Trending AI GitHub Repos
Trending open source AI and machine learning repositories on GitHub.
Showing 21 of 21 items
ZJU-LLMs/Foundations-of-LLMs
An open book and course materials on the foundations of large language models, covering theory, architectures, training, and deployment. With >14k stars, it’s quickly becoming a go‑to learning resource for people trying to move from ‘user’ to ‘builder’ of LLMs. If you want a structured, code-linked path into the guts of modern LMs, this is a strong candidate.
openai/codex
A lightweight coding agent that runs directly in your terminal, wiring OpenAI models into a loop that edits files, runs tests, and applies patches. Compared to IDE plugins, it’s closer to a shell-native ‘pair programmer’ that can operate on entire repos and workflows. Given its rapid adoption and tight integration with existing CLIs, it’s poised to become a reference design for terminal-first code agents.
virattt/ai-hedge-fund
An ‘AI hedge fund team’ repository wrapping together data pipelines, modeling code, and infra for algorithmic trading driven by AI. While not plug-and-play as a real fund, it’s a surprisingly complete example of how to glue modern ML, backtesting, and orchestration around financial strategies. It’s trending hard, partly because it’s both ambitious and unusually transparent for this domain.
thedotmack/claude-mem
A Claude Code plugin that logs your coding sessions, compresses them with Claude via the agent SDK, and feeds back relevant context into future sessions. In practice it acts like a persistent, AI-managed memory of your projects, making the assistant far more ‘aware’ of the codebase and past conversations. It’s a concrete, production-friendly take on the “long-term memory for coding agents” idea.
simstudioai/sim
A full-stack platform for visually building, running, and deploying AI agent workflows. Provides a canvas for wiring together agents, tools, vector stores, and orchestrations, with both cloud-hosted and self-hosted (Docker/Ollama) options and strong Copilot integration. It effectively turns ‘agent graphs’ into a first-class artifact, which is where a lot of production LLM work is heading.
pandas-ai
pandas-ai turns DataFrames and SQL/CSV/Parquet sources into a conversational interface, translating natural-language questions into code or SQL, running them in a (configurable) sandbox, and optionally using RAG and semantic schemas to answer more complex queries. It’s attractive for quickly giving analysts or business users an LLM front-end on top of existing data, though you do need to pay attention to security configurations given its history of prompt-injection/RCE issues that were later mitigated with new settings. ([github.com](https://github.com/sinaptik-ai/pandas-ai?utm_source=openai))
stable-diffusion-webui
stable-diffusion-webui by AUTOMATIC1111 is the de facto standard local web interface for Stable Diffusion, providing a massive feature set—txt2img, img2img, inpainting/outpainting, upscaling, LoRA/embeddings support, training utilities, and a huge extension ecosystem—on top of consumer GPUs. If you’re doing any kind of image generation or fine-tuning with Stable Diffusion in a local or lab environment, this is usually the first tool people reach for and the one most community workflows target. ([github.com](https://github.com/AUTOMATIC1111/stable-diffusion-webui?utm_source=openai))
geoai
geoai is a Python package from the opengeos ecosystem that integrates deep-learning frameworks (PyTorch, Transformers, segmentation models) with geospatial tooling to handle everything from remote-sensing data download and tiling to training, inference, and interactive map visualization. It’s aimed at practitioners who want a higher-level, batteries-included stack for tasks like land-cover classification, building footprint extraction, and change detection, without reinventing all the GIS + ML plumbing. ([github.com](https://github.com/opengeos/geoai?utm_source=openai))
GPT-SoVITS
GPT-SoVITS is a hugely popular WebUI and pipeline for few-shot TTS and voice conversion, enabling convincing voice cloning with as little as 5 seconds to 1 minute of audio, plus dataset prep tools (separation, ASR, labeling) and multi-lingual support (EN/JA/KO/ZH/Cantonese). If you’re experimenting with custom voices, VTuber-style content, or rapid TTS prototyping on consumer GPUs, this is effectively the community standard toolkit. ([github.com](https://github.com/RVC-Boss/GPT-SoVITS?utm_source=openai))
