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      "url": "https://github.com/K-Dense-AI/agentic-data-scientist",
      "verified": true
    },
    {
      "automation_level": "framework",
      "category": "research-agent",
      "description": "A library of specialized LLM agents (data cleaning, EDA, feature engineering, SQL, H2O AutoML, visualization) orchestrated by a supervisor to automate common data-science tasks.",
      "homepage": null,
      "id": "ai-data-science-team",
      "languages": [
        "python"
      ],
      "last_activity": "2025-12",
      "license": "MIT",
      "maintained": "active",
      "name": "AI Data Science Team",
      "owner_repo": "business-science/ai-data-science-team",
      "stars_approx": 5300,
      "subcategory": [
        "data-science",
        "eda",
        "automl",
        "end-to-end"
      ],
      "url": "https://github.com/business-science/ai-data-science-team",
      "verified": true
    },
    {
      "automation_level": "framework",
      "category": "research-agent",
      "description": "Fully autonomous research system (NeurIPS 2025) that runs the whole pipeline from literature review and idea generation through algorithm implementation to manuscript writing, primarily for AI/ML research.",
      "homepage": "https://novix.science/chat",
      "id": "ai-researcher",
      "languages": [
        "python"
      ],
      "last_activity": "2025-10",
      "license": "unverified",
      "maintained": "maintained",
      "name": "AI-Researcher",
      "owner_repo": "HKUDS/AI-Researcher",
      "stars_approx": 5400,
      "subcategory": [
        "end-to-end",
        "paper-generation",
        "literature-review",
        "experiment-automation",
        "idea-generation",
        "domain-ml"
      ],
      "url": "https://github.com/HKUDS/AI-Researcher",
      "verified": true
    },
    {
      "automation_level": "framework",
      "category": "research-agent",
      "description": "Tree-search ML-engineering agent that autonomously drafts, debugs, and benchmarks code to maximize a user-defined metric, reaching strong Kaggle/MLE-bench performance. (Overlaps with the data-science agent bucket.)",
      "homepage": "https://www.aide.ml/",
      "id": "aide-aideml",
      "languages": [
        "python"
      ],
      "last_activity": "2026-05",
      "license": "MIT",
      "maintained": "active",
      "name": "AIDE (aideml)",
      "owner_repo": "WecoAI/aideml",
      "stars_approx": 1300,
      "subcategory": [
        "experiment-automation",
        "code-generation",
        "domain-ml",
        "automl",
        "end-to-end"
      ],
      "url": "https://github.com/WecoAI/aideml",
      "verified": true
    },
    {
      "automation_level": "framework",
      "category": "research-agent",
      "description": "A modular multi-agent AI data-scientist platform (DSPy-based) automating cleaning, statistical analysis, scikit-learn modeling, and Plotly visualization.",
      "homepage": "https://www.autoanalyst.ai/",
      "id": "auto-analyst",
      "languages": [
        "typescript",
        "python"
      ],
      "last_activity": "2026-05",
      "license": "MIT",
      "maintained": "active",
      "name": "Auto-Analyst",
      "owner_repo": "FireBird-Technologies/Auto-Analyst",
      "stars_approx": 700,
      "subcategory": [
        "data-science",
        "eda",
        "automl",
        "end-to-end"
      ],
      "url": "https://github.com/FireBird-Technologies/Auto-Analyst",
      "verified": true
    },
    {
      "automation_level": "framework",
      "category": "research-agent",
      "description": "A cost-efficient open Deep Research alternative (built on the AutoAgent framework) that autonomously gathers and synthesizes web information; strong on GAIA.",
      "homepage": null,
      "id": "auto-deep-research",
      "languages": [
        "python"
      ],
      "last_activity": "2025-02",
      "license": "MIT",
      "maintained": "maintained",
      "name": "Auto-Deep-Research",
      "owner_repo": "HKUDS/Auto-Deep-Research",
      "stars_approx": 1600,
      "subcategory": [
        "deep-research"
      ],
      "url": "https://github.com/HKUDS/Auto-Deep-Research",
      "verified": true
    },
    {
      "automation_level": "framework",
      "category": "research-agent",
      "description": "A multi-agent system that transforms raw multimodal data (tabular, image, text, audio) into trained ML solutions end-to-end with zero human intervention, using MCTS-guided code generation over AutoGluon.",
      "homepage": null,
