NotHotDog is an open-source platform for testing, evaluating, and simulating AI agents. It offers a robust framework for generating test cases, running conversational scenarios, and analyzing agent performance.

LLM-FuzzX is an open-source user-friendly fuzz testing tool for large language models (e.g., GPT, Claude, LLaMA), equipped with advanced task-aware mutation strategies, fine-grained evaluation, and jailbreak detection capabilities. It helps researchers and developers quickly discover potential security vulnerabilities and enhance model robustness. The tool features a user-friendly web interface for visual configuration and real-time monitoring, supports various advanced mutation methods, integrates RoBERTa model for real-time jailbreak detection and evaluation, supports multiple language models like GPT, Claude, LLaMA, provides visualization analysis with seed flowcharts and experiment data statistics, and offers detailed logging support for main, mutation, and jailbreak logs.

Nerority's Advanced GPT Suite is a collection of 33 GPTs that can be controlled with natural language prompts. The suite includes tools for various tasks such as strategic consulting, business analysis, career profile building, content creation, educational purposes, image-based tasks, knowledge engineering, marketing, persona creation, programming, prompt engineering, role-playing, simulations, and task management. Users can access links, usage instructions, and guides for each GPT on their respective pages. The suite is designed for public demonstration and usage, offering features like meta-sequence optimization, AI priming, prompt classification, and optimization. It also provides tools for generating articles, analyzing contracts, visualizing data, distilling knowledge, creating educational content, exploring topics, generating marketing copy, simulating scenarios, managing tasks, and more.

Paperless-AI is an automated document analyzer tool designed for Paperless-ngx users. It utilizes the OpenAI API and Ollama (Mistral, llama, phi 3, gemma 2) to automatically scan, analyze, and tag documents. The tool offers features such as automatic document scanning, AI-powered document analysis, automatic title and tag assignment, manual mode for analyzing documents, easy setup through a web interface, document processing dashboard, error handling, and Docker support. Users can configure the tool through a web interface and access a debug interface for monitoring and troubleshooting. Paperless-AI aims to streamline document organization and analysis processes for users with access to Paperless-ngx and AI capabilities.

Cursor Lens is an open-source tool that acts as a proxy between Cursor and various AI providers, logging interactions and providing detailed analytics to help developers optimize their use of AI in their coding workflow. It supports multiple AI providers, captures and logs all requests, provides visual analytics on AI usage, allows users to set up and switch between different AI configurations, offers real-time monitoring of AI interactions, tracks token usage, estimates costs based on token usage and model pricing. Built with Next.js, React, PostgreSQL, Prisma ORM, Vercel AI SDK, Tailwind CSS, and shadcn/ui components.

CrewAI Studio is an application with a user-friendly interface for interacting with CrewAI, offering support for multiple platforms and various backend providers. It allows users to run crews in the background, export single-page apps, and use custom tools for APIs and file writing. The roadmap includes features like better import/export, human input, chat functionality, automatic crew creation, and multiuser environment support.

AITuberKit server-side is a tool that allows users to receive messages via WebSocket and obtain responses from Open Interpreter. Users can also send files to the server for storage and issue commands to Open Interpreter. The tool is designed for WebSocket operation and provides a default connection URL of `ws://127.0.0.1:8000/ws`. It supports debugging in VSCode with DEBUG_MODE=1. The tool is licensed under KillianLucas/open-interpreter and includes a guide on how to use Open Interpreter.

Flock is a workflow-based low-code platform that enables rapid development of chatbots, RAG applications, and coordination of multi-agent teams. It offers a flexible, low-code solution for orchestrating collaborative agents, supporting various node types for specific tasks, such as input processing, text generation, knowledge retrieval, tool execution, intent recognition, answer generation, and more. Flock integrates LangChain and LangGraph to provide offline operation capabilities and supports future nodes like Conditional Branch, File Upload, and Parameter Extraction for creating complex workflows. Inspired by StreetLamb, Lobe-chat, Dify, and fastgpt projects, Flock introduces new features and directions while leveraging open-source models and multi-tenancy support.

