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TimMoyence
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langchain-fundamentals

Build LangChain agents with create_agent, tool definitions, and middleware.

Updated Oct 27, 2024
Search Tags:#automation#typescript#python#middleware#langchain#agents#tools
One-click install
npx skills add https://github.com/TimMoyence/Innov-mind-museum --skill langchain-fundamentals-timmoyence
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langchain-fundamentals
Source: https://github.com/TimMoyence/Innov-mind-museum/tree/main/.claude/skills/langchain-fundamentals
Command: npx skills add https://github.com/TimMoyence/Innov-mind-museum --skill langchain-fundamentals-timmoyence

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Create robust LangChain agents quickly by using create_agent(), defining tools with the @tool decorator or tool() function, and applying middleware for human-in-the-loop and error handling to streamline production-grade AI workflows.

Core Features & Use Cases

  • Agent creation with create_agent: orchestrates the agent loop, tool invocations, and state management for reliable operation.
  • Tool definition: expose reusable capabilities using the Python @tool decorator or TypeScript tool() to enable modular, testable actions.
  • Middleware patterns: employ HumanInTheLoopMiddleware and wrap_tool_call hooks to enable approvals, logging, and error resilience in real-time tasks.
  • Persistence and memory: optionally attach a checkpointer to retain conversation or session state across invocations.
  • Use Case: build an assistant that can perform web searches, calculations, and data retrieval while enforcing human oversight when required.

Quick Start

Create an agent using create_agent() with a simple tool, and enable HumanInTheLoopMiddleware for approval workflows.

Frequently Asked Questions about langchain-fundamentals

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

FAQPage Schema
How do I build production LangChain agents with middleware for human-in-the-loop?▼

Build production LangChain agents using create_agent to orchestrate tool invocations, and apply HumanInTheLoopMiddleware to enforce real-time approval workflows and error handling.

What is the best way to define tools for LangChain agents in Python and TypeScript?▼

Define reusable tools for LangChain agents using the @tool decorator in Python or the tool() function in TypeScript, enabling modular and testable actions within the agent loop.

How do I persist conversation state across LangChain agent sessions?▼

Persist conversation state across LangChain agent sessions by optionally attaching a checkpointer to create_agent, which retains memory and session state for controlled workflows.

Does LangChain middleware support error handling and logging for agent tools?▼

LangChain middleware supports error handling and logging by employing wrap_tool_call hooks, which capture tool invocations to provide error resilience and real-time task monitoring.

Can I orchestrate web searches and calculations in a LangChain agent with human oversight?▼

Orchestrate web searches, calculations, and data retrieval in a LangChain agent by defining tools and enabling HumanInTheLoopMiddleware to enforce human oversight when required.

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