Repository History
138 repositories tagged with LLM
KBLaM: Knowledge Base Augmented Language Models for Enhanced LLMs
KBLaM, developed by Microsoft, is the official implementation of "Knowledge Base Augmented Language Models" presented at ICLR 2025. This innovative method enhances Large Language Models by directly integrating external knowledge bases, offering an efficient alternative to traditional Retrieval-Augmented Generation (RAG) and in-context learning. It eliminates external retrieval modules and scales computationally linearly with knowledge base size, rather than quadratically.
Vanna: Chat with Your SQL Database Using LLMs and Agentic Retrieval
Vanna is an open-source Python library that enables natural language interaction with SQL databases, leveraging Large Language Models (LLMs) for accurate text-to-SQL generation. Version 2.0 introduces enterprise-grade features like user-aware permissions, a modern web interface, and streaming responses, making it ideal for secure and scalable data analytics applications.
Awesome-AI-Agents: A Curated List of LLM-Powered Autonomous Agents
The Awesome-AI-Agents repository is a comprehensive collection of autonomous AI agents powered by Large Language Models (LLMs). It meticulously categorizes various projects, frameworks, and tools, making it an invaluable resource for developers and researchers exploring the rapidly evolving field of AI agents. This list covers everything from single-agent task solvers to multi-agent simulations and robust development frameworks.
AI Baby Monitor: A Local Video-LLM Powered Solution for Child Safety
The AI Baby Monitor is an innovative, privacy-first solution that leverages local Video-LLMs to enhance child supervision. It monitors a video stream against user-defined safety rules, issuing a gentle beep if a rule is broken. This tool acts as an additional pair of eyes, providing real-time alerts without compromising privacy.
Cloudflare MCP Server: Connect LLMs to Cloudflare Services
The Cloudflare MCP Server repository provides a suite of servers implementing the Model Context Protocol (MCP), enabling large language models (LLMs) to interact seamlessly with various Cloudflare services. This allows LLMs to read configurations, process information, make suggestions, and even enact changes across Cloudflare's extensive ecosystem, from security and performance to application development. It streamlines the integration of AI capabilities with your Cloudflare account through natural language.
Anthropic Courses: Educational Materials for Claude API and Prompt Engineering
The Anthropic Courses repository offers a comprehensive suite of educational materials designed to teach users how to effectively work with the Claude SDK and master prompt engineering techniques. It includes five structured courses, guiding learners from API fundamentals to advanced topics like prompt evaluations and tool use. These resources are ideal for developers and AI enthusiasts looking to enhance their skills with Anthropic's AI models.
Cheshire Cat AI Core: An AI Agent Microservice Framework
Cheshire Cat AI Core is an open-source framework designed for building custom AI agents as microservices. It offers an API-first approach, enabling easy integration of conversational layers into applications with WebSocket chat and a customizable REST API. Key features include built-in RAG with Qdrant, extensibility via plugins, function calling, and full Dockerization for straightforward deployment.
Verifiers: Environments for LLM Reinforcement Learning and Evaluation
Verifiers is a Python library by Prime Intellect AI for building environments to train and evaluate Large Language Models (LLMs). It enables the creation of custom environments with datasets, model harnesses, and reward functions, supporting reinforcement learning, capability evaluation, and synthetic data generation. This library is tightly integrated with the Prime Intellect ecosystem, including their Environments Hub and training framework.
files-to-prompt: Concatenate Files into a Single Prompt for LLMs
files-to-prompt is a command-line tool designed to concatenate the contents of multiple files from a directory into a single, structured prompt suitable for Large Language Models (LLMs). It offers flexible options for filtering files by extension, ignoring patterns, and supports various output formats including standard text, Claude XML, and Markdown. This utility streamlines the process of preparing complex codebases or documentation for AI analysis.
LLM Reasoners: Advanced Library for Large Language Model Reasoning
LLM Reasoners is a powerful Python library designed to significantly enhance the complex reasoning capabilities of Large Language Models. It offers a comprehensive suite of cutting-edge search algorithms, intuitive visualization tools, and optimized performance for efficient LLM inference. The library prioritizes rigorous implementation and reproducibility, making it a reliable tool for researchers and developers in the AI field.
RAG-Anything: The All-in-One Multimodal RAG Framework
RAG-Anything is a comprehensive, all-in-one Retrieval-Augmented Generation (RAG) framework designed to process and query diverse multimodal content. It seamlessly handles text, images, tables, and equations within a single integrated system, eliminating the need for multiple specialized tools. Built on LightRAG, this framework offers advanced multimodal retrieval capabilities for complex documents.
Rocketnotes: AI-Powered Markdown Editor for Local and Cloud Knowledge Management
Rocketnotes is an innovative AI-powered markdown editor designed for efficient knowledge management. It allows users to leverage Large Language Models (LLMs) with their documents, offering both 100% local and cloud deployment options. This versatile tool integrates advanced features like semantic search, AI chat, and agentic archiving to enhance productivity and organization.