gpt-all-star

GPT-All-Star is an AI-powered code generation tool designed for scratch development of web applications with team collaboration of autonomous AI agents. The primary focus of this research project is to explore the potential of autonomous AI agents in software development. Users can organize their team, choose leaders for each step, create action plans, and work together to complete tasks. The tool supports various endpoints like OpenAI, Azure, and Anthropic, and provides functionalities for project management, code generation, and team collaboration.

The dbgpts repository contains data apps, AWEL operators, AWEL workflow templates, and agents that are built upon DB-GPT. Users can install and manage these components within their DB-GPT environment. The repository offers functionalities such as listing available flows, installing dbgpts from the official repository, viewing installed dbgpts, running flows, and managing repositories. Users can create new workflow templates and operators using the provided commands. The repository aims to enhance the capabilities of DB-GPT by providing a collection of useful tools and resources for data processing and workflow management.

ShellGPT is a command-line productivity tool powered by AI large language models (LLMs). This command-line tool offers streamlined generation of shell commands, code snippets, documentation, eliminating the need for external resources (like Google search). Supports Linux, macOS, Windows and compatible with all major Shells like PowerShell, CMD, Bash, Zsh, etc.

LangGraph Expense tracker is a small project that explores the possibilities of LangGraph. It allows users to send pictures of invoices, which are then structured and categorized into expenses and stored in a database. The project includes functionalities for invoice extraction, database setup, and API configuration. It consists of various modules for categorizing expenses, creating database tables, and running the API. The database schema includes tables for categories, payment methods, and expenses, each with specific columns to track transaction details. The API documentation is available for reference, and the project utilizes LangChain for processing expense data.

Markdown Translation BOT is a GitHub action that translates markdown files into multiple languages using various AI models. It supports markdown, markdown-jsx, and json files only. The action can be executed by individuals with write permissions to the repository, preventing API abuse by non-trusted parties. Users can set up the action by providing their API key and configuring the workflow settings. The tool allows users to create comments with specific commands to trigger translations and automatically generate pull requests or add translated files to existing pull requests. It supports multiple file translations and can interpret any language supported by GPT-4 or GPT-3.5.

LongRecipe is a tool designed for efficient long context generalization in large language models. It provides a recipe for extending the context window of language models while maintaining their original capabilities. The tool includes data preprocessing steps, model training stages, and a process for merging fine-tuned models to enhance foundational capabilities. Users can follow the provided commands and scripts to preprocess data, train models in multiple stages, and merge models effectively.

AGiXT is a dynamic Artificial Intelligence Automation Platform engineered to orchestrate efficient AI instruction management and task execution across a multitude of providers. Our solution infuses adaptive memory handling with a broad spectrum of commands to enhance AI's understanding and responsiveness, leading to improved task completion. The platform's smart features, like Smart Instruct and Smart Chat, seamlessly integrate web search, planning strategies, and conversation continuity, transforming the interaction between users and AI. By leveraging a powerful plugin system that includes web browsing and command execution, AGiXT stands as a versatile bridge between AI models and users. With an expanding roster of AI providers, code evaluation capabilities, comprehensive chain management, and platform interoperability, AGiXT is consistently evolving to drive a multitude of applications, affirming its place at the forefront of AI technology.

StarCoder2-Instruct is an open-source pipeline that introduces StarCoder2-15B-Instruct-v0.1, a self-aligned code Large Language Model (LLM) trained with a fully permissive and transparent pipeline. It generates instruction-response pairs to fine-tune StarCoder-15B without human annotations or data from proprietary LLMs. The tool is primarily finetuned for Python code generation tasks that can be verified through execution, with potential biases and limitations. Users can provide response prefixes or one-shot examples to guide the model's output. The model may have limitations with other programming languages and out-of-domain coding tasks.

files-to-prompt is a tool that concatenates a directory full of files into a single prompt for use with Language Models (LLMs). It allows users to provide the path to one or more files or directories for processing, outputting the contents of each file with relative paths and separators. The tool offers options to include hidden files, ignore specific patterns, and exclude files specified in .gitignore. It is designed to streamline the process of preparing text data for LLMs by simplifying file concatenation and customization.

OpenAI Kotlin API client is a Kotlin client for OpenAI's API with multiplatform and coroutines capabilities. It allows users to interact with OpenAI's API using Kotlin programming language. The client supports various features such as models, chat, images, embeddings, files, fine-tuning, moderations, audio, assistants, threads, messages, and runs. It also provides guides on getting started, chat & function call, file source guide, and assistants. Sample apps are available for reference, and troubleshooting guides are provided for common issues. The project is open-source and licensed under the MIT license, allowing contributions from the community.

doc-comments-ai is a tool designed to automatically generate code documentation using language models. It allows users to easily create documentation comment blocks for methods in various programming languages such as Python, Typescript, Javascript, Java, Rust, and more. The tool supports both OpenAI and local LLMs, ensuring data privacy and security. Users can generate documentation comments for methods in files, inline comments in method bodies, and choose from different models like GPT-3.5-Turbo, GPT-4, and Azure OpenAI. Additionally, the tool provides support for Treesitter integration and offers guidance on selecting the appropriate model for comprehensive documentation needs.

