\u002Frepositories\u002F14895\" alt=\"Alibaba-NLP%2FDeepResearch | Trendshift\" style=\"width: 250px; height: 55px;\" width=\"250\" height=\"55\"\u002F>\u003C\u002Fa>\n\n👏 Welcome to try Tongyi DeepResearch via our **[\u003Cimg src=\".\u002Fassets\u002Ftongyi.png\" width=\"14px\" style=\"display:inline;\"> Modelscope online demo](https:\u002F\u002Fwww.modelscope.cn\u002Fstudios\u002Fjialongwu\u002FTongyi-DeepResearch)** or **[🤗 Huggingface online demo](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002FAlibaba-NLP\u002FTongyi-DeepResearch)** or \u003Cimg src=\".\u002FWebAgent\u002Fassets\u002Faliyun.png\" width=\"14px\" style=\"display:inline;\"> **[bailian service](https:\u002F\u002Fbailian.console.aliyun.com\u002F?spm=a2ty02.31808181.d_app-market.1.6c4974a1tFmoFc&tab=app#\u002Fapp\u002Fapp-market\u002Fdeep-search\u002F)**!\n\n> [!NOTE]\n> 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 \u003Cimg src=\".\u002FWebAgent\u002Fassets\u002Faliyun.png\" width=\"14px\" style=\"display:inline;\"> [bailian](https:\u002F\u002Fbailian.console.aliyun.com\u002F?spm=a2ty02.31808181.d_app-market.1.6c4974a1tFmoFc&tab=app#\u002Fapp\u002Fapp-market\u002Fdeep-search\u002F) and follow the guided setup.\n\n# Introduction\n\nWe present \u003Cimg src=\".\u002Fassets\u002Ftongyi.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, BrowseComp, BrowseComp-ZH, WebWalkerQA,xbench-DeepSearch, FRAMES and SimpleQA.\n\n> Tongyi DeepResearch builds upon our previous work on the \u003Cimg src=\".\u002Fassets\u002Ftongyi.png\" width=\"14px\" style=\"display:inline;\"> [WebAgent](.\u002FWebAgent\u002F) project.\n\nMore details can be found in our 📰 \u003Ca href=\"https:\u002F\u002Ftongyi-agent.github.io\u002Fblog\u002Fintroducing-tongyi-deep-research\u002F\">Tech Blog\u003C\u002Fa>.\n\n\u003Cp align=\"center\">\n  \u003Cimg width=\"100%\" src=\".\u002Fassets\u002Fperformance.png\">\n\u003C\u002Fp>\n\n## Features\n\n- ⚙️ **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.\n- 🔄 **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.\n- 🔁 **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.\n- 🤖 **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.\n\n# Model Download\n\nYou can directly download the model by following the links below.\n\n|            Model            |                                                                           Download Links                                                                           | Model Size | Context Length |\n| :-------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------: | :--------: | :------------: |\n| Tongyi-DeepResearch-30B-A3B | [🤗 HuggingFace](https:\u002F\u002Fhuggingface.co\u002FAlibaba-NLP\u002FTongyi-DeepResearch-30B-A3B)\u003Cbr> [🤖 ModelScope](https:\u002F\u002Fmodelscope.cn\u002Fmodels\u002Fiic\u002FTongyi-DeepResearch-30B-A3B) |  30B-A3B   |      128K      |\n\n# News\n\n[2025\u002F09\u002F20]🚀 Tongyi-DeepResearch-30B-A3B is now on [OpenRouter](https:\u002F\u002Fopenrouter.ai\u002Falibaba\u002Ftongyi-deepresearch-30b-a3b)! Follow the [Quick-start](https:\u002F\u002Fgithub.com\u002FAlibaba-NLP\u002FDeepResearch?tab=readme-ov-file#6-you-can-use-openrouters-api-to-call-our-model) guide.\n\n[2025\u002F09\u002F17]🔥 We have released **Tongyi-DeepResearch-30B-A3B**.\n\n# Deep Research Benchmark Results\n\u003Cp align=\"center\">\n  \u003Cimg width=\"100%\" src=\".\u002Fassets\u002Fbenchmark.png\">\n\u003C\u002Fp>\n\n## Quick Start\n\nThis guide provides instructions for setting up the environment and running inference scripts located in the [inference](.\u002Finference\u002F) folder.\n\n### 1. Environment Setup\n- Recommended Python version: **3.10.0** (using other versions may cause dependency issues).\n- It is strongly advised to create an isolated environment using `conda` or `virtualenv`.