Orra is a tool for building production-ready multi-agent applications that handle complex real-world interactions. It coordinates tasks across existing stack, agents, and tools run as services using intelligent reasoning. With features like smart pre-evaluated execution plans, domain grounding, durable execution, and automatic service health monitoring, Orra enables users to go fast with tools as services and revert state to handle failures. It provides real-time status tracking and webhook result delivery, making it ideal for developers looking to move beyond simple crews and agents.

A starter template for building AI-powered chat agents using Cloudflare's Agent platform, powered by agents-sdk. It provides a foundation for creating interactive chat experiences with AI, complete with a modern UI and tool integration capabilities. Features include interactive chat interface with AI, built-in tool system with human-in-the-loop confirmation, advanced task scheduling, dark/light theme support, real-time streaming responses, state management, and chat history. Prerequisites include a Cloudflare account and OpenAI API key. The project structure includes components for chat UI implementation, chat agent logic, tool definitions, and helper functions. Customization guide covers adding new tools, modifying the UI, and example use cases for customer support, development assistant, data analysis assistant, personal productivity assistant, and scheduling assistant.

OpenGradient Python SDK is a tool for decentralized model management and inference services on the OpenGradient platform. It provides programmatic access to distributed AI infrastructure with cryptographic verification capabilities. The SDK supports verifiable LLM inference, multi-provider support, TEE execution, model hub integration, consensus-based verification, and command-line interface. Users can leverage this SDK to build AI applications with execution guarantees through Trusted Execution Environments and blockchain-based settlement, ensuring auditability and tamper-proof AI execution.

Fraim is an AI-powered toolkit designed for security engineers to enhance their workflows by leveraging AI capabilities. It offers solutions to find, detect, fix, and flag vulnerabilities throughout the development lifecycle. The toolkit includes features like Risk Flagger for identifying risks in code changes, Code Security Analysis for context-aware vulnerability detection, and Infrastructure as Code Analysis for spotting misconfigurations in cloud environments. Fraim can be run as a CLI tool or integrated into Github Actions, making it a versatile solution for security teams and organizations looking to enhance their security practices with AI technology.

Agentarium is a powerful Python framework for managing and orchestrating AI agents with ease. It provides a flexible and intuitive way to create, manage, and coordinate interactions between multiple AI agents in various environments. The framework offers advanced agent management, robust interaction management, a checkpoint system for saving and restoring agent states, data generation through agent interactions, performance optimization, flexible environment configuration, and an extensible architecture for customization.

Graphiti is a framework for building and querying temporally-aware knowledge graphs, tailored for AI agents in dynamic environments. It continuously integrates user interactions, structured and unstructured data, and external information into a coherent, queryable graph. The framework supports incremental data updates, efficient retrieval, and precise historical queries without complete graph recomputation, making it suitable for developing interactive, context-aware AI applications.

RA.Aid is an AI software development agent powered by `aider` and advanced reasoning models like `o1`. It combines `aider`'s code editing capabilities with LangChain's agent-based task execution framework to provide an intelligent assistant for research, planning, and implementation of multi-step development tasks. It handles complex programming tasks by breaking them down into manageable steps, running shell commands automatically, and leveraging expert reasoning models like OpenAI's o1. RA.Aid is designed for everyday software development, offering features such as multi-step task planning, automated command execution, and the ability to handle complex programming tasks beyond single-shot code edits.

Strands Agents - TypeScript SDK is a lightweight and flexible SDK that takes a model-driven approach to building and running AI agents in TypeScript/JavaScript. It brings key features from the Python Strands framework to Node.js environments, enabling type-safe agent development for various applications. The SDK supports model agnostic development with first-class support for Amazon Bedrock and OpenAI, along with extensible architecture for custom providers. It also offers built-in MCP support, real-time response streaming, extensible hooks, and conversation management features. With tools for interaction with external systems and seamless integration with MCP servers, the SDK provides a comprehensive solution for developing AI agents.

Action MCP is a powerful tool for managing and automating your cloud infrastructure. It provides a user-friendly interface to easily create, update, and delete resources on popular cloud platforms. With Action MCP, you can streamline your deployment process, reduce manual errors, and improve overall efficiency. The tool supports various cloud providers and offers a wide range of features to meet your infrastructure management needs. Whether you are a developer, system administrator, or DevOps engineer, Action MCP can help you simplify and optimize your cloud operations.

SmythOS is an operating system designed for building, deploying, and managing intelligent AI agents at scale. It provides a unified SDK and resource abstraction layer for various AI services, making it easy to scale and flexible. With an agent-first design, developer-friendly SDK, modular architecture, and enterprise security features, SmythOS offers a robust foundation for AI workloads. The system is built with a philosophy inspired by traditional operating system kernels, ensuring autonomy, control, and security for AI agents. SmythOS aims to make shipping production-ready AI agents accessible and open for everyone in the coming Internet of Agents era.

