ReAct Framework

ReAct structures prompts to generate a sequential cycle: Thought (reasoning about the current state), Action (executing a tool or API call), and Observation (processing the external feedback). This loop prevents the model from hallucinating facts by forcing it to query external sources like a search engine or calculator before synthesizing a final answer.

A core mechanism for hallucination mitigation, ReAct uses the Action step to retrieve verifiable text spans from a trusted corpus. The model is conditioned to base its final reasoning solely on the Observation text returned by the retriever, effectively grounding the generation in external evidence rather than parametric memory.

ReAct combines the internal monologue of Chain-of-Thought with task-oriented actions. While CoT handles static math or commonsense logic, ReAct handles dynamic, information-seeking tasks. The prompt design alternates between Thought: (internal reasoning) and Action: (external call), allowing the model to switch between slow deliberation and fast tool use.

By exposing the model's explicit reasoning trace and the raw observations returned by tools, ReAct offers a transparent window into the decision-making process. Engineers can audit why a specific action was taken or pinpoint the exact retrieval failure that led to an incorrect answer, significantly improving agentic observability.

ReAct excels at complex queries requiring sequential information gathering. The framework decomposes a multi-hop question into a series of alternating reasoning and retrieval steps. For example, to answer 'Who directed the film starring Actor X?', it will first retrieve Actor X's filmography, then retrieve the director of that specific film.

The foundational reasoning strategy that ReAct extends. CoT instructs the model to generate intermediate reasoning steps before arriving at a final answer, improving performance on complex logic tasks. ReAct builds on this by interleaving those reasoning traces with concrete action steps and environment observations.

The broader architectural domain governing how agents securely interact with external software. ReAct provides the cognitive loop that decides when and why to invoke a tool, while the Model Context Protocol (MCP) and similar standards handle the how—authentication, schema validation, and execution.

The process of maintaining a structured representation of user goals, intents, and slot values across multiple turns. In conversational ReAct implementations, DST ensures the agent remembers what it has already done and what remains to be resolved, preventing redundant tool calls.

A generation technique that forces the model's output to strictly adhere to a predefined schema or grammar. In ReAct, this ensures the Action step always produces valid, parseable commands—preventing malformed JSON that would break the execution loop.

A set of techniques including factual grounding and constrained decoding designed to prevent models from generating incorrect information. ReAct inherently reduces hallucination by anchoring reasoning to observable outputs from tools and external data sources rather than relying solely on parametric knowledge.