      "id": "autogluon-assistant-mlzero",
      "languages": [
        "python"
      ],
      "last_activity": "2026-03",
      "license": "Apache-2.0",
      "maintained": "active",
      "name": "AutoGluon Assistant (MLZero)",
      "owner_repo": "autogluon/autogluon-assistant",
      "stars_approx": 285,
      "subcategory": [
        "automl",
        "end-to-end",
        "multimodal"
      ],
      "url": "https://github.com/autogluon/autogluon-assistant",
      "verified": true
    },
    {
      "automation_level": "framework",
      "category": "research-agent",
      "description": "A multi-agent framework with five cooperating agents that autonomously complete Kaggle tabular competitions across six pipeline phases.",
      "homepage": null,
      "id": "autokaggle",
      "languages": [
        "python"
      ],
      "last_activity": "2024-12",
      "license": "Apache-2.0",
      "maintained": "maintained",
      "name": "AutoKaggle",
      "owner_repo": "multimodal-art-projection/AutoKaggle",
      "stars_approx": 303,
      "subcategory": [
        "automl",
        "eda",
        "end-to-end"
      ],
      "url": "https://github.com/multimodal-art-projection/AutoKaggle",
      "verified": true
    },
    {
      "automation_level": "framework",
      "category": "research-agent",
      "description": "An adaptive, knowledge-grounded data-science agent using an expert knowledge base plus agentic tree search to build ML pipelines (beats AIDE on MLE-bench).",
      "homepage": null,
      "id": "automind",
      "languages": [
        "python"
      ],
      "last_activity": "2025-10",
      "license": "MIT",
      "maintained": "active",
      "name": "AutoMind",
      "owner_repo": "innovatingAI/AutoMind",
      "stars_approx": 92,
      "subcategory": [
        "automl",
        "end-to-end"
      ],
      "url": "https://github.com/innovatingAI/AutoMind",
      "verified": true
    },
    {
      "automation_level": "framework",
      "category": "research-agent",
      "description": "Self-reinforcing 23-stage autonomous research pipeline (literature discovery, multi-agent hypothesis debate, sandboxed self-healing experiments, peer review, LaTeX export) that turns an idea into a conference-ready paper.",
      "homepage": "https://huggingface.co/papers/2605.20025",
      "id": "autoresearchclaw",
      "languages": [
        "python"
      ],
      "last_activity": "2026-06",
      "license": "MIT",
      "maintained": "active",
      "name": "AutoResearchClaw",
      "owner_repo": "aiming-lab/AutoResearchClaw",
      "stars_approx": 13100,
      "subcategory": [
        "end-to-end",
        "paper-generation",
        "literature-review",
        "experiment-automation",
        "idea-generation"
      ],
      "url": "https://github.com/aiming-lab/AutoResearchClaw",
      "verified": true
    },
    {
      "automation_level": "framework",
      "category": "research-agent",
      "description": "NeurIPS 2024 method that automatically writes comprehensive literature surveys via retrieval, parallel subsection drafting by specialized LLMs, and iterative refinement with automated evaluation.",
      "homepage": null,
      "id": "autosurvey",
      "languages": [
        "python"
      ],
      "last_activity": "2025-02",
      "license": "unverified",
      "maintained": "dormant",
      "name": "AutoSurvey",
      "owner_repo": "AutoSurveys/AutoSurvey",
      "stars_approx": 470,
      "subcategory": [
        "literature-review",
        "survey-generation"
      ],
      "url": "https://github.com/AutoSurveys/AutoSurvey",
      "verified": true
    },
    {
      "automation_level": "library",
      "category": "research-agent",
      "description": "Gymnasium/framework of language-agent environments for challenging scientific tasks (literature QA, DNA manipulation, protein engineering) used to build and train autonomous research agents.",
      "homepage": "https://futurehouse.gitbook.io/futurehouse-cookbook",
      "id": "aviary",
      "languages": [
        "python"
      ],
      "last_activity": "2026-06",
      "license": "Apache-2.0",
      "maintained": "active",
      "name": "Aviary",
      "owner_repo": "Future-House/aviary",
      "stars_approx": 270,
      "subcategory": [
        "agent-framework",
        "experiment-automation",
        "scientific-discovery"
      ],
      "url": "https://github.com/Future-House/aviary",
      "verified": true
    },
    {
      "automation_level": "framework",
      "category": "research-agent",
