langchain-dependencies

Manage LangChain ecosystem dependencies across Python and TypeScript environments.

Updated Mar 8, 2026

Search Tags:#typescript #package management #python #dependencies #langchain #langgraph #langsmith

Ready to run

npx skills add https://github.com/brivaro/brivaro-ai-wizard --skill langchain-dependencies-brivaro

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: langchain-dependencies
Source: https://github.com/brivaro/brivaro-ai-wizard/tree/main/skills/langchain-dependencies
Command: npx skills add https://github.com/brivaro/brivaro-ai-wizard --skill langchain-dependencies-brivaro

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps developers manage the complex and often confusing dependency landscape of the LangChain ecosystem, ensuring compatibility and correct installation for projects.

Core Features & Use Cases

Dependency Management: Provides clear guidance on required packages, minimum versions, and best practices for LangChain, LangGraph, LangSmith, and Deep Agents.
Environment Setup: Details runtime requirements for both Python and TypeScript/Node.js environments.
Framework Choice: Clarifies the distinction and usage between LangGraph and Deep Agents.
Package Selection: Lists essential core packages, orchestration layers, model providers, and common tool/retrieval integrations.
Use Case: When starting a new AI project using LangChain, use this Skill to determine the exact packages and versions needed for your Python environment, including specific model providers and vector stores.

Quick Start

Use the langchain-dependencies skill to list the required Python packages for a LangGraph project with OpenAI integration.

Frequently Asked Questions about langchain-dependencies

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

FAQPage Schema

What LangChain packages do I need to install for a new Python project?▼

Required LangChain packages for Python include core libraries, orchestration layers like LangGraph, model providers, and specific tool integrations. Exact packages depend on your chosen integrations, such as vector stores and retrieval tools.

How do I manage LangGraph dependencies in TypeScript?▼

Managing LangGraph dependencies in TypeScript requires installing specific Node.js packages for orchestration and model providers. This skill outlines the runtime requirements and best practices for TypeScript environments.

What is the difference between LangGraph and Deep Agents for orchestration?▼

LangGraph and Deep Agents serve different orchestration roles within the LangChain ecosystem. This skill clarifies their distinctions and provides guidance on choosing the appropriate framework for your project.

Why does my LangChain environment setup fail after upgrading packages?▼

LangChain environment setup failures after upgrades often stem from package version conflicts or missing environment variables. This skill details versioning best practices and common pitfalls to avoid during setup.

Do I need separate packages for LangSmith integration?▼

Yes, LangSmith integration requires specific packages and environment variables to function correctly. This skill lists the necessary dependencies and configuration settings for seamless LangSmith setup.

What are common pitfalls when configuring LangChain model providers?▼

Common pitfalls when configuring LangChain model providers include missing environment variables and incorrect package versions. This skill provides versioning best practices and setup guidance to prevent these issues.