Behavior3Lua is a Lua framework for behavior trees in game AI. It provides a modified blackboard system where behavior trees are designed like code editors, allowing game designers to configure logic through editing trees. The framework offers various node types for creating complex AI behaviors, freeing game programmers from manual configuration. It includes composite, decorator, and action nodes, along with an API for creating and running behavior trees. The framework supports running states and provides an editor for visual tree editing. It has been successfully used in multiple projects for different game genres, enabling designers to create sophisticated AI and logic systems.

Aio-scrapy is an asyncio-based web crawling and web scraping framework inspired by Scrapy. It supports distributed crawling/scraping, implements compatibility with scrapyd, and provides options for using redis queue and rabbitmq queue. The framework is designed for fast extraction of structured data from websites. Aio-scrapy requires Python 3.9+ and is compatible with Linux, Windows, macOS, and BSD systems.

Polyfire is an all-in-one managed backend for AI apps that allows users to build AI apps directly from the frontend, eliminating the need for a separate backend. It simplifies the process by providing most backend services in just a few lines of code. With Polyfire, users can easily create chatbots, transcribe audio files to text, generate simple text, create a long-term memory, and generate images with Dall-E. The tool also offers starter guides and tutorials to help users get started quickly and efficiently.

llm.nvim is a plugin for Neovim that enables code completion using LLM models. It supports 'ghost-text' code completion similar to Copilot and allows users to choose their model for code generation via HTTP requests. The plugin interfaces with multiple backends like Hugging Face, Ollama, Open AI, and TGI, providing flexibility in model selection and configuration. Users can customize the behavior of suggestions, tokenization, and model parameters to enhance their coding experience. llm.nvim also includes commands for toggling auto-suggestions and manually requesting suggestions, making it a versatile tool for developers using Neovim.

GenAIScript is a scripting environment designed to facilitate file ingestion, prompt development, and structured data extraction. Users can define metadata and model configurations, specify data sources, and define tasks to extract specific information. The tool provides a convenient way to analyze files and extract desired content in a structured format. It offers a user-friendly interface for working with data and automating data extraction processes, making it suitable for various data processing tasks.

Instructor is a popular Python library for managing structured outputs from large language models (LLMs). It offers a user-friendly API for validation, retries, and streaming responses. With support for various LLM providers and multiple languages, Instructor simplifies working with LLM outputs. The library includes features like response models, retry management, validation, streaming support, and flexible backends. It also provides hooks for logging and monitoring LLM interactions, and supports integration with Anthropic, Cohere, Gemini, Litellm, and Google AI models. Instructor facilitates tasks such as extracting user data from natural language, creating fine-tuned models, managing uploaded files, and monitoring usage of OpenAI models.

The Clarifai Python SDK offers a comprehensive set of tools to integrate Clarifai's AI platform to leverage computer vision capabilities like classification , detection ,segementation and natural language capabilities like classification , summarisation , generation , Q&A ,etc into your applications. With just a few lines of code, you can leverage cutting-edge artificial intelligence to unlock valuable insights from visual and textual content.

ScrapeGraphAI is a Python library that uses Large Language Models (LLMs) and direct graph logic to create web scraping pipelines for websites, documents, and XML files. It allows users to extract specific information from web pages by providing a prompt describing the desired data. ScrapeGraphAI supports various LLMs, including Ollama, OpenAI, Gemini, and Docker, enabling users to choose the most suitable model for their needs. The library provides a user-friendly interface through its `SmartScraper` class, which simplifies the process of building and executing scraping pipelines. ScrapeGraphAI is open-source and available on GitHub, with extensive documentation and examples to guide users. It is particularly useful for researchers and data scientists who need to extract structured data from web pages for analysis and exploration.

