Next Generation Agentic Proxy for AI Agents and MCP servers

A curated, continuously updated reading list of 200+ papers on LLM agents: planning, memory, tool use, multi-agent, evaluation & safety. Companion to the survey 'LLM Agents: A Survey'.

Korean MTEB Retrieval Evaluators for SPLADE, Dense, and Reranking models

Pyodide is a Python distribution for the browser and Node.js based on WebAssembly

nanoRLHF: from-scratch journey into how LLMs and RLHF really work.

The official repo for “Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting”, ACL, 2025.

Embedding Atlas is a tool that provides interactive visualizations for large embeddings. It allows you to visualize, cross-filter, and search embeddings and metadata.

🙌 OpenHands: AI-Driven Development

🚀 The fast, Pythonic way to build MCP servers and clients.

Expose your FastAPI endpoints as Model Context Protocol (MCP) tools, with Auth!

The official Python SDK for Model Context Protocol servers and clients

👶🏻 신입 개발자 전공 지식 & 기술 면접 백과사전 📖

Transforms complex documents like PDFs and Office docs into LLM-ready markdown/JSON for your Agentic workflows.

Get your documents ready for gen AI

A curated list of safety-related papers, articles, and resources focused on Large Language Models (LLMs). This repository aims to provide researchers, practitioners, and enthusiasts with insights i…

Efficient Triton Kernels for LLM Training

Chat with your database or your datalake (SQL, CSV, parquet). PandasAI makes data analysis conversational using LLMs and RAG.

[ICML 2025 Oral] CodeI/O: Condensing Reasoning Patterns via Code Input-Output Prediction

Image to prompt with BLIP and CLIP

Stable Diffusion web UI

Awesome things about LLM-powered agents. Papers / Repos / Blogs / ...

KURE: 고려대학교에서 개발한, 한국어 검색에 특화된 임베딩 모델

Korean SAT leader board

Large Reasoning Models

A blazing fast inference solution for text embeddings models

PyTorch native post-training library

A high-throughput and memory-efficient inference and serving engine for LLMs

Qwen3-VL is the multimodal large language model series developed by Qwen team, Alibaba Cloud.

LangChain 공식 Document, Cookbook, 그 밖의 실용 예제를 바탕으로 작성한 한국어 튜토리얼입니다. 본 튜토리얼을 통해 LangChain을 더 쉽고 효과적으로 사용하는 방법을 배울 수 있습니다.

[NeurIPS 2024 Spotlight] Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models