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.

Talks the native Anthropic Messages format to Bedrock InvokeModel / InvokeModelWithResponseStream, unlocking Anthropic-specific features that the Converse API cannot express — tool search with deferred tool loading, prompt caching via cache_control, and structured outputs — while keeping data inside the AWS boundary. Authenticates with the standard AWS credential chain (IRSA, Pod Identity, instance profiles, env vars) via aws-sdk-core.

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

A RubyLLM provider that invokes the official Codex CLI, reuses local ChatGPT authentication, and maps RubyLLM structured-output schemas to Codex output schemas.

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).

Adds Pollinations AI support to RubyLLM — chat, image/video generation, TTS/music, and transcription via the Pollinations API. Installs as a standalone gem without modifying RubyLLM core.

A RubyLLM plugin that enables local LLM execution using the Red Candle gem. Run quantized GGUF models directly in Ruby without external API calls. Supports streaming, structured output, and multiple model architectures including Gemma, Llama, Qwen, Mistral, and Phi.

Cache RubyLLM responses based on semantic similarity, not exact string matching. Reduces costs and latency by returning cached responses for semantically equivalent queries.

Route messages to specialized RubyLLM chat agents based on semantic similarity using embeddings and kNN lookup

Provides an adapter to integrate RubyLLM with Sequel ORM.

Provides a RubyLLM::Provider that allows you to stub responses for testing purposes. You can stub individual responses or a sequence of responses, and you can also temporarily stub responses within a block.

Intuitive one-liner utility methods for common LLM tasks like text summarization, translation, data extraction, classification, grammar correction, sentiment analysis, key point extraction, text rewriting, and question answering.

ruby_llm-toolbox bundles filesystem, shell, web, git, and data tools as ready-to-use RubyLLM::Tool subclasses. One require loads everything; read-only tools are on by default while mutating/exec tools are gated behind explicit config. Tool output is token-budgeted via ruby_llm-tokenizer.

A RubyLLM plugin that integrates Top Secret to automatically filter sensitive information from LLM interactions.