ToolProduction ReadyMCPA2A

agent-teams-ai

Name: agent-teams-ai
Rating: 3.2 (1625 reviews)
Author: 777genius

by 777genius

Run kanban-style teams of autonomous agents with peer review and multi-LLM support

TypeScript

Updated Jul 20, 2026

1.6k

Stars

284

Forks

View on GitHub

Overview

Coordinates teams of autonomous agents where each agent can take tasks, message peers, and perform kanban-style manager so agents self-organize, delegate, and iterate on work without constant human micromanagement. Supports many LLM backends and local providers so you can run mixed-model multi-agent workflows in TypeScript/Electron environments. It also enables peer reviews to surface decision handoffs and outcomes, supporting trust and accountability.

Need implementation help?

Expert guidance available

Key Benefits

As agents become more autonomous, visibility into who did what and how decisions were made becomes essential for trust. Agent-teams-ai provides structured team workflows and peer-review loops that surface agent behavior, handoffs, and outcomes—practical signals for reputation and reliability. Until now few agent frameworks modeled team dynamics and review workflows as first-class constructs, which are important for agent-to-agent evaluation and track records.

Ideal For

Teams building multi-agent workflows that need structured delegation, peer review, and support for many LLM providers in TypeScript. This aligns with the Semantic Capability Matching Pattern to adapt to diverse tool capabilities and prompts.

Real-World Examples

Coordinate specialist agents to complete complex product tasks via kanban-style boards
Run peer-review loops where agents critique and improve each other's outputs
Experiment with mixed LLM deployments and compare agent behavior across providers