The agentic operators module is integrated within the OmAgent framework, providing a suite of mature algorithms designed to tackle specific problems effectively. These operators enhance the capabilities of agents by incorporating advanced reasoning and acting techniques.
These operators are seamlessly integrated into OmAgent, allowing users to import them as tools for various steps within an application. For instance, an operator can replace a specific reasoning step in an application, thereby enhancing the agent's performance across diverse tasks.
By leveraging these classic operators, users can easily improve their agents' performance in a wide range of tasks, making OmAgent a versatile and powerful tool for developing intelligent agents.
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CoT: Chain-of-thought prompting elicits reasoning in large language models (Paper | Operator)
Enhances response quality by encouraging more in-depth reasoning through prompts. -
SC-CoT: Self-Consistency Improves Chain of Thought Reasoning in Language Models (Paper | Operator)
Improves reasoning consistency in language models. -
PoT: Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks (Paper | Operator)
Disentangles computation from reasoning for numerical reasoning tasks. -
ReAct: ReAct: Synergizing Reasoning and Acting in Language Models (Paper | ReAct Operator | ReAct-Pro Operator)
Combines reasoning with acting, allowing agents to utilize external tools and information in real-time to solve problems.