LYRAIOS (LLM-based Your Reliable AI Operating System) is an advanced AI assistant platform built with FastAPI and Streamlit, designed to serve as an operating system for AI applications. It offers core features such as AI process management, memory system, and I/O system. The platform includes built-in tools like Calculator, Web Search, Financial Analysis, File Management, and Research Tools. It also provides specialized assistant teams for Python and research tasks. LYRAIOS is built on a technical architecture comprising FastAPI backend, Streamlit frontend, Vector Database, PostgreSQL storage, and Docker support. It offers features like knowledge management, process control, and security & access control. The roadmap includes enhancements in core platform, AI process management, memory system, tools & integrations, security & access control, open protocol architecture, multi-agent collaboration, and cross-platform support.

LlamaFarm is a comprehensive AI framework that empowers users to build powerful AI applications locally, with full control over costs and deployment options. It provides modular components for RAG systems, vector databases, model management, prompt engineering, and fine-tuning. Users can create differentiated AI products without needing extensive ML expertise, using simple CLI commands and YAML configs. The framework supports local-first development, production-ready components, strategy-based configuration, and deployment anywhere from laptops to the cloud.

SynthLang is a tool designed to optimize AI prompts by reducing costs and improving processing speed. It brings academic rigor to prompt engineering, creating precise and powerful AI interactions. The tool includes core components like a Translator Engine, Performance Optimization, Testing Framework, and Technical Architecture. It offers mathematical precision, academic rigor, enhanced security, a modern interface, and instant testing. Users can integrate mathematical frameworks, model complex relationships, and apply structured prompts to various domains. Security features include API key management and data privacy. The tool also provides a CLI for prompt engineering and optimization capabilities.

Persistent AI Memory System is a comprehensive tool that offers persistent, searchable storage for AI assistants. It includes features like conversation tracking, MCP tool call logging, and intelligent scheduling. The system supports multiple databases, provides enhanced memory management, and offers various tools for memory operations, schedule management, and system health checks. It also integrates with various platforms like LM Studio, VS Code, Koboldcpp, Ollama, and more. The system is designed to be modular, platform-agnostic, and scalable, allowing users to handle large conversation histories efficiently.

evi-run is a powerful, production-ready multi-agent AI system built on Python using the OpenAI Agents SDK. It offers instant deployment, ultimate flexibility, built-in analytics, Telegram integration, and scalable architecture. The system features memory management, knowledge integration, task scheduling, multi-agent orchestration, custom agent creation, deep research, web intelligence, document processing, image generation, DEX analytics, and Solana token swap. It supports flexible usage modes like private, free, and pay mode, with upcoming features including NSFW mode, task scheduler, and automatic limit orders. The technology stack includes Python 3.11, OpenAI Agents SDK, Telegram Bot API, PostgreSQL, Redis, and Docker & Docker Compose for deployment.

PAELLADOC is an intelligent documentation system that uses AI to analyze code repositories and generate comprehensive technical documentation. It offers a modular architecture with MECE principles, interactive documentation process, key features like Orchestrator and Commands, and a focus on context for successful AI programming. The tool aims to streamline documentation creation, code generation, and product management tasks for software development teams, providing a definitive standard for AI-assisted development documentation.

Local Deep Research is a powerful AI-powered research assistant that performs deep, iterative analysis using multiple LLMs and web searches. It can be run locally for privacy or configured to use cloud-based LLMs for enhanced capabilities. The tool offers advanced research capabilities, flexible LLM support, rich output options, privacy-focused operation, enhanced search integration, and academic & scientific integration. It also provides a web interface, command line interface, and supports multiple LLM providers and search engines. Users can configure AI models, search engines, and research parameters for customized research experiences.

Gemini CLI is an open-source AI agent that provides lightweight access to Gemini, offering powerful capabilities like code understanding, generation, automation, integration, and advanced features. It is designed for developers who prefer working in the command line and offers extensibility through MCP support. The tool integrates directly into GitHub workflows and offers various authentication options for individual developers, enterprise teams, and production workloads. With features like code querying, editing, app generation, debugging, and GitHub integration, Gemini CLI aims to streamline development workflows and enhance productivity.

