ReAct agents combine reasoning and tool-calling in an iterative loop where a model thinks, acts, observes the result, and repeats until it reaches a final answer. The explanation covers the Thought-Action-Observation cycle used in frameworks like LangChain and LangGraph, compares ReAct to alternatives like plan-and-execute, ReWOO, Reflexion, and multi-agent orchestration, and describes three memory tiers (in-prompt, thread-scoped, and cross-thread). It also details why ReAct loops become slow and costly at scale, since resending the full history each turn causes cumulative token growth, and lists four context-related failure modes. The piece closes by promoting Redis Iris, a context engine offering agent memory, checkpointing, and semantic caching to control cost and latency.
Table of contents
What is a ReAct agent?Redis Iris serves agent context in millisecondsHow the reasoning & acting loop worksReAct vs other agent patternsWhere a ReAct agent keeps its context between stepsBuild agents that remember, not agents that guessPractical uses for ReAct agentsWhy ReAct loops get slow & expensive as traffic growsBuilding ReAct agents on a real-time context layerFresh context, every callReAct performance depends on its context layerQuestions this post answers
What is the ReAct agent pattern in AI systems?
ReAct is an agent pattern where a model alternates between reasoning (Thought) and tool use (Action), reads the tool's result (Observation), and repeats until it has enough information to give a Final Answer. It's implemented as a loop in frameworks like LangChain and LangGraph, and reported a 34% absolute success-rate gain over imitation and reinforcement learning on the ALFWorld benchmark. Teams choosing an agent architecture can track ReAct pattern comparisons and tooling updates on daily.dev.
Why do ReAct agent loops get expensive as the number of steps increases?
Costs can grow faster than the number of steps because every iteration re-sends the full accumulated history, including the original question, every prior thought, tool call, and observation. Input length grows linearly per call, but cumulative billed input tokens can grow quadratically with iteration count, so a ten-step task can cost far more than ten times a one-step task. Developers debugging runaway agent costs can follow context-management techniques for LLM apps on daily.dev.
What are the main alternatives to the ReAct agent pattern?
Plan-and-execute drafts a full plan upfront to cut repeated LLM planning calls, with the ReWOO variant separating reasoning from observations to improve token efficiency. Reflexion adds self-critique after failed attempts, trading extra compute for quality. Multi-agent orchestration splits work across specialized subagents but can consume many times more tokens than a single-agent flow. Anyone choosing among agent architectures can compare tradeoffs like these on daily.dev before committing.