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Readme
@dimples/lra
Overview
A CLI tool for managing long-running AI agent projects. When building complex applications with AI assistants like Claude Code, context windows can fill up before the project is complete. LRA provides structured state management so AI agents can seamlessly continue work across multiple sessions.
Key Features:
- 📋 Feature Tracking - Maintain a structured list of features with priorities
- 📊 Progress Management - Track completion status across sessions
- 🔄 State Persistence - JSON-based state that survives context resets
- 🤝 AI Integration - Generates
.claude/CLAUDE.mdfor seamless Claude Code integration - 📝 Git Integration - Automatic commits with progress tracking
Installation
# Quick use (recommended)
npx @dimples/lra init my-project
# Or install globally
npm install -g @dimples/lra
lra init my-projectQuick Start
# 1. Initialize a new project
npx @dimples/lra init my-app --type web
cd my-app
# 2. Add features to track
npx @dimples/lra add "User authentication" --priority critical
npx @dimples/lra add "Dashboard view" --priority high
npx @dimples/lra add "Settings page" --priority medium
# 3. Check status
npx @dimples/lra status
# 4. Get next feature to work on
npx @dimples/lra next
# 5. After AI completes a feature
npx @dimples/lra done feat-001
npx @dimples/lra commit feat-001Commands
| Command | Description |
|---|---|
init [name] |
Initialize a new LRA project |
status |
Show project progress and statistics |
add <description> |
Add a new feature |
next |
Get the next pending feature (by priority) |
done <feature-id> |
Mark a feature as completed |
commit [feature-id] |
Commit progress to git |
list |
List all features |
export |
Export project state |
init [name]
npx @dimples/lra init my-project --type webOptions:
-t, --type <type>- Project type:web,api,cli,library(default:web)-d, --dir <directory>- Target directory (default:.)
add <description>
npx @dimples/lra add "User login" --priority critical --steps "Open login page" "Enter credentials" "Submit"Options:
-p, --priority <priority>-critical,high,medium,low(default:medium)-c, --category <category>-functional,style,performance,security-s, --steps <steps...>- Test steps for verification
status
npx @dimples/lra status
npx @dimples/lra status --json # Machine-readable outputnext
Returns the highest-priority pending feature.
npx @dimples/lra next
npx @dimples/lra next --json # Machine-readable outputProject Structure
my-project/
├── .agent/
│ ├── features.json # Feature list (the "memory")
│ ├── progress.md # Session history
│ └── sessions/ # Detailed session logs
├── .claude/
│ └── CLAUDE.md # Instructions for Claude Code
├── init.sh # Development environment script
├── app_spec.txt # Application specification
└── [your project files]How It Works
┌─────────────────────────────────────────────────────────────┐
│ Session Workflow │
├─────────────────────────────────────────────────────────────┤
│ │
│ Session 1 Session 2 Session 3 │
│ ┌──────────┐ ┌──────────┐ ┌──────────┐ │
│ │ AI │ │ AI │ │ AI │ │
│ │ (fresh) │ │ (fresh) │ │ (fresh) │ │
│ └────┬─────┘ └────┬─────┘ └────┬─────┘ │
│ │ │ │ │
│ ▼ ▼ ▼ │
│ ┌─────────────────────────────────────────────────┐ │
│ │ .agent/features.json │ │
│ │ (Persistent State/Memory) │ │
│ └─────────────────────────────────────────────────┘ │
│ │
│ Each session: │
│ 1. Read features.json → Know current state │
│ 2. Work on features → Implement code │
│ 3. Mark done → Update state │
│ 4. Commit → Persist to git │
│ │
└─────────────────────────────────────────────────────────────┘Integration with Claude Code
When you run lra init, it automatically creates .claude/CLAUDE.md with instructions for Claude Code. Every session, Claude will:
- Read
.agent/features.jsonto understand project state - Read
.agent/progress.mdto see session history - Call
lra nextto get the next feature - Implement and test the feature
- Call
lra doneandlra committo save progress
Core Principles
- Immutable Feature List - Features can only be marked done, never removed
- Incremental Progress - One feature per session
- Verification First - Test before marking done
- State Synchronization - Always commit after changes
Why LRA?
When building complex applications with AI:
| Problem | Solution |
|---|---|
| Context window fills up | Features tracked in JSON file |
| AI "forgets" previous work | Progress persisted across sessions |
| AI declares done too early | Structured feature list prevents this |
| AI tries to do too much at once | One feature at a time |
Related
- Anthropic: Effective Harnesses for Long-Running Agents - The inspiration for this tool
- Claude Code - AI coding assistant
License
MIT © dimple-smile
概述
跨多个会话管理复杂的 AI 项目
概述
一个用于管理长运行 AI Agent 项目的 CLI 工具。当使用 Claude Code 等 AI 助手构建复杂应用时，上下文窗口可能在项目完成前就被填满。LRA 提供结构化的状态管理，让 AI Agent 可以在多个会话中无缝继续工作。
核心功能：
- 📋 功能追踪 - 维护带有优先级的结构化功能列表
- 📊 进度管理 - 跨会话追踪完成状态
- 🔄 状态持久化 - 基于 JSON 的状态，不受上下文重置影响
- 🤝 AI 集成 - 自动生成
.claude/CLAUDE.md与 Claude Code 无缝集成 - 📝 Git 集成 - 自动提交并追踪进度
安装
# 快速使用（推荐）
npx @dimples/lra init my-project
# 或全局安装
npm install -g @dimples/lra
lra init my-project快速开始
# 1. 初始化新项目
npx @dimples/lra init my-app --type web
cd my-app
# 2. 添加要追踪的功能
npx @dimples/lra add "用户认证" --priority critical
npx @dimples/lra add "仪表盘视图" --priority high
npx @dimples/lra add "设置页面" --priority medium
# 3. 查看状态
npx @dimples/lra status
# 4. 获取下一个要工作的功能
npx @dimples/lra next
# 5. AI 完成功能后
npx @dimples/lra done feat-001
npx @dimples/lra commit feat-001许可证
MIT © dimple-smile