Research-Claw provides an AI-powered, local research infrastructure designed to alleviate the administrative burden on academics. It automates time-consuming tasks such as literature review, drafting, experiment replication, and citation management, allowing researchers to concentrate on core scientific discovery. This system targets researchers and students, offering advanced research capabilities directly on their personal machines, ensuring data privacy and ownership.

How It Works

This project functions as a local AI research assistant, operating entirely on the user's machine with minimal external dependencies beyond a chosen Large Language Model (LLM) API. It features a comprehensive dashboard interface tailored for academic workflows, including specialized panels for literature management, task tracking, workspace control, and a real-time research radar. Its core strength lies in a vast library of 438 pre-defined "academic skills" that encapsulate complex research tasks, enabling structured and efficient AI-driven assistance across the entire research lifecycle.

Quick Start & Requirements

• Installation: macOS/Linux users can install via a one-line script (curl -fsSL https://wentor.ai/install.sh | bash). Windows, macOS, and Linux users can opt for Docker installation using provided scripts.
• Prerequisites: Requires Node.js >= 22 (auto-installed by script) or Docker. A Large Language Model (LLM) API key (e.g., Anthropic Claude, OpenAI) is mandatory.
• Documentation: Official documentation is available via docs/00-reference-map.md.
• Links: wentor.ai, GitHub Issues: https://github.com/wentorai/Research-Claw/issues.

Highlighted Details

• 438 Academic Skills: A rich set of pre-built functionalities covering literature retrieval, research methodologies, data analysis, academic writing, and domain-specific knowledge.
• Local Experiment Replication: Automatically attempts to clone repositories, configure environments (including CUDA), and run paper experiments locally.
• 24/7 Research Radar: Continuously monitors academic databases (arXiv, OpenAlex, etc.) for specified keywords, scholars, or journals, pushing timely updates via IM.
• Integrated Dashboard: A feature-rich, responsive UI designed for academic workflows, offering structured output cards and dual-theme support.
• Data Privacy: All user data, including papers, notes, and experimental results, is stored exclusively on the local machine.

Maintenance & Community

The project is developed by Wentor AI. Community engagement is facilitated through a WeChat group and GitHub Issues for feedback. No specific roadmap or detailed contributor information is provided in the README.

Licensing & Compatibility

The project is licensed under the Business Source License 1.1 (BSL 1.1). It is free for personal and academic research use. Commercial use requires separate authorization and is restricted until March 12, 2030, after which it automatically converts to the Apache 2.0 license.

Limitations & Caveats

Commercial adoption is restricted by the BSL 1.1 license until 2030. The system relies on external LLM API keys, which may incur costs. As indicated by version v0.5.4, the project is under active development and may be subject to ongoing changes and potential instability.