langchain-fundamentals

Name: langchain-fundamentals
Availability: InStock
Author: kapilkumar88

Construct LangChain agents with middleware, tools, and human-in-the-loop feedback.

Updated Apr 17, 2026

Search Tags:#tool integration #middleware #langchain #agent creation #structured output

Ready to run

npx skills add https://github.com/KapilKumar88/ai-workspace-platform --skill langchain-fundamentals-kapilkumar88

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: langchain-fundamentals
Source: https://github.com/KapilKumar88/ai-workspace-platform/tree/main/.agents/skills/langchain-fundamentals
Command: npx skills add https://github.com/KapilKumar88/ai-workspace-platform --skill langchain-fundamentals-kapilkumar88

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill unit solves the problem of building and deploying complex LangChain agents, providing tools for human-in-the-loop interactions and error handling.

Core Features & Use Cases

LangChain Agent Creation: Guides users on how to create LangChain agents using the create_agent() function, defining tools, and integrating middleware.
Middleware Usage: Illustrates the implementation of middleware for control flow and approval processes.
Structured Output: Shows how to retrieve and use structured output from agents.
Model Configuration: Demonstrates the flexibility of the create_agent() function by accepting both model strings and instances.

Quick Start

Create an agent using the create_agent() function and include tools like 'search' and 'calculator'.

Frequently Asked Questions about langchain-fundamentals

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I create a LangChain agent with custom tools?▼

To create a LangChain agent with custom tools, use the `create_agent()` function to define your agent and integrate tool definitions like 'search' and 'calculator' for processing complex queries.

What is middleware used for in LangChain agents?▼

Middleware in LangChain agents is used for managing control flow and approval processes, enabling human-in-the-loop integration and feedback mechanisms during complex query processing.

How do I get structured output from a LangChain agent?▼

You can retrieve structured output from a LangChain agent by configuring the agent creation process to enforce specific response formats, allowing your application to reliably parse the agent's final result.

Can I pass a model string instead of an instance to create an agent?▼

Yes, the `create_agent()` function accepts both model strings and model instances, providing flexibility in how you configure the underlying language model for your agent.

How do I manage agent state and handle errors in LangChain?▼

You can manage agent state and handle errors in LangChain by implementing middleware to control execution flow, ensuring robust feedback mechanisms and human-in-the-loop error correction.