Guide to Implementing LLM Agents: ReAct and Simple Agents

In this guide, we will walk through the configuration and usage of two main types of LLM-based agents: ReAct Agent and Simple Agent. We will discuss their unique features, configuration steps, and best practices, along with how to use tool integrations to enhance their capabilities.

1. ReAct Agent: Reasoning + Action

Overview

The ReAct Agent combines reasoning and tool-based actions to handle complex, dynamic tasks. This agent operates in a loop-based framework that iteratively uses external tools, makes decisions, and processes feedback to achieve its goals.

How It Works

The ReAct approach is inspired by human reasoning and action, utilizing Chain-of-Thought (CoT) prompting to guide the agent's decision-making. By incorporating external data through tools, ReAct overcomes limitations like knowledge cutoff and hallucination issues, allowing the agent to update its knowledge and respond accurately.

Key Fields Explained

Field Name	Description
Name	Defines the agent's identifier in workflows, e.g., "searcher-assistant".
LLM	Specifies the language model to use. The LLM node is essential for this agent.
Tools	List of tools (e.g., ScaleSerp, ZenRows) the agent can access during its reasoning-action loop.
Role	Description of the agent's role, providing background and behavior instructions, e.g., "helpful AI assistant with search skills."
Max Loops	Sets the maximum number of reasoning-action cycles to prevent infinite loops.
Behavior on Max Loops	Defines what happens if max loops are reached: Raise (return error) or Return (craft an explanation and stop).

Step-by-Step Setup

Creating the agent

Configuring the agent

Saving the agent

1. Basic Configuration Start by defining the name, role, and selecting an LLM for the agent.

2. Tool Integration Select and configure tools like ScaleSerp or ZenRows. These tools are employed in the reasoning-action loop to fetch external data and assist with decision-making.

   • Example Tool Setup: Use ScaleSerp for web search and ZenRows for structured data extraction from web pages.

   • Agents equipped with search tools can access real-time information, improving response accuracy.

3. Execution Flow The ReAct Agent works in a cycle of thinking, acting, and evaluating results. The agent:

   • Receives an input query

   • Determines the necessary actions

   • Utilizes the appropriate tools

   • Assesses the gathered data

   • Loops if additional information or reasoning is needed

   • Concludes with a formatted response

4. Testing the Agent Test the ReAct agent with a series of queries to evaluate its decision-making, data handling, and loop termination when reaching max_loops.

Best Practices for ReAct Agent

• Max Loops: Adjust the max_loops parameter to a reasonable number based on task complexity to avoid infinite processing loops.

• Role Clarity: Define a clear role to help the agent understand its behavior and objectives.

• Error Handling: Configure Behavior on Max Loops to return a meaningful response instead of simply failing, enhancing the user experience.

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2. Simple Agent: Direct Prompt-Response

Overview

The Simple Agent is designed for straightforward tasks, focusing on single-turn, prompt-response interactions without the need for external tools. It is ideal for content generation, summarization, or any task that requires minimal context and processing.

Key Fields Explained

Field Name	Description
Name	Identifier for the agent, e.g., "simple-agent".
LLM	The language model used for prompt-response generation, mandatory for Simple Agents.
Role	A description of the agent’s responsibilities, e.g., “helpful AI assistant providing summaries.”

Step-by-Step Setup

Creating the simple agent

Configuring and saving the simple agent

1. Basic Configuration Define the name, select the LLM, and set up the role.

2. Role Definition Provide detailed instructions in the role field to ensure the agent’s responses align with your requirements. Example role definition:

   You are a helpful AI assistant that:
   - Provides clear, concise answers
   - Maintains a professional tone
   - Follows specific formatting guidelines
   - Avoids speculation

3. Execution Flow The Simple Agent’s flow is straightforward:

   • Receives input

   • Processes based on role

   • Directly generates a response

Best Practices for Simple Agent

• Role Specificity: A well-defined role ensures the agent’s responses are aligned with expectations.

• Usage Suitability: Ideal for tasks that do not require tool usage or iterative reasoning, such as content modification or summarization.

• Reflection Strategy: Use the role section for reflective tasks, where the agent can assess or refine content based on role instructions.

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Comparison of ReAct and Simple Agents

Feature	ReAct Agent	Simple Agent
Tool Integration	Supports multiple tools for dynamic tasks	No external tools
Reasoning Loops	Complex, multi-step reasoning-action cycles	Single-pass prompt-response
External Data Access	Yes, through tools like ScaleSerp, ZenRows	No
Best Use Cases	Real-time information gathering, dynamic queries	Content generation, summarization, modifications

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Workflow Examples

ReAct Agent Workflow

• Objective: Create a search assistant that finds real-time information on the web.

• Execution:

  • User inputs a query

  • ReAct agent engages ScaleSerp to search

  • Extracts, processes, and verifies data

  • Provides a formatted, factual response

Simple Agent Workflow

• Objective: Summarize a provided document.

• Execution:

  • User provides document text

  • Simple agent processes the text based on its summarization role

  • Returns a concise summary, maintaining a professional tone as specified in the role.

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Conclusion

In summary, the ReAct Agent is ideal for complex reasoning and tasks requiring external data, while the Simple Agent serves well for straightforward content generation or modification tasks. By defining clear roles, leveraging tool integration effectively, and setting up appropriate loop limits, these agents can be configured to perform reliably in various workflows. Regular testing and refinement are key to optimizing their performance for specific applications.