← 开源
Gentleman-Programming

gentle-ai

Gentle-AI configures the AI coding agents you already use: Claude Code, Cursor, OpenCode, Codex, Pi, and more. Choose persistent memory, Organic-Driven Development, curated skills, MCP servers, personas, and optional bounded review. Open source, no agent lock-in.

AI EngineeringGive agent toolsBuild agent workflow (SDK)Go
在 GitHub 打开
增长势头
+1824 小时新增 Star+0.2%
7.54k
Star
828
Fork
—
本周
85
贡献者
创建于 2026-02-27 · 更新于 2026-10-05 · 今日第 561 名
主要开发者
README

Gentle-AI neon rose banner: the rose blooms in, the GENTLE-AI wordmark is written on, and the tagline Ecosystem, Framework, Workflows appears

Gentle-AI™

The deterministic engineering environment for the AI agent you already use.

Release Stars 17 agents Platform License: MIT

Website • Quickstart • Docs • Wiki

Your agent writes code, then forgets everything. It has no opinion about your project, and no way to prove what it did beyond asking you to read every line. Gentle-AI gives it memory, a workflow, and evidence.

See it in action

One prompt, from idea to reviewed commit: memory, workflow, and evidence in a real session.

https://github.com/user-attachments/assets/fa5c0cfe-06e7-4c0d-bd6e-8ac7cb934339

Prefer Spanish subtitles?

https://github.com/user-attachments/assets/6d2bc422-a4dd-4ecf-a04b-fcd3bea7fea9

If Gentle-AI made your agent worth trusting, a star helps other people find it.

Star History Chart

WORKS WITH THE AGENT YOU ALREADY HAVE

Pi · OpenCode · Claude Code · Codex · Cursor · VS Code Copilot · Gemini CLI · Kilo Code

Kimi Code · Kiro IDE · Qwen Code · Hermes · Antigravity · Windsurf · OpenClaw · Trae · Conductor

17 integrations · native configuration · compare capabilities →

Features


Engram™ — Keep your project context

Three work sessions separated by a restart and by context compaction. Each break cuts the session layer but stops at the memory layer underneath. The first session saves a decision, the next one asks memory before asking you, and weeks later the same question is answered from memory instead of by re-reading the repository.

The cost of a fresh session is not the tokens — it is you, re-explaining the same decisions every morning. Engram removes that: your agent writes down what it learns as it goes and reaches for it before it reaches for you, so context accumulates instead of resetting.

Docs →


ODD — Keep small work small

ODD authorizes and understands a request. Read-only work ends separately; authorized work stays lightweight when small or keeps a recoverable record when substantial, then is implemented, checked, and closed.

Small changes should not need a planning pipeline, and larger work should not lose its context between sessions. Organic Driven Development (ODD) keeps understood changes lightweight and gives substantial, authorized work one recoverable feature document. The agent explores before changing code, checks the results, and keeps progress current so work can resume without rebuilding the plan.

Docs →


Test-first by default — Prove each requested rule

ODD applies test-first development by default when a relevant runnable test and a clear expected outcome exist: capture a failing behavior test before implementation, make it pass, then refactor while tests stay green. Each requested rule gets one RED test, each touched existing command or option gets one test proving its previous behavior still holds, and nothing else is padded in. Without a meaningful runnable test, the agent explains the exception and runs applicable functional checks anyway.

Docs →


RDD — Check finished work at the right depth

How RDD checks a finished change. The exact change is frozen to a lineage, revision and target, then a read-only risk assessment picks the depth: passive gets a structural readback with zero reviewer lenses, medium gets one focused lens, high gets the canonical 4R — Risk, Resilience, Readability and Reliability. At most one bounded correction is allowed, and one exact acknowledgement closes the transaction. Delivery stays human-owned.

Receipt-Driven Development (RDD) is on by default and opt-out: run gentle-ai review mode disable to turn it off. Explicit global or clone-local OFF choices remain OFF. Its point is that a review cannot drift: the candidate is frozen before anything reads it, so the evidence belongs to the exact version you are about to rely on — not to whatever the worktree looked like a moment later. The depth comes from that frozen candidate rather than from the model's judgment, and the result is informational. Commit, push and release stay your call.

Docs →


Deterministic by design — Know the next valid step

A different agent, a different model and a brand-new session all converge on the gentle-ai binary. It reads the change state from files on disk and returns the only valid next transition, so no model votes on what comes next. The answer is always one of four public states: Working, Checking, Ready, or Needs your decision.

