The Prompt Fuzzer is an open-source tool that helps you assess the security of your GenAI application's system prompt against various dynamic LLM-based attacks. It provides a security evaluation based on the outcome of these attack simulations, enabling you to strengthen your system prompt as needed. The Prompt Fuzzer dynamically tailors its tests to your application's unique configuration and domain. The Fuzzer also includes a Playground chat interface, giving you the chance to iteratively improve your system prompt, hardening it against a wide spectrum of generative AI attacks.

The MCP TypeScript Server Template is a production-grade framework for building powerful and scalable Model Context Protocol servers with TypeScript. It features built-in observability, declarative tooling, robust error handling, and a modular, DI-driven architecture. The template is designed to be AI-agent-friendly, providing detailed rules and guidance for developers to adhere to best practices. It enforces architectural principles like 'Logic Throws, Handler Catches' pattern, full-stack observability, declarative components, and dependency injection for decoupling. The project structure includes directories for configuration, container setup, server resources, services, storage, utilities, tests, and more. Configuration is done via environment variables, and key scripts are available for development, testing, and publishing to the MCP Registry.

AutoAgent is a fully-automated and zero-code framework that enables users to create and deploy LLM agents through natural language alone. It is a top performer on the GAIA Benchmark, equipped with a native self-managing vector database, and allows for easy creation of tools, agents, and workflows without any coding. AutoAgent seamlessly integrates with a wide range of LLMs and supports both function-calling and ReAct interaction modes. It is designed to be dynamic, extensible, customized, and lightweight, serving as a personal AI assistant.

Evalchemy is a unified and easy-to-use toolkit for evaluating language models, focusing on post-trained models. It integrates multiple existing benchmarks such as RepoBench, AlpacaEval, and ZeroEval. Key features include unified installation, parallel evaluation, simplified usage, and results management. Users can run various benchmarks with a consistent command-line interface and track results locally or integrate with a database for systematic tracking and leaderboard submission.

PentAGI is an innovative tool for automated security testing that leverages cutting-edge artificial intelligence technologies. It is designed for information security professionals, researchers, and enthusiasts who need a powerful and flexible solution for conducting penetration tests. The tool provides secure and isolated operations in a sandboxed Docker environment, fully autonomous AI-powered agent for penetration testing steps, a suite of 20+ professional security tools, smart memory system for storing research results, web intelligence for gathering information, integration with external search systems, team delegation system, comprehensive monitoring and reporting, modern interface, API integration, persistent storage, scalable architecture, self-hosted solution, flexible authentication, and quick deployment through Docker Compose.

AgenticSeek is a voice-enabled AI assistant powered by DeepSeek R1 agents, offering a fully local alternative to cloud-based AI services. It allows users to interact with their filesystem, code in multiple languages, and perform various tasks autonomously. The tool is equipped with memory to remember user preferences and past conversations, and it can divide tasks among multiple agents for efficient execution. AgenticSeek prioritizes privacy by running entirely on the user's hardware without sending data to the cloud.

xFasterTransformer is an optimized solution for Large Language Models (LLMs) on the X86 platform, providing high performance and scalability for inference on mainstream LLM models. It offers C++ and Python APIs for easy integration, along with example codes and benchmark scripts. Users can prepare models in a different format, convert them, and use the APIs for tasks like encoding input prompts, generating token ids, and serving inference requests. The tool supports various data types and models, and can run in single or multi-rank modes using MPI. A web demo based on Gradio is available for popular LLM models like ChatGLM and Llama2. Benchmark scripts help evaluate model inference performance quickly, and MLServer enables serving with REST and gRPC interfaces.

The ComfyUI Web Viewer by vrch.ai is a real-time AI-generated interactive art framework that integrates realtime streaming into ComfyUI workflows. It supports keyboard control nodes, OSC control nodes, sound input nodes, and more, accessible from any device with a web browser. It enables real-time interaction with AI-generated content, ideal for interactive visual projects and enhancing ComfyUI workflows with efficient content management and display.

WebUI is a user-friendly tool built on Gradio that enhances website accessibility for AI agents. It supports various Large Language Models (LLMs) and allows custom browser integration for seamless interaction. The tool eliminates the need for re-login and authentication challenges, offering high-definition screen recording capabilities.