Agent Squad is a flexible, lightweight open-source framework for orchestrating multiple AI agents to handle complex conversations. It intelligently routes queries, maintains context across interactions, and offers pre-built components for quick deployment. The system allows easy integration of custom agents and conversation messages storage solutions, making it suitable for various applications from simple chatbots to sophisticated AI systems, scaling efficiently.

Agentic Company Researcher is a multi-agent tool that generates comprehensive company research reports by utilizing a pipeline of AI agents to gather, curate, and synthesize information from various sources. It features multi-source research, AI-powered content filtering, real-time progress streaming, dual model architecture, modern React frontend, and modular architecture. The tool follows an agentic framework with specialized research and processing nodes, leverages separate models for content generation, uses a content curation system for relevance scoring and document processing, and implements a real-time communication system via WebSocket connections. Users can set up the tool quickly using the provided setup script or manually, and it can also be deployed using Docker and Docker Compose. The application can be used for local development and deployed to various cloud platforms like AWS Elastic Beanstalk, Docker, Heroku, and Google Cloud Run.

SGR Agent Core is an open-source agentic framework for building intelligent research agents using Schema-Guided Reasoning. It provides a core library with an extendable BaseAgent interface implementing a two-phase architecture and multiple ready-to-use research agent implementations. The library includes tools for search, reasoning, and clarification, real-time streaming responses, and an OpenAI-compatible REST API. It works with any OpenAI-compatible LLM, including local models for fully private research. The framework is production-ready, with comprehensive test coverage and Docker support.

Code Puppy is an AI-powered code generation agent designed to understand programming tasks, generate high-quality code, and explain its reasoning. It supports multi-language code generation, interactive CLI, and detailed code explanations. The tool requires Python 3.9+ and API keys for various models like GPT, Google's Gemini, Cerebras, and Claude. It also integrates with MCP servers for advanced features like code search and documentation lookups. Users can create custom JSON agents for specialized tasks and access a variety of tools for file management, code execution, and reasoning sharing.

GPT Computer Assistant (GCA) is an open-source framework designed to build vertical AI agents that can automate tasks on Windows, macOS, and Ubuntu systems. It leverages the Model Context Protocol (MCP) and its own modules to mimic human-like actions and achieve advanced capabilities. With GCA, users can empower themselves to accomplish more in less time by automating tasks like updating dependencies, analyzing databases, and configuring cloud security settings.

Hayhooks is a tool that simplifies the deployment and serving of Haystack pipelines as REST APIs. It allows users to wrap their pipelines with custom logic and expose them via HTTP endpoints, including OpenAI-compatible chat completion endpoints. With Hayhooks, users can easily convert their Haystack pipelines into API services with minimal boilerplate code.

This repository contains Jupyter-based tutorials for NLP, ML, AI in Python for classes in Computational Linguistics, Natural Language Processing (NLP), Machine Learning (ML), and Artificial Intelligence (AI) at Indiana University.

Open Parse is a Python library for visually discerning document layouts and chunking them effectively. It is designed to fill the gap in open-source libraries for handling complex documents. Unlike text splitting, which converts a file to raw text and slices it up, Open Parse visually analyzes documents for superior LLM input. It also supports basic markdown for parsing headings, bold, and italics, and has high-precision table support, extracting tables into clean Markdown formats with accuracy that surpasses traditional tools. Open Parse is extensible, allowing users to easily implement their own post-processing steps. It is also intuitive, with great editor support and completion everywhere, making it easy to use and learn.

The MoonshotAI-Cookbook provides example code and guides for accomplishing common tasks with the MoonshotAI API. To run these examples, you'll need an MoonshotAI account and associated API key. Most code examples are written in Python, though the concepts can be applied in any language.

This repository is dedicated to the learning and exchange of resources for the School of Artificial Intelligence at Anhui University. Notes will be published on this website first: https://www.aoaoaoao.cn and will be synchronized to the repository regularly. You can also contact me at [email protected].

This repository provides a comprehensive guide to modern AI for beginners, covering both theoretical foundations and practical implementation. It emphasizes the importance of understanding both the mathematical principles and the code implementation of AI models. The repository includes resources on PyTorch, deep learning fundamentals, mathematical foundations, transformer-based LLMs, diffusion models, software engineering, and full-stack development. It also features tutorials on natural language processing with transformers, reinforcement learning, and practical deep learning for coders.

This repository is for the book 'Building AI Applications with ChatGPT APIs' published by Packt. It provides code examples and instructions for mastering ChatGPT, Whisper, and DALL-E APIs through building innovative AI projects. Readers will learn to develop AI applications using ChatGPT APIs, integrate them with frameworks like Flask and Django, create AI-generated art with DALL-E APIs, and optimize ChatGPT models through fine-tuning.

This repository contains a collection of sample applications and Jupyter Notebooks for hands-on experience with Pinecone vector databases and common AI patterns, tools, and algorithms. It includes production-ready examples for review and support, as well as learning-optimized examples for exploring AI techniques and building applications. Users can contribute, provide feedback, and collaborate to improve the resource.

