Ahnlich is a tool that provides multiple components for storing and searching similar vectors using linear or non-linear similarity algorithms. It includes 'ahnlich-db' for in-memory vector key value store, 'ahnlich-ai' for AI proxy communication, 'ahnlich-client-rs' for Rust client, and 'ahnlich-client-py' for Python client. The tool is not production-ready yet and is still in testing phase, allowing AI/ML engineers to issue queries using raw input such as images/text and features off-the-shelf models for indexing and querying.

starknet-agent-kit is a NestJS-based toolkit for creating AI agents that can interact with the Starknet blockchain. It allows users to perform various actions such as retrieving account information, creating accounts, transferring assets, playing with DeFi, interacting with dApps, and executing RPC read methods. The toolkit provides a secure environment for developing AI agents while emphasizing caution when handling sensitive information. Users can make requests to the Starknet agent via API endpoints and utilize tools from Langchain directly.

vue-renderer-markdown is a high-performance tool designed for streaming and rendering Markdown content in real-time. It is optimized for handling incomplete or rapidly changing Markdown blocks, making it ideal for scenarios like AI model responses, live content updates, and real-time Markdown rendering. The tool offers features such as ultra-high performance, streaming-first design, Monaco integration, progressive Mermaid rendering, custom components integration, complete Markdown support, real-time updates, TypeScript support, and zero configuration setup. It solves challenges like incomplete syntax blocks, rapid content changes, cursor positioning complexities, and graceful handling of partial tokens with a streaming-optimized architecture.

Cache-Augmented Generation (CAG) is an alternative paradigm to Retrieval-Augmented Generation (RAG) that eliminates real-time retrieval delays and errors by preloading all relevant resources into the model's context. CAG leverages extended context windows of large language models (LLMs) to generate responses directly, providing reduced latency, improved reliability, and simplified design. While CAG has limitations in knowledge size and context length, advancements in LLMs are addressing these issues, making CAG a practical and scalable alternative for complex applications.

Models.dev is an open-source database providing detailed specifications, pricing, and capabilities of various AI models. It serves as a centralized platform for accessing information on AI models, allowing users to contribute and utilize the data through an API. The repository contains data stored in TOML files, organized by provider and model, along with SVG logos. Users can contribute by adding new models following specific guidelines and submitting pull requests for validation. The project aims to maintain an up-to-date and comprehensive database of AI model information.

Python-tgpt is a Python package that enables seamless interaction with over 45 free LLM providers without requiring an API key. It also provides image generation capabilities. The name _python-tgpt_ draws inspiration from its parent project tgpt, which operates on Golang. Through this Python adaptation, users can effortlessly engage with a number of free LLMs available, fostering a smoother AI interaction experience.

Cursive is a universal and intuitive framework for interacting with LLMs. It is extensible, allowing users to hook into any part of a completion life cycle. Users can easily describe functions that LLMs can use with any supported model. Cursive aims to bridge capabilities between different models, providing a single interface for users to choose any model. It comes with built-in token usage and costs calculations, automatic retry, and model expanding features. Users can define and describe functions, generate Pydantic BaseModels, hook into completion life cycle, create embeddings, and configure retry and model expanding behavior. Cursive supports various models from OpenAI, Anthropic, OpenRouter, Cohere, and Replicate, with options to pass API keys for authentication.

Golf is a simple command-line tool for calculating the distance between two geographic coordinates. It uses the Haversine formula to accurately determine the distance between two points on the Earth's surface. This tool is useful for developers working on location-based applications or projects that require distance calculations. With Golf, users can easily input latitude and longitude coordinates and get the precise distance in kilometers or miles. The tool is lightweight, easy to use, and can be integrated into various programming workflows.

OpenedAI Speech is a free, private text-to-speech server compatible with the OpenAI audio/speech API. It offers custom voice cloning and supports various models like tts-1 and tts-1-hd. Users can map their own piper voices and create custom cloned voices. The server provides multilingual support with XTTS voices and allows fixing incorrect sounds with regex. Recent changes include bug fixes, improved error handling, and updates for multilingual support. Installation can be done via Docker or manual setup, with usage instructions provided. Custom voices can be created using Piper or Coqui XTTS v2, with guidelines for preparing audio files. The tool is suitable for tasks like generating speech from text, creating custom voices, and multilingual text-to-speech applications.

ClickClickClick is a framework designed to enable autonomous Android and computer use using various LLM models, both locally and remotely. It supports tasks such as drafting emails, opening browsers, and starting games, with current support for local models via Ollama, Gemini, and GPT 4o. The tool is highly experimental and evolving, with the best results achieved using specific model combinations. Users need prerequisites like `adb` installation and USB debugging enabled on Android phones. The tool can be installed via cloning the repository, setting up a virtual environment, and installing dependencies. It can be used as a CLI tool or script, allowing users to configure planner and finder models for different tasks. Additionally, it can be used as an API to execute tasks based on provided prompts, platform, and models.

The MCP LLMS-TXT Documentation Server is an open-source server that provides developers full control over tools used by applications like Cursor, Windsurf, and Claude Code/Desktop. It allows users to create a user-defined list of `llms.txt` files and use a `fetch_docs` tool to read URLs within these files, enabling auditing of tool calls and context returned. The server supports various applications and provides a way to connect to them, configure rules, and test tool calls for tasks related to documentation retrieval and processing.

