This is an info Alert.
Full logo
  • Home
  • Blog
  • Agentic Frameworks
  • Use Cases
LangChain

LangChain

by LangChain Inc
RAG & Knowledge
Intermediate
MIT
Framework for building context-aware LLM applications
Visit WebsiteDocumentationGitHub

Overview

LangChain is a comprehensive framework for developing applications powered by large language models. It simplifies every stage of the LLM application lifecycle through development, productionization, and deployment.

Key Statistics

Overall Rating

4.6/5

GitHub Stars

117,000

Last Updated

2025-10

Version

0.3.13

Features

Sequential workflows

Sequential workflows capabilities

Component chaining

Component chaining capabilities

RAG pipelines

RAG pipelines capabilities

Getting Started

Installation
pip install langchain
Quick Start

Install LangChain and set API keys to start building

Code Example
from langchain_openai import ChatOpenAI

Pros & Cons

Advantages

Largest ecosystem of integrations (700+) in LLM space

Well-established with strong community support (2000+ contributors)

Excellent documentation and learning resources

MIT license allows commercial use

Strong backing and funding from Sequoia and Benchmark

Production-ready with LangSmith observability

Easy to get started with high-level API

Model agnostic - swap providers easily

Limitations

Linear chain-based architecture may be limiting for complex workflows

Can be overkill for simple applications

Learning curve for understanding the full ecosystem

Some features require understanding of LangGraph for advanced use

Abstractions may add overhead

Rapid evolution means documentation can lag behind releases

Technical Details
Primary Language

Python

Supported Languages
Python
TypeScript
License

MIT

Enterprise Ready

Yes

Community Size

Very Large

Pricing
Open Source

Free open source under MIT. Commercial: LangSmith (observability) and LangGraph Platform

Performance Metrics

easeOfUse

4/5

scalability

5/5

documentation

5/5

community

5/5

performance

4/5

Common Use Cases

Chatbots and conversational AI

Question-answering systems over documents

Retrieval-Augmented Generation (RAG) applications

Document analysis and summarization

Code generation and analysis

Internal knowledge bases and support bots

Content generation workflows

API integration and data augmentation

Back to Framework Overview
Full logo

10X your AI agents' Impact by letting the AI Agents get the right context!

Let’s stay in touch
Ubscribe to our newsletter to receive latest articles to your inbox weekly.
  • Use Cases
    • Healthcare
    • n8n
  • About
    • About
    • Contact

© All rights reserved.Help center
Terms of service