LangForge is your comprehensive resource for mastering the LangChain ecosystem - the industry-leading framework for building intelligent, production-ready applications with Large Language Models (LLMs). Whether you're a beginner exploring AI possibilities or an experienced developer building enterprise solutions, our documentation provides everything you need to succeed.
- 🔥 Up-to-Date: Always current with the latest LangChain releases and best practices
- 📈 Production-Focused: Real-world examples and patterns used by top companies
- 🚀 Quick Start: Get from zero to production in minutes, not hours
- 🔧 Hands-On: Interactive examples with both Python and TypeScript
- 🏆 Expert-Curated: Written by industry professionals and community contributors
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The Foundation Framework Build sophisticated LLM applications with chains, agents, and memory systems |
Debug & Monitor Trace, evaluate, and optimize your LLM applications in production |
🕸️ LangGraphStateful Workflows Create complex, multi-agent systems with persistent state |
Deploy APIs Transform chains into production-ready REST APIs instantly |
# Install: pip install langchain openai
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
# 1. Initialize your LLM
llm = OpenAI(temperature=0.7)
# 2. Create a dynamic prompt
template = """You are an AI assistant for {company_type} companies.
Generate a creative business idea for: {industry}
Focus on: {focus_area}"""
prompt = PromptTemplate(
input_variables=["company_type", "industry", "focus_area"],
template=template
)
# 3. Create and run the chain
chain = LLMChain(llm=llm, prompt=prompt)
result = chain.run({
"company_type": "sustainable tech",
"industry": "renewable energy",
"focus_area": "AI optimization"
})
print(f"💡 Business Idea: {result}")// Install: npm install langchain @langchain/openai
import { OpenAI } from "@langchain/openai";
import { PromptTemplate } from "@langchain/core/prompts";
import { LLMChain } from "langchain/chains";
const llm = new OpenAI({ temperature: 0.7 });
const prompt = PromptTemplate.fromTemplate(`
Generate a {style} marketing slogan for {product}
Target audience: {audience}
`);
const chain = new LLMChain({ llm, prompt });
const result = await chain.call({
style: "catchy",
product: "AI-powered fitness app",
audience: "busy professionals"
});
console.log(`🎯 Marketing Slogan: ${result.text}`);- Getting Started Guide - Your first LLM application
- Core Concepts - Understanding the fundamentals
- Basic Examples - Simple, working code samples
- Advanced Patterns - Production-ready architectures
- Best Practices - Industry-tested approaches
- API References - Complete function documentation
- Production Deployment - Scaling to millions of users
- Monitoring & Analytics - Track performance and costs
- Multi-Agent Systems - Complex workflow orchestration
🤖 Customer Support Automation
Build intelligent chatbots that understand context, access knowledge bases, and escalate to humans when needed.
📊 Document Analysis & QA
Process PDFs, contracts, and documents with LLMs for intelligent question-answering and summarization.
🔍 Semantic Search
Build vector databases and semantic search systems for finding relevant information from large datasets.
🧠 AI Agents & Workflows
Create autonomous agents that can use tools, make decisions, and complete complex multi-step tasks.
| Feature | Description | Benefit |
|---|---|---|
| 🔄 Live Examples | All code samples are tested and working | Copy-paste and run immediately |
| 🌐 Multi-Language | Python and TypeScript examples | Use your preferred language |
| 📱 Mobile-Friendly | Responsive documentation design | Read anywhere, anytime |
| 🔍 Advanced Search | Find exactly what you need quickly | Save time and boost productivity |
| 📊 Visual Diagrams | Complex concepts explained visually | Understand architectures faster |
| 🚀 Performance Tips | Optimization strategies included | Build faster, more efficient apps |
- Python 3.8+ or Node.js 16+
- OpenAI API key (Get one here)
# Install core packages
pip install langchain langsmith langgraph langserve
# Install LLM providers
pip install openai anthropic cohere
# Set your API key
export OPENAI_API_KEY='your-api-key-here'# Install core packages
npm install langchain @langchain/openai @langchain/anthropic
# Create environment file
echo "OPENAI_API_KEY=your-api-key-here" > .env📖 Detailed Installation Guide →
- 💡 Questions? Open a discussion
- 🐛 Found a bug? Report it here
- 🤝 Want to contribute? Read our guide
- LangChain Framework - Complete guide to building LLM applications
- LangSmith Platform - Debugging, monitoring, and evaluation tools
- LangGraph Library - Stateful, multi-actor application workflows
- LangServe Deployment - Turn chains into production APIs
- Getting Started - From zero to your first app
- Examples & Tutorials - Working code for common use cases
- Best Practices - Industry-proven patterns and approaches
- Advanced Topics - Complex architectures and optimization
- Component Overview - All available components at a glance
- API Reference - Function signatures and parameters
- Troubleshooting - Common issues and solutions
- Migration Guides - Upgrading between versions
"LangForge documentation helped us build a customer support bot that reduced response time by 80% and improved satisfaction scores."
— Sarah Chen, CTO at TechStartup Inc.
"The examples are incredibly practical. We went from prototype to production in just 2 weeks."
— Marcus Rodriguez, Lead Developer at Enterprise Corp.
"Finally, documentation that actually helps you build real applications, not just toy examples."
— Dr. Priya Patel, AI Research Lead
- 🏢 Enterprise-Ready: Used by startups to Fortune 500 companies
- 🌟 Community-Driven: 1000+ developers contributing and improving
- 🔄 Always Updated: Stays current with rapid AI ecosystem changes
- 🎯 Practical Focus: Real solutions for real problems
- 📈 Proven Results: Thousands of successful applications built
This project is licensed under the MIT License - see the LICENSE file for details.
Want to contribute? We'd love your help! Check out our Contributing Guidelines to get started.
🎯 Start Building Now → • 📚 Browse Examples → • 🔧 Advanced Patterns →
Crafted with ❤️ by Muhammad Mazhar Saeed (Professor) and the LangForge Community
Empowering developers to build intelligent applications that matter