Camel is an open-source AI agent framework that facilitates role-playing between multiple agents to collaboratively solve complex problems. By assigning specific roles and enabling communication between agents, Camel allows for dynamic problem-solving and decision-making processes. This framework is particularly useful for scenarios requiring diverse perspectives and expertise, such as strategic planning and creative brainstorming.
Facilitating strategic planning sessions with diverse AI perspectives.
Conducting creative brainstorming for content creation.
Simulating negotiations and decision-making processes.
Enhancing problem-solving by leveraging multiple AI roles.
CAMEL demonstrates high autonomy through its multi-agent framework enabling role-based collaboration without direct human intervention. The system supports dynamic task execution through conversational chains between specialized agents (e.g., programmer and stock trader roles). However, autonomy is constrained by its reliance on predefined role specifications and the need for initial task prompting. While agents exhibit self-directed conversation patterns using LLMs like LLaMA and ChatGPT, complete end-to-end task execution still requires human oversight for complex workflows.
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