agents-in-a-box

agent
Guvenlik Denetimi
Uyari
Health Uyari
  • No license — Repository has no license file
  • Description — Repository has a description
  • Active repo — Last push 0 days ago
  • Low visibility — Only 8 GitHub stars
Code Gecti
  • Code scan — Scanned 12 files during light audit, no dangerous patterns found
Permissions Gecti
  • Permissions — No dangerous permissions requested
Purpose
This tool provides a terminal-native ecosystem for AI-assisted development, offering context engineering to help manage and run autonomous coding agents.

Security Assessment
The automated code scan of 12 files found no dangerous patterns, no hardcoded secrets, and no dangerous permissions requested. However, because it is a complex agent framework built with 115 Rust modules designed to assist with coding, it inherently interacts with the local file system. The overall risk is rated as Low from a malicious code perspective, but developers should always exercise standard caution when granting autonomous agents write access to their projects.

Quality Assessment
The project is highly active, with its last commit pushed today. Despite the massive scope mentioned in the documentation (such as 71 skills and 37 agents), the repository currently has very low community visibility with only 8 GitHub stars. There is a discrepancy in the repository's health data: the automated scan flagged the project for having no license file, even though the README displays an MIT license badge. This could mean the license exists but was overlooked by the scanner, or the badge is inaccurate. The low star count means this tool has not yet been widely vetted by the broader open-source community.

Verdict
Use with caution — the code appears clean and is actively maintained, but lacks community vetting and has an unresolved discrepancy regarding its software license.
SUMMARY

context engineering for agentic coding

README.md

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A complete ecosystem for AI-assisted development

CI Toolkit Validation Release Rust Platform License

115 Rust Modules · 71 Skills · 37 Agents · 9 AI Tools · Knowledge Graph


A terminal-native ecosystem for managing AI coding agents. Built around a Rust TUI that orchestrates Claude Code sessions with git worktree isolation, and a portable toolkit of skills, agents, and workflows that plug into 9 different AI coding tools.

ainb demo
Creating a new Claude Code session with model selection


What's Inside

Component What it does Scale
ainb TUI Rust terminal app for managing Claude Code sessions 115 modules
Toolkit Portable skills, agents, and workflows for AI coding tools 71 skills, 37 agents
Knowledge System GraphRAG + QMD learning capture and retrieval Architecture docs

Why agents-in-a-box?

Most AI coding setups are a loose collection of dotfiles. This project treats the problem as an engineering system:

  • One toolkit, many tools — Write a skill once, deploy it to Claude Code, Codex, Gemini, Cursor, Copilot, Amazon Q, Cline, Roo, or Clawdhub
  • Session isolation — Each coding session gets its own git worktree and tmux session. No cross-contamination
  • Agents that compose — 37 specialized agents (backend-developer, security-agent, architecture-reviewer, etc.) that can be orchestrated into swarms
  • Memory that persists — A two-tier knowledge system (GraphRAG + QMD) that captures learnings and retrieves them across sessions and projects
  • Production Rust — The TUI isn't a shell script. It's 115 modules of typed, tested, async Rust with clippy pedantic/nursery lints

Quick Start

# Install the TUI
brew tap stevengonsalvez/ainb && brew install ainb

# Install the toolkit for your AI tool
cd toolkit && npm install && node create-rule.js --tool=claude-code-4.5

# Launch
ainb

ainb — Terminal UI

A Rust-based terminal application for managing Claude Code development sessions with git worktree isolation, model selection, and persistent tmux sessions.

