GStars
    Alibaba-NLP

    Alibaba-NLP/DeepResearch

    Tongyi Deep Research, the Leading Open-source Deep Research Agent

    ai-agents
    ai
    llm
    agent
    alibaba
    artificial-intelligence
    deep-research
    deepresearch
    Python
    Apache-2.0
    20.1K stars
    1.5K forks
    127 GitHub watchers
    Updated Nov 10, 2026
    View on GitHub

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    Where it stands

    Rank, growth pace and release cadence relative to everything GStars tracks.

    The project lists 27 contributors on GitHub.

    Peaked at #2 in the weekly ranking the week of Sep 21, its best in the past year.

    Use Cases & Benefits

    • Implements a large language model agent specialized in long-horizon, deep information-seeking tasks using advanced reinforcement learning and synthetic data generation.
    • Delivers state-of-the-art performance on agentic search benchmarks by combining scalable data synthesis, continual pre-training, and token-level policy optimization.
    • Use for developing AI agents that perform complex web-based research and information retrieval over extended interactions.
    • Use for integrating advanced reasoning and search capabilities into applications requiring dynamic, multi-step question answering.
    • Use for experimenting with reinforcement learning techniques to improve large language model agents in non-stationary, real-world environments.

    Should you depend on it?

    License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.

    License

    Apache 2.0

    Permissive: commercial use, modification and redistribution are allowed with attribution; includes an explicit patent grant from contributors.

    Community files

    • README: present

    Watch-outs

    • No commits in the last six months (last push Feb 2026).
    • No GitHub releases; pin a commit or tag instead of a release.

    General guidance from repository metadata, not legal advice. How we score

    From the README

    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 project.

    More details can be found in our 📰 Tech Blog.

    You can directly download the model by following the links below.

    Excerpt from the Alibaba-NLP/DeepResearch README on GitHub (Apache-2.0 license). © its authors.Read the full README on GitHub

    Daily snapshots

    Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots

    Forks

    1.5K+27

    Open issues

    103+11

    Compared with peers in AI Agents

    Fastest-growing repositories GStars tracks in this cohort for the week of Nov 8, by 7-day star growth

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