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langchain-fundamentals

Builds LangChain agents with create_agent, custom tools, middleware approvals, and structured outputs in Python/TypeScript.

1|Updated Apr 25, 2026
Search Tags:#middleware#langchain#tool calling#agents#human-in-the-loop#structured output#state persistence
One-click install
npx skills add https://github.com/collat-labs/collat --skill langchain-fundamentals-collat-labs
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langchain-fundamentals
Source: https://github.com/collat-labs/collat/tree/main/.claude/skills/langchain-fundamentals%20copy
Command: npx skills add https://github.com/collat-labs/collat --skill langchain-fundamentals-collat-labs

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

LangChain users struggle to build reliable, production-ready agents that correctly decide when to call tools, persist state, and safely handle human approval and error-prone flows.

Core Features & Use Cases

  • Production-ready agent construction with create_agent(): Use the recommended agent loop that handles tool execution and state management instead of outdated alternatives.
  • Tool definition for agent calling: Define tools via the @tool decorator (Python) or tool() with clear schemas and descriptions so the model knows when to use them.
  • Middleware-driven control for HITL and resilience: Apply middleware patterns for human-in-the-loop approvals and custom hook logic, including interrupt/resume workflows.
  • Structured outputs for validated responses: Produce typed, schema-validated outputs using response_format or with_structured_output.
  • Use cases: Create assistants that perform web/tool actions, require human approval for risky tool calls, remember user context across sessions, and return validated JSON-like structures for downstream systems.

Quick Start

Instruct the AI to help you implement a LangChain agent using create_agent with your tools, add HITL middleware, and return structured output validated by a schema.

Frequently Asked Questions about langchain-fundamentals

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

FAQPage Schema
How do I build a LangChain agent that reliably calls tools in production?▼

Build LangChain agents using the create_agent API to handle tool execution and state management. This replaces outdated alternatives by providing a reliable agent loop that correctly decides when to call defined tools.

How do I add human-in-the-loop approvals for risky tool calls in LangChain?▼

Add human-in-the-loop approvals in LangChain by applying middleware patterns to your agent. Middleware enables custom hook logic, allowing you to interrupt and resume workflows for human approval before executing risky tools.

How do I get structured, schema-validated outputs from a LangChain agent?▼

Get structured outputs from LangChain agents using response_format or with_structured_output. This produces typed, schema-validated JSON-like structures for downstream systems to consume reliably.

Does LangChain support state persistence across multiple user sessions?▼

LangChain supports state persistence across user sessions by optionally adding checkpointers to your agent. This allows the agent to remember user context and maintain state over time.

What is the best way to define tools for a LangChain agent to call?▼

Define tools for LangChain agents using the @tool decorator in Python or tool() in TypeScript. Provide clear schemas and descriptions so the model knows exactly when and how to use them.

Can I use LangChain agents for workflow automation in both Python and TypeScript?▼

LangChain agents support workflow automation across both Python and TypeScript. The create_agent construction API and tool definitions are designed for cross-language chatbot and automation development.

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