Build a ReAct AI Agent from Scratch with LangGraph and MiniMax-M2.5

### From theory to practice: build a ReAct AI agent with LangGraph using MiniMax-M2.5.

AI agents are becoming a key part of modern AI systems, enabling applications that can reason, plan, and take actions to complete tasks.
In this hands-on session, we’ll show you how to build a ReAct AI agent with LangGraph using SambaNova’s MiniMax-M2.5 model. Starting with the core agent loop (Reason → Act → Observe), we’ll progressively add tool integration, state management, and structured outputs.
You’ll also learn the design patterns that make AI agents reliable, including structured tool execution, error handling, and context management across multi-step tasks.
By the end of the session, you’ll have a working AI agent with a virtual file system and TODO planner, along with a clear understanding of how modern agent frameworks orchestrate agent behavior.

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### What You’ll Build:

During this session, we will build a ReAct AI agent with LangGraph that can:
• Reason through tasks step-by-step
• Use tools to perform actions
• Maintain context across multiple steps
• Manage tasks using a simple TODO planner
The final agent will include a virtual file system tool and a multi-step reasoning loop.

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### What We’ll Cover:

• The ReAct agent loop (Reason → Act → Observe)
• How to build a ReAct AI agent with LangGraph
• Tool integration and command execution
• Managing state and context in agents
• Design patterns for reliable AI agents
• Overview of leading AI agent frameworks (LangGraph, CrewAI, OpenAI Agents SDK, Claude Agents SDK)

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### Who Should Attend:

This webinar is ideal for:
• AI engineers and developers
• Machine learning engineers
• Developers exploring agentic AI
• Anyone interested in building AI agents

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### Technologies Used:

• LangGraph
• MiniMax-M2.5 (SambaNova)
• Python
• ReAct Agent Architecture