Building ReAct Agent Workflows

Part 1: The ReAct Framework - Thought, Action, Observation

The ReAct framework is an elegant solution that synergizes reasoning and acting. It's an iterative loop that mimics a human's problem-solving process.

The loop consists of three key steps:

1. Thought: The agent uses the LLM to analyze the current problem and its state. It thinks about what it knows, what it doesn't know, and what its next step should be to get closer to the solution. This is its internal monologue.
2. Action: Based on its thought process, the agent decides to use one of its available tools. It specifies the tool to call and the input for that tool.
3. Observation: The agent executes the action (i.e., calls the tool) and gets a result back. This result is the observation. The observation is then fed back into the agent to start the next cycle with a new "Thought".

This loop continues until the agent's "Thought" process concludes that it has enough information to provide a final answer, at which point it exits the loop.

To see a clear, high-level explanation of this concept, let's watch the beginning of the following video.

Part 2: Building a ReAct Agent from Scratch

Given your background and preference for implementation, the best way to truly understand ReAct is to build it from the ground up. This will demystify what frameworks like LangChain do under the hood and give you full control over the agent's logic.

We will continue with the video from the previous section, as it provides a complete, code-along tutorial for building a ReAct agent using only Python and a fast LLM API (Groq).

2.1 The System Prompt: The Agent's Operating Instructions

The entire ReAct loop is orchestrated by a cleverly designed system prompt. This builds directly on the prompt engineering skills from our last lesson. The prompt must explicitly instruct the LLM to follow the Thought, Action, PAUSE, Observation sequence.

The video details how to craft such a prompt. You can also refer to the article "Create a ReACT agent from scratch..." for a similar prompt structure. Notice how it provides:

• A clear definition of the loop.
• A description of the available tools (calculate, wikipedia).
• A few-shot example showing the desired interaction pattern.

2.2 The Orchestration Loop: Automating the Cycle

Now for the core of the implementation: a Python loop that automates the agent's reasoning process. This loop will serve as the conductor, managing the conversation between the LLM and the tools.

The logic is as follows:

1. Initialize the agent with the system prompt and the user's query.
2. Start a while loop that runs for a maximum number of iterations (to prevent infinite loops).
3. In each iteration, call the agent (LLM) with the current prompt.
4. Parse the LLM's response.
   • If the response contains Action: and PAUSE, use regular expressions to extract the tool name and its arguments.
   • Execute the corresponding Python function (the tool).
   • Format the function's return value into an Observation: string. This string becomes the next_prompt.
   • continue to the next loop iteration.
   • If the response contains Answer:, the agent is finished. break the loop.
5. Return the final result.

The final part of the video walks through building this exact loop.

For your reference, here is the essential logic of the orchestration loop discussed in the video.

# Simplified logic of the agent loop
import re

# Assume 'agent' is an initialized class that calls the LLM
# Assume 'tools' is a dictionary mapping tool names to functions

next_prompt = "What is the mass of the Earth plus the mass of Mercury?"
max_iterations = 10

for i in range(max_iterations):
    # 1. Call the LLM
    result = agent(next_prompt)
    print(result)

    # 2. Check for final answer
    if "Answer" in result:
        print("Agent has finished.")
        break

    # 3. Check for an action
    if "Action" in result and "PAUSE" in result:
        # Parse the action and arguments
        action_match = re.search(r"Action: (\w+): (.*)", result)
        if action_match:
            tool_name, tool_input = action_match.groups()
            
            # Execute the tool
            if tool_name in tools:
                tool_function = tools[tool_name]
                # Be cautious with eval in production; here it's used for clarity
                observation = tool_function(eval(tool_input)) 
                next_prompt = f"Observation: {observation}"
                print(f"--- Observation Sent to Agent: {observation} ---")
            else:
                next_prompt = "Observation: Tool not found."
            
            continue # Go to the next loop iteration

Part 3: Production-Ready ReAct with LangGraph

Building a ReAct agent from scratch is insightful, but for complex, real-world applications, using a framework is more robust and scalable. LangGraph is a library from the creators of LangChain designed specifically for building stateful, multi-agent applications by representing them as graphs. It provides a much cleaner way to implement the ReAct loop.

The core concepts in LangGraph are:

• State: A central object (like a Python dictionary) that holds the current state of the workflow (e.g., the list of messages).
• Nodes: Python functions that perform work and update the state. In our case, we'll have a reasoner node (calls the LLM) and a tools node (executes tools).
• Edges: Links that define the flow. We'll use a conditional edge to decide whether to call a tool or finish, and a regular edge to always go back to the reasoner after a tool is used.

This video provides an excellent walkthrough of this exact pattern.

The key advantage of the LangGraph approach is that you are no longer manually parsing strings with regular expressions. The framework handles the function-calling mechanism, error handling, and state management, allowing you to focus on defining the nodes and the logic of the graph.

Conclusion

Today you have bridged the gap between reasoning and acting. You have implemented the core loop that drives autonomous agents, transforming your LLM from a simple text generator into a problem-solver that can formulate a plan and execute it.

Key Takeaways:

• ReAct = Reason + Act: This framework enables an agent to iteratively think about a problem, decide on an action (tool use), and observe the outcome to refine its next thought.
• The Prompt is the Conductor: A well-engineered system prompt is essential to force the LLM to follow the Thought -> Action -> Observation structure.
• Orchestration is Key: Whether built from scratch with a Python loop or with a framework like LangGraph, an orchestration layer is needed to manage the flow between the LLM and the tools.
• Frameworks Abstract Complexity: LangGraph simplifies ReAct implementation by providing pre-built components for state management, tool execution, and conditional routing, making your agent more robust and easier to maintain.

In this lesson, our agent's "memory" was confined to the history of a single agent_loop execution. If you ran the loop again, it would start from scratch. In our next lesson, "Implement short-term memory for an agent to maintain context across multiple interactions," we will address this. You will learn how to give your agent a memory that persists across separate conversations, allowing it to remember past interactions and provide more contextual and personalized responses.