The 'free-coding-models' repository provides a tool for finding and utilizing coding-focused LLM models optimized for code generation. It offers a multi-provider approach with models from various providers like NVIDIA NIM, Groq, Cerebras, and Hugging Face Inference. The tool allows users to ping models in real-time, select the fastest ones, and integrate them seamlessly with tools like OpenCode and OpenClaw. It features a settings screen for managing API keys, tier filtering, and profile saving. The stability score feature helps users identify models with consistent performance, and the tool offers interactive selection, startup mode menu, and tier filtering functionalities.

OpenClaw Dashboard is a beautiful, zero-dependency command center for OpenClaw AI agents. It provides a single local page that collects gateway health, costs, cron status, active sessions, sub-agent runs, model usage, and git log in one place, refreshed automatically without login or external dependencies. It aims to offer an at-a-glance overview layer for users to monitor the health of their OpenClaw setup and make informed decisions quickly without the need to search through log files or run CLI commands.

Memorix is a cross-agent memory bridge tool designed to prevent AI assistants from forgetting important information during chats or when switching between different agents. It allows users to store and retrieve architecture decisions, bug fixes, technical explanations, code changes, insights, design choices, and more across various agents seamlessly. With Memorix, users can avoid re-explaining concepts, prevent context loss when switching agents, collaborate effectively, sync workspaces, generate project skills, and utilize a knowledge graph for intelligent retrieval. The tool offers 24 MCP tools for smart memory management, cross-agent workspace sync, a knowledge graph compatible with MCP Official Memory Server, various observation types, a visual dashboard, auto-memory hooks, and optional vector search for semantic similarity. Memorix ensures project isolation, local data storage, and zero cross-contamination, making it a valuable tool for enhancing productivity and knowledge retention in AI-driven workflows.

Tokscale is a high-performance CLI tool and visualization dashboard for tracking token usage and costs across multiple AI coding agents. It helps monitor and analyze token consumption from various AI coding tools, providing real-time pricing calculations using LiteLLM's pricing data. Inspired by the Kardashev scale, Tokscale measures token consumption as users scale the ranks of AI-augmented development. It offers interactive TUI mode, multi-platform support, real-time pricing, detailed breakdowns, web visualization, flexible filtering, and social platform features.

AgentField is an open-source control plane designed for autonomous AI agents, providing infrastructure for agents to make decisions beyond chatbots. It offers features like scaling infrastructure, routing & discovery, async execution, durable state, observability, trust infrastructure with cryptographic identity, verifiable credentials, and policy enforcement. Users can write agents in Python, Go, TypeScript, or interact via REST APIs. The tool enables the creation of AI backends that reason autonomously within defined boundaries, offering predictability and flexibility. AgentField aims to bridge the gap between AI frameworks and production-ready infrastructure for AI agents.

QuantaAlpha is a framework designed for factor mining in quantitative alpha research. It combines LLM intelligence with evolutionary strategies to automatically mine, evolve, and validate alpha factors through self-evolving trajectories. The framework provides a trajectory-based approach with diversified planning initialization and structured hypothesis-code constraint. Users can describe their research direction and observe the automatic factor mining process. QuantaAlpha aims to transform how quantitative alpha factors are discovered by leveraging advanced technologies and self-evolving methodologies.

Shimmy is a 5.1MB single-binary local inference server providing OpenAI-compatible endpoints for GGUF models. It offers fast, reliable AI inference with sub-second responses, zero configuration, and automatic port management. Perfect for developers seeking privacy, cost-effectiveness, speed, and easy integration with popular tools like VSCode and Cursor. Shimmy is designed to be invisible infrastructure that simplifies local AI development and deployment.

MindNLP is an open-source NLP library based on MindSpore. It provides a platform for solving natural language processing tasks, containing many common approaches in NLP. It can help researchers and developers to construct and train models more conveniently and rapidly. Key features of MindNLP include: * Comprehensive data processing: Several classical NLP datasets are packaged into a friendly module for easy use, such as Multi30k, SQuAD, CoNLL, etc. * Friendly NLP model toolset: MindNLP provides various configurable components. It is friendly to customize models using MindNLP. * Easy-to-use engine: MindNLP simplified complicated training process in MindSpore. It supports Trainer and Evaluator interfaces to train and evaluate models easily. MindNLP supports a wide range of NLP tasks, including: * Language modeling * Machine translation * Question answering * Sentiment analysis * Sequence labeling * Summarization MindNLP also supports industry-leading Large Language Models (LLMs), including Llama, GLM, RWKV, etc. For support related to large language models, including pre-training, fine-tuning, and inference demo examples, you can find them in the "llm" directory. To install MindNLP, you can either install it from Pypi, download the daily build wheel, or install it from source. The installation instructions are provided in the documentation. MindNLP is released under the Apache 2.0 license. If you find this project useful in your research, please consider citing the following paper: @misc{mindnlp2022, title={{MindNLP}: a MindSpore NLP library}, author={MindNLP Contributors}, howpublished = {\url{https://github.com/mindlab-ai/mindnlp}}, year={2022} }