daytona
Daytona is a secure, elastic runtime for executing AI-generated code and agent workflows in isolated sandboxes, with Python and TypeScript SDKs to spin up environments in sub‑100ms and run arbitrary code, processes, or dev tools. It’s quickly becoming a go-to “agent runtime” layer for teams that need safe, persistent, and massively parallel sandboxes (including LangChain’s open-source coding agent), instead of gluing together ad‑hoc Docker or VM setups. ([github.com](https://github.com/daytonaio/daytona?utm_source=openai))
tinker-cookbook
tinker-cookbook provides practical, end‑to‑end examples of post‑training LLMs using Tinker, a managed fine‑tuning API from Thinking Machines Lab that handles distributed training while you control the algorithms and data. The repo includes recipes for instruction tuning, math reasoning, RLHF-style preference learning, tool use, prompt distillation, and multi-agent setups, making it a strong starting point if you want to fine‑tune open-weight models like Llama or Qwen without building your own training stack. ([github.com](https://github.com/thinking-machines-lab/tinker-cookbook?utm_source=openai))
Depixelization_poc
A proof-of-concept attack showing how pixelated screenshots can be reverse-engineered to recover underlying text using computer vision. A stark reminder that naive anonymization in UIs is often not privacy-safe. ([github.com](https://github.com/trending?since=daily))
mindsdb
Markets itself as a "federated query engine for AI" and "the only MCP server you’ll ever need," exposing AI models and tools through a unified interface. Useful if you’re standardizing on MCP and want a batteries-included orchestration backend. ([github.com](https://github.com/trending?since=daily))
hello-agents
A Chinese-language tutorial project titled "从零开始构建智能体" (Building Agents from Scratch), walking through agent principles and practical implementations. Good onboarding material if you want to upskill teammates on modern agentic patterns. ([github.com](https://github.com/trending?since=daily))
WeKnora
Tencent’s LLM-powered framework for deep document understanding, semantic retrieval, and context-aware answering with a RAG paradigm. Essentially a production-grade answer engine stack rather than a toy demo. ([github.com](https://github.com/trending?since=daily))
CopilotKit
React UI components plus backend infrastructure for building in-app AI copilots, chatbots, and agentic workflows. It’s becoming a go-to choice if you want "agentic frontends" without wiring everything from scratch. ([github.com](https://github.com/trending?since=daily))
next-ai-draw-io
A Next.js web app that layers natural-language-driven AI editing on top of draw.io diagrams, letting you create and modify diagrams through prompts. Great if your team lives in diagrams and you want AI to help refactor system designs. ([github.com](https://github.com/trending?since=daily))
dify
A very popular production-ready platform for building agentic workflows and applications, with UI, orchestration, and deployment all in one. Given its star growth, it’s becoming a de facto choice for many teams moving beyond simple RAG bots. ([github.com](https://github.com/trending?since=daily))
agents.md
Defines AGENTS.md, a simple open format for describing coding agents: their tools, capabilities, and expectations. It’s trying to do for agents what README and OpenAPI did for repos and APIs—standardize how we document them. ([github.com](https://github.com/trending?since=daily))
chrome-devtools-mcp
An MCP server that exposes Chrome DevTools to coding agents, enabling them to inspect and manipulate web pages programmatically. This is a big enabler for realistic browser-based agents that need deep debugging and automation capabilities. ([github.com](https://github.com/trending?since=daily))
nanoGPT
Karpathy’s minimalist GPT training repo continues to trend, reflecting ongoing interest in from-scratch pretraining and fine-tuning for medium-sized LLMs. Still one of the best learning references if you want to understand the guts of GPT-style models. ([github.com](https://github.com/trending?since=daily))