AIA runs large language models from the terminal. Prompts are Markdown files with YAML front matter: parameters that AIA asks for, directives such as /include and /shell, ERB and shell expansion, and keys that pick the model, temperature or a special mode. Run a prompt once, chain prompts into a pipeline, make a prompt file executable, or open an interactive chat with history, checkpoints and skills. Every session is a crew of robots built on robot_lab. Name several models to compare them side by side, ask for a consensus or a debate, have two independent answers reconciled with /verify, or decompose and delegate work to specialists. Robots use RubyLLM tools and MCP servers through ruby_llm 2.1's built-in client, with per-tool approval and automatic tool filtering. Any provider RubyLLM registers can be used, local or remote, plus optional ruby_llm-providers-* gems that run Claude Code, Codex or GitHub Copilot as models. Requires Ruby 4.0.9 or newer.

FactDb implements the Event Clock concept - capturing organizational reasoning through temporal facts with validity periods (valid_at/invalid_at), entity resolution, and audit trails back to source content. Built on PostgreSQL with pgvector support for semantic search.

HTM (Hierarchical Temporal Memory) provides intelligent memory/context management for LLM-based applications. It implements a two-tier memory system with durable long-term storage (PostgreSQL) and token-limited working memory, enabling applications to recall context from past conversations using RAG (Retrieval-Augmented Generation) techniques.

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), document storage (robot_lab-document_store), and OS-level skill-script confinement (robot_lab-sandbox). Core itself imposes no execution limitations.