What is the ReAct pattern and why use it?
How ReAct Works
In ReAct, the agent generates a thought (reasoning about what to do), then takes an action (e.g., call a tool), then observes the result, and repeats. This loop continues until the task is complete. The reasoning traces make the agent's decision process more interpretable and allow it to correct mistakes.
For example, to answer a question about current weather, the agent might think 'I need to check the weather API,' call the API, observe the result, and then formulate a response. This differs from a single-shot prompt where the model might hallucinate details.
- Thought: internal reasoning about the next step
- Action: tool call or API request
- Observation: result from the action
- Repeat until final answer
Benefits and Trade-offs
ReAct improves reliability for tasks requiring external data or multi-step reasoning. It also makes debugging easier because you can see the chain of thought. However, it can increase latency and cost due to multiple LLM calls. It's best for tasks where accuracy matters more than speed.
ReAct is widely used in frameworks like LangChain and is a foundational pattern for many agents. It works well with tool-using models like GPT-4 or Claude.
Common mistakes
- Thinking ReAct is the only pattern—other patterns like plan-and-execute may be better for certain tasks.
- Assuming ReAct always improves performance; for simple queries, it can add unnecessary overhead.
- Forgetting to limit the number of reasoning steps to avoid infinite loops.