Neural Speed is an innovative library designed to support the efficient inference of large language models (LLMs) on Intel platforms through the state-of-the-art (SOTA) low-bit quantization powered by Intel Neural Compressor. The work is inspired by llama.cpp and further optimized for Intel platforms with our innovations in NeurIPS' 2023

llm-gemini is a plugin that provides API access to Google's Gemini models. It allows users to configure and run various Gemini models for tasks such as generating text, processing images, transcribing audio, and executing code. The plugin supports multi-modal inputs including images, audio, and video, and can output JSON objects. Additionally, it enables chat interactions with the model and supports different embedding models for text processing. Users can also run similarity searches on embedded data. The plugin is designed to work in conjunction with LLM and offers extensive documentation for development and usage.

LangServe Ollama is a tool that allows users to fine-tune Korean language models for local hosting, including RAG. Users can load HuggingFace gguf files, create model chains, and monitor GPU usage. The tool provides a seamless workflow for customizing and deploying language models in a local environment.

HippoRAG is a novel retrieval augmented generation (RAG) framework inspired by the neurobiology of human long-term memory that enables Large Language Models (LLMs) to continuously integrate knowledge across external documents. It provides RAG systems with capabilities that usually require a costly and high-latency iterative LLM pipeline for only a fraction of the computational cost. The tool facilitates setting up retrieval corpus, indexing, and retrieval processes for LLMs, offering flexibility in choosing different online LLM APIs or offline LLM deployments through LangChain integration. Users can run retrieval on pre-defined queries or integrate directly with the HippoRAG API. The tool also supports reproducibility of experiments and provides data, baselines, and hyperparameter tuning scripts for research purposes.

Roast is a convention-oriented framework for creating structured AI workflows maintained by the Augmented Engineering team at Shopify. It provides a structured, declarative approach to building AI workflows with convention over configuration, built-in tools for file operations, search, and AI interactions, Ruby integration for custom steps, shared context between steps, step customization with AI models and parameters, session replay, parallel execution, function caching, and extensive instrumentation for monitoring workflow execution, AI calls, and tool usage.

Ahnlich is a tool that provides multiple components for storing and searching similar vectors using linear or non-linear similarity algorithms. It includes 'ahnlich-db' for in-memory vector key value store, 'ahnlich-ai' for AI proxy communication, 'ahnlich-client-rs' for Rust client, and 'ahnlich-client-py' for Python client. The tool is not production-ready yet and is still in testing phase, allowing AI/ML engineers to issue queries using raw input such as images/text and features off-the-shelf models for indexing and querying.

aider.el is an AI pair programming tool for Emacs that provides an interactive interface to communicate with Aider. It offers features such as pop-up menu for commands, Git repository-specific sessions, batch file adding from dired buffer, region-based refactor support, and the ability to add custom Elisp functions. Users can install aider.el and dependencies to enhance their pair programming experience within Emacs.

Next AI Draw.io is a next.js web application that integrates AI capabilities with draw.io diagrams. It allows users to create, modify, and enhance diagrams through natural language commands and AI-assisted visualization. Features include LLM-Powered Diagram Creation, Image-Based Diagram Replication, Diagram History, Interactive Chat Interface, and Smart Editing. The application uses Next.js for frontend framework, @ai-sdk/react for chat interface and AI interactions, and react-drawio for diagram representation and manipulation. Diagrams are represented as XML that can be rendered in draw.io, with AI processing commands to generate or modify the XML accordingly.

A provider agnostic PHP AI client SDK to communicate with any generative AI models of various capabilities using a uniform API. It is a PHP SDK that can be installed as a Composer package and used in any PHP project, not limited to WordPress. The project aims to bridge the gap between AI models and PHP applications, providing flexibility and ease of communication with AI providers.

This repository provides resources, demos, and recipes for working with Large Language Models (LLMs) on OpenShift using OpenShift AI or Open Data Hub. It includes instructions for deploying inference servers for LLMs, such as vLLM, Hugging Face TGI, Caikit-TGIS-Serving, and Ollama. Additionally, it offers guidance on deploying serving runtimes, such as vLLM Serving Runtime and Hugging Face Text Generation Inference, in the Single-Model Serving stack of Open Data Hub or OpenShift AI. The repository also covers vector databases that can be used as a Vector Store for Retrieval Augmented Generation (RAG) applications, including Milvus, PostgreSQL+pgvector, and Redis. Furthermore, it provides examples of inference and application usage, such as Caikit, Langchain, Langflow, and UI examples.

This repository showcases a project that indexes millions of Wikipedia articles using Upstash Vector. It includes a semantic search engine and a RAG chatbot SDK. The project involves preparing and embedding Wikipedia articles, indexing vectors, building a semantic search engine, and implementing a RAG chatbot. Key features include indexing over 144 million vectors, multilingual support, cross-lingual semantic search, and a RAG chatbot. Technologies used include Upstash Vector, Upstash Redis, Upstash RAG Chat SDK, SentenceTransformers, and Meta-Llama-3-8B-Instruct for LLM provider.

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.