      "description": "A general-purpose autonomous biomedical research agent combining LLM reasoning, retrieval-augmented planning, and code execution over a large library of biomedical tools.",
      "homepage": null,
      "id": "biomni",
      "languages": [
        "python"
      ],
      "last_activity": "2025-10",
      "license": "Apache-2.0",
      "maintained": "active",
      "name": "Biomni",
      "owner_repo": "snap-stanford/Biomni",
      "stars_approx": 3200,
      "subcategory": [
        "domain-biomed",
        "deep-research",
        "end-to-end"
      ],
      "url": "https://github.com/snap-stanford/Biomni",
      "verified": true
    },
    {
      "automation_level": "framework",
      "category": "research-agent",
      "description": "An LLM agent augmented with chemistry tools (RDKit, paper-qa, reaction/retrosynthesis databases) that autonomously solves reasoning-intensive chemistry tasks.",
      "homepage": null,
      "id": "chemcrow",
      "languages": [
        "python"
      ],
      "last_activity": "2024-03",
      "license": "MIT",
      "maintained": "dormant",
      "name": "ChemCrow",
      "owner_repo": "ur-whitelab/chemcrow-public",
      "stars_approx": 920,
      "subcategory": [
        "domain-chem"
      ],
      "url": "https://github.com/ur-whitelab/chemcrow-public",
      "verified": true
    },
    {
      "automation_level": "framework",
      "category": "research-agent",
      "description": "An LLM-driven autonomous lab agent (from the Nature paper) that plans, designs, and optimizes chemical experiments and synthesis.",
      "homepage": null,
      "id": "coscientist",
      "languages": [
        "python"
      ],
      "last_activity": null,
      "license": "Apache-2.0 (Commons Clause)",
      "maintained": "dormant",
      "name": "Coscientist",
      "owner_repo": "gomesgroup/coscientist",
      "stars_approx": 205,
      "subcategory": [
        "domain-chem"
      ],
      "url": "https://github.com/gomesgroup/coscientist",
      "verified": true
    },
    {
      "automation_level": "framework",
      "category": "research-agent",
      "description": "AI agent framework for rigorous, automated scientific experimentation that handles the full hypothesis-to-analysis loop (experiment design, environment setup, execution, analysis) with reproducibility guarantees.",
      "homepage": "https://www.just-curieous.com/",
      "id": "curie",
      "languages": [
        "python"
      ],
      "last_activity": "2025-09",
      "license": "Apache-2.0",
      "maintained": "maintained",
      "name": "Curie",
      "owner_repo": "Just-Curieous/Curie",
      "stars_approx": 360,
      "subcategory": [
        "experiment-automation",
        "scientific-discovery",
        "domain-ml"
      ],
      "url": "https://github.com/Just-Curieous/Curie",
      "verified": true
    },
    {
      "automation_level": "framework",
      "category": "research-agent",
      "description": "Open-source ecosystem of trained models (CycleResearcher + CycleReviewer) that iteratively generate research papers and improve them via automated peer review, focused on ML research.",
      "homepage": "https://wengsyx.github.io/Researcher/",
      "id": "cycleresearcher",
      "languages": [
        "python"
      ],
      "last_activity": "2026-03",
      "license": "unverified",
      "maintained": "active",
      "name": "CycleResearcher",
      "owner_repo": "zhu-minjun/Researcher",
      "stars_approx": 390,
      "subcategory": [
        "paper-generation",
        "automated-review",
        "domain-ml"
      ],
      "url": "https://github.com/zhu-minjun/Researcher",
      "verified": true
    },
    {
      "automation_level": "framework",
      "category": "research-agent",
      "description": "An AI tool with data-loading, exploration, and chart-style-refinement agents that transform and visualize data via a blend of UI interactions and natural language.",
      "homepage": null,
      "id": "data-formulator",
      "languages": [
        "typescript",
        "python"
      ],
      "last_activity": "2026-05",
      "license": "MIT",
      "maintained": "active",
      "name": "Data Formulator",
      "owner_repo": "microsoft/data-formulator",
      "stars_approx": 15800,
      "subcategory": [
        "data-science",
        "eda"
      ],
      "url": "https://github.com/microsoft/data-formulator",
      "verified": true
    },
    {
      "automation_level": "framework",
      "category": "research-agent",
      "description": "An LLM agent that self-designs interface tools then dispatches them to autonomously query, process, analyze, and visualize (financial) data.",
