Stop Using AI Frameworks Blindly: Build Your Own ReAct Agent
Tobiloba OdejinmiBy Tobiloba Odejinmi
Education
May 30, 2026 • 8:11 PM
7m7 min read
Verified
Source: Unsplash
The Core Insight
This guide demystifies the 'ReAct' (Reasoning and Acting) pattern, the engine behind popular AI agent frameworks like CrewAI and LangChain. By breaking down the cycle of Thought, Action, and Observation, the article explains how to move beyond black-box libraries to build custom, transparent, and reliable AI agents using Python and LLMs.
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Education Specialist & Editor
Tobiloba Odejinmi
Tobiloba Odejinmi is an education specialist dedicated to helping students and lifelong learners discover the best scholarship opportunities, study techniques, and career pathways.
The Kodawire Editorial Team consists of experienced journalists and subject matter experts dedicated to delivering accurate, well-researched, and engaging content.
The Hidden Logic of AI Agents: Demystifying the ReAct Pattern
Building advanced AI agents has been simplified by high-level frameworks. These tools are excellent for production, but they often hide the underlying logic that makes an agent tick. If you have wondered how an agent decides to perform a web search or a calculation, you are looking at the ReAct (Reasoning and Acting) pattern. Understanding this mechanism is essential for debugging, optimization, and moving beyond pre-packaged libraries, especially when you are mastering LLM context engineering to improve performance.
What You Need to Know
The Core Loop: ReAct agents operate in a cycle of Thought, Action, Observation, and Final Answer.
Prompt Engineering is Key: ReAct is not a built-in LLM feature; it is a design pattern enforced by strict prompt templates.
Transparency: By forcing the model to show its work in a structured format, you gain the ability to debug the decision-making chain.
Tool Integration: Real-world utility comes from connecting LLMs to external tools like search engines or calculators.
What is the ReAct Pattern?
ReAct is a paradigm for AI agent design where an agent uses chain-of-thought reasoning and tool-using actions in aggregation. Unlike a basic chatbot that relies solely on its static training data, a ReAct agent thinks step-by-step and can perform intermediate actions, like looking something up or calculating a value, before finalizing its answer. This approach is critical when you move beyond accuracy to ensure your agent is actually performing useful work.
The ReAct pattern requires careful implementation of reasoning loops. (Credit: Glenn Carstens-Peters via Unsplash)
The most effective way to visualize this is to look at the raw trace. The model’s output typically follows this structure:
"Thought: I should calculate the total.
Action: Calculator('123 + 456')
Observation: 579
Thought: Now I have the sum; next, I need to multiply it.
Action: Calculator('579 * 789')
Observation: 456,831.
Thought: I have the final result.
Final Answer: 456,831."
This reasoning trace helps the model plan and keep track of its progress, while the actions allow it to reach out to external sources. It is an LLM brain coordinating reasoning and action in a structured, adaptable way.
Behind the Scenes & Transparency Log
I have deconstructed the internal loops of agentic frameworks. By stripping away the abstraction layers of libraries like CrewAI, I have verified that the functionality is a combination of controlled I/O and rigid prompt templates. My analysis is based on the practical implementation of these loops in Python, ensuring that the logic described here is reproducible in any environment, whether you are using OpenAI or local models via Ollama. For those scaling these systems, understanding strategic LLM deployment is vital.
A ReAct agent operates in a loop of Thought → Action → Observation, repeating until it reaches a solution. This is analogous to how humans solve problems: we think about what to do, perform an action, observe the result, and incorporate that into our next thought.
The ReAct loop functions as a decision-making node in an agentic system. (Credit: Javier Miranda via Unsplash)
The mechanism behind this is the Action Protocol. When you use tools, the agent is instructed to follow a strict schema. This prevents the model from hallucinating tools that do not exist and ensures that the environment can parse the agent's requests reliably.
The Hands-On Experience
When building these agents, the verbose=True flag is your best friend. It exposes the internal self-talk of the agent. In testing, I used the SerperDevTool to provide live web search capabilities. The agent is given a specific prompt template that defines the available tools and the required response format. If the agent deviates from the Thought -> Action -> Action Input -> Observation schema, the loop breaks. This rigidity is what makes the system deterministic and debuggable.
The Contrarian's Corner
Most people believe that agentic behavior is an inherent capability of newer, larger models. I disagree. While larger models are better at following complex instructions, the ReAct pattern is fundamentally a design constraint. You can make a smaller, less capable model perform well as an agent simply by enforcing a strict, repetitive prompt structure. The intelligence is often in the loop, not just the model weights.
Interactive Decision-Making Tool
Not every task requires a ReAct agent. Use this guide to decide:
Static Knowledge Task? (e.g., "What is the capital of France?") → Standard LLM call.
Dynamic/Real-time Data? (e.g., "What is the current stock price?") → ReAct Agent.
Multi-step Reasoning? (e.g., "Find the latest news on X and summarize it for a CEO.") → Multi-Agent ReAct Pipeline.
Debugging agent loops requires close inspection of the reasoning trace. (Credit: Mick Haupt via Unsplash)
My Personal Toolkit
Ollama: For running local models like Llama3 when I need to keep data private or avoid API costs.
Serper.dev: The most reliable, low-latency tool for adding web search capabilities to custom agents.
Jupyter Notebooks: Essential for testing the agent loop step-by-step before deploying it into a production script.
Synthesis: Moving Beyond Frameworks
ReAct is not a built-in LLM feature; it is a design pattern. The power lies in controlling the I/O loop manually. By transitioning from using pre-packaged libraries to custom-built agent architectures, you gain full control over the agent’s behavior. This makes it easier to optimize for specific tasks and troubleshoot when things go wrong.
Do you prefer the convenience of high-level frameworks like CrewAI, or do you find that building your own ReAct loops from scratch gives you the control you need for complex production systems? I will be replying to every comment in the next 24 hours.
ReAct (Reasoning and Acting) is a design pattern for AI agents that forces the model to cycle through 'Thought', 'Action', and 'Observation' steps to solve problems, rather than relying solely on static training data.
No, ReAct is not a built-in feature of LLMs. It is a design pattern enforced by strict prompt templates that guide the model to follow a specific reasoning and action schema.
Use a ReAct agent for tasks requiring real-time data (like stock prices) or multi-step reasoning. Standard LLM calls are sufficient for static knowledge tasks.
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