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Every tracked project

36 projects tracked. Filter by category, language, and trend.

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Category:AllAI & Machine Learning36Web Frameworks20CLI & Developer Tools21Databases & Storage26DevOps & Infrastructure26Mobile Development13Data Science & Notebooks20Game Development8
Language:Python22TypeScript5Go3C++2Rust2Jupyter Notebook1C1
Sort:PopScoreTrending (7d)StarsNewestDensityLanguage
Showing 31–36 of 36
  • 31

    weaviate/weaviate

    Weaviate is an open-source vector database.

    SlowingMature
    Pop
    0
    Go#vector-search#ai#semantic-search
    11.4k720
  • 32

    openai/openai-node

    Official Node.js / Deno library for the OpenAI API.

    SlowingEnterprise
    Pop
    0
    TypeScript#openai#llm#sdk
    8.9k1.2k
  • 33

    langchain-ai/langgraph

    Build stateful, multi-actor agent workflows with LangChain.

    SlowingMature
    Pop
    0
    Python#agents#llm#graph
    6.8k920
    AIGraph-based orchestration for stateful LLM agents with cycles, retries, and persistence.expand
    Graph-based orchestration for stateful LLM agents with cycles, retries, and persistence.
  • 34

    smol-ai/smol-course

    A hands-on course on aligning small language models.

    SlowingMature
    Pop
    0
    Jupyter Notebook#llm#course#alignment
    6.4k580
    AIHands-on, code-first course for aligning small LLMs (SFT + DPO + eval) on a single GPU.expand
    Hands-on, code-first course for aligning small LLMs (SFT + DPO + eval) on a single GPU.
  • 35

    e2b-dev/e2b

    Open-source secure cloud sandboxes for AI agents.

    SlowingEnterprise
    Pop
    0
    TypeScript#agents#sandbox#llm
    6.4k580
    AIEphemeral firecracker sandboxes for AI agents — safe code execution in <200ms cold start.expand
    Ephemeral firecracker sandboxes for AI agents — safe code execution in <200ms cold start.
  • 36

    lmsys/lmsys-chat-1m

    1M real LLM conversations with human preferences (research dataset).

    SlowingMature
    Pop
    0
    Python#dataset#llm#research
    3.8k380

How GitPop ranks projects

Read the full methodology
What is PopScore?
A 0–100 score combining star velocity (35%), fork engagement (20%), activity (25%), mentions (10%), and a noise filter (10%). Details →
Why not just use GitHub Trending?
Trending is dominated by awesome-* lists and tutorial repos. We weight commits and merged PRs above raw stars, and apply a noise penalty for content-only repos. Details →
How is the AI Snapshot generated?
An LLM summarizes each repo's README into strict JSON — TL;DR, 3 key features, difficulty rating. Cached and refreshed automatically. Details →