LangChain Workflow Patterns

Workflow patterns are proven approaches to common AI tasks. Instead of starting from scratch, you can use these battle-tested patterns as templates and adapt them to your specific needs.

Think of patterns like recipes - they give you the basic structure and ingredients, but you can adjust them to taste.

Content Processing Patterns

Simple Analysis Chain

Purpose: Analyze any content with AI and get structured results

graph LR
    Input[Content Input] --> Extract[Extract/Load]
    Extract --> Analyze[AI Analysis]
    Analyze --> Format[Format Results]
    Format --> Output[Structured Output]
    
    style Analyze fill:#6d28d9,stroke:#fff,color:#fff

Components used:

• Content source (GetAllTextFromLink, Document Loader)
• Basic LLM Chain (analysis)
• EditFields (formatting)

Perfect for:

• Blog post analysis
• Product review summarization
• Document classification
• Content quality assessment

Blog Analysis

Goal: Extract key insights from blog posts

Workflow:

1. GetAllTextFromLink → extract blog content
2. Basic LLM Chain → analyze with prompt: “Extract main topic, key points, target audience, and writing style”
3. EditFields → structure results into categories

Output: Structured analysis with topic, insights, and recommendations

Review Summarization

Goal: Summarize product reviews into actionable insights

Workflow:

1. Multiple GetAllTextFromLink → collect reviews from different sources
2. Merge → combine all review content
3. Basic LLM Chain → summarize with prompt: “Identify common themes, pros/cons, and overall sentiment”
4. EditFields → format into pros, cons, and recommendation score

Output: Comprehensive review summary with ratings

Multi-Step Processing Pipeline

Purpose: Complex content processing that requires multiple AI operations

graph TD
    Input[Raw Content] --> Step1[Extract Key Info]
    Step1 --> Step2[Analyze Sentiment]
    Step2 --> Step3[Generate Summary]
    Step3 --> Step4[Create Action Items]
    Step4 --> Output[Complete Analysis]
    
    style Step1 fill:#e1f5fe
    style Step2 fill:#e8f5e8
    style Step3 fill:#fff3e0
    style Step4 fill:#f3e5f5

Real-world example: Customer Feedback Processor

1. Extract: GetAllTextFromLink → collect feedback from multiple sources
2. Categorize: Basic LLM Chain → classify feedback types (bug, feature request, complaint)
3. Analyze: Sentiment Analysis Chain → determine emotional tone
4. Prioritize: Basic LLM Chain → assign urgency scores
5. Summarize: Basic LLM Chain → create executive summary
6. Format: EditFields → structure for reporting

Knowledge-Based Patterns

Smart Q&A System

Purpose: Answer questions accurately using your documents

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

Components used:

• Document Loader (prepare content)
• Text Splitter (chunk documents)
• Embeddings (create searchable vectors)
• Vector Store (smart storage)
• RAG Node (question answering)

Setup process:

1. Prepare documents: Load your knowledge base using Document Loader

2. Chunk content: Use Recursive Character Text Splitter to break into searchable pieces

3. Create embeddings: Convert chunks to vectors using OpenAI or Ollama Embeddings

4. Store vectors: Save in Local Knowledge or cloud vector store

5. Set up Q&A: Connect RAG Node to search and answer questions

Perfect for:

• Company knowledge bases
• Technical documentation search
• Customer support automation
• Research assistance

Contextual Knowledge Assistant

Purpose: AI that builds knowledge over time and provides increasingly relevant answers

graph LR
    Question[User Question] --> Memory[Check Memory]
    Memory --> Search[Search Knowledge]
    Search --> Combine[Combine Context]
    Combine --> Answer[Contextual Answer]
    Answer --> Update[Update Memory]
    Update --> Memory
    
    style Memory fill:#6d28d9,stroke:#fff,color:#fff
    style Search fill:#6d28d9,stroke:#fff,color:#fff

Components used:

• Vector Store (knowledge base)
• Conversation Memory (context tracking)
• RAG Node (intelligent search)
• Basic LLM Chain (response generation)

Example: Personal Research Assistant

• Remembers what topics you’re interested in
• Builds knowledge about your research areas over time
• Provides increasingly relevant and personalized responses
• Connects new information to previous conversations