\n\n```bash\n# Example with Conda\nconda create -n react_infer_env python=3.10.0\nconda activate react_infer_env\n```\n\n### 2. Installation\n\nInstall the required dependencies:\n```bash\npip install -r requirements.txt\n```\n\n### 3. Environment Configuration and Prepare Evaluation Data\n\n#### Environment Configuration\n\nConfigure your API keys and settings by copying the example environment file:\n\n```bash\n# Copy the example environment file\ncp .env.example .env\n```\n\nEdit the `.env` file and provide your actual API keys and configuration values:\n\n- **SERPER_KEY_ID**: Get your key from [Serper.dev](https:\u002F\u002Fserper.dev\u002F) for web search and Google Scholar\n- **JINA_API_KEYS**: Get your key from [Jina.ai](https:\u002F\u002Fjina.ai\u002F) for web page reading\n- **API_KEY\u002FAPI_BASE**: OpenAI-compatible API for page summarization from [OpenAI](https:\u002F\u002Fplatform.openai.com\u002F)\n- **DASHSCOPE_API_KEY**: Get your key from [Dashscope](https:\u002F\u002Fdashscope.aliyun.com\u002F) for file parsing\n- **SANDBOX_FUSION_ENDPOINT**: Python interpreter sandbox endpoints (see [SandboxFusion](https:\u002F\u002Fgithub.com\u002Fbytedance\u002FSandboxFusion))\n- **MODEL_PATH**: Path to your model weights\n- **DATASET**: Name of your evaluation dataset\n- **OUTPUT_PATH**: Directory for saving results\n\n> **Note**: The `.env` file is gitignored, so your secrets will not be committed to the repository.\n\n#### Prepare Evaluation Data\n\nThe system supports two input file formats: **JSON** and **JSONL**.\n\n#### Supported File Formats:\n\n**Option 1: JSONL Format (recommended)**\n- Create your data file with `.jsonl` extension (e.g., `my_questions.jsonl`)\n- Each line must be a valid JSON object with `question` and `answer` keys:\n  ```json\n  {\"question\": \"What is the capital of France?\", \"answer\": \"Paris\"}\n  {\"question\": \"Explain quantum computing\", \"answer\": \"\"}\n  ```\n\n**Option 2: JSON Format**\n- Create your data file with `.json` extension (e.g., `my_questions.json`)\n- File must contain a JSON array of objects, each with `question` and `answer` keys:\n  ```json\n  [\n    { \"question\": \"What is the capital of France?\", \"answer\": \"Paris\" },\n    { \"question\": \"Explain quantum computing\", \"answer\": \"\" }\n  ]\n  ```\n\n**Important Note:** The `answer` field contains the **ground truth\u002Freference 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.\n\n#### File References for Document Processing:\n\n- If using the _file parser_ tool, **prepend the filename to the `question` field**\n- Place referenced files in `eval_data\u002Ffile_corpus\u002F` directory\n- Example: `{\"question\": \"(Uploaded 1 file: ['report.pdf'])\\n\\nWhat are the key findings?\", \"answer\": \"...\"}`\n\n#### File Organization:\n```\nproject_root\u002F\n├── eval_data\u002F\n│   ├── my_questions.jsonl          # Your evaluation data\n│   └── file_corpus\u002F                # Referenced documents\n│       ├── report.pdf\n│       └── data.xlsx\n```\n\n### 4. Configure the Inference Script\n\n- Open `run_react_infer.sh` and modify the following variables as instructed in the comments:\n  - `MODEL_PATH` - path to the local or remote model weights.\n  - `DATASET` - full path to your evaluation file, e.g. `eval_data\u002Fmy_questions.jsonl` or `\u002Fpath\u002Fto\u002Fmy_questions.json`.\n  - `OUTPUT_PATH` - path for saving the prediction results, e.g. `.\u002Foutputs`.\n- 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.\n\n### 5. Run the Inference Script\n\n```bash\nbash run_react_infer.sh\n```\n---\n\nWith 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.\n\n### 6. You can use OpenRouter's API to call our model\n\nTongyi-DeepResearch-30B-A3B is now available at [OpenRouter](https:\u002F\u002Fopenrouter.ai\u002Falibaba\u002Ftongyi-deepresearch-30b-a3b). You can run the inference without any GPUs.\n\nYou need to modify the following in the file [inference\u002Freact_agent.py](https:\u002F\u002Fgithub.com\u002FAlibaba-NLP\u002FDeepResearch\u002Fblob\u002Fmain\u002Finference\u002Freact_agent.py):\n\n- In the call_server function: Set the API key and URL to your OpenRouter account’s API and URL.\n- Change the model name to alibaba\u002Ftongyi-deepresearch-30b-a3b.\n- Adjust the content concatenation way as described in the comments on lines **88–90.**\n\n## Benchmark Evaluation\n\nWe provide benchmark evaluation scripts for various datasets. Please refer to the [evaluation scripts](.\u002Fevaluation\u002F) directory for more details.\n\n## FAQ\n\nPlease refer to the [FAQ](.