An AI-powered code documentation generator that automatically analyzes repositories and creates comprehensive documentation using advanced language models. The system employs a multi-agent architecture to perform specialized code analysis and generate structured documentation.

opcode is a powerful desktop application built with Tauri 2 that serves as a command center for interacting with Claude Code. It offers a visual GUI for managing Claude Code sessions, creating custom agents, tracking usage, and more. Users can navigate projects, create specialized AI agents, monitor usage analytics, manage MCP servers, create session checkpoints, edit CLAUDE.md files, and more. The tool bridges the gap between command-line tools and visual experiences, making AI-assisted development more intuitive and productive.

RepoMaster is an AI agent that leverages GitHub repositories to solve complex real-world tasks. It transforms how coding tasks are solved by automatically finding the right GitHub tools and making them work together seamlessly. Users can describe their tasks, and RepoMaster's AI analysis leads to auto discovery and smart execution, resulting in perfect outcomes. The tool provides a web interface for beginners and a command-line interface for advanced users, along with specialized agents for deep search, general assistance, and repository tasks.

Enferno is a modern Flask framework optimized for AI-assisted development workflows. It combines carefully crafted development patterns, smart Cursor Rules, and modern libraries to enable developers to build sophisticated web applications with unprecedented speed. Enferno's intelligent patterns and contextual guides help create production-ready SAAS applications faster than ever. It includes features like modern stack, authentication, OAuth integration, database support, task queue, frontend components, security measures, Docker readiness, and more.

Monoscope is an open-source monitoring and observability platform that uses artificial intelligence to understand and monitor systems automatically. It allows users to ingest and explore logs, traces, and metrics in S3 buckets, query in natural language via LLMs, and create AI agents to detect anomalies. Key capabilities include universal data ingestion, AI-powered understanding, natural language interface, cost-effective storage, and zero configuration. Monoscope is designed to reduce alert fatigue, catch issues before they impact users, and provide visibility across complex systems.

AgC is an open-core platform designed for deploying, running, and orchestrating AI agents at scale. It treats agents as first-class compute units, providing a modular, observable, cloud-neutral, and production-ready environment. Open Agentic Compute empowers developers and organizations to run agents like cloud-native workloads without lock-in.

DeepSeekAI is a browser extension plugin that allows users to interact with AI by selecting text on web pages and invoking the DeepSeek large model to provide AI responses. The extension enhances browsing experience by enabling users to get summaries or answers for selected text directly on the webpage. It features context text selection, API key integration, draggable and resizable window, AI streaming replies, Markdown rendering, one-click copy, re-answer option, code copy functionality, language switching, and multi-turn dialogue support. Users can install the extension from Chrome Web Store or Edge Add-ons, or manually clone the repository, install dependencies, and build the extension. Configuration involves entering the DeepSeek API key in the extension popup window to start using the AI-driven responses.

Hacx GPT is a cutting-edge AI tool developed by BlackTechX, inspired by WormGPT, designed to push the boundaries of natural language processing. It is an advanced broken AI model that facilitates seamless and powerful interactions, allowing users to ask questions and perform various tasks. The tool has been rigorously tested on platforms like Kali Linux, Termux, and Ubuntu, offering powerful AI conversations and the ability to do anything the user wants. Users can easily install and run Hacx GPT on their preferred platform to explore its vast capabilities.

Financial Datasets is an open-source Python library that allows users to create question and answer financial datasets using Large Language Models (LLMs). With this library, users can easily generate realistic financial datasets from 10-K, 10-Q, PDF, and other financial texts. The library provides three main methods for generating datasets: from any text, from a 10-K filing, or from a PDF URL. Financial Datasets can be used for a variety of tasks, including financial analysis, research, and education.

This repository contains a collection of articles and tutorials on quantitative finance, including topics such as machine learning, statistical arbitrage, and risk management. The articles are written in a clear and concise style, and they are suitable for both beginners and experienced practitioners. The repository also includes a number of Jupyter notebooks that demonstrate how to use Python for quantitative finance.

finagg is a Python package that provides implementations of popular and free financial APIs, tools for aggregating historical data from those APIs into SQL databases, and tools for transforming aggregated data into features useful for analysis and AI/ML. It offers documentation, installation instructions, and basic usage examples for exploring various financial APIs and features. Users can install recommended datasets from 3rd party APIs into a local SQL database, access Bureau of Economic Analysis (BEA) data, Federal Reserve Economic Data (FRED), Securities and Exchange Commission (SEC) filings, and more. The package also allows users to explore raw data features, install refined data features, and perform refined aggregations of raw data. Configuration options for API keys, user agents, and data locations are provided, along with information on dependencies and related projects.

