DeGPT is a tool designed to optimize decompiler output using Large Language Models (LLM). It requires manual installation of specific packages and setting up API key for OpenAI. The tool provides functionality to perform optimization on decompiler output by running specific scripts.

GenAssist is an AI-powered platform for managing and leveraging various AI workflows, focusing on conversation management, analytics, and agent-based interactions. It provides user management, AI agents configuration, knowledge base management, analytics, conversation management, and audit logging features. The platform is built with React, TypeScript, Vite, Tailwind CSS, FastAPI, SQLAlchemy ORM, and PostgreSQL database. GenAssist offers integration options for React, JavaScript Widget, and iOS, along with UI test automation and backend testing capabilities.

The MCP LLM Bridge is a tool that acts as a bridge connecting Model Context Protocol (MCP) servers to OpenAI-compatible LLMs. It provides a bidirectional protocol translation layer between MCP and OpenAI's function-calling interface, enabling any OpenAI-compatible language model to leverage MCP-compliant tools through a standardized interface. The tool supports primary integration with the OpenAI API and offers additional compatibility for local endpoints that implement the OpenAI API specification. Users can configure the tool for different endpoints and models, facilitating the execution of complex queries and tasks using cloud-based or local models like Ollama and LM Studio.

OmniSteward is an AI-powered steward system based on large language models that can interact with users through voice or text to help control smart home devices and computer programs. It supports multi-turn dialogue, tool calling for complex tasks, multiple LLM models, voice recognition, smart home control, computer program management, online information retrieval, command line operations, and file management. The system is highly extensible, allowing users to customize and share their own tools.

DocETL is a tool for creating and executing data processing pipelines, especially suited for complex document processing tasks. It offers a low-code, declarative YAML interface to define LLM-powered operations on complex data. Ideal for maximizing correctness and output quality for semantic processing on a collection of data, representing complex tasks via map-reduce, maximizing LLM accuracy, handling long documents, and automating task retries based on validation criteria.

AISDK Prompt Optimizer is an open-source tool designed to transform AI interactions by optimizing prompts. It utilizes the GEPA reflective optimizer to evolve textual components of AI systems, providing features such as reflective prompt mutation, rich textual feedback, and Pareto-based selection. Users can teach their AI desired behaviors, collect ideal samples, run optimization to generate optimized prompts, and deploy the results in their applications. The tool leverages advanced optimization algorithms to guide AI through interactive conversations and refine prompt candidates for improved performance.

Chunkr is an open-source document intelligence API that provides a production-ready service for document layout analysis, OCR, and semantic chunking. It allows users to convert PDFs, PPTs, Word docs, and images into RAG/LLM-ready chunks. The API offers features such as layout analysis, OCR with bounding boxes, structured HTML and markdown output, and VLM processing controls. Users can interact with Chunkr through a Python SDK, enabling them to upload documents, process them, and export results in various formats. The tool also supports self-hosted deployment options using Docker Compose or Kubernetes, with configurations for different AI models like OpenAI, Google AI Studio, and OpenRouter. Chunkr is dual-licensed under the GNU Affero General Public License v3.0 (AGPL-3.0) and a commercial license, providing flexibility for different usage scenarios.

ControlLLM is a framework that empowers large language models to leverage multi-modal tools for solving complex real-world tasks. It addresses challenges like ambiguous user prompts, inaccurate tool selection, and inefficient tool scheduling by utilizing a task decomposer, a Thoughts-on-Graph paradigm, and an execution engine with a rich toolbox. The framework excels in tasks involving image, audio, and video processing, showcasing superior accuracy, efficiency, and versatility compared to existing methods.

XLang™ is a cutting-edge language designed for AI and IoT applications, offering exceptional dynamic and high-performance capabilities. It excels in distributed computing and seamless integration with popular languages like C++, Python, and JavaScript. Notably efficient, running 3 to 5 times faster than Python in AI and deep learning contexts. Features optimized tensor computing architecture for constructing neural networks through tensor expressions. Automates tensor data flow graph generation and compilation for specific targets, enhancing GPU performance by 6 to 10 times in CUDA environments.

webapp-starter is a modern full-stack application template built with Turborepo, featuring a Hono + Bun API backend and Next.js frontend. It provides an easy way to build a SaaS product. The backend utilizes technologies like Bun, Drizzle ORM, and Supabase, while the frontend is built with Next.js, Tailwind CSS, Shadcn/ui, and Clerk. Deployment can be done using Vercel and Render. The project structure includes separate directories for API backend and Next.js frontend, along with shared packages for the main database. Setup involves installing dependencies, configuring environment variables, and setting up services like Bun, Supabase, and Clerk. Development can be done using 'turbo dev' command, and deployment instructions are provided for Vercel and Render. Contributions are welcome through pull requests.

An open-source mono-repository containing the MyCoder agent and CLI. It leverages Anthropic's Claude API for intelligent decision making, has a modular architecture with various tool categories, supports parallel execution with sub-agents, can modify code by writing itself, features a smart logging system for clear output, and is human-compatible using README.md, project files, and shell commands to build its own context.

