Module 5: Exploring ReAct Agents and Tool Use
In this final module, we delve into the exciting world of ReAct agents. Building on the basic agent concepts you’ve learned, ReAct (Reasoning and Acting) agents introduce a powerful loop of Thought, Action (using tools), and Observation that allows them to tackle more complex problems dynamically. Unlike simpler agents, ReAct agents can plan multistep solutions and adapt their approach based on the results they get from using various tools.
This module will showcase how Llama Stack facilitates building sophisticated agents capable of chaining tool use together to achieve goals.
By the end of this module, you will be able to:
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Understand the core principles of the ReAct framework and its Reason-Act-Observe loop.
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See how a ReAct agent combines step-by-step reasoning with tool execution.
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Utilize both built-in and custom tools within a ReAct agent workflow.
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Add and manage different models optimized for Agentic capabilities within Llama Stack.
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Analyze the detailed steps ("Chain of Thought") a ReAct agent takes to solve a problem.
Ready to experience the power of agents that can reason and act?
Open the 05_React_Agents.ipynb notebook and follow along to build and observe a ReAct agent in action!
This Module provides an overview of Agents and Tools within the Llama Stack framework.
Reference Material
ReAct agents employ a powerful iterative process to solve problems. This diagram visualizes the core Reason-Act-Observe loop that defines their behavior, allowing them to perform complex, multistep tasks by dynamically planning and using tools based on prior observations.
The diagram illustrates how the agent cycles through thinking (Thought), performing an action (Action/Tool Call), and processing the result of that action (Observation). This loop repeats until the agent determines it has enough information to provide a final answer, showcasing a more dynamic and capable approach compared to simpler agent types.