AutoMem is a production-grade long-term memory system for AI assistants, achieving 90.53% accuracy on the LoCoMo benchmark. It combines FalkorDB (Graph) and Qdrant (Vectors) storage systems to store, recall, connect, learn, and perform with memories. AutoMem enables AI assistants to remember, connect, and evolve their understanding over time, similar to human long-term memory. It implements techniques from peer-reviewed memory research and offers features like multi-hop bridge discovery, knowledge graphs that evolve, 9-component hybrid scoring, memory consolidation cycles, background intelligence, 11 relationship types, and more. AutoMem is benchmark-proven, research-validated, and production-ready, with features like sub-100ms recall, concurrent writes, automatic retries, health monitoring, dual storage redundancy, and automated backups.

Headroom is a tool designed to optimize the context layer for Large Language Models (LLMs) applications by compressing redundant boilerplate outputs. It intercepts context from tool outputs, logs, search results, and intermediate agent steps, stabilizes dynamic content like timestamps and UUIDs, removes low-signal content, and preserves original data for retrieval only when needed by the LLM. It ensures provider caching works efficiently by aligning prompts for cache hits. The tool works as a transparent proxy with zero code changes, offering significant savings in token count and enabling reversible compression for various types of content like code, logs, JSON, and images. Headroom integrates seamlessly with frameworks like LangChain, Agno, and MCP, supporting features like memory, retrievers, agents, and more.

A blazing-fast, fully autonomous AI coding agent with a gorgeous TUI, multi-model support, swarm coordination, persistent memory, and 30+ built-in tools - all running natively in your terminal. Engineered to be absurdly efficient, jcode runs as a single compiled binary that sips resources, offering features like sub-millisecond rendering, multiple providers, no API keys needed, persistent memory, swarm mode, 30+ built-in tools, MCP support, self-updating, and more. It is lightweight, efficient, and built with Rust, providing a unique coding experience with high performance and resource efficiency.

OpenAkita is a self-evolving AI Agent framework that autonomously learns new skills, performs daily self-checks and repairs, accumulates experience from task execution, and persists until the task is done. It auto-generates skills, installs dependencies, learns from mistakes, and remembers preferences. The framework is standards-based, multi-platform, and provides a Setup Center GUI for intuitive installation and configuration. It features self-learning and evolution mechanisms, a Ralph Wiggum Mode for persistent execution, multi-LLM endpoints, multi-platform IM support, desktop automation, multi-agent architecture, scheduled tasks, identity and memory management, a tool system, and a guided wizard for setup.

Remember-Me is a military-grade, offline 'Second Brain' AI tool that lives entirely on your hard drive. It combines local LLM inference with Quantum Dream Memory Architecture to help you think, remember everything, and answer to no one but you. The tool ensures 0% Cloud, 0% Spying, and 100% Ownership, providing a sovereign ecosystem for users to enhance their cognitive capabilities without relying on external servers or services.

Edit Banana is a universal content re-editor that allows users to transform fixed content into fully manipulatable assets. Powered by SAM 3 and multimodal large models, it enables high-fidelity reconstruction while preserving original diagram details and logical relationships. The platform offers advanced segmentation, fixed multi-round VLM scanning, high-quality OCR, user system with credits, multi-user concurrency, and a web interface. Users can upload images or PDFs to get editable DrawIO (XML) or PPTX files in seconds. The project structure includes components for segmentation, text extraction, frontend, models, and scripts, with detailed installation and setup instructions provided. The tool is open-source under the Apache License 2.0, allowing commercial use and secondary development.

ZeroClaw is a fast, small, and fully autonomous AI assistant infrastructure built with Rust. It features a lean runtime, cost-efficient deployment, fast cold starts, and a portable architecture. It is secure by design, fully swappable, and supports OpenAI-compatible provider support. The tool is designed for low-cost boards and small cloud instances, with a memory footprint of less than 5MB. It is suitable for tasks like deploying AI assistants, swapping providers/channels/tools, and pluggable everything.

