Gentle-AI

Gentle-AI is an open-source configuration layer for AI coding agents. It does not replace Claude Code, Cursor, OpenCode, Codex, Copilot, or similar tools. Instead, it installs a structured ecosystem around them: persistent memory, spec-driven development workflows, curated skills, MCP servers, personas, and optional review practices. The project is aimed at developers who already use coding agents but want them to behave less like stateless chatbots and more like configured engineering partners. The official site frames the problem around context amnesia, chaotic workflows, and tool sprawl. A normal coding-agent setup can forget past architecture decisions, skip verification steps, and scatter memory across unrelated tools. Gentle-AI tries to solve that by bundling Engram persistent memory, SDD workflows, skill packs, documentation access through MCP, and a senior-architect persona. The site also describes Receipt-Driven Review as an optional bounded review mode for frozen code candidates. The project is practical because it starts from existing agents. Its supported-agent list includes common IDE and CLI tools such as Claude Code, Cursor, VS Code Copilot, OpenCode, Codex, Gemini CLI, Windsurf, Kilo Code, OpenClaw, Kimi Code, Qwen Code, Pi, and Hermes. Users can choose a preset such as full-gentleman, ecosystem-only, minimal, or custom, depending on how much structure they want to add. Gentle-AI is strongest for solo builders and engineering teams that keep re-explaining project context to agents. Persistent memory helps carry decisions across sessions. Spec-driven workflows help push vague requests through exploration, proposal, design, application, and verification. Curated skills make recurring tasks repeatable. MCP documentation and memory servers give agents a path to current project and framework context. The caveat is that Gentle-AI is an ecosystem installer and configuration project, not a single model or standalone IDE. Teams still need to understand the agents and tools they connect. The site also presents different counts for supported agents and skills in different sections, so exact inventory should be verified against the current release. If you want a structured AI coding environment without locking into one assistant, Gentle-AI is a useful project to evaluate. For OpenTools readers, Gentle-AI is worth tracking because agent configuration is becoming its own layer of the developer stack. Teams are no longer asking only which model to use; they are asking how to preserve memory, enforce specs, load skills, and review changes across several agents. Gentle-AI gives that layer a concrete open-source shape. A practical test is to install a minimal preset on one active repository, run a real feature through the SDD workflow, and compare the review quality, context recall, and setup repeatability against an unconfigured agent session. If the workflow feels heavy, users can choose a smaller preset instead of adopting the whole stack.