Getting Started with LangChain

LangChain in Agentic WorkFlow makes building AI applications as simple as connecting visual nodes. Instead of writing complex code, you drag and drop components to create sophisticated AI workflows that can think, reason, and act.

Think of it like building with smart LEGO blocks - each piece has a specific AI capability, and you connect them to create powerful automation.

Your first LangChain workflow

Let’s build a simple content analyzer that reads web pages and extracts key insights:

1. Add content source: Drag a “GetAllTextFromLink” node to extract text from any webpage

2. Add AI analysis: Connect a “Basic LLM Chain” node to analyze the content

3. Configure the prompt: Tell the AI what kind of analysis you want

4. Run and see results: Execute the workflow and get intelligent insights

graph LR
    URL[Web Page URL] --> Extract[GetAllTextFromLink]
    Extract --> AI[Basic LLM Chain]
    AI --> Results[Key Insights]
    
    style Extract fill:#e1f5fe
    style AI fill:#6d28d9,stroke:#fff,color:#fff
    style Results fill:#e8f5e8

Core LangChain concepts

Chains

What they are: Sequences of AI operations that process information step by step

Think of it like: An assembly line where each station adds something to the product

Example:

1. Extract text from webpage
2. Summarize the content
3. Extract key topics
4. Generate action items

Best for: Predictable, multi-step AI processing tasks

Agents

What they are: AI that can think, plan, and choose which tools to use

Think of it like: A smart assistant who figures out the best way to help you

Example:

• Goal: “Research competitor pricing”
• Agent decides: Visit websites → Extract prices → Compare data → Generate report

Best for: Complex tasks where the exact steps aren’t known in advance

Memory

What it is: Gives AI the ability to remember previous conversations and context

Think of it like: A notebook that AI uses to keep track of what happened before

Example:

• User: “Analyze this product page”
• AI: “I see it’s a smartphone with these features…”
• User: “How does it compare to the iPhone?”
• AI: “Compared to the smartphone we just analyzed…” (remembers context)

Best for: Conversational AI and workflows that build on previous interactions

Building blocks you’ll use

Language Models (LLMs)

The “brain” of your AI workflows:

Model Type	Best For	Example Use
OpenAI GPT	General intelligence, creative tasks	Content writing, analysis, conversation
Anthropic Claude	Careful reasoning, safety-focused	Research, fact-checking, sensitive content
Local Models (Ollama)	Privacy, offline use	Personal data processing, no internet required

Tools and Utilities

Extend what your AI can do:

Tool Category	Purpose	Examples
Web Tools	Internet access and search	Wikipedia lookup, web search, API calls
Data Tools	Process and transform information	Calculate numbers, format data, parse files
Browser Tools	Interact with web pages	Extract content, fill forms, navigate sites

Vector Stores

Smart document storage that understands meaning:

Traditional search: Finds exact word matches Vector search: Understands what you mean, even with different words

Example:

• Search: “customer complaints”
• Finds: “user feedback”, “service issues”, “client concerns”

Common workflow patterns

Content Analysis Pipeline

Perfect for analyzing articles, reviews, or documents:

graph TD
    Input[Document/URL] --> Extract[Extract Content]
    Extract --> Analyze[AI Analysis]
    Analyze --> Structure[Format Results]
    Structure --> Output[Structured Insights]
    
    style Analyze fill:#6d28d9,stroke:#fff,color:#fff

Components used:

• GetAllTextFromLink (content extraction)
• Basic LLM Chain (analysis)
• EditFields (formatting)

Smart Q&A System

Build AI that answers questions about your documents:

graph TD
    Docs[Your Documents] --> Embed[Create Embeddings]
    Embed --> Store[Vector Store]
    Question[User Question] --> Search[Search Documents]
    Store --> Search
    Search --> Answer[AI Answer + Sources]
    
    style Store fill:#6d28d9,stroke:#fff,color:#fff
    style Answer fill:#e8f5e8

Components used:

• Document loaders (prepare content)
• Embeddings (understand meaning)
• Vector stores (smart storage)
• RAG Node (question answering)

Intelligent Agent Workflow

AI that can use multiple tools to accomplish goals:

graph TD
    Goal[User Goal] --> Agent[Tools Agent]
    Agent --> Tool1[Web Search]
    Agent --> Tool2[Content Extract]
    Agent --> Tool3[Data Analysis]
    Tool1 --> Result[Final Report]
    Tool2 --> Result
    Tool3 --> Result
    
    style Agent fill:#6d28d9,stroke:#fff,color:#fff

Components used:

• Tools Agent (intelligent coordinator)
• Multiple tools (web search, extraction, analysis)
• Memory (maintain context)

Start simple, then expand

Begin with a basic chain (like content analysis), then gradually add memory, tools, and agent capabilities as you become more comfortable with the concepts.

Practical examples

Blog Post Analyzer

Goal: Automatically analyze blog posts for key insights

Workflow:

1. Input: Blog post URL
2. Extract: GetAllTextFromLink node
3. Analyze: Basic LLM Chain with prompt: “Extract the main topic, key points, and target audience”
4. Output: Structured analysis

Customer Support Assistant

Goal: Answer customer questions using company documentation

Workflow:

1. Prepare: Load company docs into vector store
2. Question: Customer asks about policies
3. Search: RAG Node finds relevant documentation
4. Answer: AI provides accurate response with sources

Research Automation

Goal: Gather competitive intelligence automatically

Workflow:

1. Plan: Tools Agent receives research goal
2. Execute: Agent chooses tools (web search, content extraction, analysis)
3. Adapt: Agent adjusts approach based on what it finds
4. Report: Comprehensive research summary

Next steps

Learn the components: Explore the Components Guide to understand all available LangChain nodes

Try workflow patterns: Check out Workflow Patterns for proven approaches to common tasks

Browser integration: Learn how to combine AI with web automation in Browser Integration

Advanced techniques: Master complex workflows with Advanced Patterns

LangChain in Agentic WorkFlow makes sophisticated AI accessible to everyone - no coding required, just smart workflow design.