Fill in the npm and pip package names for LangChain's OpenAI provider integration

Fill in blank·Easy·4.0 · 0·~1 min·Asked atMicrosoft Cursor Perplexity

Attempt it

Modern LangChain splits provider integrations into separate packages under a per-vendor namespace. The OpenAI integration ships as on npm (TypeScript) and on pip (Python); installing it gives you the `ChatOpenAI` class without pulling every other provider's SDK as a transitive dependency.

Quick reference

TL;DR

LangChain ships provider integrations as separate packages: `@langchain/openai` on npm, `langchain-openai` on pip; one prefix per ecosystem, the vendor name is the suffix.

Memory aid

Easy to grasp

Think of the way a phone comes with a charging cable in the box but the cable for the next phone you buy lives in a different box. Old LangChain shipped every vendor's cable inside one giant box; you got a working OpenAI cable and 30 cables you would never plug in, plus the weight of all of them. New LangChain ships the core box (the phone) and you grab only the cables you actually need. The npm world labels its cable boxes with an organisation prefix and a slash; the pip world labels its boxes with a hyphen. Same idea, two stickers.

Key concepts

LLM frameworks

Detailed answer & concept explanation

~6 min read

Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example. Click to expand.

Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example.

Quick scan · 2–4 min

Everything important, quickly.

4 min: the three-layer split + per-provider package shape + npm vs pip conventions + version-skew traps + JS/Python parity + langchain-community tier.

Real-world examples

Real products, models, and research that use this idea.

Production Next.js apps using `@langchain/openai` typically also install `@langchain/core` directly to pin the Runnable protocol version.
Python serverless deploys on AWS Lambda or Cloud Run use `langchain-openai` instead of the umbrella `langchain` to keep cold-start install size under the runtime limits.
The LangChain v1 docs in 2026 use the per-provider package names exclusively in code samples; the umbrella package is mentioned only for migration.
OpenInference and Langfuse integration guides assume the per-provider package shape; their callback handlers attach to `ChatOpenAI` imported from `@langchain/openai`.
The langchain-community / @langchain/community packages are explicitly second-tier and intended for integrations that are not maintained by the core team.

Interviewer follow-ups

What an interviewer would ask next. Try answering before peeking at the approach.

QWhat is in `langchain-core` versus `langchain` versus `langchain-openai`?

QHow do you handle version skew across `langchain-core` and a provider package in CI?

QWhere does `langchain-community` fit and when should you avoid it?

Don't say this

Red flags and common mistakes that signal junior thinking. Click to expand.

Most common mistake

Installing the umbrella `langchain` package and importing `ChatOpenAI` from it. That works through a re-export but pulls extra weight; modern code imports from the provider-specific package directly.

60-second cheat sheet

The night-before-the-interview bullets. Scan these on the way to the call.

References

Primary sources. Skim if you want the original framing.