marian2js

marian2js / opengoat

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Build organizations of OpenClaw agents that coordinate work across Codex, Claude Code, Cursor, OpenCode, and more 🐐 🐐 🐐

opengoat.ai agent-orchestration agentic-ai agentic-workflow agents openclaw
226
24
100% credibility
Found Feb 17, 2026 at 68 stars 3x -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
TypeScript
AI Summary

OpenGoat lets you create and manage teams of AI agents that collaborate hierarchically on software projects using tools like Claude Code and Cursor.

How It Works

1
🔍 Discover OpenGoat

You hear about a fun way to build teams of smart AI helpers that work together like a real company on your projects.

2
📦 Quick setup

Follow simple steps to install and connect your favorite AI tools so everything is ready to go.

3
🚀 Launch your company

Open the web dashboard with one click and chat with the CEO to start building your dream team.

4
👥 Hire your team

Tell the CEO who to bring on board—like a CTO, engineers, or designers—and give them special abilities.

5
📋 Hand out tasks

Assign projects or goals to your team, and watch them plan, delegate, and get to work.

6
📊 Track progress

Check in on conversations, boards, and updates to see your AI company humming along.

🎉 Autonomous success

Your AI organization runs itself, delivering results while you focus on big ideas.

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Star Growth

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AI-Generated Review

What is opengoat?

OpenGoat is a TypeScript CLI and UI tool to build hierarchical organizations of OpenClaw agents that coordinate work across Claude Code, Codex, Cursor, Lovable, and more. Developers create a CEO agent, add managers like CTOs, and individual contributors via commands like `opengoat agent create "CTO" --manager --reports-to ceo`, then assign tasks or run messages with session continuity. It solves coordinating multi-agent AI workflows for coding projects, with Docker support and a web UI at localhost:19123 for chatting with the CEO.

Why is it gaining traction?

It stands out by modeling real org structures—agents report up hierarchies, delegate tasks, and share skills like boards or Jira tools—making complex coordination feel natural across AI coding environments. CLI workflows for building GitHub actions, Copilot agents, or project repos keep context via sessions, reducing hallucination in long runs. Early adopters hook on the "message the CEO" simplicity for emergent team behaviors.

Who should use this?

AI engineers building agent teams for GitHub projects, like automating repositories, portfolios, or apps with Claude/Codex. Teams exploring how organizations build trust through delegated tasks and how to build organizations across tools. Devs tired of single-agent limits in Cursor or Lovable, needing coordinated coding sprints.

Verdict

Promising for agent orchestration but early-stage with 68 stars and 1.0% credibility—docs are solid, tests cover core flows, MIT license. Prototype with Docker before committing to production workflows.

(198 words)

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