5 Multiplayer AI Tools for Team Collaboration

AI coding tools have mostly been built around the idea of one developer working with one AI assistant.

But that model starts to feel limiting when you have multiple agents working on different parts of the same project.

I’ve been exploring tools that approach this problem from different angles. Here are 5 worth looking at:

1. Sharkly.ai

Sharkly.ai focuses on the collaboration layer between humans and AI agents.

Instead of managing agents as isolated coding sessions, the idea is to have multiple agents participate in the same project while humans can assign work, coordinate tasks, and review what comes back.

For teams experimenting with agent-based development, I find this approach interesting because the problem isn't only “Can the agent write the code?” — it's also “How do we work with several agents without creating coordination overhead?”

1. CrewAI

CrewAI takes a more orchestration-oriented approach.

Developers can create teams of specialized agents with different roles, goals, and responsibilities. This makes it useful when a workflow naturally breaks into several autonomous tasks.

It’s particularly interesting for developers who want to build their own multi-agent systems programmatically.

1. Microsoft Copilot Studio

Microsoft Copilot Studio approaches agents from the enterprise side.

Teams can create agents and connect them to business data, applications, and existing workflows. The focus is less on building an experimental multi-agent architecture and more on putting agents into practical organizational processes.

1. LangGraph

LangGraph is aimed at developers who need more control over agent execution.

It provides primitives for building stateful, graph-based workflows where agents can interact, make decisions, maintain state, and hand work between different parts of the system.

For complex agent workflows, that level of control can be valuable.

1. OpenAI Agents SDK

The OpenAI Agents SDK provides building blocks for developers creating agent-based applications.

Agents can use tools, maintain context, and hand tasks to other agents. It's a good fit when you want to build the underlying agent experience yourself rather than adopt a complete collaboration environment.

The bigger question

These tools solve different parts of the same emerging problem.

Some focus on orchestration, some on agent frameworks, some on enterprise workflows, and others on human-agent collaboration.

Personally, I think the next interesting step is moving from:

Developer + AI assistant

to:

Team + multiple AI contributors

And that's where I'm particularly interested in what Sharkly.ai is trying to do: make the collaboration between humans and multiple agents part of the workflow itself.

For developers already experimenting with multi-agent development, what does your setup look like today?