win4r

win4r / team-tasks

Public

Multi-agent pipeline coordination: Linear, DAG, and Debate modes for AI agent orchestration

www.youtube.com@AIsuperdomain openclaw openclaw-skill openclaw-skills
337
67
100% credibility
Found Feb 09, 2026 at 32 stars 11x -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A standalone script that organizes AI agent teams for projects using step-by-step sequences, dependency chains, or structured debates, tracking everything in plain files.

How It Works

1
💡 Discover Team Tasks

You hear about a friendly organizer that helps teams of smart AI helpers work together on projects like building apps or reviewing ideas.

2
🚀 Start Your Project

Name your project and share the big goal, like creating a simple website with tests and instructions.

3
Choose Team Style
➡️
One After Another

Helpers hand off work smoothly in order.

🔗
Team with Waits

Helpers jump in parallel once their piece is ready.

🗣️
Idea Debate

Helpers share views, review each other, and agree.

4
📊 See the Big Picture

Glance at your colorful progress board showing who's up next, what's done, and how close you are to the finish.

5
✅ Guide Your Helpers

Tell a helper to start their part, save their results when done, and watch the next one light up automatically.

🎉 Mission Accomplished

Your project is complete with every helper's work combined perfectly, ready to use.

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Star Growth

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AI-Generated Review

What is team-tasks?

Team-tasks is a Python CLI tool for coordinating multi-agent AI workflows via shared JSON files, offering linear pipelines, DAG graphs for parallel dispatch, and debate modes for agent deliberation. It tackles the mess of manual task handoffs in setups like claude multi agent github projects or langgraph multi agent github flows, letting you init projects, assign tasks with deps, and track progress with commands like `next`, `ready`, and `round`. Stdlib-only with no external deps, it stores everything in JSON for easy integration with OpenClaw or custom agent orchestrators.

Why is it gaining traction?

Unlike heavyweight jenkins multi agent pipeline tools or multi agent orchestration github copilot frameworks, it delivers instant value with zero setup—just clone and run CLI commands for auto-advance sequencing, cycle-detecting DAGs, and structured debates that output synthesis prompts. The JSON API shines for multi agent llm pipeline dispatch loops, passing dep outputs to ready tasks, making it a go-to for quick multi agent rag pipeline prototypes over bloated multi agent platform github alternatives.

Who should use this?

AI devs orchestrating agent teams for bug fixes, feature specs, or code reviews in multi agent system pipeline setups. OpenClaw users needing a team task tracker for linear handoffs or parallel DAG execution. Experimenters in multi-agent-pathfinding github-style coordination or debate-driven architecture without full multi agent ppo github complexity.

Verdict

Solid for prototyping multi agent pipelines—40 stars show niche appeal, but 1.0% credibility flags its early stage with thin tests. Use in sandboxes now; production waits on more polish.

(198 words)

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