RAGFlow is an open-source Retrieval-Augmented Generation (RAG) engine that combines deep document understanding with Large Language Models (LLMs) to provide accurate question-answering capabilities. It offers a streamlined RAG workflow for businesses of all sizes, enabling them to extract knowledge from unstructured data in various formats, including Word documents, slides, Excel files, images, and more. RAGFlow's key features include deep document understanding, template-based chunking, grounded citations with reduced hallucinations, compatibility with heterogeneous data sources, and an automated and effortless RAG workflow. It supports multiple recall paired with fused re-ranking, configurable LLMs and embedding models, and intuitive APIs for seamless integration with business applications.

LLM Functions is a project that enables the enhancement of large language models (LLMs) with custom tools and agents developed in bash, javascript, and python. Users can create tools for their LLM to execute system commands, access web APIs, or perform other complex tasks triggered by natural language prompts. The project provides a framework for building tools and agents, with tools being functions written in the user's preferred language and automatically generating JSON declarations based on comments. Agents combine prompts, function callings, and knowledge (RAG) to create conversational AI agents. The project is designed to be user-friendly and allows users to easily extend the capabilities of their language models.

Backend.AI is a streamlined, container-based computing cluster platform that hosts popular computing/ML frameworks and diverse programming languages, with pluggable heterogeneous accelerator support including CUDA GPU, ROCm GPU, TPU, IPU and other NPUs. It allocates and isolates the underlying computing resources for multi-tenant computation sessions on-demand or in batches with customizable job schedulers with its own orchestrator. All its functions are exposed as REST/GraphQL/WebSocket APIs.

ppl.llm.serving is a serving component for Large Language Models (LLMs) within the PPL.LLM system. It provides a server based on gRPC and supports inference for LLaMA. The repository includes instructions for prerequisites, quick start guide, model exporting, server setup, client usage, benchmarking, and offline inference. Users can refer to the LLaMA Guide for more details on using this serving component.

Pipecat is an open-source framework designed for building generative AI voice bots and multimodal assistants. It provides code building blocks for interacting with AI services, creating low-latency data pipelines, and transporting audio, video, and events over the Internet. Pipecat supports various AI services like speech-to-text, text-to-speech, image generation, and vision models. Users can implement new services and contribute to the framework. Pipecat aims to simplify the development of applications like personal coaches, meeting assistants, customer support bots, and more by providing a complete framework for integrating AI services.

Superflows is an open-source alternative to OpenAI's Assistant API. It allows developers to easily add an AI assistant to their software products, enabling users to ask questions in natural language and receive answers or have tasks completed by making API calls. Superflows can analyze data, create plots, answer questions based on static knowledge, and even write code. It features a developer dashboard for configuration and testing, stateful streaming API, UI components, and support for multiple LLMs. Superflows can be set up in the cloud or self-hosted, and it provides comprehensive documentation and support.

GPT-All-Star is an AI-powered code generation tool designed for scratch development of web applications with team collaboration of autonomous AI agents. The primary focus of this research project is to explore the potential of autonomous AI agents in software development. Users can organize their team, choose leaders for each step, create action plans, and work together to complete tasks. The tool supports various endpoints like OpenAI, Azure, and Anthropic, and provides functionalities for project management, code generation, and team collaboration.

Blum Airdrop Bot automates interactions with the Blum airdrop platform, allowing users to claim rewards, manage farming sessions, complete tasks, and play games automatically. It includes features like claiming farm rewards, starting farming sessions, auto-completing tasks, auto-playing games, and claiming daily rewards. Users can choose between Default Flow for manual task selection and One-time Flow for continuous automated tasks. The setup requires Node.js, npm, and setting up a `.env` file with `QUERY_ID`. The bot can be started with `npm start` and supports donations in Solana, EVM, and BTC.

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

ONNX Runtime Generative AI is a library that provides the generative AI loop for ONNX models, including inference with ONNX Runtime, logits processing, search and sampling, and KV cache management. Users can call a high level `generate()` method, or run each iteration of the model in a loop. It supports greedy/beam search and TopP, TopK sampling to generate token sequences, has built in logits processing like repetition penalties, and allows for easy custom scoring.

Mistral.rs is a fast LLM inference platform written in Rust. We support inference on a variety of devices, quantization, and easy-to-use application with an Open-AI API compatible HTTP server and Python bindings.

The Google AI Python SDK is the easiest way for Python developers to build with the Gemini API. The Gemini API gives you access to Gemini models created by Google DeepMind. Gemini models are built from the ground up to be multimodal, so you can reason seamlessly across text, images, and code.

This repo hosts tutorial documentation for running generative AI models on NVIDIA Jetson devices. The documentation is auto-generated and hosted on GitHub Pages using their CI/CD feature to automatically generate/update the HTML documentation site upon new commits.

Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.