DOM to Semantic Markdown is a tool that converts HTML DOM to Semantic Markdown for use in Large Language Models (LLMs). It maximizes semantic information, token efficiency, and preserves metadata to enhance LLMs' processing capabilities. The tool captures rich web content structure, including semantic tags, image metadata, table structures, and link destinations. It offers customizable conversion options and supports both browser and Node.js environments.

Superlinked is a compute framework for information retrieval and feature engineering systems, focusing on converting complex data into vector embeddings for RAG, Search, RecSys, and Analytics stack integration. It enables custom model performance in machine learning with pre-trained model convenience. The tool allows users to build multimodal vectors, define weights at query time, and avoid postprocessing & rerank requirements. Users can explore the computational model through simple scripts and python notebooks, with a future release planned for production usage with built-in data infra and vector database integrations.

Bespoke Curator is an open-source tool for data curation and structured data extraction. It provides a Python library for generating synthetic data at scale, with features like programmability, performance optimization, caching, and integration with HuggingFace Datasets. The tool includes a Curator Viewer for dataset visualization and offers a rich set of functionalities for creating and refining data generation strategies.

This repository provides advanced AI explainability for PyTorch, offering state-of-the-art methods for Explainable AI in computer vision. It includes a comprehensive collection of Pixel Attribution methods for various tasks like Classification, Object Detection, Semantic Segmentation, and more. The package supports high performance with full batch image support and includes metrics for evaluating and tuning explanations. Users can visualize and interpret model predictions, making it suitable for both production and model development scenarios.

Tongyi DeepResearch is an agentic large language model with 30.5 billion total parameters, designed for long-horizon, deep information-seeking tasks. It demonstrates state-of-the-art performance across various search benchmarks. The model features a fully automated synthetic data generation pipeline, large-scale continual pre-training on agentic data, end-to-end reinforcement learning, and compatibility with two inference paradigms. Users can download the model directly from HuggingFace or ModelScope. The repository also provides benchmark evaluation scripts and information on the Deep Research Agent Family.

TorchRL is an open-source Reinforcement Learning (RL) library for PyTorch. It provides pytorch and **python-first** , low and high level abstractions for RL that are intended to be **efficient** , **modular** , **documented** and properly **tested**. The code is aimed at supporting research in RL. Most of it is written in python in a highly modular way, such that researchers can easily swap components, transform them or write new ones with little effort.

Simple-OpenAI is a Java library that provides a simple way to interact with the OpenAI API. It offers consistent interfaces for various OpenAI services like Audio, Chat Completion, Image Generation, and more. The library uses CleverClient for HTTP communication, Jackson for JSON parsing, and Lombok to reduce boilerplate code. It supports asynchronous requests and provides methods for synchronous calls as well. Users can easily create objects to communicate with the OpenAI API and perform tasks like text-to-speech, transcription, image generation, and chat completions.

mcp-agent is a simple, composable framework designed to build agents using the Model Context Protocol. It handles the lifecycle of MCP server connections and implements patterns for building production-ready AI agents in a composable way. The framework also includes OpenAI's Swarm pattern for multi-agent orchestration in a model-agnostic manner, making it the simplest way to build robust agent applications. It is purpose-built for the shared protocol MCP, lightweight, and closer to an agent pattern library than a framework. mcp-agent allows developers to focus on the core business logic of their AI applications by handling mechanics such as server connections, working with LLMs, and supporting external signals like human input.

ChatRex is a Multimodal Large Language Model (MLLM) designed to seamlessly integrate fine-grained object perception and robust language understanding. By adopting a decoupled architecture with a retrieval-based approach for object detection and leveraging high-resolution visual inputs, ChatRex addresses key challenges in perception tasks. It is powered by the Rexverse-2M dataset with diverse image-region-text annotations. ChatRex can be applied to various scenarios requiring fine-grained perception, such as object detection, grounded conversation, grounded image captioning, and region understanding.

CrackSQL is a powerful SQL dialect translation tool that integrates rule-based strategies with large language models (LLMs) for high accuracy. It enables seamless conversion between dialects (e.g., PostgreSQL → MySQL) with flexible access through Python API, command line, and web interface. The tool supports extensive dialect compatibility, precision & advanced processing, and versatile access & integration. It offers three modes for dialect translation and demonstrates high translation accuracy over collected benchmarks. Users can deploy CrackSQL using PyPI package installation or source code installation methods. The tool can be extended to support additional syntax, new dialects, and improve translation efficiency. The project is actively maintained and welcomes contributions from the community.

LLMRec is a PyTorch implementation for the WSDM 2024 paper 'Large Language Models with Graph Augmentation for Recommendation'. It is a novel framework that enhances recommenders by applying LLM-based graph augmentation strategies to recommendation systems. The tool aims to make the most of content within online platforms to augment interaction graphs by reinforcing u-i interactive edges, enhancing item node attributes, and conducting user node profiling from a natural language perspective.

ScrapeGraphAI is a web scraping Python library that utilizes LLM and direct graph logic to create scraping pipelines for websites and local documents. It offers various standard scraping pipelines like SmartScraperGraph, SearchGraph, SpeechGraph, and ScriptCreatorGraph. Users can extract information by specifying prompts and input sources. The library supports different LLM APIs such as OpenAI, Groq, Azure, and Gemini, as well as local models using Ollama. ScrapeGraphAI is designed for data exploration and research purposes, providing a versatile tool for extracting information from web pages and generating outputs like Python scripts, audio summaries, and search results.

