Problem solved

Coordinating multiple AI agents to work together on complex tasks without human intervention, ensuring tasks are completed by keeping agents in a loop until finished.

Use cases

• Automating multi-step reasoning tasks with LLM agents
• Continuous task execution without manual intervention
• Orchestrating agent workflows for complex problem-solving
• Building autonomous AI systems that self-correct and iterate
• Managing stateful conversations between multiple agents
• Implementing task chains where agents pass roles (hats) to each other

Audience

• Developers
• ML Engineers
• Data Scientists
• Researchers

Pros

• Open source and free to use
• Keeps agents running until task completion
• Simple hat-based metaphor for task passing
• Easy to integrate with existing AI agents
• Python-based and extensible

Cons

• Limited documentation and community
• Early-stage project with potential instability
• May not scale well for highly complex orchestrations
• Dependent on underlying agent reliability
• Lacks advanced monitoring and debugging tools