      "homepage": null,
      "id": "data-copilot",
      "languages": [
        "python"
      ],
      "last_activity": "2023",
      "license": "MIT",
      "maintained": "dormant",
      "name": "Data-Copilot",
      "owner_repo": "zwq2018/Data-Copilot",
      "stars_approx": 1500,
      "subcategory": [
        "data-science",
        "eda",
        "domain-finance"
      ],
      "url": "https://github.com/zwq2018/Data-Copilot",
      "verified": true
    },
    {
      "automation_level": "framework",
      "category": "research-agent",
      "description": "Multi-agent system that goes from a raw dataset and research goal to a verifiable, data-traceable scientific paper, emphasizing reproducibility in data-driven (e.g. biomedical/clinical) research.",
      "homepage": null,
      "id": "data-to-paper",
      "languages": [
        "python"
      ],
      "last_activity": "2025-07",
      "license": "MIT",
      "maintained": "maintained",
      "name": "data-to-paper",
      "owner_repo": "Technion-Kishony-lab/data-to-paper",
      "stars_approx": 800,
      "subcategory": [
        "end-to-end",
        "paper-generation",
        "experiment-automation",
        "data-analysis"
      ],
      "url": "https://github.com/Technion-Kishony-lab/data-to-paper",
      "verified": true
    },
    {
      "automation_level": "framework",
      "category": "research-agent",
      "description": "An open data-synthesis + agent-training recipe yielding generalist data-analytic LLMs (DataMind-7B/14B) that do multi-step, code-based reasoning over CSV/Excel/SQLite.",
      "homepage": null,
      "id": "datamind",
      "languages": [
        "python"
      ],
      "last_activity": "2026-06",
      "license": "Apache-2.0",
      "maintained": "active",
      "name": "DataMind",
      "owner_repo": "zjunlp/DataMind",
      "stars_approx": 102,
      "subcategory": [
        "data-science",
        "eda"
      ],
      "url": "https://github.com/zjunlp/DataMind",
      "verified": true
    },
    {
      "automation_level": "framework",
      "category": "research-agent",
      "description": "A compact open-source deep-research agent that recursively searches, scrapes, and reasons over the web to produce reports, tracking goals across iterations.",
      "homepage": null,
      "id": "deep-research-dzhng",
      "languages": [
        "typescript"
      ],
      "last_activity": "2026-04",
      "license": "MIT",
      "maintained": "active",
      "name": "deep-research (dzhng)",
      "owner_repo": "dzhng/deep-research",
      "stars_approx": 19000,
      "subcategory": [
        "deep-research"
      ],
      "url": "https://github.com/dzhng/deep-research",
      "verified": true
    },
    {
      "automation_level": "framework",
      "category": "research-agent",
      "description": "An agentic LLM (DeepAnalyze-8B) that autonomously runs the end-to-end data-science pipeline from raw structured/semi-structured/unstructured data to analyst-grade research reports.",
      "homepage": "https://ruc-deepanalyze.github.io/",
      "id": "deepanalyze",
      "languages": [
        "python"
      ],
      "last_activity": "2026-03",
      "license": "MIT",
      "maintained": "active",
      "name": "DeepAnalyze",
      "owner_repo": "ruc-datalab/DeepAnalyze",
      "stars_approx": 4200,
      "subcategory": [
        "data-science",
        "eda",
        "deep-research",
        "end-to-end",
        "notebook"
      ],
      "url": "https://github.com/ruc-datalab/DeepAnalyze",
      "verified": true
    },
    {
      "automation_level": "framework",
      "category": "research-agent",
      "description": "A production-ready 'self-driving' data agent system that autonomously orchestrates multi-step workflows to produce dashboards, analytical reports, and data videos from heterogeneous data.",
      "homepage": null,
      "id": "deepeye",
      "languages": [
        "python",
        "typescript"
      ],
      "last_activity": "2026-05",
      "license": "Apache-2.0",
      "maintained": "active",
      "name": "DeepEye",
      "owner_repo": "HKUSTDial/DeepEye",
      "stars_approx": 199,
      "subcategory": [
        "data-science",
        "eda",
        "end-to-end"
      ],
      "url": "https://github.com/HKUSTDial/DeepEye",
      "verified": true
    },
    {
      "automation_level": "framework",
      "category": "research-agent",
      "description": "An ICML'24 data-science agent that uses case-based reasoning over Kaggle expert knowledge to iteratively build and train ML models across tabular/text/time-series.",
      "homepage": null,