LLM-Term is a Rust-based CLI tool that generates and executes terminal commands using OpenAI's language models or local Ollama models. It offers configurable model and token limits, works on both PowerShell and Unix-like shells, and provides a seamless user experience for generating commands based on prompts. Users can easily set up the tool, customize configurations, and leverage different models for command generation.

askrepo is a tool that reads the content of Git-managed text files in a specified directory, sends it to the Google Gemini API, and provides answers to questions based on a specified prompt. It acts as a question-answering tool for source code by using a Google AI model to analyze and provide answers based on the provided source code files. The tool leverages modules for file processing, interaction with the Google AI API, and orchestrating the entire process of extracting information from source code files.

OramaCore is a database designed for AI projects, answer engines, copilots, and search functionalities. It offers features such as a full-text search engine, vector database, LLM interface, and various utilities. The tool is currently under active development and not recommended for production use due to potential API changes. OramaCore aims to provide a comprehensive solution for managing data and enabling advanced AI capabilities in projects.

An LLM-powered code generation tool that relies on the built-in Node.js API Typescript Template Project to easily generate clean, well-structured CRUD module code from text description. It orchestrates 3 LLM micro-agents (`Developer`, `Troubleshooter` and `TestsFixer`) to generate code, fix compilation errors, and ensure passing E2E tests. The process includes module code generation, DB migration creation, seeding data, and running tests to validate output. By cycling through these steps, it guarantees consistent and production-ready CRUD code aligned with vertical slicing architecture.

A production-ready FastAPI template for building AI agent applications with LangGraph integration. This template provides a robust foundation for building scalable, secure, and maintainable AI agent services. It includes features like FastAPI for high-performance async API endpoints, LangGraph integration, structured logging, rate limiting, PostgreSQL for data persistence, Docker support, security measures like JWT-based authentication and input sanitization, developer-friendly features like environment-specific configuration and type hints, a model evaluation framework with automated metric-based evaluation and detailed JSON reports, and a configuration system with environment-specific settings.

LLM4EDA is a repository dedicated to showcasing the emerging progress in utilizing Large Language Models for Electronic Design Automation. The repository includes resources, papers, and tools that leverage LLMs to solve problems in EDA. It covers a wide range of applications such as knowledge acquisition, code generation, code analysis, verification, and large circuit models. The goal is to provide a comprehensive understanding of how LLMs can revolutionize the EDA industry by offering innovative solutions and new interaction paradigms.

DeGPT is a tool designed to optimize decompiler output using Large Language Models (LLM). It requires manual installation of specific packages and setting up API key for OpenAI. The tool provides functionality to perform optimization on decompiler output by running specific scripts.

Code2Prompt is a powerful command-line tool that generates comprehensive prompts from codebases, designed to streamline interactions between developers and Large Language Models (LLMs) for code analysis, documentation, and improvement tasks. It bridges the gap between codebases and LLMs by converting projects into AI-friendly prompts, enabling users to leverage AI for various software development tasks. The tool offers features like holistic codebase representation, intelligent source tree generation, customizable prompt templates, smart token management, Gitignore integration, flexible file handling, clipboard-ready output, multiple output options, and enhanced code readability.

SinkFinder + LLM is a closed-source semi-automatic vulnerability discovery tool that performs static code analysis on jar/war/zip files. It enhances the capability of LLM large models to verify path reachability and assess the trustworthiness score of the path based on the contextual code environment. Users can customize class and jar exclusions, depth of recursive search, and other parameters through command-line arguments. The tool generates rule.json configuration file after each run and requires configuration of the DASHSCOPE_API_KEY for LLM capabilities. The tool provides detailed logs on high-risk paths, LLM results, and other findings. Rules.json file contains sink rules for various vulnerability types with severity levels and corresponding sink methods.

OpenRepoWiki is a tool designed to automatically generate a comprehensive wiki page for any GitHub repository. It simplifies the process of understanding the purpose, functionality, and core components of a repository by analyzing its code structure, identifying key files and functions, and providing explanations. The tool aims to assist individuals who want to learn how to build various projects by providing a summarized overview of the repository's contents. OpenRepoWiki requires certain dependencies such as Google AI Studio or Deepseek API Key, PostgreSQL for storing repository information, Github API Key for accessing repository data, and Amazon S3 for optional usage. Users can configure the tool by setting up environment variables, installing dependencies, building the server, and running the application. It is recommended to consider the token usage and opt for cost-effective options when utilizing the tool.

CodebaseToPrompt is a simple tool that converts a local directory into a structured prompt for Large Language Models (LLMs). It allows users to select specific files for code review, analysis, or documentation by exploring and filtering through the file tree in a browser-based interface. The tool generates a formatted output that can be directly used with AI tools, provides token count estimates, and supports local storage for saving selections. Users can easily copy the selected files in the desired format for further use.

air is an R formatter and language server written in Rust. It is currently in alpha stage, so users should expect breaking changes in both the API and formatting results. The tool draws inspiration from various sources like roslyn, swift, rust-analyzer, prettier, biome, and ruff. It provides formatters and language servers, influenced by design decisions from these tools. Users can install air using standalone installers for macOS, Linux, and Windows, which automatically add air to the PATH. Developers can also install the dev version of the air CLI and VS Code extension for further customization and development.

Code-graph is a tool composed of FalkorDB Graph DB, Code-Graph-Backend, and Code-Graph-Frontend. It allows users to store and query graphs, manage backend logic, and interact with the website. Users can run the components locally by setting up environment variables and installing dependencies. The tool supports analyzing C & Python source files with plans to add support for more languages in the future. It provides a local repository analysis feature and a live demo accessible through a web browser.

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.