Features
- Chains for composing multi-step LLM workflows
- RAG support with document loaders and vector stores
- Agent framework with tool integration
- 100+ integrations with LLMs, databases, and tools
Pros
- Most comprehensive LLM framework with widest integrations
- Excellent for RAG and document processing pipelines
- Large community with extensive examples
Cons
- Over-abstracted for simple use cases
- API changes frequently between versions
- Performance overhead from abstraction layers
Overview
LangChain is a framework for developing applications powered by large language models. It provides abstractions for common LLM patterns: chaining multiple prompts together, augmenting LLMs with external knowledge (RAG), building agents that use tools, and managing conversation memory.
The framework offers a vast integration ecosystem with 100+ LLM providers, vector stores, document loaders, and tools. This makes it particularly strong for building RAG (Retrieval-Augmented Generation) applications that need to process documents, store embeddings, and retrieve relevant context for LLM queries.
LangChain is available for both Python and JavaScript/TypeScript, with the Python version being more mature and feature-complete.
When to Use
Choose LangChain for RAG applications, complex multi-step LLM pipelines, and projects that need extensive third-party integrations. For simpler chat applications or when using a single provider, the Vercel AI SDK or direct provider SDKs may be more appropriate.
Getting Started
npm install langchain @langchain/anthropicimport { ChatAnthropic } from '@langchain/anthropic'
const model = new ChatAnthropic({
modelName: 'claude-sonnet-4-5-20250929'
})
const response = await model.invoke('What is RAG?')