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langchain-dependencies

Configure LangChain 1.0+ dependency selections and version constraints for Python and TypeScript projects.

Updated May 26, 2026
Search Tags:#dependency management#langchain#langgraph#version pinning#deep agents#langsmith#python typescript
One-click install
npx skills add https://github.com/anukkrit149/anukkrit-skills --skill langchain-dependencies-anukkrit149
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langchain-dependencies
Source: https://github.com/anukkrit149/anukkrit-skills/tree/main/cloud/skills/langchain-dependencies
Command: npx skills add https://github.com/anukkrit149/anukkrit-skills --skill langchain-dependencies-anukkrit149

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Prevents broken builds and incompatible runtime behavior caused by incorrect package choices and unsafe version upgrades across the LangChain, LangGraph, LangSmith, and Deep Agents ecosystem.

Core Features & Use Cases

  • Clarifies the correct dependency split between core, orchestration, and provider integrations so you don’t over-install or mix incompatible packages.
  • Provides minimum versions and environment requirements for Python and TypeScript/Node so projects start from a stable baseline.
  • Supports framework selection guidance (LangGraph vs Deep Agents) to keep orchestration consistent with your intended architecture.
  • Addresses common upgrade risks including how to treat langchain-community (non-semver) and recommended “latest within compatible major” handling for other integrations.

Quick Start

Ask an AI using this skill to propose a minimal, provider-agnostic dependency set and a safe versioning/upgrade strategy for your chosen orchestration layer in a new LangChain 1.0+ project.

Frequently Asked Questions about langchain-dependencies

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

FAQPage Schema
How do I fix broken builds caused by incorrect LangChain package versions?▼

Correct LangChain dependency management requires explicitly installing core packages, enforcing LangChain 1.0+ LTS baselines, and selecting one orchestration layer like LangGraph or Deep Agents to avoid mixing incompatible packages.

What is the correct way to split LangChain dependencies between core and provider integrations?▼

The correct LangChain dependency split involves explicitly installing core packages separately from provider integrations. This prevents over-installing and ensures you do not mix incompatible community packages with your base LangChain setup.

Do I need Python 3.10 or Node.js 20 to run LangChain 1.0+ projects?▼

Yes, LangChain 1.0+ projects require Python 3.10+ or Node.js 20+ as minimum runtime environments. Meeting these version requirements ensures your project starts from a stable baseline for both Python and TypeScript setups.

How do I safely upgrade langchain-community packages that do not follow semantic versioning?▼

Safely upgrade non-semver langchain-community packages by pinning their versions conservatively. For other LangChain provider integrations, apply a recommended latest within compatible major strategy to manage upgrade risks effectively.

Should I use LangGraph or Deep Agents for my LangChain orchestration layer?▼

Choosing between LangGraph and Deep Agents depends on your intended architecture. Selecting one consistent orchestration layer for your LangChain project prevents runtime conflicts and keeps your dependency set minimal and aligned.

Why does adding LangSmith tracing break my LangChain runtime environment?▼

LangSmith tracing breaks runtime environments when core packages are not explicitly installed or baseline versions mismatch. Establishing correct LangChain dependency selections and version constraints for tracing plus provider integrations prevents this incompatible behavior.

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