Phronomy is a Ruby AI agent framework that provides composable building blocks — Agents, Workflows, Tools, Filters, and Tracing — for building AI agents in Ruby. Powered by RubyLLM for LLM abstraction.

pikuri-core is the lean, audit-friendly foundation of the pikuri family: Pikuri::Agent (a thin wrapper around ruby_llm's chat loop) with its Configurator + Extension protocol, the strict Pikuri::Tool framework, a listener surface for rendering / budgets / sub-agents, and four bundled stateless tools (calculator, web search, web scrape, fetch). Extensions (skills, MCP, workspace, coding stack, named-agent personas) live in sibling gems so a privacy-conscious user can install just this core and audit a minimal dependency tree. For the convenience bundle that pulls in everything, see the +pikuri+ metagem.

Probatio Diabolica runs custom *_spec.rb files with a DSL inspired by RSpec and supports text/image/PDF reporting.

Under development. Contributors welcome.

Ragents is a pure Ruby, Ractor-based AI agent framework designed for concurrent, isolated agent execution. It provides message-passing architecture, tool-based actions, and provider-agnostic LLM integration without Rails dependencies.

Ragnar is a high-performance RAG system that leverages Rust libraries through Ruby bindings for embeddings, vector search, and topic modeling. It provides a complete CLI for indexing documents and querying with LLMs.

Unified tool definition pipeline for Rails 8 applications using FastMCP and RubyLLM.

RainbowLLM provides intelligent routing and failover capabilities for multiple LLM providers. Automatically tries providers in sequence until one responds successfully, ensuring reliable access to LLM services.

RLM.rb is a Ruby runtime spine for Recursive Language Models. It runs bounded, typed, auditable AI jobs over files, records, and application context. RLM.rb includes RubyLLM provider access, a dspy.rb signature adapter, the recursive prompt loop, file/context mounting, recursive sub-LM calls, typed final output, budget controls, trace events, and a best-effort trace_store hook.

RobotLab is a Ruby framework for building and orchestrating multi-robot LLM workflows. Built on ruby_llm, it provides robots with template-based prompts, tools, and shared memory; networks for coordinating multiple robots with intelligent routing; MCP (Model Context Protocol) integration for external tool servers; and a memory system with Redis backend and semantic caching. Optional gems add Rails integration (robot_lab-rails), durable learning (robot_lab-durable), Ractor concurrency (robot_lab-ractor), and document storage (robot_lab-document_store).

A standalone, self-contained coding and automation agent built on ruby_llm. Provides an agent loop, persistent memory, SQLite sessions, context compaction, a job system, a tool registry, and an extensible UI layer.

Interactive rake tasks to generate context files for AI coding assistants (Claude, Cursor, Windsurf, Codex, llm.txt). Uses RubyLLM to analyze your codebase and create platform-specific context that helps AI understand your project.

RubyCoded is a terminal-based AI coding assistant built in Ruby. It provides a full TUI chat interface with support for multiple LLM providers (OpenAI, Anthropic, etc.), an agent mode with filesystem tools for reading, writing, and editing project files, a plan mode for structured task planning, and a plugin system for extensibility.

RubyLLM Code will provide an AI-powered coding assistant. This is a placeholder release - full implementation coming soon!

A comprehensive Ruby gem that provides Docker management capabilities through RubyLLM tools. Enables AI assistants to interact with Docker containers, images, networks, and volumes using natural language. Ported from DockerMCP to work directly with RubyLLM without requiring an external MCP server.

LLM evaluation engine for Rails.

Access Claude and GPT models through GitLab Duo via the RubyLLM interface. One provider, automatic routing to Anthropic or OpenAI backends.

Validates and coerces unstructured LLM responses directly into rich, schema-validated Ruby objects with automatic self-correction loops.

Drop-in Mongoid replacement for ruby_llm's ActiveRecord integration. Provides acts_as_chat, acts_as_message, acts_as_tool_call, and acts_as_model macros backed by MongoDB via Mongoid.

A Rails engine providing comprehensive observability for LLM-powered applications. Features include session tracking, trace analysis, prompt management, cost monitoring, and optional chat/agent testing UI (with RubyLLM integration).