      "id": "ds-agent",
      "languages": [
        "python"
      ],
      "last_activity": "2024",
      "license": "unverified",
      "maintained": "dormant",
      "name": "DS-Agent",
      "owner_repo": "guosyjlu/DS-Agent",
      "stars_approx": 233,
      "subcategory": [
        "automl",
        "end-to-end"
      ],
      "url": "https://github.com/guosyjlu/DS-Agent",
      "verified": true
    },
    {
      "automation_level": "framework",
      "category": "research-agent",
      "description": "Customizable multi-agent framework (ManagerAgent orchestrating Ideation/Experiment/Writeup agents) for building personalized systems that run continuous autonomous research toward publication-grade reports.",
      "homepage": "https://freephdlabor.github.io/",
      "id": "freephdlabor",
      "languages": [
        "python"
      ],
      "last_activity": "2026-05",
      "license": "MIT",
      "maintained": "active",
      "name": "freephdlabor",
      "owner_repo": "ltjed/freephdlabor",
      "stars_approx": 560,
      "subcategory": [
        "end-to-end",
        "paper-generation",
        "idea-generation",
        "experiment-automation",
        "multi-agent"
      ],
      "url": "https://github.com/ltjed/freephdlabor",
      "verified": true
    },
    {
      "automation_level": "framework",
      "category": "research-agent",
      "description": "Autonomous deep-research agent that plans sub-questions, scrapes and aggregates many web/local sources, and synthesizes a long-form cited research report. (Also relevant to the data-science/deep-research bucket.)",
      "homepage": "https://gptr.dev/",
      "id": "gpt-researcher",
      "languages": [
        "python",
        "typescript"
      ],
      "last_activity": "2026-05",
      "license": "Apache-2.0",
      "maintained": "active",
      "name": "GPT Researcher",
      "owner_repo": "assafelovic/gpt-researcher",
      "stars_approx": 27500,
      "subcategory": [
        "literature-review",
        "report-generation",
        "deep-research"
      ],
      "url": "https://github.com/assafelovic/gpt-researcher",
      "verified": true
    },
    {
      "automation_level": "framework",
      "category": "research-agent",
      "description": "A JupyterLab extension (v3) connecting agentic AI models to notebooks so they can read/write files, run code, and act via a built-in MCP server for data work.",
      "homepage": null,
      "id": "jupyter-ai",
      "languages": [
        "python"
      ],
      "last_activity": "2026-04",
      "license": "BSD-3-Clause",
      "maintained": "active",
      "name": "Jupyter AI",
      "owner_repo": "jupyterlab/jupyter-ai",
      "stars_approx": 4300,
      "subcategory": [
        "notebook",
        "data-science"
      ],
      "url": "https://github.com/jupyterlab/jupyter-ai",
      "verified": true
    },
    {
      "automation_level": "library",
      "category": "research-agent",
      "description": "An LLM agent that automatically summarizes data, generates analysis goals, and writes/executes/edits visualization code (treating viz as code) across grammars.",
      "homepage": null,
      "id": "lida",
      "languages": [
        "python"
      ],
      "last_activity": "2024-03",
      "license": "MIT",
      "maintained": "dormant",
      "name": "LIDA",
      "owner_repo": "microsoft/lida",
      "stars_approx": 3300,
      "subcategory": [
        "eda"
      ],
      "url": "https://github.com/microsoft/lida",
      "verified": true
    },
    {
      "automation_level": "framework",
      "category": "research-agent",
      "description": "Multi-agent framework whose Data Interpreter (and SELA tree-search AutoML extension) agent plans, writes, and self-debugs code to solve data-analysis, ML, and modeling tasks.",
      "homepage": null,
      "id": "metagpt-data-interpreter-sela",
      "languages": [
        "python"
      ],
      "last_activity": "2026-01",
      "license": "MIT",
      "maintained": "active",
      "name": "MetaGPT (Data Interpreter / SELA)",
      "owner_repo": "geekan/MetaGPT",
      "stars_approx": 68500,
      "subcategory": [
        "data-science",
        "automl",
        "eda",
        "end-to-end"
      ],
      "url": "https://github.com/geekan/MetaGPT",
      "verified": true
    },
    {
      "automation_level": "framework",
      "category": "research-agent",
      "description": "An AI companion that autonomously builds ML/AI baselines and end-to-end solutions (incl. Kaggle) with integrated arXiv/paper search.",
      "homepage": null,
      "id": "mle-agent",
