Build intelligent AI agents using LangChain v1, OpenClaw integration, tool calling, middleware, and deep agents.
What you'll learn
✓ Understand the fundamentals of LangChain v1 and how it is used to build AI-powered applications and intelligent agents.
✓ Build and run AI agents using LangChain, including tool calling, structured outputs, and agent workflows.
✓ Integrate Large Language Models (LLMs) such as OpenAI or Gemini into LangChain for different AI tasks.
✓ Create custom tools and enable tool calling so agents can interact with APIs, databases, and external systems.
✓ Use middleware features such as summarization and human-in-the-loop to control and enhance agent behavior.
✓ Build Deep Agents with the DeepAgents framework capable of handling complex, multi-step tasks.
✓ Automate real-world workflows using OpenClaw and connect agents with messaging platforms like WhatsApp.
Requirements
● Basic Python knowledge is required.
● Basic familiarity with Large Language Models (LLMs) such as OpenAI or Google Gemini models is recommended.
Description
AI agents are transforming how applications interact with data, tools, and users. In this course, you will learn how to build powerful AI agents using LangChain v1.
We start with the core concepts of LangChain, where you will learn how to work with LLMs, configure model parameters, and run models using different invocation methods such as invoke, stream, and batch. You will then explore structured outputs, allowing models to return responses in well-defined formats using Pydantic, TypedDict, and JSON schemas.
Next, you will dive into tool calling, one of the most powerful capabilities of LangChain. You will learn how to connect AI agents with external tools, APIs, and custom functions, enabling agents to perform real-world tasks.
The course also covers middleware in LangChain, which allows you to control and enhance agent behavior without modifying the core logic. You will learn how to implement features such as summarization middleware, human-in-the-loop approvals, model fallback, and tool call limits to build reliable and production-ready agents.
You will then explore Deep Agents, a powerful framework built on top of LangChain that enables agents to handle complex multi-step tasks, task planning, file systems for context management, subagents, and long-term memory.
Finally, you will learn how to integrate OpenClaw with AI agents, enabling automation and communication workflows through external platforms such as messaging systems.
By the end of this course, you will have the skills to design, build, and deploy intelligent AI agents capable of solving complex tasks and automating real-world workflows.
What You'll Learn
• Work with LLMs in LangChain v1
• Configure model parameters and invocation methods
• Generate structured outputs using Pydantic, TypedDict, and JSON Schema
• Implement tool calling for APIs, databases, and custom functions
• Use middleware to control and enhance agent behavior
• Implement human-in-the-loop approval workflows
• Build Deep Agents capable of planning and complex task execution
• Integrate OpenClaw for AI-powered automation
Who this course is for
■ Software Engineers that want to learn how to build Generative AI based applications with LangChain.
■ AI/ML Engineers and Developers who want to build advanced AI agent workflows and autonomous applications.