langchain-fundamentals

Name: langchain-fundamentals
Availability: InStock
Author: reofdev07

Orchestrate LangChain agents with create_agent(), tools, and middleware patterns.

Updated Jul 13, 2025

Search Tags:#middleware #langchain #agents #memory #tools #human-in-the-loop #create_agent

Ready to run

npx skills add https://github.com/Reofdev07/osai --skill langchain-fundamentals-reofdev07

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: langchain-fundamentals
Source: https://github.com/Reofdev07/osai/tree/main/.windsurf/skills/langchain-fundamentals
Command: npx skills add https://github.com/Reofdev07/osai --skill langchain-fundamentals-reofdev07

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Build production-ready LangChain agents by standardizing agent creation via create_agent(), tool wiring with @tool decorator or tool(), and middleware for human-in-the-loop and error handling.

Core Features & Use Cases

Agent orchestration with create_agent() handling the loop, state, and tool execution.
Tool definition via Python decorators or TypeScript tool() with clear schemas.
Middleware patterns for approval workflows, error handling, and logging, including memory persistence.

Quick Start

Create an agent with create_agent(), wire a tool, and apply middleware for a human-in-the-loop workflow.

Frequently Asked Questions about langchain-fundamentals

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

FAQPage Schema

How do I add human-in-the-loop approval to LangChain agents?▼

You can implement human-in-the-loop approval by applying middleware patterns to create_agent(), which intercepts the agent loop to pause tool execution and request manual approval before proceeding.

What's the best way to wire tools into a LangChain agent?▼

Wire tools into a LangChain agent by defining them with the @tool decorator in Python or the tool() function in TypeScript, passing these schema-defined tools directly to create_agent().

How does middleware handle errors and persistent state in LangChain?▼

Middleware handles errors and persistent state by wrapping the create_agent() loop, enabling logging, error recovery, and memory persistence to maintain context across multiple agent interactions.

Do I need LangChain installed to use create_agent for production workflows?▼

Yes, LangChain is required to use create_agent(). The framework provides the core orchestration loop, state management, and tool execution capabilities required to build and run production-grade agent workflows.

Can I use create_agent with custom Python decorators for tool schemas?▼

Yes, create_agent works with custom Python decorators. You can use the @tool decorator to define custom tools with clear input schemas, which are then automatically wired into the agent's execution loop.