GraphLLM is a graph-based framework designed to process data using LLMs. It offers a set of tools including a web scraper, PDF parser, YouTube subtitles downloader, Python sandbox, and TTS engine. The framework provides a GUI for building and debugging graphs with advanced features like loops, conditionals, parallel execution, streaming of results, hierarchical graphs, external tool integration, and dynamic scheduling. GraphLLM is a low-level framework that gives users full control over the raw prompt and output of models, with a steeper learning curve. It is tested with llama70b and qwen 32b, under heavy development with breaking changes expected.

TrustGraph is a tool that deploys private GraphRAG pipelines to build a RDF style knowledge graph from data, enabling accurate and secure `RAG` requests compatible with cloud LLMs and open-source SLMs. It showcases the reliability and efficiencies of GraphRAG algorithms, capturing contextual language flags missed in conventional RAG approaches. The tool offers features like PDF decoding, text chunking, inference of various LMs, RDF-aligned Knowledge Graph extraction, and more. TrustGraph is designed to be modular, supporting multiple Language Models and environments, with a plug'n'play architecture for easy customization.

Chaindesk is a no-code platform that allows users to easily set up a semantic search system for personal data without technical knowledge. It supports loading data from various sources such as raw text, web pages, files (Word, Excel, PowerPoint, PDF, Markdown, Plain Text), and upcoming support for web sites, Notion, and Airtable. The platform offers a user-friendly interface for managing datastores, querying data via a secure API endpoint, and auto-generating ChatGPT Plugins for each datastore. Chaindesk utilizes a Vector Database (Qdrant), Openai's text-embedding-ada-002 for embeddings, and has a chunk size of 1024 tokens. The technology stack includes Next.js, Joy UI, LangchainJS, PostgreSQL, Prisma, and Qdrant, inspired by the ChatGPT Retrieval Plugin.

Infinity is an AI-native database designed for LLM applications, providing incredibly fast full-text and vector search capabilities. It supports a wide range of data types, including vectors, full-text, and structured data, and offers a fused search feature that combines multiple embeddings and full text. Infinity is easy to use, with an intuitive Python API and a single-binary architecture that simplifies deployment. It achieves high performance, with 0.1 milliseconds query latency on million-scale vector datasets and up to 15K QPS.

Tiktoken is a high-performance implementation focused on token count operations. It provides various encodings like o200k_base, cl100k_base, r50k_base, p50k_base, and p50k_edit. Users can easily encode and decode text using the provided API. The repository also includes a benchmark console app for performance tracking. Contributions in the form of PRs are welcome.

KTransformers is a flexible Python-centric framework designed to enhance the user's experience with advanced kernel optimizations and placement/parallelism strategies for Transformers. It provides a Transformers-compatible interface, RESTful APIs compliant with OpenAI and Ollama, and a simplified ChatGPT-like web UI. The framework aims to serve as a platform for experimenting with innovative LLM inference optimizations, focusing on local deployments constrained by limited resources and supporting heterogeneous computing opportunities like GPU/CPU offloading of quantized models.

Nestia is a set of helper libraries for NestJS, providing super-fast/easy decorators, advanced WebSocket routes, Swagger generator, SDK library generator for clients, mockup simulator for client applications, automatic E2E test functions generator, test program utilizing e2e test functions, benchmark program using e2e test functions, super A.I. chatbot by Swagger document, Swagger-UI with online TypeScript editor, and a CLI tool. It enhances performance significantly and offers a collection of typed fetch functions with DTO structures like tRPC, along with a mockup simulator that is fully automated.

Deeppowers is a powerful Python library for deep learning applications. It provides a wide range of tools and utilities to simplify the process of building and training deep neural networks. With Deeppowers, users can easily create complex neural network architectures, perform efficient training and optimization, and deploy models for various tasks. The library is designed to be user-friendly and flexible, making it suitable for both beginners and experienced deep learning practitioners.

Ivy-Framework is a powerful tool for building internal applications with AI assistance using C# codebase. It provides a CLI for project initialization, authentication integrations, database support, LLM code generation, secrets management, container deployment, hot reload, dependency injection, state management, routing, and external widget framework. Users can easily create data tables for sorting, filtering, and pagination. The framework offers a seamless integration of front-end and back-end development, making it ideal for developing robust internal tools and dashboards.

FastFlowLM is a Python library for efficient and scalable language model inference. It provides a high-performance implementation of language model scoring using n-gram language models. The library is designed to handle large-scale text data and can be easily integrated into natural language processing pipelines for tasks such as text generation, speech recognition, and machine translation. FastFlowLM is optimized for speed and memory efficiency, making it suitable for both research and production environments.

Trafilatura is a Python package and command-line tool for gathering text on the Web and simplifying the process of turning raw HTML into structured, meaningful data. It includes components for web crawling, downloads, scraping, and extraction of main texts, metadata, and comments. The tool aims to focus on actual content, avoid noise, and make sense of data and metadata. It is robust, fast, and widely used by companies and institutions. Trafilatura outperforms other libraries in text extraction benchmarks and offers various features like support for sitemaps, parallel processing, configurable extraction of key elements, multiple output formats, and optional add-ons. The tool is actively maintained with regular updates and comprehensive documentation.

TS-Bench is a performance benchmarking tool for TypeScript projects. It provides detailed insights into the performance of TypeScript code, helping developers optimize their projects. With TS-Bench, users can measure and compare the execution time of different code snippets, functions, or modules. The tool offers a user-friendly interface for running benchmarks and analyzing the results. TS-Bench is a valuable asset for developers looking to enhance the performance of their TypeScript applications.

