How deeply does Building LLM Applications with DSPy cover ReAct?

Explains iterative tool selection through trajectories of reasoning, tools, parameters, and execution outputs.

ReAct

As explained in Building LLM Applications with DSPy

The ReAct pattern guides an agent to interleave reasoning with actions such as selecting and invoking tools.

ReAct lets an agent reason and act by choosing among tools wrapped as Python functions and supplying their parameters. DSPy provides a built-in ReAct module for this pattern. section 9.7

Across iterations, the agent uses a trajectory of reasoning, selected tools, and execution outputs to choose what to do next. A long-running loop can keep an agent available; chatbots can use ReAct, RAG, or both. section 9.7

How Building LLM Applications with DSPy covers it

Cover of Building LLM Applications with DSPy

liveBook manning.com

Read Building LLM Applications with DSPy on liveBook Get the book at manning.com

Also explained in

liveBook manning.com

AI Agents in Action, Second EditionReAct is an agent reasoning technique that interleaves verbal reasoning with tool actions and observations in a feedback loop.
liveBook manning.com

Domain-Specific Small Language ModelsAn agent framework that interleaves reasoning and tool use to solve tasks by iterating toward a solution.
liveBook manning.com

Build AI-Enhanced Web AppsReAct is an agentic reasoning technique in which a language model interleaves reasoning about a task with actions that gather information or affect an environment.
liveBook manning.com

Build an AI Agent (From Scratch)The ReAct pattern is an agent design in which reasoning and action alternate so the agent can incorporate tool results and adapt its subsequent behavior.
liveBook manning.com

Building Reliable AI SystemsAn agent framework that interleaves reasoning, actions, and observations to guide task execution.
liveBook manning.com

Prompt Engineering in PracticeReACT is a prompting pattern for agentic workflows in which a model alternates between reasoning, actions, and observations.
liveBook manning.com

AI Agents and ApplicationsA ReAct agent is an agent architecture that interleaves reasoning with actions such as invoking tools and observing their results.
liveBook manning.com

Hugging Face in ActionReAct is an agent technique in which a language model alternates between reasoning, taking actions such as tool calls, observing results, and continuing reasoning.
liveBook manning.com

Build Python Web Apps with StreamlitA ReAct agent is an AI agent pattern that interleaves reasoning about a task with actions performed through callable tools.
liveBook manning.com

LLMs in ProductionReAct is a prompting framework that interleaves a language model's reasoning with external actions and observations to solve tasks.

Compare how 11 books explain ReAct →

Related concepts

memoryhas part · Memory is a storage resource or mechanism that retains data or computational state for access during computation or later use.
tooluses · A tool is an instrument or software capability that enables a person or system to perform a task, obtain information, or interact with resources.
AI agentkind of · An AI agent is a computational system that uses artificial intelligence to interpret information, select actions, and pursue goals through interaction with an environment.
tool usageuses · Tool use is an AI system's invocation of external software functions, services, or other capabilities to obtain information or perform actions.

This explainer was generated by Manning's knowledge graph from the text of Building LLM Applications with DSPy. The section links point to the parts of the book it draws on.