LinGoose is a modular Go framework designed for building AI/LLM applications. It offers the flexibility to import only the necessary modules, abstracts features for customization, and provides a comprehensive solution for developing AI/LLM applications from scratch. The framework simplifies the process of creating intelligent applications by allowing users to choose preferred implementations or create their own. LinGoose empowers developers to leverage its capabilities to streamline the development of cutting-edge AI and LLM projects.

**Prompt flow** is a suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, evaluation to production deployment and monitoring. It makes prompt engineering much easier and enables you to build LLM apps with production quality.

DeepEval is a simple-to-use, open-source LLM evaluation framework specialized for unit testing LLM outputs. It incorporates various metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., and runs locally on your machine for evaluation. It provides a wide range of ready-to-use evaluation metrics, allows for creating custom metrics, integrates with any CI/CD environment, and enables benchmarking LLMs on popular benchmarks. DeepEval is designed for evaluating RAG and fine-tuning applications, helping users optimize hyperparameters, prevent prompt drifting, and transition from OpenAI to hosting their own Llama2 with confidence.

MegaDetector is an AI model that identifies animals, people, and vehicles in camera trap images (which also makes it useful for eliminating blank images). This model is trained on several million images from a variety of ecosystems. MegaDetector is just one of many tools that aims to make conservation biologists more efficient with AI. If you want to learn about other ways to use AI to accelerate camera trap workflows, check out our of the field, affectionately titled "Everything I know about machine learning and camera traps".

LeapfrogAI is a self-hosted AI platform designed to be deployed in air-gapped resource-constrained environments. It brings sophisticated AI solutions to these environments by hosting all the necessary components of an AI stack, including vector databases, model backends, API, and UI. LeapfrogAI's API closely matches that of OpenAI, allowing tools built for OpenAI/ChatGPT to function seamlessly with a LeapfrogAI backend. It provides several backends for various use cases, including llama-cpp-python, whisper, text-embeddings, and vllm. LeapfrogAI leverages Chainguard's apko to harden base python images, ensuring the latest supported Python versions are used by the other components of the stack. The LeapfrogAI SDK provides a standard set of protobuffs and python utilities for implementing backends and gRPC. LeapfrogAI offers UI options for common use-cases like chat, summarization, and transcription. It can be deployed and run locally via UDS and Kubernetes, built out using Zarf packages. LeapfrogAI is supported by a community of users and contributors, including Defense Unicorns, Beast Code, Chainguard, Exovera, Hypergiant, Pulze, SOSi, United States Navy, United States Air Force, and United States Space Force.

This Docker image for LLaVA (Large Language and Vision Assistant) provides a convenient way to run LLaVA locally or on RunPod. LLaVA is a powerful AI tool that combines natural language processing and computer vision capabilities. With this Docker image, you can easily access LLaVA's functionalities for various tasks, including image captioning, visual question answering, text summarization, and more. The image comes pre-installed with LLaVA v1.2.0, Torch 2.1.2, xformers 0.0.23.post1, and other necessary dependencies. You can customize the model used by setting the MODEL environment variable. The image also includes a Jupyter Lab environment for interactive development and exploration. Overall, this Docker image offers a comprehensive and user-friendly platform for leveraging LLaVA's capabilities.

The 'carrot' repository on GitHub provides a list of free and user-friendly ChatGPT mirror sites for easy access. The repository includes sponsored sites offering various GPT models and services. Users can find and share sites, report errors, and access stable and recommended sites for ChatGPT usage. The repository also includes a detailed list of ChatGPT sites, their features, and accessibility options, making it a valuable resource for ChatGPT users seeking free and unlimited GPT services.

TrustLLM is a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. The document explains how to use the trustllm python package to help you assess the performance of your LLM in trustworthiness more quickly. For more details about TrustLLM, please refer to project website.

AI-YinMei is an AI virtual anchor Vtuber development tool (N card version). It supports fastgpt knowledge base chat dialogue, a complete set of solutions for LLM large language models: [fastgpt] + [one-api] + [Xinference], supports docking bilibili live broadcast barrage reply and entering live broadcast welcome speech, supports Microsoft edge-tts speech synthesis, supports Bert-VITS2 speech synthesis, supports GPT-SoVITS speech synthesis, supports expression control Vtuber Studio, supports painting stable-diffusion-webui output OBS live broadcast room, supports painting picture pornography public-NSFW-y-distinguish, supports search and image search service duckduckgo (requires magic Internet access), supports image search service Baidu image search (no magic Internet access), supports AI reply chat box [html plug-in], supports AI singing Auto-Convert-Music, supports playlist [html plug-in], supports dancing function, supports expression video playback, supports head touching action, supports gift smashing action, supports singing automatic start dancing function, chat and singing automatic cycle swing action, supports multi scene switching, background music switching, day and night automatic switching scene, supports open singing and painting, let AI automatically judge the content.