Open-Source Evaluation for LLM Applications. `continuous-eval` is an open-source package created for granular and holistic evaluation of GenAI application pipelines. It offers modularized evaluation, a comprehensive metric library covering various LLM use cases, the ability to leverage user feedback in evaluation, and synthetic dataset generation for testing pipelines. Users can define their own metrics by extending the Metric class. The tool allows running evaluation on a pipeline defined with modules and corresponding metrics. Additionally, it provides synthetic data generation capabilities to create user interaction data for evaluation or training purposes.

LLM4Decompile is an open-source large language model dedicated to decompilation of Linux x86_64 binaries, supporting GCC's O0 to O3 optimization levels. It focuses on assessing re-executability of decompiled code through HumanEval-Decompile benchmark. The tool includes models with sizes ranging from 1.3 billion to 33 billion parameters, available on Hugging Face. Users can preprocess C code into binary and assembly instructions, then decompile assembly instructions into C using LLM4Decompile. Ongoing efforts aim to expand capabilities to support more architectures and configurations, integrate with decompilation tools like Ghidra and Rizin, and enhance performance with larger training datasets.

RAID is the largest and most comprehensive dataset for evaluating AI-generated text detectors. It contains over 10 million documents spanning 11 LLMs, 11 genres, 4 decoding strategies, and 12 adversarial attacks. RAID is designed to be the go-to location for trustworthy third-party evaluation of popular detectors. The dataset covers diverse models, domains, sampling strategies, and attacks, making it a valuable resource for training detectors, evaluating generalization, protecting against adversaries, and comparing to state-of-the-art models from academia and industry.

WordLlama is a fast, lightweight NLP toolkit optimized for CPU hardware. It recycles components from large language models to create efficient word representations. It offers features like Matryoshka Representations, low resource requirements, binarization, and numpy-only inference. The tool is suitable for tasks like semantic matching, fuzzy deduplication, ranking, and clustering, making it a good option for NLP-lite tasks and exploratory analysis.

DOM to Semantic Markdown is a tool that converts HTML DOM to Semantic Markdown for use in Large Language Models (LLMs). It maximizes semantic information, token efficiency, and preserves metadata to enhance LLMs' processing capabilities. The tool captures rich web content structure, including semantic tags, image metadata, table structures, and link destinations. It offers customizable conversion options and supports both browser and Node.js environments.

1filellm is a command-line data aggregation tool designed for LLM ingestion. It aggregates and preprocesses data from various sources into a single text file, facilitating the creation of information-dense prompts for large language models. The tool supports automatic source type detection, handling of multiple file formats, web crawling functionality, integration with Sci-Hub for research paper downloads, text preprocessing, and token count reporting. Users can input local files, directories, GitHub repositories, pull requests, issues, ArXiv papers, YouTube transcripts, web pages, Sci-Hub papers via DOI or PMID. The tool provides uncompressed and compressed text outputs, with the uncompressed text automatically copied to the clipboard for easy pasting into LLMs.

AudioNotes is a system built on FunASR and Qwen2 that can quickly extract content from audio and video, and organize it using large models into structured markdown notes for easy reading. Users can interact with the audio and video content, install Ollama, pull models, and deploy services using Docker or locally with a PostgreSQL database. The system provides a seamless way to convert audio and video into structured notes for efficient consumption.

Scrape It Now is a versatile tool for scraping websites with features like decoupled architecture, CLI functionality, idempotent operations, and content storage options. The tool includes a scraper component for efficient scraping, ad blocking, link detection, markdown extraction, dynamic content loading, and anonymity features. It also offers an indexer component for creating AI search indexes, chunking content, embedding chunks, and enabling semantic search. The tool supports various configurations for Azure services and local storage, providing flexibility and scalability for web scraping and indexing tasks.

Open Deep Research is an open-source tool designed to generate AI-powered reports from web search results efficiently. It combines Bing Search API for search results retrieval, JinaAI for content extraction, and customizable report generation. Users can customize settings, export reports in multiple formats, and benefit from rate limiting for stability. The tool aims to streamline research and report creation in a user-friendly platform.

DevDocs is a platform designed to simplify the process of digesting technical documentation for software engineers and developers. It automates the extraction and conversion of web content into markdown format, making it easier for users to access and understand the information. By crawling through child pages of a given URL, DevDocs provides a streamlined approach to gathering relevant data and integrating it into various tools for software development. The tool aims to save time and effort by eliminating the need for manual research and content extraction, ultimately enhancing productivity and efficiency in the development process.

mcp-omnisearch is a Model Context Protocol (MCP) server that acts as a unified gateway to multiple search providers and AI tools. It integrates Tavily, Perplexity, Kagi, Jina AI, Brave, Exa AI, and Firecrawl to offer a wide range of search, AI response, content processing, and enhancement features through a single interface. The server provides powerful search capabilities, AI response generation, content extraction, summarization, web scraping, structured data extraction, and more. It is designed to work flexibly with the API keys available, enabling users to activate only the providers they have keys for and easily add more as needed.

Datalore is a terminal tool for generating structured datasets from local files like PDFs, Word docs, images, and text. It extracts content, uses semantic search to understand context, applies instructions through a generated schema, and outputs clean, structured data. Perfect for converting raw or unstructured local documents into ready-to-use datasets for training, analysis, or experimentation, all without manual formatting.

Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

The VisionCraft API is a free API for using over 100 different AI models. From images to sound.

Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.

Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.

SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.

Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.