ReAct Framework
A core AI agent framework proposed by Google Research that enables interpretable multi-step task execution by having a model alternate cyclically between Reasoning and Acting. The framework interleaves chain-of-thought reasoning traces with concrete action steps, allowing the model to dynamically adjust its plan based on environmental feedback. It is widely regarded as a key paradigm for transitioning language models from conversational assistants toward action-capable agents.
Core Facts
Timeline (last 90 days)
The basics section covers concepts such as the ReAct framework, planning and reasoning, and memory mechanisms, and guides learners through building their first Agent Demo.
Common solutions to long-horizon task planning include task graph mechanisms, the ReAct framework with self-correction capabilities, and the introduction of external memory modules.
The cognitive section covers concepts such as the ReAct framework, planning and reasoning, and memory mechanisms, and guides learners through running their first Agent demo.
All Facts (4)
Agent planning capabilities are typically implemented through the ReAct (Reasoning + Acting) framework.
90%UnverifiedThe basics section covers concepts such as the ReAct framework, planning and reasoning, and memory mechanisms, and guides learners through building their first Agent Demo.
50%UnverifiedCommon solutions to long-horizon task planning include task graph mechanisms, the ReAct framework with self-correction capabilities, and the introduction of external memory modules.
50%UnverifiedThe cognitive section covers concepts such as the ReAct framework, planning and reasoning, and memory mechanisms, and guides learners through running their first Agent demo.
50%