This is a summary of the full tutorial published on howtostartprogramming.in.
TLDR: Building a ReAct Agent from Scratch with LangChain 2026 In this blog post, we will explore how to build a ReAct agent from scratch using LangChain 2026. The process involves setting up the environment, defining the agent's architecture, and implementing the necessary components. Table of Contents TLDR: Building a ReAct Agent from Scratch with LangChain 2026 Introduction to Building a ReAct Agent from Scratch with LangChain 2026 Prerequisites Core Concepts Step-by-Step Guide Full Example Common Mistakes Incorrect Import Statements Invalid Prompt Templates Common Issues and Solutions Production Tips Agent Configuration Deployment Strategies Monitoring and Logging Frequently Asked Questions General Questions Building a ReAct Agent Training a ReAct Agent Deploying a ReAct Agent Takeaways
📖 Read the Full Tutorial
🔗 How to build a ReAct agent from scratch with LangChain 2026 — Full Guide with Code Examples
The full article includes:
- ✅ Step-by-step code examples (copy-paste ready)
- ✅ Complete working project (Spring Boot / Java)
- ✅ Common mistakes + fixes
- ✅ Production tips and benchmarks
- ✅ FAQ section
Published on How to Start Programming — practical AI and Java tutorials for developers.
Top comments (1)
Building ReAct from scratch instead of importing a prebuilt agent is the best way to actually understand it, because the reason-act-observe loop is deceptively simple on paper and full of sharp edges in practice. Two of them are worth flagging for anyone following along. First, the loop needs hard bounds: a max-iteration cap and a real stop condition, otherwise a confused agent will happily think-act-think-act forever, burning tokens and money chasing a goal it can't reach. Unbounded loops are the number one way these quietly get expensive. Second, the observation step is a trust boundary, the agent treats tool output as ground truth, so if a tool returns an error string or garbage and you feed it back raw, the model reasons confidently off bad input and the whole chain derails. Validating observations before they re-enter the prompt is what separates a robust agent from a flaky demo. Reason and act is the famous part; bound the loop and trust-check the observation is the part that makes it production-safe. That cap-the-loop-and-verify-the-feedback instinct is core to how I think about agents in Moonshift. In your from-scratch version, did you add an iteration limit and any check on tool output, or keep it minimal to show the core loop?