.1 200 OK Date: Thu, 02 Jul 2026 16:53:59 GMT Content-Type: text/html; charset=utf-8 Vary: X-PJAX, X-PJAX-Container, Turbo-Visit, Turbo-Frame, X-Requested-With, Sec-Fetch-Site,Accept-Encoding, Accept, X-Requested-With ETag: W/"563ef1aa4a8f6d2e7b65a34501152d39" Cache-Control: max-age=0, private, must-revalidate Strict-Transport-Security: max-age=31536000; includeSubdomains; preload X-Frame-Options: deny X-Content-Type-Options: nosniff X-XSS-Protection: 0 Referrer-Policy: no-referrer-when-downgrade Content-Security-Policy: default-src 'none'; base-uri 'self'; child-src github.githubassets.com github.com/assets-cdn/worker/ github.com/assets/ gist.github.com/assets-cdn/worker/; connect-src 'self' uploads.github.com www.githubstatus.com collector.github.com raw.githubusercontent.com api.github.com github-cloud.s3.amazonaws.com github-production-repository-file-5c1aeb.s3.amazonaws.com github-production-upload-manifest-file-7fdce7.s3.amazonaws.com github-production-user-asset-6210df.s3.amazonaws.com *.rel.tunnels.api.visualstudio.com wss://*.rel.tunnels.api.visualstudio.com github.githubassets.com objects-origin.githubusercontent.com copilot-proxy.githubusercontent.com proxy.individual.githubcopilot.com proxy.business.githubcopilot.com proxy.enterprise.githubcopilot.com *.actions.githubusercontent.com wss://*.actions.githubusercontent.com productionresultssa0.blob.core.windows.net productionresultssa1.blob.core.windows.net productionresultssa2.blob.core.windows.net productionresultssa3.blob.core.windows.net productionresultssa4.blob.core.windows.net productionresultssa5.blob.core.windows.net productionresultssa6.blob.core.windows.net productionresultssa7.blob.core.windows.net productionresultssa8.blob.core.windows.net productionresultssa9.blob.core.windows.net productionresultssa10.blob.core.windows.net productionresultssa11.blob.core.windows.net productionresultssa12.blob.core.windows.net productionresultssa13.blob.core.windows.net productionresultssa14.blob.core.windows.net productionresultssa15.blob.core.windows.net productionresultssa16.blob.core.windows.net productionresultssa17.blob.core.windows.net productionresultssa18.blob.core.windows.net productionresultssa19.blob.core.windows.net github-production-repository-image-32fea6.s3.amazonaws.com github-production-release-asset-2e65be.s3.amazonaws.com insights.github.com wss://alive.github.com wss://alive-staging.github.com api.githubcopilot.com api.individual.githubcopilot.com api.business.githubcopilot.com api.enterprise.githubcopilot.com wss://production-copilot-host.webpubsub.azure.com; font-src github.githubassets.com; form-action 'self' github.com gist.github.com copilot-workspace.githubnext.com objects-origin.githubusercontent.com; frame-ancestors 'none'; frame-src viewscreen.githubusercontent.com notebooks.githubusercontent.com; img-src 'self' data: blob: github.githubassets.com media.githubusercontent.com camo.githubusercontent.com identicons.github.com avatars.githubusercontent.com private-avatars.githubusercontent.com github-cloud.s3.amazonaws.com objects.githubusercontent.com release-assets.githubusercontent.com secured-user-images.githubusercontent.com user-images.githubusercontent.com private-user-images.githubusercontent.com opengraph.githubassets.com marketplace-screenshots.githubusercontent.com copilotprodattachments.blob.core.windows.net/github-production-copilot-attachments/ github-production-user-asset-6210df.s3.amazonaws.com customer-stories-feed.github.com spotlights-feed.github.com explore-feed.github.com objects-origin.githubusercontent.com *.githubusercontent.com; manifest-src 'self'; media-src github.com user-images.githubusercontent.com secured-user-images.githubusercontent.com private-user-images.githubusercontent.com github-production-user-asset-6210df.s3.amazonaws.com gist.github.com github.githubassets.com; script-src github.githubassets.com; style-src 'unsafe-inline' github.githubassets.com; upgrade-insecure-requests; worker-src github.githubassets.com github.com/assets-cdn/worker/ github.com/assets/ gist.github.com/assets-cdn/worker/ Server: github.com Accept-Ranges: bytes Set-Cookie: _gh_sess=YInIUCAnOwGJsSkAgnu5x%2FXtS%2F%2B1MuTeyI0I03%2FblAsbY%2BjPWCpIqGGS0TITKu8N7liwDVpIDLsuoD5kGE1eF1%2B4e0Asdqwi1XIuIH9XaqamcrzAAl%2BfT1qVnvc6x%2FqBQpSc0CzzS6JCFgBfBFrc6jUfys0EBE1iqLeQTiieMbyC1WHIU66CyVmN8yw53KYAReO6d98vr94%2B7UKw7JMm07tdYuLgVhWS9dBKBd%2FyDNOBNBnwNF%2B7MA9gVXqS3QJ7XYwycLHD2vedu6f7CDpviQ%3D%3D--VbYJr1ax9ZCGZPNY--mVEllDY27yZ4KeT1qxxZ2w%3D%3D; path=/; HttpOnly; secure; SameSite=Lax Set-Cookie: _octo=GH1.1.448724858.1783011238; expires=Fri, 02 Jul 2027 16:53:58 GMT; domain=.github.com; path=/; secure; SameSite=Lax Set-Cookie: logged_in=no; expires=Fri, 02 Jul 2027 16:53:58 