Privacy and advertising choices
Git-Stars uses essential storage for site operation. Optional analytics and ad-measurement scripts stay disabled unless you accept them; partners such as Google may then use cookies or similar identifiers where required. Privacy Policy
You're the boss, agents are your team. They handle tasks on their own, message each other, and review each other's work. You just watch the kanban board and give high-level commands. Codex/Claude/OpenCode/Cursor/Grok/GitHub Copilot/Kiro/Z.AI/MiniMax/Kimi(200+ models, 75+ LLM providers, free models no auth). Build your AI company with multiple teams
This repository page has enough original analysis, source evidence, and comparison context to be treated as an indexable Git-Stars review.
100
review score
Problem solved
It solves the coordination overhead of managing multiple AI agents by providing a structured team framework with role assignment, autonomous task execution, and peer review, unlike single-agent tools or manual multi-agent setups. It also eliminates the need for API keys or signups for initial use, lowering the barrier to entry.
Deployment reality
The available setup signal starts with: Download the appropriate installer for your OS from the GitHub releases page (e.g., macOS .dmg, Windows .exe, Linux .AppImage).. Treat this as a starting point, then ask Codex or Claude to inspect the README, environment variables, runtime version, and deployment target before production use.
Commercial use
The recorded license is AGPL-3.0. This is a useful commercial-use signal, but teams should still verify license text, dependencies, model/API terms, and trademark constraints.
Capability ceiling
Developers can build multi-agent coding workflows, project management pipelines, and automated review systems where agents act as team members with specific roles (e.g., coder, reviewer). Real-world use cases include autonomous code generation with built-in code review, multi-step project execution with agent-to-agent communication, and integrating diverse LLM providers in a single orchestrated environment. The ceiling includes creating entire AI-driven organizations with multiple teams, budget tracking, and complex task dependencies.
Git-Stars is independent and not affiliated with GitHub or this project. Analysis may be AI-assisted and based on public repository metadata plus short README-derived summaries. We do not mirror full README files, docs, issues, or social comments.
777genius/agent-teams-ai is tracked as a TypeScript project in the AI Agent, Automation, MCP Server, LLM Tool, AI App area. This evaluation combines public GitHub metadata, category signals, short source summaries, and Git-Stars editorial rules rather than copying project documentation.
Momentum check: the repository has 1.7k total stars, with +46 today, +0 this week, and +0 this month. These growth windows help distinguish durable adoption from short-lived visibility spikes.
Maintenance check: current activity is Active; the latest push was 26 days ago, and the open issue queue is 17, about 0.98% of total stars. Treat this as an adoption signal, not a substitute for engineering due diligence.
Adoption check: 303 forks and 1.7k watchers suggest how often the project is reused or followed. License signal: AGPL-3.0. Always verify license compatibility before commercial or internal use.
Practical fit: this project is most relevant when you need AI prototypes, LLM workflows, and agent-style applications. Be cautious when you need a legal review, security audit, or production SLA.
Source check: Git-Stars currently has 2 explicit source reference(s) for this report and a recent growth signal of 46. Follow the original GitHub repository for final install, security, and release information.
Analysis
ReadyOriginal problem, capability, and verdict guidance are available.
Sources
ReadyRepository metadata and README/source references are attached.
License
ReadyAGPL-3.0 is recorded for review.
Maintenance
ReadyRecent activity is visible in repository metadata.
Alternatives
ReadyEnough nearby projects exist for comparison.
Momentum
1.7k Stars
Reuse
303 Forks
Attention
1.7k Watchers
Maintenance
active
License
AGPL-3.0
Open issues
17
Agent Teams AI is a free desktop app for managing AI agent teams. It supports multiple AI providers (Claude Code, Codex, OpenCode, Cursor, etc.) and offers a visual workspace for task management, code review, and team collaboration.
Key Features
- Supports multiple AI providers and models (Claude Code, Codex, OpenCode, Cursor, SuperGrok, GitHub Copilot, etc.) with no signup required for free models. - Provides a Kanban board, task detail views, execution logs, code review with file-level controls, and organization hierarchy management. - Includes built-in analytics for token usage, costs, and budget tracking, plus notification settings and triggers.
AI Agent
Agent frameworks, autonomous workflows, and tool-use systems
Automation
Workflow automation, integration glue, and orchestration
MCP Server
Model Context Protocol servers, clients, and integrations
LLM Tool
Libraries and tools for LLM apps, RAG, prompts, and evals
AI App
End-user AI applications and AI-native product examples
Download the appropriate installer for your OS from the GitHub releases page (e.g., macOS .dmg, Windows .exe, Linux .AppImage).73
Health Score
Active
Commit Activity
Feb 21, 2026
Created
Jul 28, 2026
Last push
+44
Today's growth
+44
7-day growth
+44
30-day growth
Forks
Open
Watchers
777genius
GitHub profileopenclaw/openclaw
Your own personal AI assistant. Any OS. Any Platform. The lobster way. 🦞
obra/superpowers
An agentic skills framework & software development methodology that works.
affaan-m/ECC
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
NousResearch/hermes-agent
The agent that grows with you
mattpocock/skills
Skills for Real Engineers. Straight from my .agents directory.
✓
License
✓
Forked
✓ Active
Maintained
Problem Solved
It solves the coordination overhead of managing multiple AI agents by providing a structured team framework with role assignment, autonomous task execution, and peer review, unlike single-agent tools or manual multi-agent setups. It also eliminates the need for API keys or signups for initial use, lowering the barrier to entry.
Capabilities
Developers can build multi-agent coding workflows, project management pipelines, and automated review systems where agents act as team members with specific roles (e.g., coder, reviewer). Real-world use cases include autonomous code generation with built-in code review, multi-step project execution with agent-to-agent communication, and integrating diverse LLM providers in a single orchestrated environment. The ceiling includes creating entire AI-driven organizations with multiple teams, budget tracking, and complex task dependencies.
Bottom Line
This is ideal for developers and teams wanting to experiment with multi-agent orchestration without upfront costs or complex setup, especially for coding projects. It may not suit those needing production-grade reliability or extensive customization beyond the provided framework. The key trade-off is ease of use and flexibility versus potential complexity in managing many agents and providers.