llama.cpp runs large language models locally, from command-line inference to an OpenAI-compatible API server.
Open Source: #llm
Catalog projects marked with #llm. Tags work as dedicated landing pages, so related tools are easier to find and connect.
This collection holds 55 projects with a combined 4,548,466 GitHub stars. Main languages: Python, TypeScript, Go.
About the LLM tools
Large language models stopped being research-lab magic — people now run them locally, fine-tune them on their data, and ship them inside products. The open source ecosystem around LLMs moves faster than any other: new tools land literally every week.
This collection covers inference engines, RAG and agent frameworks, API wrappers, and local runners. If you are building on top of language models, start here: these are proven projects under active development.
Repositories
Awesome LLM Apps collects runnable language-model application examples: agents, RAG, voice scenarios, and MCP integrations.
DeepSeek-V3 is DeepSeek’s open model repository with a MoE architecture, 671B total parameters, 37B activated parameters, and materials for running and studying it.
Browser Use is a Python tool for browser AI agents: the model gets a browser action space, profile, domain limits, and a task execution loop.
LLMs from scratch is Sebastian Raschka’s educational repository for implementing a GPT-like language model step by step in PyTorch.
DeepSeek-R1 is DeepSeek’s open reasoning-model series, including R1, R1-Zero, and distilled variants based on Qwen and Llama.
NextChat is a lightweight client for chatting with language models on web, desktop, and mobile platforms, with support for multiple providers.
autoresearch is Andrej Karpathy’s experiment with AI agents that automatically run research around single-GPU nanochat training.
TradingAgents is a Python framework for experimenting with multi-agent LLM approaches to market analysis, risk, and portfolio decisions.
MoneyPrinterTurbo is a Python application for generating short videos using language models, voice synthesis, subtitles, and a media pipeline.
vLLM is a high-performance engine for LLM inference and serving with an OpenAI-compatible API, batching, and efficient memory management.
RAGFlow is an open source RAG platform for document search, chunking, source citations, and agent workflows around LLMs.