The LLM Platform, also known as Inference Hub, is an open-source tool designed to simplify the development and deployment of large language model applications at scale. It provides a unified framework for integrating and managing multiple LLM vendors, models, and related services through a flexible approach. The platform supports various LLM providers, document processing, RAG, advanced AI workflows, infrastructure operations, and flexible configuration using YAML files. Its modular and extensible architecture allows developers to plug in different providers and services as needed. Key components include completers, embedders, renderers, synthesizers, transcribers, document processors, segmenters, retrievers, summarizers, translators, AI workflows, tools, and infrastructure components. Use cases range from enterprise AI applications to scalable LLM deployment and custom AI pipelines. Integrations with LLM providers like OpenAI, Azure OpenAI, Anthropic, Google Gemini, AWS Bedrock, Groq, Mistral AI, xAI, Hugging Face, and more are supported.

RAG/LLM Security Scanner is a professional security testing tool designed for Retrieval-Augmented Generation (RAG) systems and LLM applications. It identifies critical vulnerabilities in AI-powered applications such as chatbots, virtual assistants, and knowledge retrieval systems. The tool offers features like prompt injection detection, data leakage assessment, function abuse testing, context manipulation identification, professional reporting with JSON/HTML formats, and easy integration with OpenAI, HuggingFace, and custom RAG systems.

ONE is a modern web and AI agent development toolkit that empowers developers to build AI-powered applications with high performance, beautiful UI, AI integration, responsive design, type safety, and great developer experience. It is perfect for building modern web applications, from simple landing pages to complex AI-powered platforms.

Buster is a modern analytics platform designed with AI in mind, focusing on self-serve experiences powered by Large Language Models. It addresses pain points in existing tools by advocating for AI-centric app development, cost-effective data warehousing, improved CI/CD processes, and empowering data teams to create powerful, user-friendly data experiences. The platform aims to revolutionize AI analytics by enabling data teams to build deep integrations and own their entire analytics stack.

ScaBench is a comprehensive framework designed for evaluating security analysis tools and AI agents on real-world smart contract vulnerabilities. It provides curated datasets from recent audits and official tooling for consistent evaluation. The tool includes features such as curated datasets from Code4rena, Cantina, and Sherlock audits, a baseline runner for security analysis, a scoring tool for evaluating findings, a report generator for HTML reports with visualizations, and pipeline automation for complete workflow execution. Users can access curated datasets, generate new datasets, download project source code, run security analysis using LLMs, and evaluate tool findings against benchmarks using LLM matching. The tool enforces strict matching policies to ensure accurate evaluation results.

LLM Context is a tool designed to assist developers in quickly injecting relevant content from code/text projects into Large Language Model chat interfaces. It leverages `.gitignore` patterns for smart file selection and offers a streamlined clipboard workflow using the command line. The tool also provides direct integration with Large Language Models through the Model Context Protocol (MCP). LLM Context is optimized for code repositories and collections of text/markdown/html documents, making it suitable for developers working on projects that fit within an LLM's context window. The tool is under active development and aims to enhance AI-assisted development workflows by harnessing the power of Large Language Models.

VimLM is an AI-powered coding assistant for Vim that integrates AI for code generation, refactoring, and documentation directly into your Vim workflow. It offers native Vim integration with split-window responses and intuitive keybindings, offline first execution with MLX-compatible models, contextual awareness with seamless integration with codebase and external resources, conversational workflow for iterating on responses, project scaffolding for generating and deploying code blocks, and extensibility for creating custom LLM workflows with command chains.

This repository is a monorepo for NPM packages published under the `@elevenlabs` scope. It contains multiple packages in the `packages` folder. The setup allows for easy development, linking packages, creating new packages, and publishing them with GitHub actions.

ArkFlow is a high-performance Rust stream processing engine that seamlessly integrates AI capabilities, providing powerful real-time data processing and intelligent analysis. It supports multiple input/output sources and processors, enabling easy loading and execution of machine learning models for streaming data and inference, anomaly detection, and complex event processing. The tool is built on Rust and Tokio async runtime, offering excellent performance and low latency. It features built-in SQL queries, Python script, JSON processing, Protobuf encoding/decoding, and batch processing capabilities. ArkFlow is extensible with a modular design, making it easy to extend with new components.

VASA-1-hack is a repository containing the VASA implementation separated from EMOPortraits, with all components properly configured for standalone training. It provides detailed setup instructions, prerequisites, project structure, configuration details, running training modes, troubleshooting tips, monitoring training progress, development information, and acknowledgments. The repository aims to facilitate training volumetric avatar models with configurable parameters and logging levels for efficient debugging and testing.

The MCP Server for Apache Spark History Server is a tool that connects AI agents to Apache Spark History Server for intelligent job analysis and performance monitoring. It enables AI agents to analyze job performance, identify bottlenecks, and provide insights from Spark History Server data. The server bridges AI agents with existing Apache Spark infrastructure, allowing users to query job details, analyze performance metrics, compare multiple jobs, investigate failures, and generate insights from historical execution data.

