Loading patterns…
🎯
ReAct
Combines reasoning with acting through external tool use
Complexity: highReasoning Techniques
In 30 seconds
- What
- Agent loops through thought, calls external tools, observes results, then reasons on new data to decide next action.
- When to use
- Tasks requiring real-time information lookup, verification, or multi-step tool chains where each step depends on live feedback.
- Watch out
- Tool calls can fail or return stale data, forcing the agent into retry loops or hallucinated recovery attempts.
Ask the AI expert about this pattern
Opens the assistant with your question prefilled. You review it before sending.
ReAct: Overview
Combines reasoning with acting through external tool use
- Thought → Action → Observation loop
- Integrates external tools
- Dynamic strategy adaptation
- Real-time environmental feedback
The Agent Architect
One pattern, one tradeoff, one production failure story. A short weekly briefing for people building agentic systems.
Weekly email, one-click unsubscribe. We only use your address to send the briefing.
References
The papers, specifications, and repositories this pattern is based on.
- ReAct: Synergizing Reasoning and Acting in Language Models (Yao et al., 2022)arXiv:2210.03629
- Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (Wei et al., 2022)arXiv:2201.11903
- Toolformer: Language Models Can Teach Themselves to Use Tools (Schick et al., 2023)arXiv:2302.04761
- WebGPT: Browser-assisted question-answering with human feedback (Nakano et al., 2021)arXiv:2112.09332
- LangChain ReAct Agent Documentation