AI Chunking is a powerful Python library for semantic document chunking and enrichment using AI. It provides intelligent document chunking capabilities with various strategies to split text while preserving semantic meaning, particularly useful for processing markdown documentation. The library offers multiple chunking strategies such as Recursive Text Splitting, Section-based Semantic Chunking, and Base Chunking. Users can configure chunk sizes, overlap, and support various text formats. The tool is easy to extend with custom chunking strategies, making it versatile for different document processing needs.

Acte is a framework designed to build GUI-like tools for AI Agents. It aims to address the issues of cognitive load and freedom degrees when interacting with multiple APIs in complex scenarios. By providing a graphical user interface (GUI) for Agents, Acte helps reduce cognitive load and constraints interaction, similar to how humans interact with computers through GUIs. The tool offers APIs for starting new sessions, executing actions, and displaying screens, accessible via HTTP requests or the SessionManager class.

This project implements a web API that offers a unified interface to Google Gemini and Claude 3. It provides a self-hosted, lightweight, and scalable solution for accessing these AI models through a streaming API. The API supports both Claude and Gemini models, allowing users to interact with them in real-time. The project includes a user-friendly web UI for configuration and documentation, making it easy to get started and explore the capabilities of the API.

PartialJson is a Python library that allows users to parse partial and incomplete JSON data with ease. With just 3 lines of Python code, users can parse JSON data that may be missing key elements or contain errors. The library provides a simple solution for handling JSON data that may not be well-formed or complete, making it a valuable tool for data processing and manipulation tasks.

The gpt-bitcoin repository is focused on creating an automated trading system for Bitcoin using GPT AI technology. It provides different versions of trading strategies utilizing various data sources such as OHLCV, Moving Averages, RSI, Stochastic Oscillator, MACD, Bollinger Bands, Orderbook Data, news data, fear/greed index, and chart images. Users can set up the system by creating a .env file with necessary API keys and installing required dependencies. The repository also includes instructions for setting up the environment on local machines and AWS EC2 Ubuntu servers. The future plan includes expanding the system to support other cryptocurrency exchanges like Bithumb, Binance, Coinbase, OKX, and Bybit.

VideoKit is a full-featured user-generated content solution for Unity Engine, enabling video recording, camera streaming, microphone streaming, social sharing, and conversational interfaces. It is cross-platform, with C# source code available for inspection. Users can share media, save to camera roll, pick from camera roll, stream camera preview, record videos, remove background, caption audio, and convert text commands. VideoKit requires Unity 2022.3+ and supports Android, iOS, macOS, Windows, and WebGL platforms.

100xPhoto is a powerful AI image platform that enables users to generate stunning images and train custom AI models. It provides an intuitive interface for creating unique AI-generated artwork and training personalized models on image datasets. The platform is built with cutting-edge technology and offers robust capabilities for AI image generation and model training.

nvim.ai is a powerful Neovim plugin that enables AI-assisted coding and chat capabilities within the editor. Users can chat with buffers, insert code with an inline assistant, and utilize various LLM providers for context-aware AI assistance. The plugin supports features like interacting with AI about code and documents, receiving relevant help based on current work, code insertion, code rewriting (Work in Progress), and integration with multiple LLM providers. Users can configure the plugin, add API keys to dotfiles, and integrate with nvim-cmp for command autocompletion. Keymaps are available for chat and inline assist functionalities. The chat dialog allows parsing content with keywords and supports roles like /system, /you, and /assistant. Context-aware assistance can be accessed through inline assist by inserting code blocks anywhere in the file.

Behavior3Lua is a Lua framework for behavior trees in game AI. It provides a modified blackboard system where behavior trees are designed like code editors, allowing game designers to configure logic through editing trees. The framework offers various node types for creating complex AI behaviors, freeing game programmers from manual configuration. It includes composite, decorator, and action nodes, along with an API for creating and running behavior trees. The framework supports running states and provides an editor for visual tree editing. It has been successfully used in multiple projects for different game genres, enabling designers to create sophisticated AI and logic systems.

**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.