Ranking System for Open-Source Artificial Intelligence Projects
In the rapidly evolving world of artificial intelligence, a leaderboard is making waves among developers, researchers, and AI enthusiasts. This leaderboard, available on GitHub, showcases various AI projects, including foundational frameworks, cutting-edge language models, and innovative tools.
One of the standout projects on the leaderboard is Kimi's K2, an open-source AI agent model that leads among open-source entries in the Agent Leaderboard v2. With a strong Action Completion (AC) score of 0.53 and a Tool Selection Quality (TSQ) score of 0.90, Kimi's K2 demonstrates high engagement and practical effectiveness among community-contributed projects.
Another notable project is Vicuna-13B, an open-source chatbot fine-tuned from Meta’s LLaMA model using user-shared conversations from ShareGPT. Its popularity reflects high community interest in dialogue agents that leverage open conversational data, showing strong growth and adoption in open source.
Alpaca 7B, another LLaMA derivative, is widely used and contributed to for research and development of instruction-tuned open source models. Phind-70B, an open-source model noted for matching and exceeding GPT-4's coding abilities while running 5x faster, has also attracted significant contributor interest.
A tool-augmented framework for detecting factual errors in LLM-generated texts, FacTool, represents growth in auxiliary AI tools focusing on accuracy and quality assurance in open-source projects.
The Entelligence Engineering Leaderboard, launched in August 2025, tracks over 1,000 developers contributing real code evaluated every 5 minutes by impact score. This provides an objective view of developer engagement and growth in AI open source ecosystems, measuring code quality, bug avoidance, and code review helpfulness.
The trends reflected in these platforms include an increasing emphasis on enterprise-grade evaluation frameworks, strong momentum for open source large language models derived from Meta’s LLaMA base, and growth of adjacent tooling for evaluation, error detection, and customized deployment.
The AI Leaderboard, one of the resources mentioned, updates weekly, providing insights into community growth, contributor engagement, and emerging trends in the AI development landscape. The team behind the AI open source leaderboard also offers weekly insights on AI trends, technical deep-dives, and market analysis, available at BusinessEngineer.ai.
Thousands of stars are gained daily by AI open source projects on GitHub, indicating the strong interest and active development in the field. With these top open source AI projects leading the way, the future of artificial intelligence is looking bright and promising.
- The growth of artificial intelligence is evident in the increasing number of stars gained daily by AI open source projects on GitHub, which signifies strong interest and active development.
- Technology advancements in artificial intelligence, such as the development of tool-augmented frameworks for detecting factual errors in LLM-generated texts, demonstrate growth in auxiliary AI tools focusing on accuracy and quality assurance in open-source projects.