This repository provides sample applications and tutorials to showcase the power of Amazon Bedrock with Python. It helps Python developers understand how to harness Amazon Bedrock in building generative AI-enabled applications. The resources also demonstrate integration with vector databases using RAG (Retrieval-augmented generation) and services like Amazon Aurora, RDS, and OpenSearch. Additionally, it explores using langchain and streamlit to create effective experimental applications.

CodeFuse IDE is an AI-native integrated development environment that leverages AI technologies to enhance productivity and streamline workflows. It supports seamless integration of various models, enabling developers to customize and extend functionality. The platform is compatible with VS Code extensions, providing access to a rich ecosystem of plugins. CodeFuse IDE uses electron-forge for packaging desktop applications and supports development, building, packaging, and auto updates.

Julep is an advanced platform for creating stateful and functional AI apps powered by large language models. It offers features like statefulness by design, automatic function calling, production-ready deployment, cron-like asynchronous functions, 90+ built-in tools, and the ability to switch between different LLMs easily. Users can build AI applications without the need to write code for embedding, saving, and retrieving conversation history, and can connect to third-party applications using Composio. Julep simplifies the process of getting started with AI apps, whether they are conversational, functional, or agentic.

Easy Voice Toolkit is a toolkit based on open source voice projects, providing automated audio tools including speech model training. Users can seamlessly integrate functions like audio processing, voice recognition, voice transcription, dataset creation, model training, and voice conversion to transform raw audio files into ideal speech models. The toolkit supports multiple languages and is currently only compatible with Windows systems. It acknowledges the contributions of various projects and offers local deployment options for both users and developers. Additionally, cloud deployment on Google Colab is available. The toolkit has been tested on Windows OS devices and includes a FAQ section and terms of use for academic exchange purposes.

Gemini Multimodal Playground is a basic Python app for voice conversations with Google's Gemini 2.0 AI model. It features real-time voice input and text-to-speech responses. Users can configure settings through the GUI and interact with Gemini by speaking into the microphone. The application provides options for voice selection, system prompt customization, and enabling Google search. Troubleshooting tips are available for handling audio feedback loop issues that may occur during interactions.

Resume Job Matcher is a Python script that automates the process of matching resumes to a job description using AI. It leverages the Anthropic Claude API or OpenAI's GPT API to analyze resumes and provide a match score along with personalized email responses for candidates. The tool offers comprehensive resume processing, advanced AI-powered analysis, in-depth evaluation & scoring, comprehensive analytics & reporting, enhanced candidate profiling, and robust system management. Users can customize font presets, generate PDF versions of unified resumes, adjust logging level, change scoring model, modify AI provider, and adjust AI model. The final score for each resume is calculated based on AI-generated match score and resume quality score, ensuring content relevance and presentation quality are considered. Troubleshooting tips, best practices, contribution guidelines, and required Python packages are provided.

llmaz is an easy, advanced inference platform for large language models on Kubernetes. It aims to provide a production-ready solution that integrates with state-of-the-art inference backends. The platform supports efficient model distribution, accelerator fungibility, SOTA inference, various model providers, multi-host support, and scaling efficiency. Users can quickly deploy LLM services with minimal configurations and benefit from a wide range of advanced inference backends. llmaz is designed to optimize cost and performance while supporting cutting-edge researches like Speculative Decoding or Splitwise on Kubernetes.

LLM Zoomcamp is a free online course focusing on real-life applications of Large Language Models (LLMs). Over 10 weeks, participants will learn to build an AI bot capable of answering questions based on a knowledge base. The course covers topics such as LLMs, RAG, open-source LLMs, vector databases, orchestration, monitoring, and advanced RAG systems. Pre-requisites include comfort with programming, Python, and the command line, with no prior exposure to AI or ML required. The course features a pre-course workshop and is led by instructors Alexey Grigorev and Magdalena Kuhn, with support from sponsors and partners.