Features

  • Session management — Create, monitor, switch between isolated Claude Code sessions
  • Git worktree isolation — Each session gets its own branch and working directory
  • Model selection — Choose Sonnet, Opus, or Haiku per session
  • Live log streaming — Real-time log viewer with level filtering and search
  • tmux integration — Persistent sessions that survive disconnects
  • Keyboard-driven — Vim-style navigation throughout
Screenshots
Home screen
Main dashboard with active sessions
New session
Session creation with model selection
Live logs
Real-time log streaming
Session view
Attached Claude Code session

Installation

Homebrew (macOS / Linux)
brew tap stevengonsalvez/ainb
brew install ainb
One-liner install
curl -fsSL https://raw.githubusercontent.com/stevengonsalvez/agents-in-a-box/v2/ainb-tui/install.sh | bash
Cargo (any platform)
cargo install --git https://github.com/stevengonsalvez/agents-in-a-box --branch v2 agents-box
# Optionally alias: alias ainb="agents-box"
Windows (WSL)
# 1. Install WSL2
wsl --install

# 2. Inside Ubuntu/Debian
curl -fsSL https://raw.githubusercontent.com/stevengonsalvez/agents-in-a-box/v2/ainb-tui/install.sh | bash
sudo apt update && sudo apt install -y tmux
ainb

ainb requires tmux for persistent sessions, which is Unix-only. WSL provides the best Windows experience.

Keyboard Shortcuts

Key Action
j/k or ↑/↓ Navigate sessions
Enter Attach to session
n New session
d Delete session
r Restart Claude in session
l View logs
q Quit

Platform Support

Platform Status Method
macOS Apple Silicon Pre-built binary
macOS Intel Build from source
Linux x86_64 Pre-built binary
Linux ARM64 Build from source
Windows (WSL2) Install script
Windows (Native) Use WSL

Requirements

  • tmux — persistent session management
  • git — worktree operations
  • Claude Code CLI — the claude command

Toolkit

A portable AI coding agent toolkit: skills, agents, workflows, and configurations that deploy to 9 different AI coding tools from a single source.

Full toolkit documentation →

Supported AI Tools

Tool Deploy target Method
Claude Code ~/.claude/ Home directory
Codex ~/.codex/ Home directory
GitHub Copilot ~/.copilot/ Home directory
Gemini CLI .gemini/ Project directory
Amazon Q .amazonq/rules/ Project directory
Cursor Project root Project directory
Cline Project root Project directory
Roo Project root Project directory
Clawdhub Project root Project directory

Skills (71)

Skills are reusable capabilities that any supported AI tool can invoke.

Workflow & Planning

plan · plan-tdd · plan-gh · implement · validate · workflow · brainstorm · critique · discuss · expose · interview

Code Quality & Testing

commit · find-missing-tests · webapp-testing · security-audit · security-scan · simplify

DevOps & Infrastructure

start-local · start-ios · start-android · spawn-agent · tmux-monitor · tmux-status · expose · debug-bridge

Knowledge & Learning

reflect · global-learnings · research · research-cache · instincts · compound-docs · prime

Session Management

health-check · session-info · session-metrics · session-summary · handover · recover-sessions · plugins

Swarm Orchestration

swarm-create · swarm-join · swarm-inbox · swarm-status · swarm-shutdown · swarm-orchestration · swarm-agent-troubleshooting

GitHub & Issues

gh-issue · make-github-issues · do-issues · merge-agent-work · list-agent-worktrees · attach-agent-worktree · cleanup-agent-worktree

Design & Frontend

ui-ux-pro-max · frontend-design · frontend-slides · tui-style-guide · tui-screen · liquid-glass · remotion-best-practices

Research & Analysis

crypto-research · oracle · notebooklm · sentry-cli · ats-resume-matcher · resume-formatter · retro-pdf

Agent Architecture

skill-creator · agent-ops · autonomous-loops · cost-aware-pipeline · media-processing · nano-banana-pro · sync-learnings · claude-developer-platform

Agents (37)

Specialized AI agents organized by domain. Each agent has a defined persona, tool access, and area of expertise.

Category Agents
Universal backend-developer · frontend-developer · superstar-engineer
Orchestrators tech-lead-orchestrator · project-analyst · team-configurator
Engineering api-architect · architecture-reviewer · code-archaeologist · code-reviewer · dev-cleanup-wizard · devops-automator · documentation-specialist · gatekeeper · integration-tests · lead-orchestrator · migration · performance-optimizer · planner · playwright-test-validator · property-mutation · release-manager · security-agent · service-codegen · solution-architect · tailwind-css-expert · test-writer-fixer
Design ui-designer
Swarm swarm-leader · swarm-worker
Meta agentmaker · reflect
Root distinguished-engineer · web-search-researcher

Knowledge System

A two-tier learning system that captures insights during development and retrieves them across sessions and projects.

