Gentle-AI is a CLI-driven application that manages AI coding agents through installation, configuration, and review workflows. It provides a deterministic engineering environment with features like persistent memory (Engram™), receipt-driven development (RDD) for verifiable code reviews, and telemetry collection. The architecture is modular, separating concerns into CLI commands, agent adapters, components (like Skills and Context7), review transaction logic, state management, and a text-based user interface. The system interacts with external AI agents (Claude Code, OpenCode, Cursor, etc.) via adapter patterns, manages state in $HOME/.gentle-ai, and supports cross-platform operation (Linux, macOS, Windows). Key entry points include the gentle-ai CLI command and TUI screens for interactive workflows.

Engram is a persistent memory system for AI coding agents, providing local-first storage (SQLite with FTS5) and optional cloud sync (Postgres). It exposes memory tools via the Model Context Protocol (MCP), a local HTTP API, a Bubble Tea terminal UI, and plugins for Claude Code, Codex, OpenCode, Gemini CLI, and Obsidian. The architecture is a modular monolith: a single Go binary that can run as a CLI, background daemon, or HTTP server. Core data flows involve agents saving observations (memories) via MCP tools, which are stored locally and optionally replicated to a cloud backend. The cloud component includes a sync manager, authentication (managed tokens, project grants), a browser dashboard (HTMX + templ), and an admin API. The repository also includes a diagnostic/repair framework, project detection, and a self-test suite. Although primarily an application, it contains some data engineering patterns (chunk-based sync, mutation journal, audit logs) that are more typical of data pipelines. The codebase is well-structured with extensive tests, but its complexity and tight coupling of internal packages limit the reusability of individual components outside the project.