NotHotDog is an open-source platform for testing, evaluating, and simulating AI agents. It offers a robust framework for generating test cases, running conversational scenarios, and analyzing agent performance.

EvoMaster is an open-source AI-driven tool that automatically generates system-level test cases for web/enterprise applications. It uses Evolutionary Algorithm and Dynamic Program Analysis to evolve test cases, maximizing code coverage and fault detection. It supports REST, GraphQL, and RPC APIs, with whitebox testing for JVM-compiled APIs. The tool generates JUnit tests in Java or Kotlin, focusing on fault detection, self-contained tests, SQL handling, and authentication. Known limitations include manual driver creation for whitebox testing and longer execution times for better results. EvoMaster has been funded by ERC and RCN grants.

Repopack is a powerful tool that packs your entire repository into a single, AI-friendly file. It optimizes your codebase for AI comprehension, is simple to use with customizable options, and respects Gitignore files for security. The tool generates a packed file with clear separators and AI-oriented explanations, making it ideal for use with Generative AI tools like Claude or ChatGPT. Repopack offers command line options, configuration settings, and multiple methods for setting ignore patterns to exclude specific files or directories during the packing process. It includes features like comment removal for supported file types and a security check using Secretlint to detect sensitive information in files.

EvoMaster is an open-source AI-driven tool that automatically generates system-level test cases for web/enterprise applications. It uses an Evolutionary Algorithm and Dynamic Program Analysis to evolve test cases, maximizing code coverage and fault detection. The tool supports REST, GraphQL, and RPC APIs, with whitebox testing for JVM-compiled languages. It generates JUnit tests, detects faults, handles SQL databases, and supports authentication. EvoMaster has been funded by the European Research Council and the Research Council of Norway.

Ianvs is a distributed synergy AI benchmarking project incubated in KubeEdge SIG AI. It aims to test the performance of distributed synergy AI solutions following recognized standards, providing end-to-end benchmark toolkits, test environment management tools, test case control tools, and benchmark presentation tools. It also collaborates with other organizations to establish comprehensive benchmarks and related applications. The architecture includes critical components like Test Environment Manager, Test Case Controller, Generation Assistant, Simulation Controller, and Story Manager. Ianvs documentation covers quick start, guides, dataset descriptions, algorithms, user interfaces, stories, and roadmap.

Beehave is a powerful addon for Godot Engine that enables users to create robust AI systems using behavior trees. It simplifies the design of complex NPC behaviors, challenging boss battles, and other advanced setups. Beehave allows for the creation of highly adaptive AI that responds to changes in the game world and overcomes unexpected obstacles, catering to both beginners and experienced developers. The tool is currently in development for version 3.0.

The Portkey Python SDK is a control panel for AI apps that allows seamless integration of Portkey's advanced features with OpenAI methods. It provides features such as AI gateway for unified API signature, interoperability, automated fallbacks & retries, load balancing, semantic caching, virtual keys, request timeouts, observability with logging, requests tracing, custom metadata, feedback collection, and analytics. Users can make requests to OpenAI using Portkey SDK and also use async functionality. The SDK is compatible with OpenAI SDK methods and offers Portkey-specific methods like feedback and prompts. It supports various providers and encourages contributions through Github issues or direct contact via email or Discord.

Agno is a lightweight library for building multi-modal Agents. It is designed with core principles of simplicity, uncompromising performance, and agnosticism, allowing users to create blazing fast agents with minimal memory footprint. Agno supports any model, any provider, and any modality, making it a versatile container for AGI. Users can build agents with lightning-fast agent creation, model agnostic capabilities, native support for text, image, audio, and video inputs and outputs, memory management, knowledge stores, structured outputs, and real-time monitoring. The library enables users to create autonomous programs that use language models to solve problems, improve responses, and achieve tasks with varying levels of agency and autonomy.

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.