ReAct: Synergizing Reasoning and Acting
The foundation of practical agent loops: reasoning traces combined with tool use and environment interaction.
ReAct gives a model a loop: think, use a tool, observe the result, then think again.
It is one of the roots of modern agents and tool-using LLM workflows.
ReAct is the basic agent rhythm: reasoning gets stronger when the model can check the world and update.
The quick digest
A normal model answer is one shot: prompt in, text out. ReAct changes the rhythm. The model writes a thought, chooses an action like search or lookup, reads the observation, and continues from there.
That matters because many tasks cannot be solved from memory alone. The model may need current information, calculation, environment feedback, or a chance to correct a bad assumption. ReAct gives the model a structured way to combine internal reasoning with external evidence.
Modern agent systems are more engineered than the paper’s format, but the core loop remains: reasoning is stronger when it can act, observe, and update.
What to remember
Read it like this
- First pass: Follow one trajectory line by line.
- Second pass: Notice where observations correct the model.
- Then build taste: Then map the pattern onto MCP/tool-calling systems.
Make a two-tool agent that searches notes, performs one calculation, and explains when each observation changed its answer.