LLM4S provides a simple, robust, and scalable framework for building Large Language Models (LLM) applications in Scala. It aims to leverage Scala's type safety, functional programming, JVM ecosystem, concurrency, and performance advantages to create reliable and maintainable AI-powered applications. The framework supports multi-provider integration, execution environments, error handling, Model Context Protocol (MCP) support, agent frameworks, multimodal generation, and Retrieval-Augmented Generation (RAG) workflows. It also offers observability features like detailed trace logging, monitoring, and analytics for debugging and performance insights.

The 'free-coding-models' repository provides a tool for finding and utilizing coding-focused LLM models optimized for code generation. It offers a multi-provider approach with models from various providers like NVIDIA NIM, Groq, Cerebras, and Hugging Face Inference. The tool allows users to ping models in real-time, select the fastest ones, and integrate them seamlessly with tools like OpenCode and OpenClaw. It features a settings screen for managing API keys, tier filtering, and profile saving. The stability score feature helps users identify models with consistent performance, and the tool offers interactive selection, startup mode menu, and tier filtering functionalities.

ChatGPT Web Sea is an open-source project based on ChatGPT-web for secondary development. It supports all models that comply with the OpenAI interface standard, allows for model selection, configuration, and extension, and is compatible with OneAPI. The tool includes a Chinese ChatGPT tuning guide, supports file uploads, and provides model configuration options. Users can interact with the tool through a web interface, configure models, and perform tasks such as model selection, API key management, and chat interface setup. The project also offers Docker deployment options and instructions for manual packaging.

Farfalle is an open-source AI-powered search engine that allows users to run their own local LLM or utilize the cloud. It provides a tech stack including Next.js for frontend, FastAPI for backend, Tavily for search API, Logfire for logging, and Redis for rate limiting. Users can get started by setting up prerequisites like Docker and Ollama, and obtaining API keys for Tavily, OpenAI, and Groq. The tool supports models like llama3, mistral, and gemma. Users can clone the repository, set environment variables, run containers using Docker Compose, and deploy the backend and frontend using services like Render and Vercel.

Tara is a powerful node for ComfyUI that integrates Large Language Models (LLMs) to enhance and automate workflow processes. With Tara, you can create complex, intelligent workflows that refine and generate content, manage API keys, and seamlessly integrate various LLMs into your projects. It comprises nodes for handling OpenAI-compatible APIs, saving and loading API keys, composing multiple texts, and using predefined templates for OpenAI and Groq. Tara supports OpenAI and Grok models with plans to expand support to together.ai and Replicate. Users can install Tara via Git URL or ComfyUI Manager and utilize it for tasks like input guidance, saving and loading API keys, and generating text suitable for chaining in workflows.

This repository contains a Rest-Backend for a Conversational Agent that allows embedding documents, semantic search, QA based on documents, and document processing with Large Language Models. It uses Aleph Alpha and OpenAI Large Language Models to generate responses to user queries, includes a vector database, and provides a REST API built with FastAPI. The project also features semantic search, secret management for API keys, installation instructions, and development guidelines for both backend and frontend components.

ChatGPT-Next-Web-Pro is a tool that provides an enhanced version of ChatGPT-Next-Web with additional features and functionalities. It offers complete ChatGPT-Next-Web functionality, file uploading and storage capabilities, drawing and video support, multi-modal support, reverse model support, knowledge base integration, translation, customizations, and more. The tool can be deployed with or without a backend, allowing users to interact with AI models, manage accounts, create models, manage API keys, handle orders, manage memberships, and more. It supports various cloud services like Aliyun OSS, Tencent COS, and Minio for file storage, and integrates with external APIs like Azure, Google Gemini Pro, and Luma. The tool also provides options for customizing website titles, subtitles, icons, and plugin buttons, and offers features like voice input, file uploading, real-time token count display, and more.

APIMyLlama is a server application that provides an interface to interact with the Ollama API, a powerful AI tool to run LLMs. It allows users to easily distribute API keys to create amazing things. The tool offers commands to generate, list, remove, add, change, activate, deactivate, and manage API keys, as well as functionalities to work with webhooks, set rate limits, and get detailed information about API keys. Users can install APIMyLlama packages with NPM, PIP, Jitpack Repo+Gradle or Maven, or from the Crates Repository. The tool supports Node.JS, Python, Java, and Rust for generating responses from the API. Additionally, it provides built-in health checking commands for monitoring API health status.

IntelliChat is an open-source AI chatbot tool designed to accelerate the integration of multiple language models into chatbot apps. Users can select their preferred AI provider and model from the UI, manage API keys, and access data using Intellinode. The tool is built with Intellinode and Next.js, and supports various AI providers such as OpenAI ChatGPT, Google Gemini, Azure Openai, Cohere Coral, Replicate, Mistral AI, Anthropic, and vLLM. It offers a user-friendly interface for developers to easily incorporate AI capabilities into their chatbot 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.