GMT; domain=.github.com; path=/; HttpOnly; secure; SameSite=Lax X-GitHub-Request-Id: CBBA:23ED7E:48CAA4:643786:6A46979C Transfer-Encoding: chunked 8000 GitHub - Alibaba-NLP/DeepResearch: Tongyi Deep Research, the Leading Open-source Deep Research Agent · GitHub Skip to content Navigation Menu Toggle navigation Sign in Appearance settings Platform AI CODE CREATION GitHub Copilot Write better code with AI GitHub Copilot app Direct agents from issue to merge MCP Registry New Integrate external tools DEVELOPER WORKFLOWS Actions Automate any workflow Codespaces Instant dev environments Issues Plan and track work Code Review Manage code changes APPLICATION SECURITY GitHub Advanced Security Find and fix vulnerabilities Code security Secure your code as you build Secret protection Stop leaks before they start EXPLORE Why GitHub Documentation Blog Changelog Marketplace View all features Solutions BY COMPANY SIZE Enterprises Small and medium teams Startups Nonprofits BY USE CASE App Modernization DevSecOps DevOps CI/CD View all use cases BY INDUSTRY Healthcare Financial services Manufacturing Government View all industries View all solutions Resources EXPLORE BY TOPIC AI Software Development DevOps Security View all topics EXPLORE BY TYPE Customer stories Events & webinars Ebooks & reports Business insights GitHub Skills SUPPORT & SERVICES Documentation Customer support Community forum Trust center Partners View all resources Open Source COMMUNITY GitHub Sponsors Fund open source developers PROGRAMS Security Lab Maintainer Community Accelerator GitHub Stars Archive Program REPOSITORIES Topics Trending Collections Enterprise ENTERPRISE SOLUTIONS Enterprise platform AI-powered developer platform AVAILABLE ADD-ONS GitHub Advanced Security Enterprise-grade security features Copilot for Business Enterprise-grade AI features Premium Support Enterprise-grade 24/7 support Pricing Search or jump to... Search code, repositories, users, issues, pull requests... --> Search Clear Search syntax tips Provide feedback --> We read every piece of feedback, and take your input very seriously. Include my email address so I can be contacted Cancel Submit feedback Saved searches Use saved searches to filter your results more quickly --> Name Query To see all available qualifiers, see our documentation . Cancel Create saved search Sign in Sign up Appearance settings Resetting focus You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. You switched accounts on another tab or window. Reload to refresh your session. Dismiss alert {{ message }} Uh oh! There was an error while loading. Please reload this page . Alibaba-NLP / DeepResearch Public Notifications You must be signed in to change notification settings Fork 1.5k Star 19.6k Code Issues 82 Pull requests 9 Actions Projects Security and quality 0 Insights Additional navigation options Code Issues Pull requests Actions Projects Security and quality Insights Alibaba-NLP/DeepResearch main Branches Tags Go to file Code Open more actions menu Folders and files Name Name Last commit message Last commit date Latest commit History 300 Commits 300 Commits Agent Agent WebAgent WebAgent assets assets evaluation evaluation inference inference .env.example .env.example .gitignore .gitignore FAQ.md FAQ.md LICENSE LICENSE README.md README.md Tech_Report.pdf Tech_Report.pdf requirements.txt requirements.txt View all files Repository files navigation README Apache-2.0 license More items 🤗 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 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. 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 ： yongjiang.jy@alibaba-inc.com Contact Information For communications, please contact Yong Jiang ( yongjiang.jy@alibaba-inc.com ). 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 } } About Tongyi Deep Research, the Leading Open-source Deep Research Agent tongyi-agent.github.io/blog/introducing-tongyi-deep-research/ Topics agent artificial-intelligence alibaba web-agent information-seeking llm tongyi deep-research deepresearch Resources Readme License Apache-2.0 license Uh oh! There was an error while loading. Please reload this page . Activity Custom properties Stars 19.6k stars Watchers 126 watching Forks 1.5k forks Report repository Releases No releases published Packages 0 Uh oh! There was an error while loading. Please reload this page . Uh oh! There was an error while loading. Please reload this page . Contributors Uh oh! There was an error while loading. Please reload this page . Languages Python 97.7% Shell 2.2% Dockerfile 0.1% Footer © 2026 GitHub, Inc. Footer navigation Terms Privacy Security Status Community Docs Contact Manage cookies Do not share my personal information You can’t perform that action at this time. 0