** tttt** (Takes Two To Tango)는 AI 에이전트 관현악을 위한 Rust 기반 터미널 다중화기입니다. 그것은 뿌리 AI 대리인 (예를들면, 클로드 Opus)가 스파덴에, 감시자, 조정 다수 노동자 대리인 (예를들면, 클로드 Sonnet, Qwen 같이 국부적으로 모형) - 그것의 자신의 PTY 회의에서 각 달리는 - MCP (모형 Context 의정서) 공구를 통해서 가득 차있는 통제를 유지하고.

AI Agent**: Session Management, Screen reading, keystroke Injection, Pattern-based notifications, live reload 및 Remote monitoring을 위해 설계된 ** tmux로 생각하십시오. AI Agent가 프로그래밍으로 호출 할 수있는 MCP 도구로 모든 노출.

**WARNING: 이 소프트웨어는 명령을 실행하고 키 입력을 보내고 프로세스를 관리 할 수있는 기능을 포함하여 터미널 세션에 AI 에이전트 직접 액세스를 제공합니다. Session Sandboxing은 아직 구현되지 않았습니다. 실험/증강-of-concept 환경에서만 사용하며 항상 실행 에이전트를 감독합니다. 적절한 고립없이 민감한 데이터를 가진 생산 또는 시스템에 배포하지 마십시오. ALSO: 입력 로깅을 활성화하면 비밀번호 키 입력을 포함한 모든 것은 ~/.local/share/tttt/tttt로 로그인됩니다.

tttt — Takes Two To Tango

tttt (Takes Two To Tango) is a Rust-based terminal multiplexer purpose-built for AI agent orchestration. It allows a root AI agent (e.g., Claude Opus) to spawn, monitor, and coordinate multiple worker agents (e.g., Claude Sonnet, local models like Qwen) — each running in its own PTY session — while maintaining full control through MCP (Model Context Protocol) tools.

Think of it as tmux designed for AI agents: session management, screen reading, keystroke injection, pattern-based notifications, live reload, and remote monitoring — all exposed as MCP tools that AI agents can call programmatically.

**WARNING: This software gives AI agents direct access to terminal sessions, including the ability to execute commands, send keystrokes, and manage processes. Session sandboxing is not yet implemented. Use only in experimental / proof-of-concept environments and always supervise the running agents. Do not deploy in production or on systems with sensitive data without proper isolation in place. ALSO: If you enable the input logging, everything including your password keystrokes are logged into ~/.local/share/tttt/tttt.db - keep this in mind and avoid entering any sensitive info ! **

Why This Exists

Current AI coding assistants run as single agents in a single terminal. When tasks get complex, you want multiple agents working in parallel — but coordinating them requires infrastructure that doesn’t exist in standard terminal multiplexers.

tttt was built to answer: What if the AI agent could manage its own workspace?

The result: Claude Opus running inside tttt can autonomously launch Sonnet workers, delegate implementation tasks, monitor progress, review and commit code, restart itself to pick up new tools, and even message the human on Webex when it needs input — all without human intervention.

Demo: What a Real Session Looks Like

In a single session, Claude Opus (running as the root agent inside tttt):

1. Launched a Sonnet worker in a new terminal and sent it a feature spec
2. Monitored progress using tttt_get_status (idle time, last output line)
3. Approved permissions when Sonnet hit permission prompts
4. Reviewed the code, ran tests, and committed — all while Sonnet worked on the next task
5. Live-reloaded tttt with SIGUSR1 to pick up code changes without losing any sessions
6. Restarted itself with SIGUSR2 to discover newly-added MCP tools, auto-continuing via --resume
7. Messaged the human on Webex when it needed input, read the reply, and continued working

All of this happened in a single 3-hour session where 16 features were implemented — most by delegating to Sonnet.

Features

Terminal Management (MCP Tools)

• tttt_pty_launch — Spawn new terminal sessions with custom commands, dimensions, working directories