Layer Technology Purpose
Fast local QMD (Quick Markdown Documents) Semantic search over structured learning notes
Deep graph GraphRAG (nano-graphrag) Entity-relationship graph with community detection for cross-project knowledge retrieval

The /reflect skill captures learnings. The /research and /prime skills retrieve them. The /global-learnings skill manages the knowledge base directly.

How the knowledge system works →


Architecture

agents-in-a-box/
│
├── ainb-tui/                   # Rust TUI application
│   ├── src/                    # 115 modules
│   │   ├── app/                #   State machine & event handling
│   │   ├── components/         #   TUI screen components
│   │   ├── widgets/            #   Reusable UI widgets
│   │   ├── docker/             #   Container management
│   │   ├── tmux/               #   Session & PTY integration
│   │   ├── git/                #   Worktree operations
│   │   ├── claude/             #   Claude API client
│   │   ├── models/             #   Data models
│   │   └── config/             #   Configuration handling
│   ├── deny.toml               #   License & security policy
│   ├── Formula/                #   Homebrew formula
│   └── install.sh              #   One-liner installer
│
├── toolkit/                    # Portable AI agent toolkit
│   ├── packages/
│   │   ├── skills/             #   71 reusable skills
│   │   ├── agents/             #   37 agent definitions
│   │   │   ├── universal/      #     Cross-stack specialists
│   │   │   ├── engineering/    #     Backend & infra agents
│   │   │   ├── orchestrators/  #     Team coordination
│   │   │   ├── design/         #     UI/UX specialists
│   │   │   ├── swarm/          #     Multi-agent coordination
│   │   │   └── meta/           #     Agent creation & reflection
│   │   ├── workflows/          #   Structured delivery workflows
│   │   └── utilities/          #   Shared utilities
│   ├── bootstrap.js            #   Multi-tool deployment engine
│   └── create-rule.js          #   CLI installer
│
├── docs/                       # Documentation
│   └── how-reflection-works.md #   Knowledge system architecture
│
└── .github/workflows/
    ├── ci.yml                  #   Rust CI (fmt, clippy, test, deny, machete)
    ├── toolkit-validation.yml  #   Toolkit structure & install validation
    └── release.yml             #   Cross-platform binary releases

CI/CD & Quality

Check Tool What it catches
Format rustfmt Style inconsistencies
Lint clippy (pedantic + nursery) Logic errors, anti-patterns, code smells
Test cargo-nextest (Ubuntu + macOS) Regressions across platforms
Security cargo-deny (RustSec) Known vulnerabilities in dependencies
Licenses cargo-deny Non-compliant dependency licenses
Dead deps cargo-machete Unused crate declarations
Toolkit structure Custom validation Package counts, template substitution, install verification

The Rust codebase enforces unsafe_code = "forbid" and runs clippy with pedantic, nursery, and cargo lint groups enabled.


Development

Building from source

cd ainb-tui
cargo build --release
./target/release/agents-box

Running tests

cd ainb-tui
cargo test                              # Unit tests
cargo test --features visual-debug      # With terminal output
cargo test --features vt100-tests       # VT100 screen verification
cargo nextest run                       # With nextest (parallel)

Linting & checks

cd ainb-tui
cargo fmt --check                       # Format check
cargo clippy --all-targets              # Lint
cargo deny check                        # Security + licenses

Installing the toolkit

cd toolkit
npm install
node create-rule.js --tool=claude-code-4.5    # Deploy to ~/.claude/
node create-rule.js --tool=gemini             # Deploy to .gemini/
node create-rule.js --tool=codex              # Deploy to ~/.codex/

Contributing

  1. Fork the repository
  2. Create a feature branch (git checkout -b feature/amazing-feature)
  3. Commit your changes (git commit -m 'feat: add amazing feature')
  4. Push to the branch (git push origin feature/amazing-feature)
  5. Open a Pull Request

Links


License

MIT — see LICENSE for details.

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