CurateGPT is a prototype web application and framework designed for general purpose AI-guided curation and curation-related operations over collections of objects. It provides functionalities for loading example data, building indexes, interacting with knowledge bases, and performing tasks such as chatting with a knowledge base, querying Pubmed, interacting with a GitHub issue tracker, term autocompletion, and all-by-all comparisons. The tool is built to work best with the OpenAI gpt-4 model and OpenAI ada-text-embedding-002 for embedding, but also supports alternative models through a plugin architecture.

CurateGPT is a prototype web application and framework for performing general purpose AI-guided curation and curation-related operations over collections of objects. It allows users to load JSON, YAML, or CSV data, build vector database indexes for ontologies, and interact with various data sources like GitHub, Google Drives, Google Sheets, and more. The tool supports ontology curation, knowledge base querying, term autocompletion, and all-by-all comparisons for objects in a collection.

LlamaBot is a Pythonic bot interface to Large Language Models (LLMs), providing an easy way to experiment with LLMs in Jupyter notebooks and build Python apps utilizing LLMs. It supports all models available in LiteLLM. Users can access LLMs either through local models with Ollama or by using API providers like OpenAI and Mistral. LlamaBot offers different bot interfaces like SimpleBot, ChatBot, QueryBot, and ImageBot for various tasks such as rephrasing text, maintaining chat history, querying documents, and generating images. The tool also includes CLI demos showcasing its capabilities and supports contributions for new features and bug reports from the community.

DevQualityEval is an evaluation benchmark and framework designed to compare and improve the quality of code generation of Language Model Models (LLMs). It provides developers with a standardized benchmark to enhance real-world usage in software development and offers users metrics and comparisons to assess the usefulness of LLMs for their tasks. The tool evaluates LLMs' performance in solving software development tasks and measures the quality of their results through a point-based system. Users can run specific tasks, such as test generation, across different programming languages to evaluate LLMs' language understanding and code generation capabilities.

Aisuite is a simple, unified interface to multiple Generative AI providers. It allows developers to easily interact with various Language Model (LLM) providers like OpenAI, Anthropic, Azure, Google, AWS, and more through a standardized interface. The library focuses on chat completions and provides a thin wrapper around python client libraries, enabling creators to test responses from different LLM providers without changing their code. Aisuite maximizes stability by using HTTP endpoints or SDKs for making calls to the providers. Users can install the base package or specific provider packages, set up API keys, and utilize the library to generate chat completion responses from different models.

MultiPL-E is a system for translating unit test-driven neural code generation benchmarks to new languages. It is part of the BigCode Code Generation LM Harness and allows for evaluating Code LLMs using various benchmarks. The tool supports multiple versions with improvements and new language additions, providing a scalable and polyglot approach to benchmarking neural code generation. Users can access a tutorial for direct usage and explore the dataset of translated prompts on the Hugging Face Hub.

2P-Kt is a Kotlin-based and multi-platform reboot of tuProlog (2P), a multi-paradigm logic programming framework written in Java. It consists of an open ecosystem for Symbolic Artificial Intelligence (AI) with modules supporting logic terms, unification, indexing, resolution of logic queries, probabilistic logic programming, binary decision diagrams, OR-concurrent resolution, DSL for logic programming, parsing modules, serialisation modules, command-line interface, and graphical user interface. The tool is designed to support knowledge representation and automatic reasoning through logic programming in an extensible and flexible way, encouraging extensions towards other symbolic AI systems than Prolog. It is a pure, multi-platform Kotlin project supporting JVM, JS, Android, and Native platforms, with a lightweight library leveraging the Kotlin common library.

A Pythonic framework to simplify AI service building. The LeptonAI Python library allows you to build an AI service from Python code with ease. Key features include a Pythonic abstraction Photon, simple abstractions to launch models like those on HuggingFace, prebuilt examples for common models, AI tailored batteries, a client to automatically call your service like native Python functions, and Pythonic configuration specs to be readily shipped in a cloud environment.

LiveBench is a benchmark tool designed for Language Model Models (LLMs) with a focus on limiting contamination through monthly new questions based on recent datasets, arXiv papers, news articles, and IMDb movie synopses. It provides verifiable, objective ground-truth answers for accurate scoring without an LLM judge. The tool offers 18 diverse tasks across 6 categories and promises to release more challenging tasks over time. LiveBench is built on FastChat's llm_judge module and incorporates code from LiveCodeBench and IFEval.

