Open Source Research Projects
Research is the systematic process of asking questions, gathering evidence, and developing or testing explanations. It helps people investigate complex problems, evaluate ideas, and produce findings that others can review and build on. Research spans many fields and methods, from analyzing datasets and running simulations to reviewing literature, designing experiments, and documenting results. Open tools can make these workflows more accessible, reproducible, and adaptable to different needs.
Open source research tools include data collection and analysis software, reference managers, simulation environments, collaboration platforms, and curated collections of papers, datasets, and methods. When choosing a tool, consider whether it is actively maintained, how its license fits your work, what skills and computing resources it requires, and whether it integrates with your existing workflow. These tools can support students, educators, independent researchers, and teams seeking transparent, reusable ways to conduct and share research.
7 repositories · updated October 3, 2026
Awesome-Self-Evolving-Agents: A Curated List for AI Agent Research
Awesome-Self-Evolving-Agents is a comprehensive GitHub repository offering a curated collection of resources on self-evolving agents. It includes a systematic survey, research papers, benchmarks, and open-source projects, providing valuable insights into this rapidly advancing field of AI. This repository serves as an essential guide for researchers and developers exploring model-centric, environment-centric, and co-evolutionary approaches.
Awesome-Self-Improving-Agents: A Curated List for Agentic AI Self-Improvement
Awesome-Self-Improving-Agents is a comprehensive GitHub repository featuring a curated and continuously updated list of resources on self-improvement in foundation model-based agentic systems. It serves as a central hub for researchers and practitioners, offering papers, benchmarks, and various media. This resource is essential for anyone exploring the cutting edge of self-evolving AI agents.
AgentSkills: A Curated Collection for LLM Agent Skills and Resources
AgentSkills is an extensive curated collection of resources, papers, tools, projects, and frameworks focused on building and deploying skills for large language models. This repository serves as a central hub for understanding the LLM skills ecosystem, from Anthropic's official systems to academic research and open-source agent frameworks. It is an invaluable resource for anyone exploring the rapidly evolving field of AI agents.
AWESOME-OCR-LLM: Curated Reading List for OCR in the LLM Era
AWESOME-OCR-LLM is a continuously updated reading list focusing on Optical Character Recognition (OCR) in the era of large language models (LLMs). It covers key areas like document parsing, understanding, visual text generation, and benchmarks, highlighting research from the past five years. This resource is invaluable for anyone tracking the rapid advancements in multimodal document AI.
feynman: Research Papers with an AI Agent
Feynman is a TypeScript CLI agent for investigating research topics, reviewing papers, and comparing sources. It suits researchers and developers who want structured, citation-aware literature work and can configure a model provider.
Kimi-k1.5: Train Multimodal LLMs with Reinforcement Learning
Kimi k1.5 is a research project describing reinforcement learning methods for training long-context, multimodal language models. It is aimed at researchers studying LLM reasoning and training, rather than users looking for a ready-to-install model.
RecDebiasing: A Comprehensive Collection of Recommendation Debiasing Methods
RecDebiasing is a valuable GitHub repository that curates a wide array of debiasing methods for recommendation systems. It compiles recent research papers, relevant datasets, and associated codebases, offering a centralized resource for understanding and addressing various biases. This collection is essential for researchers and practitioners focused on building more fair and accurate recommender systems.