Smart Ralph is a Claude Code plugin designed for spec-driven development. It helps users turn vague feature ideas into structured specs and executes them task-by-task. The tool operates within a self-contained execution loop without external dependencies, providing a seamless workflow for feature development. Named after the Ralph agentic loop pattern, Smart Ralph simplifies the development process by focusing on the next task at hand, akin to the simplicity of the Springfield student, Ralph.

bmalph is a tool that bundles and installs two AI development systems, BMAD-METHOD for planning agents and workflows (Phases 1-3) and Ralph for autonomous implementation loop (Phase 4). It provides commands like `bmalph init` to install both systems, `bmalph upgrade` to update to the latest versions, `bmalph doctor` to check installation health, and `/bmalph-implement` to transition from BMAD to Ralph. Users can work through BMAD phases 1-3 with commands like BP, MR, DR, CP, VP, CA, etc., and then transition to Ralph for implementation.

Agentic InfraOps is a multi-agent orchestration system for Azure infrastructure development that transforms how you build Azure infrastructure with AI agents. It provides a structured 7-step workflow that coordinates specialized AI agents through a complete infrastructure development cycle: Requirements → Architecture → Design → Plan → Code → Deploy → Documentation. The system enforces Azure Well-Architected Framework (WAF) alignment and Azure Verified Modules (AVM) at every phase, combining the speed of AI coding with best practices in cloud engineering.

The 'Everything Claude Code' repository is a comprehensive collection of production-ready agents, skills, hooks, commands, rules, and MCP configurations developed over 10+ months. It includes guides for setup, foundations, and philosophy, as well as detailed explanations of various topics such as token optimization, memory persistence, continuous learning, verification loops, parallelization, and subagent orchestration. The repository also provides updates on bug fixes, multi-language rules, installation wizard, PM2 support, OpenCode plugin integration, unified commands and skills, and cross-platform support. It offers a quick start guide for installation, ecosystem tools like Skill Creator and Continuous Learning v2, requirements for CLI version compatibility, key concepts like agents, skills, hooks, and rules, running tests, contributing guidelines, OpenCode support, background information, important notes on context window management and customization, star history chart, and relevant links.

The @ai-coders/context repository provides the Ultimate MCP for AI Agent Orchestration, Context Engineering, and Spec-Driven Development. It simplifies context engineering for AI by offering a universal process called PREVC, which consists of Planning, Review, Execution, Validation, and Confirmation steps. The tool aims to address the problem of context fragmentation by introducing a single `.context/` directory that works universally across different tools. It enables users to create structured documentation, generate agent playbooks, manage workflows, provide on-demand expertise, and sync across various AI tools. The tool follows a structured, spec-driven development approach to improve AI output quality and ensure reproducible results across projects.

Roam is a tool that builds a semantic graph of your codebase and allows AI agents to query it with one shell command. It pre-indexes your codebase into a semantic graph stored in a local SQLite DB, providing architecture-level graph queries offline, cross-language, and compact. Roam understands functions, modules, tests coverage, and overall architecture structure. It is best suited for agent-assisted coding, large codebases, architecture governance, safe refactoring, and multi-repo projects. Roam is not suitable for real-time type checking, dynamic/runtime analysis, small scripts, or pure text search. It offers speed, dependency-awareness, LLM-optimized output, fully local operation, and CI readiness.

PaperBanana is an automated academic illustration tool designed for AI scientists. It implements an agentic framework for generating publication-quality academic diagrams and statistical plots from text descriptions. The tool utilizes a two-phase multi-agent pipeline with iterative refinement, Gemini-based VLM planning, and image generation. It offers a CLI, Python API, and MCP server for IDE integration, along with Claude Code skills for generating diagrams, plots, and evaluating diagrams. PaperBanana is not affiliated with or endorsed by the original authors or Google Research, and it may differ from the original system described in the paper.

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.

Multi-agent RALPH (Reinforcement Learning with Probabilistic Hierarchies) Loop is a framework for multi-agent reinforcement learning research. It provides a flexible and extensible platform for developing and testing multi-agent reinforcement learning algorithms. The framework supports various environments, including grid-world environments, and allows users to easily define custom environments. Multi-agent RALPH Loop is designed to facilitate research in the field of multi-agent reinforcement learning by providing a set of tools and utilities for experimenting with different algorithms and scenarios.

Claude Code Mastery is a comprehensive tool for maximizing Claude Code, offering a production-ready project template with 16 slash commands, deterministic hook enforcement, MongoDB wrapper, live AI monitoring, and three-layer security. It provides a security gatekeeper, project scaffolding blueprint, MCP server integration, workflow automation through custom commands, and emphasizes the importance of single-purpose chats to avoid performance degradation.

Claude Craft is a comprehensive framework for AI-assisted development with Claude Code, providing standardized rules, agents, and commands across multiple technology stacks. It includes autonomous sprint capabilities, documentation accuracy improvements, CI hardening, and test coverage enhancements. With support for 10 technology stacks, 5 languages, 40 AI agents, 157 slash commands, and various project management features like BMAD v6 framework, Ralph Wiggum loop execution, skills, templates, checklists, and hooks system, Claude Craft offers a robust solution for project development and management. The tool also supports workflow methodology, development tracks, document generation, BMAD v6 project management, quality gates, batch processing, backlog migration, and Claude Code hooks integration.