\u002FFAQ.md) for more details.\n\n## Deep Research Agent Family\n\n\u003Cp align=\"center\">\n  \u003Cimg width=\"100%\" src=\".\u002Fassets\u002Ffamily17.png\">\n\u003C\u002Fp>\n\nTongyi DeepResearch also has an extensive deep research agent family. You can find more information in the following paper:\n\n[1] [WebWalker: Benchmarking LLMs in Web Traversal](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2501.07572) (ACL 2025)\u003Cbr>\n[2] [WebDancer: Towards Autonomous Information Seeking Agency](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2505.22648) (NeurIPS 2025)\u003Cbr>\n[3] [WebSailor: Navigating Super-human Reasoning for Web Agent](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2507.02592)\u003Cbr>\n[4] [WebShaper: Agentically Data Synthesizing via Information-Seeking Formalization](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2507.15061)\u003Cbr>\n[5] [WebWatcher: Breaking New Frontier of Vision-Language Deep Research Agent](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2508.05748)\u003Cbr>\n[6] [WebResearcher: Unleashing unbounded reasoning capability in Long-Horizon Agents](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2509.13309)\u003Cbr>\n[7] [ReSum: Unlocking Long-Horizon Search Intelligence via Context Summarization](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2509.13313)\u003Cbr>\n[8] [WebWeaver: Structuring Web-Scale Evidence with Dynamic Outlines for Open-Ended Deep Research](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2509.13312)\u003Cbr>\n[9] [WebSailor-V2: Bridging the Chasm to Proprietary Agents via Synthetic Data and Scalable Reinforcement Learning](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2509.13305)\u003Cbr>\n[10] [Scaling Agents via Continual Pre-training](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2509.13310)\u003Cbr>\n[11] [Towards General Agentic Intelligence via Environment Scaling](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2509.13311)\u003Cbr>\n[12] [AgentFold: Long-Horizon Web Agents with Proactive Context Management](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2510.24699)\u003Cbr>\n[13] [WebLeaper: Empowering Efficient, Info-Rich Seeking for Web Agents](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2510.24697)\u003Cbr>\n[14] [BrowseConf: Confidence-Guided Test-Time Scaling for Web Agents](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2510.23458)\u003Cbr>\n[15] [Repurposing Synthetic Data for Fine-grained Search Agent Supervision](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2510.24694)\u003Cbr>\n[16] [ParallelMuse: Agentic Parallel Thinking for Deep Information Seeking](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2510.24698)\u003Cbr>\n[17] [AgentFrontier: Expanding the Capability Frontier of LLM Agents with ZPD-Guided Data Synthesis](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2510.24695)\u003Cbr>\n[18] [Nested Browser-Use Learning for Agentic Information Seeking](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2512.23647)\u003Cbr>\n\n## 🌟 Misc\n\n\u003Cdiv align=\"center\">\n\n[![Star History Chart](https:\u002F\u002Fapi.star-history.com\u002Fsvg?repos=Alibaba-NLP\u002FDeepResearch&type=Date)](https:\u002F\u002Fwww.star-history.com\u002F#Alibaba-NLP\u002FDeepResearch&Date)\n\n\u003C\u002Fdiv>\n\n## 🚩 Talent Recruitment\n\n🔥🔥🔥 We are hiring! Research intern positions are open (based in Hangzhou、Beijing、Shanghai)\n\n📚 **Research Area**：Web Agent, Search Agent, Agent RL, MultiAgent RL, Agentic RAG\n\n☎️ **Contact**：[yongjiang.jy@alibaba-inc.com]()\n\n## Contact Information\n\nFor communications, please contact Yong Jiang (yongjiang.jy@alibaba-inc.com).\n\n## Citation\n\n```bibtex\n@article{tongyidr,\n  title={Tongyi DeepResearch Technical Report},\n  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},\n  journal={arXiv preprint arXiv:2510.24701},\n  year={2025}\n}\n```\n","Tongyi DeepResearch 是一个专为长时间深度信息检索任务设计的开源研究代理。它基于一个具有305亿参数的大规模语言模型，每处理一个token仅激活33亿参数，这使得它在处理复杂查询时既高效又强大。该项目利用了先进的自然语言处理技术，支持多轮对话和上下文理解，能够从互联网上获取并整合信息，以帮助用户进行深入的研究或学习。适用于需要大量信息搜集与分析的场景，比如学术研究、市场调研等。项目采用Python编写，并遵循Apache License 2.0许可协议。",2,"2026-06-11 02:50:16","top_language"]