Awesome-LLM: a curated list of Large Language Model
Awesome-LLM is a curated list: papers, models, tooling and courses about large language models, collected in one place and kept current by a community of contributors. It is not software and it will not do anything for you. What it is good for is orientation. If you need to find the important paper on a topic, or work out which open models exist in a category, or hand somebody a starting point that is not a search engine, a maintained list saves hours of wandering. Lists like this age quickly in a field that moves this fast, so check the dates on what you find, and treat it as a map rather than a verdict on what is best.
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.
Stop renting your intelligence. Own it with AnythingLLM. Everything you need for a powerful local-first agent experience
AnythingLLM is an application for chatting with your own documents. You drop in PDFs, notes, spreadsheets or links, it indexes them, and then you ask questions and get answers drawn from those files rather than from the model's general training. It runs as a desktop app or in Docker, and it can point at a local model or at a commercial one, which is the reason many people choose it: the documents can stay on your own machine. It supports separate workspaces, so a set of client files does not bleed into a set of personal notes. Setting it up takes more than opening a website, and the quality of the answers depends heavily on how clean the source documents are, which is true of every tool in this category and is worth knowing before you judge it.
Awesome lists about all kinds of interesting topics [NOTE: Pull requests are temporarily disabled until I have a chance to catch up with the existing ones]
An AI-powered research assistant that performs iterative, deep research on any topic by combining search engines, web scraping, and large language models. The goal of this repo is to provide the simplest implementation of a deep research agent - e.g. an agent that can refine its research direction overtime and deep dive into a topic.
Written mainly in TypeScript. Released under the MIT licence.
An open-source long-horizon SuperAgent harness that researches, codes, and creates. With the help of sandboxes, memories, tools, skill, subagents and message gateway, it handles different levels of tasks that could take minutes to hours.
Written mainly in Python. Released under the MIT licence.
Your AI second brain. Self-hostable. Get answers from the web or your docs. Build custom agents, schedule automations, do deep research. Turn any online or local LLM into your personal, autonomous AI (gpt, claude, gemini, llama, qwen, mistral). Get started - free.
Written mainly in Python. Released under the AGPL-3.0 licence.
Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
Written mainly in Rust. Released under the Apache-2.0 licence.
Opiniated RAG for integrating GenAI in your apps Focus on your product rather than the RAG. Easy integration in existing products with customisation! Any LLM: GPT4, Groq, Llama. Any Vectorstore: PGVector, Faiss. Any Files. Anyway you want.
RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs
Written mainly in Go. Released under the Apache-2.0 licence.
Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database.