Agent-Based Patterns

Goal-Oriented Research Agent

Purpose: AI that can plan and execute complex research tasks

graph TD
    Goal[Research Goal] --> Plan[Create Plan]
    Plan --> Tool1[Web Search]
    Plan --> Tool2[Content Extract]
    Plan --> Tool3[Data Analysis]
    Tool1 --> Evaluate[Evaluate Progress]
    Tool2 --> Evaluate
    Tool3 --> Evaluate
    Evaluate --> Complete{Goal Achieved?}
    Complete -->|No| Adapt[Adapt Plan]
    Complete -->|Yes| Report[Final Report]
    Adapt --> Plan
    
    style Plan fill:#6d28d9,stroke:#fff,color:#fff
    style Evaluate fill:#6d28d9,stroke:#fff,color:#fff

Components used:

• Tools Agent (intelligent coordinator)
• Web search tools (information gathering)
• Content extraction tools (data collection)
• Analysis tools (insight generation)
• Memory (maintain context)

Example workflow:

1. Goal: “Research competitor pricing for SaaS products”
2. Planning: Agent decides to search for competitors, visit their sites, extract pricing
3. Execution: Uses web search → content extraction → data analysis
4. Adaptation: If pricing not found on main pages, tries pricing pages or contact forms
5. Reporting: Compiles comprehensive competitive analysis

Multi-Tool Coordination Agent

Purpose: AI that can use multiple tools intelligently to accomplish complex tasks

Available tool categories:

• Information gathering: Web search, content extraction, API calls
• Data processing: Analysis, formatting, calculations
• Content creation: Writing, summarization, report generation
• Browser automation: Form filling, navigation, interaction

Pattern structure:

1. Goal definition: Clear objective for the agent
2. Tool selection: Choose relevant tools for the domain
3. Execution limits: Set maximum steps to prevent infinite loops
4. Progress monitoring: Track agent decisions and results
5. Result compilation: Format final output appropriately

Conversational Patterns

Context-Aware Chatbot

Purpose: Conversational AI that maintains context and can take actions

graph LR
    Message[User Message] --> Memory[Load Context]
    Memory --> Understand[Understand Intent]
    Understand --> Decide[Decide Action]
    Decide --> Tools[Use Tools if Needed]
    Decide --> Respond[Generate Response]
    Tools --> Respond
    Respond --> Save[Save to Memory]
    Save --> Memory
    
    style Memory fill:#6d28d9,stroke:#fff,color:#fff
    style Understand fill:#6d28d9,stroke:#fff,color:#fff

Components used:

• Chat Model (conversation)
• Conversation Memory (context)
• Tools (actions)
• Basic LLM Chain (response generation)

Conversation flow example:

User: "Find information about our refund policy"
Bot: "I'll search our knowledge base for refund information..."
     [Uses RAG tool to search company docs]
     "According to our policy document, customers can request refunds within 30 days..."

User: "What if it's been 35 days?"
Bot: "Based on our previous discussion about the 30-day policy, requests after 30 days require manager approval..."

Specialized Domain Assistant

Purpose: Expert AI for specific domains (legal, medical, technical, etc.)

Pattern components:

• Domain knowledge: Specialized vector store with expert content
• Domain tools: Specific tools for the field (calculators, databases, APIs)
• Safety checks: Validation and disclaimer systems
• Expert prompting: Specialized prompts for domain expertise

Performance Optimization Patterns

Efficient Processing Pipeline

Purpose: Handle large volumes of content efficiently

Optimization strategies:

• Batch processing: Process multiple items together
• Caching: Store frequently used results
• Streaming: Process content as it arrives
• Parallel processing: Use multiple chains simultaneously

Cost-Optimized Workflows

Purpose: Minimize AI API costs while maintaining quality

Cost reduction techniques:

• Model selection: Use appropriate model for each task (GPT-3.5 for simple, GPT-4 for complex)
• Prompt optimization: Shorter, more focused prompts
• Result caching: Avoid re-processing identical content
• Local models: Use Ollama for privacy and cost control

Start with simple patterns

Begin with basic patterns like Simple Analysis Chain, then gradually add complexity. Each pattern can be customized and combined with others to create sophisticated workflows.

Pattern Selection Guide

Choose based on your goal:

Content Analysis → Simple Analysis Chain or Multi-Step Pipeline Question Answering → Smart Q&A System or Contextual Knowledge Assistant
Research Tasks → Goal-Oriented Research Agent or Multi-Tool Coordination Conversations → Context-Aware Chatbot or Specialized Domain Assistant High Volume → Efficient Processing Pipeline with optimization patterns

Consider your constraints:

Budget-conscious → Use local models and cost-optimized patterns Privacy-focused → Local Knowledge + Ollama models Speed-critical → Simpler patterns with faster models Quality-critical → More sophisticated patterns with premium models

These patterns provide proven starting points for building intelligent workflows. Mix, match, and modify them to create solutions perfectly suited to your specific needs.