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name: langchain description: Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments. version: 1.0.0 author: Orchestra Research license: MIT tags: [Agents, LangChain, RAG, Tool Calling, ReAct, Memory Management, Vector Stores, LLM Applications, Chatbots, Production] dependencies: [langchain, langchain-core, langchain-openai, langchain-anthropic]

LangChain - Build LLM Applications with Agents & RAG

The most popular framework for building LLM-powered applications.

When to use LangChain

Use LangChain when:

• Building agents with tool calling and reasoning (ReAct pattern)
• Implementing RAG (retrieval-augmented generation) pipelines
• Need to swap LLM providers easily (OpenAI, Anthropic, Google)
• Creating chatbots with conversation memory
• Rapid prototyping of LLM applications
• Production deployments with LangSmith observability

Metrics:

• 119,000+ GitHub stars
• 272,000+ repositories use LangChain
• 500+ integrations (models, vector stores, tools)
• 3,800+ contributors

Use alternatives instead:

• LlamaIndex: RAG-focused, better for document Q&A
• LangGraph: Complex stateful workflows, more control
• Haystack: Production search pipelines
• Semantic Kernel: Microsoft ecosystem

Quick start

Installation

# Core library (Python 3.10+)
pip install -U langchain

# With OpenAI
pip install langchain-openai

# With Anthropic
pip install langchain-anthropic

# Common extras
pip install langchain-community  # 500+ integrations
pip install langchain-chroma     # Vector store

Basic LLM usage

from langchain_anthropic import ChatAnthropic

# Initialize model
llm = Cha