OCode is a sophisticated terminal-native AI coding assistant that provides deep codebase intelligence and autonomous task execution. It seamlessly works with local Ollama models, bringing enterprise-grade AI assistance directly to your development workflow. OCode offers core capabilities such as terminal-native workflow, deep codebase intelligence, autonomous task execution, direct Ollama integration, and an extensible plugin layer. It can perform tasks like code generation & modification, project understanding, development automation, data processing, system operations, and interactive operations. The tool includes specialized tools for file operations, text processing, data processing, system operations, development tools, and integration. OCode enhances conversation parsing, offers smart tool selection, and provides performance improvements for coding tasks.

MassGen is a cutting-edge multi-agent system that leverages the power of collaborative AI to solve complex tasks. It assigns a task to multiple AI agents who work in parallel, observe each other's progress, and refine their approaches to converge on the best solution to deliver a comprehensive and high-quality result. The system operates through an architecture designed for seamless multi-agent collaboration, with key features including cross-model/agent synergy, parallel processing, intelligence sharing, consensus building, and live visualization. Users can install the system, configure API settings, and run MassGen for various tasks such as question answering, creative writing, research, development & coding tasks, and web automation & browser tasks. The roadmap includes plans for advanced agent collaboration, expanded model, tool & agent integration, improved performance & scalability, enhanced developer experience, and a web interface.

ck (seek) is a semantic grep tool that finds code by meaning, not just keywords. It replaces traditional grep by understanding the user's search intent. It allows users to search for code based on concepts like 'error handling' and retrieves relevant code even if the exact keywords are not present. ck offers semantic search, drop-in grep compatibility, hybrid search combining keyword precision with semantic understanding, agent-friendly output in JSONL format, smart file filtering, and various advanced features. It supports multiple search modes, relevance scoring, top-K results, and smart exclusions. Users can index projects for semantic search, choose embedding models, and search specific files or directories. The tool is designed to improve code search efficiency and accuracy for developers and AI agents.

QuantaLogic is a ReAct framework for building advanced AI agents that seamlessly integrates large language models with a robust tool system. It aims to bridge the gap between advanced AI models and practical implementation in business processes by enabling agents to understand, reason about, and execute complex tasks through natural language interaction. The framework includes features such as ReAct Framework, Universal LLM Support, Secure Tool System, Real-time Monitoring, Memory Management, and Enterprise Ready components.

Graph-Code is an accurate Retrieval-Augmented Generation (RAG) system that analyzes multi-language codebases using Tree-sitter. It builds comprehensive knowledge graphs, enabling natural language querying of codebase structure and relationships, along with editing capabilities. The system supports various languages, uses Tree-sitter for parsing, Memgraph for storage, and AI models for natural language to Cypher translation. It offers features like code snippet retrieval, advanced file editing, shell command execution, interactive code optimization, reference-guided optimization, dependency analysis, and more. The architecture consists of a multi-language parser and an interactive CLI for querying the knowledge graph.

COMFYUI LLM PARTY is a node library designed for LLM workflow development in ComfyUI, an extremely minimalist UI interface primarily used for AI drawing and SD model-based workflows. The project aims to provide a complete set of nodes for constructing LLM workflows, enabling users to easily integrate them into existing SD workflows. It features various functionalities such as API integration, local large model integration, RAG support, code interpreters, online queries, conditional statements, looping links for large models, persona mask attachment, and tool invocations for weather lookup, time lookup, knowledge base, code execution, web search, and single-page search. Users can rapidly develop web applications using API + Streamlit and utilize LLM as a tool node. Additionally, the project includes an omnipotent interpreter node that allows the large model to perform any task, with recommendations to use the 'show_text' node for display output.

n8n is a workflow automation platform that combines the flexibility of code with the speed of no-code. It offers 400+ integrations, native AI capabilities, and a fair-code license, empowering users to create powerful automations while maintaining control over data and deployments. With features like code customization, AI agent workflows, self-hosting options, enterprise-ready functionalities, and an active community, n8n provides a comprehensive solution for technical teams seeking efficient workflow automation.

The LLM Platform, also known as Inference Hub, is an open-source tool designed to simplify the development and deployment of large language model applications at scale. It provides a unified framework for integrating and managing multiple LLM vendors, models, and related services through a flexible approach. The platform supports various LLM providers, document processing, RAG, advanced AI workflows, infrastructure operations, and flexible configuration using YAML files. Its modular and extensible architecture allows developers to plug in different providers and services as needed. Key components include completers, embedders, renderers, synthesizers, transcribers, document processors, segmenters, retrievers, summarizers, translators, AI workflows, tools, and infrastructure components. Use cases range from enterprise AI applications to scalable LLM deployment and custom AI pipelines. Integrations with LLM providers like OpenAI, Azure OpenAI, Anthropic, Google Gemini, AWS Bedrock, Groq, Mistral AI, xAI, Hugging Face, and more are supported.

XTuner is an efficient, flexible, and full-featured toolkit for fine-tuning large models. It supports various LLMs (InternLM, Mixtral-8x7B, Llama 2, ChatGLM, Qwen, Baichuan, ...), VLMs (LLaVA), and various training algorithms (QLoRA, LoRA, full-parameter fine-tune). XTuner also provides tools for chatting with pretrained / fine-tuned LLMs and deploying fine-tuned LLMs with any other framework, such as LMDeploy.

The LLM Hosting Container repository provides Dockerfile and associated resources for building and hosting containers for large language models, specifically the HuggingFace Text Generation Inference (TGI) container. This tool allows users to easily deploy and manage large language models in a containerized environment, enabling efficient inference and deployment of language-based applications.

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.