ML-Retreat is a comprehensive machine learning library designed to simplify and streamline the process of building and deploying machine learning models. It provides a wide range of tools and utilities for data preprocessing, model training, evaluation, and deployment. With ML-Retreat, users can easily experiment with different algorithms, hyperparameters, and feature engineering techniques to optimize their models. The library is built with a focus on scalability, performance, and ease of use, making it suitable for both beginners and experienced machine learning practitioners.

Code Web Chat is a versatile, free, and open-source AI pair programming tool with a unique web-based workflow. Users can select files, type instructions, and initialize various chatbots like ChatGPT, Gemini, Claude, and more hands-free. The tool helps users save money with free tiers and subscription-based billing and save time with multi-file edits from a single prompt. It supports chatbot initialization through the Connector browser extension and offers API tools for code completions, editing context, intelligent updates, and commit messages. Users can handle AI responses, code completions, and version control through various commands. The tool is privacy-focused, operates locally, and supports any OpenAI-API compatible provider for its utilities.

JADX-AI-MCP is a plugin for the JADX decompiler that integrates with Model Context Protocol (MCP) to provide live reverse engineering support with LLMs like Claude. It allows for quick analysis, vulnerability detection, and AI code modification, all in real time. The tool combines JADX-AI-MCP and JADX MCP SERVER to analyze Android APKs effortlessly. It offers various prompts for code understanding, vulnerability detection, reverse engineering helpers, static analysis, AI code modification, and documentation. The tool is part of the Zin MCP Suite and aims to connect all android reverse engineering and APK modification tools with a single MCP server for easy reverse engineering of APK files.

The verl-tool is a versatile command-line utility designed to streamline various tasks related to version control and code management. It provides a simple yet powerful interface for managing branches, merging changes, resolving conflicts, and more. With verl-tool, users can easily track changes, collaborate with team members, and ensure code quality throughout the development process. Whether you are a beginner or an experienced developer, verl-tool offers a seamless experience for version control operations.

GraphLLM is a graph-based framework designed to process data using LLMs. It offers a set of tools including a web scraper, PDF parser, YouTube subtitles downloader, Python sandbox, and TTS engine. The framework provides a GUI for building and debugging graphs with advanced features like loops, conditionals, parallel execution, streaming of results, hierarchical graphs, external tool integration, and dynamic scheduling. GraphLLM is a low-level framework that gives users full control over the raw prompt and output of models, with a steeper learning curve. It is tested with llama70b and qwen 32b, under heavy development with breaking changes expected.

CodeBox is a cloud infrastructure tool designed for running Python code in an isolated environment. It also offers simple file input/output capabilities and will soon support vector database operations. Users can install CodeBox using pip and utilize it by setting up an API key. The tool allows users to execute Python code snippets and interact with the isolated environment. CodeBox is currently in early development stages and requires manual handling for certain operations like refunds and cancellations. The tool is open for contributions through issue reporting and pull requests. It is licensed under MIT and can be contacted via email at [email protected].

Orion is a web-based chat interface that simplifies interactions with multiple AI model providers. It provides a unified platform for chatting and exploring various large language models (LLMs) such as Ollama, OpenAI (GPT model), Cohere (Command-r models), Google (Gemini models), Anthropic (Claude models), Groq Inc., Cerebras, and SambaNova. Users can easily navigate and assess different AI models through an intuitive, user-friendly interface. Orion offers features like browser-based access, code execution with Google Gemini, text-to-speech (TTS), speech-to-text (STT), seamless integration with multiple AI models, customizable system prompts, language translation tasks, document uploads for analysis, and more. API keys are stored locally, and requests are sent directly to official providers' APIs without external proxies.

LYRAIOS (LLM-based Your Reliable AI Operating System) is an advanced AI assistant platform built with FastAPI and Streamlit, designed to serve as an operating system for AI applications. It offers core features such as AI process management, memory system, and I/O system. The platform includes built-in tools like Calculator, Web Search, Financial Analysis, File Management, and Research Tools. It also provides specialized assistant teams for Python and research tasks. LYRAIOS is built on a technical architecture comprising FastAPI backend, Streamlit frontend, Vector Database, PostgreSQL storage, and Docker support. It offers features like knowledge management, process control, and security & access control. The roadmap includes enhancements in core platform, AI process management, memory system, tools & integrations, security & access control, open protocol architecture, multi-agent collaboration, and cross-platform support.

LangManus is a community-driven AI automation framework that combines language models with specialized tools for tasks like web search, crawling, and Python code execution. It implements a hierarchical multi-agent system with agents like Coordinator, Planner, Supervisor, Researcher, Coder, Browser, and Reporter. The framework supports LLM integration, search and retrieval tools, Python integration, workflow management, and visualization. LangManus aims to give back to the open-source community and welcomes contributions in various forms.

Talemate is a roleplay tool that allows users to interact with AI agents for dialogue, narration, summarization, direction, editing, world state management, character/scenario creation, text-to-speech, and visual generation. It supports multiple AI clients and APIs, offers long-term memory using ChromaDB, and provides tools for managing NPCs, AI-assisted character creation, and scenario creation. Users can customize prompts using Jinja2 templates and benefit from a modern, responsive UI. The tool also integrates with Runpod for enhanced functionality.

Kuebiko is a Twitch Chat Bot that reads twitch chat and generates text-to-speech responses using Google Cloud API and OpenAI's GPT-3 text completion model. It allows users to set up their own VTuber AI similar to 'Neuro-Sama'. The project is built with Python and requires setting up various API keys and configurations to enable the bot functionality. Users can customize the voice of their VTuber and route audio using VBAudio Cable. Kuebiko provides a unique way to interact with viewers through chat responses and captions in OBS.

AIP Community Registry is a collection of community-built applications and projects leveraging Palantir's AIP Platform. It showcases real-world implementations from developers using AIP in production. The registry features various solutions demonstrating practical implementations and integration patterns across different use cases.

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.