RayLLM (formerly known as Aviary) is an LLM serving solution that makes it easy to deploy and manage a variety of open source LLMs, built on Ray Serve. It provides an extensive suite of pre-configured open source LLMs, with defaults that work out of the box. RayLLM supports Transformer models hosted on Hugging Face Hub or present on local disk. It simplifies the deployment of multiple LLMs, the addition of new LLMs, and offers unique autoscaling support, including scale-to-zero. RayLLM fully supports multi-GPU & multi-node model deployments and offers high performance features like continuous batching, quantization and streaming. It provides a REST API that is similar to OpenAI's to make it easy to migrate and cross test them. RayLLM supports multiple LLM backends out of the box, including vLLM and TensorRT-LLM.

Renumics RAG is a retrieval-augmented generation assistant demo that utilizes LangChain and Streamlit. It provides a tool for indexing documents and answering questions based on the indexed data. Users can explore and visualize RAG data, configure OpenAI and Hugging Face models, and interactively explore questions and document snippets. The tool supports GPU and CPU setups, offers a command-line interface for retrieving and answering questions, and includes a web application for easy access. It also allows users to customize retrieval settings, embeddings models, and database creation. Renumics RAG is designed to enhance the question-answering process by leveraging indexed documents and providing detailed answers with sources.

Dravid (DRD) is an advanced, AI-powered CLI coding framework designed to follow user instructions until the job is completed, including fixing errors. It can generate code, fix errors, handle image queries, manage file operations, integrate with external APIs, and provide a development server with error handling. Dravid is extensible and requires Python 3.7+ and CLAUDE_API_KEY. Users can interact with Dravid through CLI commands for various tasks like creating projects, asking questions, generating content, handling metadata, and file-specific queries. It supports use cases like Next.js project development, working with existing projects, exploring new languages, Ruby on Rails project development, and Python project development. Dravid's project structure includes directories for source code, CLI modules, API interaction, utility functions, AI prompt templates, metadata management, and tests. Contributions are welcome, and development setup involves cloning the repository, installing dependencies with Poetry, setting up environment variables, and using Dravid for project enhancements.

PolyMind is a multimodal, function calling powered LLM webui designed for various tasks such as internet searching, image generation, port scanning, Wolfram Alpha integration, Python interpretation, and semantic search. It offers a plugin system for adding extra functions and supports different models and endpoints. The tool allows users to interact via function calling and provides features like image input, image generation, and text file search. The application's configuration is stored in a `config.json` file with options for backend selection, compatibility mode, IP address settings, API key, and enabled features.

FastTrackML is an experiment tracking server focused on speed and scalability, fully compatible with MLFlow. It provides a user-friendly interface to track and visualize your machine learning experiments, making it easy to compare different models and identify the best performing ones. FastTrackML is open source and can be easily installed and run with pip or Docker. It is also compatible with the MLFlow Python package, making it easy to integrate with your existing MLFlow workflows.

This prototype synthesizes verified code with an LLM using Monte Carlo Tree Search (MCTS). It explores the space of possible generation of a verified program and checks at every step that it's on the right track by calling the verifier. This prototype uses Dafny, Coq, Lean, Scala, or Rust. By using this technique, weaker models that might not even know the generated language all that well can compete with stronger models.

Langroid-examples is a repository containing examples of using the Langroid Multi-Agent Programming framework to build LLM applications. It provides a collection of scripts and instructions for setting up the environment, working with local LLMs, using OpenAI LLMs, and running various examples. The repository also includes optional setup instructions for integrating with Qdrant, Redis, Momento, GitHub, and Google Custom Search API. Users can explore different scenarios and functionalities of Langroid through the provided examples and documentation.

CurateGPT is a prototype web application and framework for performing general purpose AI-guided curation and curation-related operations over collections of objects. It allows users to load JSON, YAML, or CSV data, build vector database indexes for ontologies, and interact with various data sources like GitHub, Google Drives, Google Sheets, and more. The tool supports ontology curation, knowledge base querying, term autocompletion, and all-by-all comparisons for objects in a collection.

CurateGPT is a prototype web application and framework designed for general purpose AI-guided curation and curation-related operations over collections of objects. It provides functionalities for loading example data, building indexes, interacting with knowledge bases, and performing tasks such as chatting with a knowledge base, querying Pubmed, interacting with a GitHub issue tracker, term autocompletion, and all-by-all comparisons. The tool is built to work best with the OpenAI gpt-4 model and OpenAI ada-text-embedding-002 for embedding, but also supports alternative models through a plugin architecture.

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.