# What I learned from looking at 900 most popular open source AI tools

DevFeed: [What I learned from looking at 900 most popular open source AI tools](<https://devfeed.tech/articles/what-i-learned-from-looking-at-900-most-popular-open-source-ai-tools-31798.md>)

Original publisher: [Read original article](<https://huyenchip.com//2024/03/14/ai-oss.html>)

Author: Chip Huyen

Published: 2024-03-14T00:00:00Z

Content type: article

Language: en

Sources: [Chip Huyen](<https://devfeed.tech/sources/chip-huyen.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [foundation-models](<https://devfeed.tech/topics/foundation-models.md>), [Development](<https://devfeed.tech/topics/development.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [AI Engineering](<https://devfeed.tech/topics/ai-engineering.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Application Development](<https://devfeed.tech/topics/application-development.md>), [GitHub](<https://devfeed.tech/topics/github.md>), [Prompt Engineering](<https://devfeed.tech/topics/prompt-engineering.md>), [Retrieval Augmented Generation (RAG)](<https://devfeed.tech/topics/retrieval-augmented-generation-rag.md>), [PyTorch](<https://devfeed.tech/topics/pytorch.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [foundation-models](<https://devfeed.tech/tags/foundation-models.md>), [github](<https://devfeed.tech/tags/github.md>), [model](<https://devfeed.tech/tags/model.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [open-source-ai](<https://devfeed.tech/tags/open-source-ai.md>), [pytorch](<https://devfeed.tech/tags/pytorch.md>), [skypilot](<https://devfeed.tech/tags/skypilot.md>), [transformers](<https://devfeed.tech/tags/transformers.md>), [vector](<https://devfeed.tech/tags/vector.md>)

## AI overview

The article analyzes 896 popular open-source AI repositories, focusing on 845 software repositories after excluding tutorials and aggregated lists. It organizes the ecosystem around foundation models into infrastructure, model development, and application development, and notes differences between China's open-source ecosystem and Western ecosystems.

## Source excerpt

[Hacker News discussion, LinkedIn discussion, Twitter thread] Update (Feb 2026): The full list of open source AI repos is hosted at Good AI List, updated daily. It's balooned to 15K repos, and you can submit missing repos. You can also find some of them on my cool-llm-repos list on GitHub. Four years ago, I did an analysis of the open source ML ecosystem. Since then, the landscape has changed, so I revisited the topic. This time, I focused exclusively on the stack around foundation models. Data I searched GitHub using the keywords gpt, llm, and generative ai. If AI feels so overwhelming right now, it's because it is. There are 118K results for gpt alone. To make my life easier, I limited my search to the repos with at least 500 stars. There were 590 results for llm, 531 for gpt, and 38 for generative ai. I also occasionally checked GitHub trending and social media for new repos. After MANY hours, I found 896 repos. Of these, 51 are tutorials (e.g. dair-ai/Prompt-Engineering-Guide) and aggregated lists (e.g. f/awesome-chatgpt-prompts). While these tutorials and lists are helpful, I'm more interested in software. I still include them in the final list, but the analysis is done with the 845 software repositories. It was a painful but rewarding process. It gave me a much better understanding of what people are working on, how incredibly collaborative the open source community is, and just how much China's open source ecosystem diverges from the Western one. The New AI Stack I think of the AI stack as consisting of 3 layers: infrastructure, model development, and application development. Infrastructure At the bottom is the stack is infrastructure, which includes toolings for serving (vllm, NVIDIA's Triton), compute management (skypilot), vector search and database (faiss, milvus, qdrant, lancedb), .... Model development This layer provides toolings for developing models, including frameworks for modeling & training (transformers, pytorch, DeepSpeed), inference optimization