A model that guesses the next step guesses differently tomorrow, and differently again for your teammate. That is the gap between a workflow and a suggestion. The gentle-ai binary owns native RDD review transitions; ODD guidance keeps ordinary work proportional to the request. Review evidence is bound to the candidate rather than a model's recollection.

Docs →


Gentle Shell — A complete workspace for Pi

Gentle Shell development workspace with the todo list and live context and spend information

Gentle Shell is a separate Pi integration package. Gentle AI configures supported agents; Gentle Shell owns its own Pi runtime, agents, and interface. Installing or updating this binary does not itself establish Pi behavior parity.

Docs →


17 agents — Keep the agent you already use

The installer configuring multiple agents

Gentle-AI brings its shared workflow to Pi, OpenCode, Claude Code, Codex, and thirteen more agents. Each integration uses that agent's native capabilities, so available features such as delegation and RDD review can differ.

Docs →


Also in the box

Component What it does
Skills library Loaded automatically when the task matches
Context7 MCP Optional, selectable live framework and library documentation
CodeGraph Read-only symbol graph of your codebase
Security deny-list Blocks ~/.ssh, .env and credential files
Config backups Snapshotted before every single write
Doctor gentle-ai doctor — read-only health report
Personas Optional personas; Gentleman is a caring but rigorous mentor who guides you toward your goal
Themes Gentleman and Gentleman-Cute
Model assignment Configure supported agent and review-role models where available

Every component, skill and preset: Full breakdown →

Back to top

Get started

# macOS (Homebrew)
brew install gentleman-programming/tap/gentle-ai

# macOS / Linux (curl)
curl -fsSL https://raw.githubusercontent.com/Gentleman-Programming/gentle-ai/main/scripts/install.sh | bash

# Windows (PowerShell) — source install of the latest release, needs Go 1.25.10+
go install github.com/gentleman-programming/gentle-ai/v4/cmd/[email protected]
gentle-ai          # pick your agents, components and persona
gentle-ai doctor   # verify — read-only, changes nothing

Then use your agent normally. Your configs are snapshotted before every write, and Gentle-AI never installs an AI agent for you — it configures what you already have.

Beta channel and per-distro prerequisites: Quickstart → · Signature verification: Release signing →

Back to top

Documentation

Where to go What you'll find
Intended Usage The mental model. If you read one page, read this one.
Quickstart · Usage Install, prerequisites, every CLI command and flag
Agents Feature matrix and per-agent notes for all 17
ODD · Routing Everyday direct and delegated work
Review · Architecture The RDD contract, lifecycle and threat model
Engram · Components Memory commands, skills, presets and personas
Contributing · Codebase Guide Extend or contribute
Telemetry What we count, and how to turn it off

Back to top

Community

Everything labelled up-for-grabs is scoped, approved and unclaimed — pick one and it's yours.

Community Roadmap Contributing Guide Contributors

Gentle-AI contributors

This project exists because of these people.

Back to top

Built with Gentle-AI

Shipped something with Gentle-AI? Wear the rose. Paste this into your README and the badge links back here:

Built with Gentle-AI

[![Built with Gentle-AI](https://raw.githubusercontent.com/Gentleman-Programming/gentle-ai/main/docs/assets/brand/built-with-gentle-ai.png)](https://github.com/Gentleman-Programming/gentle-ai)

Prefer plain Markdown?

[![Built with Gentle-AI](https://raw.githubusercontent.com/Gentleman-Programming/gentle-ai/main/docs/assets/brand/built-with-gentle-ai.png)](https://github.com/Gentleman-Programming/gentle-ai)

Keep the image URL exactly as shown — it is how I find and feature the projects that carry the badge.

Back to top

About the author

Built by Alan Buscaglia (Gentleman Programming): 15 years of enterprise architecture, a community of thousands of developers testing these tools daily, and one rule for AI-assisted work — verifying beats generating.

Teams adopting AI and finding it isn't working — resistance, everyone prompting their own way, no shared quality bar — can reach out about engagements built on these same open-source tools →.

Website YouTube GitHub Email


Gentle-AI is crafted with Gentle-AI

License: MIT

Trademark notice: The Gentle AI™ and Engram™ names and logos are trademarks of Alan Buscaglia. Both marks are used throughout this document; the symbol appears on the first prominent mention of each, and this notice covers the rest. The MIT License applies to the code; it does not permit implying endorsement or official affiliation. See TRADEMARKS.md.