A Model Context Protocol (MCP) plugin named Claude Talk to Figma MCP that enables Claude Desktop and other AI tools to interact directly with Figma for AI-assisted design capabilities. It provides document interaction, element creation, smart modifications, text mastery, and component integration. Users can connect the plugin to Figma, start designing, and utilize various tools for document analysis, element creation, modification, text manipulation, and component management. The project offers installation instructions, AI client configuration options, usage patterns, command references, troubleshooting support, testing guidelines, architecture overview, contribution guidelines, version history, and licensing information.

Legacy Modernization Agents is an open source migration framework developed to demonstrate AI Agents capabilities for converting legacy COBOL code to Java or C# .NET. The framework uses Microsoft Agent Framework with a dual-API architecture to analyze COBOL code and dependencies, then convert to either Java Quarkus or C# .NET. The web portal provides real-time visualization of migration progress, dependency graphs, and AI-powered Q&A.

Terminator is an AI-powered desktop automation tool that is open source, MIT-licensed, and cross-platform. It works across all apps and browsers, inspired by GitHub Actions & Playwright. It is 100x faster than generic AI agents, with over 95% success rate and no vendor lock-in. Users can create automations that work across any desktop app or browser, achieve high success rates without costly consultant armies, and pre-train workflows as deterministic code.

AgentScope is a multi-agent platform designed to empower developers to build multi-agent applications with large-scale models. It features three high-level capabilities: Easy-to-Use, High Robustness, and Actor-Based Distribution. AgentScope provides a list of `ModelWrapper` to support both local model services and third-party model APIs, including OpenAI API, DashScope API, Gemini API, and ollama. It also enables developers to rapidly deploy local model services using libraries such as ollama (CPU inference), Flask + Transformers, Flask + ModelScope, FastChat, and vllm. AgentScope supports various services, including Web Search, Data Query, Retrieval, Code Execution, File Operation, and Text Processing. Example applications include Conversation, Game, and Distribution. AgentScope is released under Apache License 2.0 and welcomes contributions.

AI agent tooling for data engineering workflows. Includes an MCP server for Airflow, a CLI tool for interacting with Airflow from your terminal, and skills that extend AI coding agents with specialized capabilities for working with Airflow and data warehouses. Works with Claude Code, Cursor, and other agentic coding tools. The tool provides a comprehensive set of features for data discovery & analysis, data lineage, DAG development, dbt integration, migration, and more. It also offers user journeys for data analysis flow and DAG development flow. The Airflow CLI tool allows users to interact with Airflow directly from the terminal. The tool supports various databases like Snowflake, PostgreSQL, Google BigQuery, and more, with auto-detected SQLAlchemy databases. Skills are invoked automatically based on user queries or can be invoked directly using specific commands.

Smart Ralph is a Claude Code plugin designed for spec-driven development. It helps users turn vague feature ideas into structured specs and executes them task-by-task. The tool operates within a self-contained execution loop without external dependencies, providing a seamless workflow for feature development. Named after the Ralph agentic loop pattern, Smart Ralph simplifies the development process by focusing on the next task at hand, akin to the simplicity of the Springfield student, Ralph.

A collection of AI-powered prompts designed to streamline software development workflows. The library contains prompts at various stages of development, with structured sequences of connected prompts, project initialization support, development assistance, and documentation generation. It aims to provide consistent guidance across different development phases, promote systematic development processes, and enable progress tracking and validation.

Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

ChatFAQ is an open-source comprehensive platform for creating a wide variety of chatbots: generic ones, business-trained, or even capable of redirecting requests to human operators. It includes a specialized NLP/NLG engine based on a RAG architecture and customized chat widgets, ensuring a tailored experience for users and avoiding vendor lock-in.

AgentCloud is an open-source platform that enables companies to build and deploy private LLM chat apps, empowering teams to securely interact with their data. It comprises three main components: Agent Backend, Webapp, and Vector Proxy. To run this project locally, clone the repository, install Docker, and start the services. The project is licensed under the GNU Affero General Public License, version 3 only. Contributions and feedback are welcome from the community.

LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

The VisionCraft API is a free API for using over 100 different AI models. From images to sound.

AutoGen is a framework that enables the development of LLM applications using multiple agents that can converse with each other to solve tasks. AutoGen agents are customizable, conversable, and seamlessly allow human participation. They can operate in various modes that employ combinations of LLMs, human inputs, and tools.

The llama-recipes repository provides a scalable library for fine-tuning Llama 2, along with example scripts and notebooks to quickly get started with using the Llama 2 models in a variety of use-cases, including fine-tuning for domain adaptation and building LLM-based applications with Llama 2 and other tools in the LLM ecosystem. The examples here showcase how to run Llama 2 locally, in the cloud, and on-prem.

LLMWare is a framework for quickly developing LLM-based applications including Retrieval Augmented Generation (RAG) and Multi-Step Orchestration of Agent Workflows. This project provides a comprehensive set of tools that anyone can use - from a beginner to the most sophisticated AI developer - to rapidly build industrial-grade, knowledge-based enterprise LLM applications. Our specific focus is on making it easy to integrate open source small specialized models and connecting enterprise knowledge safely and securely.