The RAG ChatBot project combines Lama.cpp, Chroma, and Streamlit to build a Conversation-aware Chatbot and a Retrieval-augmented generation (RAG) ChatBot. The RAG Chatbot works by taking a collection of Markdown files as input and provides answers based on the context provided by those files. It utilizes a Memory Builder component to load Markdown pages, divide them into sections, calculate embeddings, and save them in an embedding database. The chatbot retrieves relevant sections from the database, rewrites questions for optimal retrieval, and generates answers using a local language model. It also remembers previous interactions for more accurate responses. Various strategies are implemented to deal with context overflows, including creating and refining context, hierarchical summarization, and async hierarchical summarization.

Hound is a security audit automation pipeline for AI-assisted code review that mirrors how expert auditors think, learn, and collaborate. It features graph-driven analysis, sessionized audits, provider-agnostic models, belief system and hypotheses, precise code grounding, and adaptive planning. The system employs a senior/junior auditor pattern where the Scout actively navigates the codebase and annotates knowledge graphs while the Strategist handles high-level planning and vulnerability analysis. Hound is optimized for small-to-medium sized projects like smart contract applications and is language-agnostic.

Build your own conversation-based search with AI, a simple implementation with Node.js & Vue3. Live Demo Features: * Built-in support for LLM: OpenAI, Google, Lepton, Ollama(Free) * Built-in support for search engine: Bing, Sogou, Google, SearXNG(Free) * Customizable pretty UI interface * Support dark mode * Support mobile display * Support local LLM with Ollama * Support i18n * Support Continue Q&A with contexts.

Comp AI is an open-source compliance automation platform designed to assist companies in achieving compliance with standards like SOC 2, ISO 27001, and GDPR. It transforms compliance into an engineering problem solved through code, automating evidence collection, policy management, and control implementation while maintaining data and infrastructure control.

Dexto is a lightweight runtime for creating and running AI agents that turn natural language into real-world actions. It serves as the missing intelligence layer for building AI applications, standalone chatbots, or as the reasoning engine inside larger products. Dexto features a powerful CLI and Web UI for running AI agents, supports multiple interfaces, allows hot-swapping of LLMs from various providers, connects to remote tool servers via the Model Context Protocol, is config-driven with version-controlled YAML, offers production-ready core features, extensibility for custom services, and enables multi-agent collaboration via MCP and A2A.

LEANN is an innovative vector database that democratizes personal AI, transforming your laptop into a powerful RAG system that can index and search through millions of documents using 97% less storage than traditional solutions without accuracy loss. It achieves this through graph-based selective recomputation and high-degree preserving pruning, computing embeddings on-demand instead of storing them all. LEANN allows semantic search of file system, emails, browser history, chat history, codebase, or external knowledge bases on your laptop with zero cloud costs and complete privacy. It is a drop-in semantic search MCP service fully compatible with Claude Code, enabling intelligent retrieval without changing your workflow.

Well-Architected Infrastructure as Code (IaC) Analyzer is a project demonstrating how generative AI can evaluate infrastructure code for alignment with best practices. It features a modern web application allowing users to upload IaC documents, complete IaC projects, or architecture diagrams for assessment. The tool provides insights into infrastructure code alignment with AWS best practices, offers suggestions for improving cloud architecture designs, and can generate IaC templates from architecture diagrams. Users can analyze CloudFormation, Terraform, or AWS CDK templates, architecture diagrams in PNG or JPEG format, and complete IaC projects with supporting documents. Real-time analysis against Well-Architected best practices, integration with AWS Well-Architected Tool, and export of analysis results and recommendations are included.

The Prompt Fuzzer is an open-source tool that helps you assess the security of your GenAI application's system prompt against various dynamic LLM-based attacks. It provides a security evaluation based on the outcome of these attack simulations, enabling you to strengthen your system prompt as needed. The Prompt Fuzzer dynamically tailors its tests to your application's unique configuration and domain. The Fuzzer also includes a Playground chat interface, giving you the chance to iteratively improve your system prompt, hardening it against a wide spectrum of generative AI attacks.

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.