LYRAIOS (LLM-based Your Reliable AI Operating System) is an advanced AI assistant platform built with FastAPI and Streamlit, designed to serve as an operating system for AI applications. It offers core features such as AI process management, memory system, and I/O system. The platform includes built-in tools like Calculator, Web Search, Financial Analysis, File Management, and Research Tools. It also provides specialized assistant teams for Python and research tasks. LYRAIOS is built on a technical architecture comprising FastAPI backend, Streamlit frontend, Vector Database, PostgreSQL storage, and Docker support. It offers features like knowledge management, process control, and security & access control. The roadmap includes enhancements in core platform, AI process management, memory system, tools & integrations, security & access control, open protocol architecture, multi-agent collaboration, and cross-platform support.

This repository contains code for research conducted by the LLM-Random research group at IDEAS NCBR in Warsaw, Poland. The group focuses on developing and using this repository to conduct research. For more information about the group and its research, refer to their blog, llm-random.github.io.

InternLM-XComposer2 is a groundbreaking vision-language large model (VLLM) based on InternLM2-7B excelling in free-form text-image composition and comprehension. It boasts several amazing capabilities and applications: * **Free-form Interleaved Text-Image Composition** : InternLM-XComposer2 can effortlessly generate coherent and contextual articles with interleaved images following diverse inputs like outlines, detailed text requirements and reference images, enabling highly customizable content creation. * **Accurate Vision-language Problem-solving** : InternLM-XComposer2 accurately handles diverse and challenging vision-language Q&A tasks based on free-form instructions, excelling in recognition, perception, detailed captioning, visual reasoning, and more. * **Awesome performance** : InternLM-XComposer2 based on InternLM2-7B not only significantly outperforms existing open-source multimodal models in 13 benchmarks but also **matches or even surpasses GPT-4V and Gemini Pro in 6 benchmarks** We release InternLM-XComposer2 series in three versions: * **InternLM-XComposer2-4KHD-7B** 🤗: The high-resolution multi-task trained VLLM model with InternLM-7B as the initialization of the LLM for _High-resolution understanding_ , _VL benchmarks_ and _AI assistant_. * **InternLM-XComposer2-VL-7B** 🤗 : The multi-task trained VLLM model with InternLM-7B as the initialization of the LLM for _VL benchmarks_ and _AI assistant_. **It ranks as the most powerful vision-language model based on 7B-parameter level LLMs, leading across 13 benchmarks.** * **InternLM-XComposer2-VL-1.8B** 🤗 : A lightweight version of InternLM-XComposer2-VL based on InternLM-1.8B. * **InternLM-XComposer2-7B** 🤗: The further instruction tuned VLLM for _Interleaved Text-Image Composition_ with free-form inputs. Please refer to Technical Report and 4KHD Technical Reportfor more details.

Awesome LLM is a curated list of resources related to Large Language Models (LLMs), including models, projects, datasets, benchmarks, materials, papers, posts, GitHub repositories, HuggingFace repositories, and reading materials. It provides detailed information on various LLMs, their parameter sizes, announcement dates, and contributors. The repository covers a wide range of LLM-related topics and serves as a valuable resource for researchers, developers, and enthusiasts interested in the field of natural language processing and artificial intelligence.

Autonomous agents are designed to achieve specific objectives through self-guided instructions. With the emergence and growth of large language models (LLMs), there is a growing trend in utilizing LLMs as fundamental controllers for these autonomous agents. This repository conducts a comprehensive survey study on the construction, application, and evaluation of LLM-based autonomous agents. It explores essential components of AI agents, application domains in natural sciences, social sciences, and engineering, and evaluation strategies. The survey aims to be a resource for researchers and practitioners in this rapidly evolving field.

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.