      "languages": [
        "python"
      ],
      "last_activity": "2024-10",
      "license": "MIT",
      "maintained": "dormant",
      "name": "MLE-Agent",
      "owner_repo": "MLSysOps/MLE-agent",
      "stars_approx": 1600,
      "subcategory": [
        "automl",
        "end-to-end"
      ],
      "url": "https://github.com/MLSysOps/MLE-agent",
      "verified": true
    },
    {
      "automation_level": "framework",
      "category": "research-agent",
      "description": "Machine-learning research assistant framework where LLM agents autonomously generate research ideas from papers and implement/execute the corresponding experiments.",
      "homepage": null,
      "id": "mlr-copilot",
      "languages": [
        "python"
      ],
      "last_activity": "2025-03",
      "license": "unverified",
      "maintained": "maintained",
      "name": "MLR-Copilot",
      "owner_repo": "du-nlp-lab/MLR-Copilot",
      "stars_approx": 70,
      "subcategory": [
        "idea-generation",
        "experiment-automation",
        "domain-ml"
      ],
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      ],
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      ],
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      ],
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      ],
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        "literature-review"
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      ],
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    {
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      ],
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        "report-generation",
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    {
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      ],
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        "paper-generation"
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      ],
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      ],
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      "name": "The AI Scientist",
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        "scientific-discovery",
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        "idea-generation",
        "domain-ml"
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    {
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      "languages": [
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      "license": "AI Scientist Source Code License v1.0 (custom, Responsible-AI based)",
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      "owner_repo": "SakanaAI/AI-Scientist-v2",
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        "paper-generation",
        "scientific-discovery",
        "experiment-automation",
        "idea-generation",
        "domain-ml"
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      "verified": true
    },
    {
      "automation_level": "framework",
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      "description": "Team of LLM agents (AI PI, domain researchers, scientific critic) that hold structured meetings to autonomously design scientific pipelines, demonstrated by designing new SARS-CoV-2 nanobodies.",
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      "languages": [
        "python"
      ],
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      "license": "MIT",
      "maintained": "maintained",
      "name": "The Virtual Lab",
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        "experiment-automation",
        "multi-agent",
        "domain-bio"
      ],
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      "verified": true
    },
    {
      "automation_level": "framework",
      "category": "research-agent",
      "description": "Multi-agent 'science of science' system (ACL 2025) that simulates teams of scientist agents through team organization and inter/intra-team discussion to autonomously generate and evaluate novel research ideas.",
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      "id": "virtual-scientists-virsci",
      "languages": [
        "python"
      ],
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      "maintained": "maintained",
      "name": "Virtual Scientists (VirSci)",
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      "subcategory": [
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        "multi-agent",
        "scientific-discovery"
      ],
      "url": "https://github.com/InternScience/Virtual-Scientists",
      "verified": true
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  ]
}
