LangGraph Framework Guide

Complex workflows with multi-step reasoning and stateful conversations

LangGraph Framework Guide

The LangGraph Framework enables complex, stateful workflows for AI agents that require multi-step reasoning, conditional logic, and sophisticated tool orchestration. It’s built on LangChain’s LangGraph library for creating sophisticated agent workflows.

When to Use LangGraph Framework

✅ Perfect for:

Complex customer service workflows
Multi-step research and analysis tasks
Conditional logic between operations
Stateful conversation management
Advanced tool orchestration and chaining
Decision trees and branching workflows
Long-running task coordination

❌ Not ideal for:

Simple chat interactions
High-throughput, low-latency applications
Basic Q&A scenarios
Minimal resource environments
Straightforward tool usage

Performance Characteristics

Response Time: ~1-5 seconds (workflow dependent)
Resource Usage: Higher CPU and memory requirements
Concurrency: Moderate - manages stateful sessions
Scalability: Good vertical scaling, moderate horizontal scaling
Debugging: Visual workflow debugging capabilities

Quick Example

apiVersion: ai.example.com/v1
kind: Agent
metadata:
  name: workflow-support
  namespace: customer-service
spec:
  framework: langgraph      # Complex workflows
  provider: openai
  model: gpt-4
  systemPrompt: "You are an advanced customer service agent with systematic problem-solving capabilities."
  apiSecretRef:
    name: openai-secret
    key: api-key
  
  # Define the workflow
  langgraphConfig:
    graphType: conditional
    nodes:
    - name: analyze_issue
      type: llm
      prompt: "Analyze this customer issue: {user_input}"
      outputs: ["issue_type", "priority"]
    
    - name: lookup_data
      type: tool
      tool: customer_lookup
      condition: "issue_type == 'account'"
      inputs: ["customer_id"]
      outputs: ["customer_data"]
    
    - name: resolve_issue
      type: llm
      prompt: "Resolve based on: {customer_data}"
      outputs: ["resolution"]
    
    edges:
    - from: analyze_issue
      to: lookup_data
      condition: "issue_type == 'account'"
    - from: lookup_data
      to: resolve_issue
    
    entrypoint: analyze_issue
    endpoints: [resolve_issue]
  
  tools:
  - name: customer_lookup
    description: Look up customer information
    inputSchema:
      type: object
      properties:
        customer_id: {type: string}
      required: ["customer_id"]
  
  replicas: 1
  resources:
    requests:
      cpu: 500m
      memory: 1Gi
    limits:
      cpu: 1
      memory: 2Gi

Workflow Components

Node Types

LLM Nodes: Call language models with prompts

- name: analyze_request
  type: llm
  prompt: "Analyze: {user_input}"
  outputs: ["analysis_result"]

Tool Nodes: Execute external tools and APIs

- name: lookup_data
  type: tool
  tool: database_search
  inputs: ["search_query"]
  outputs: ["search_results"]

Action Nodes: Perform system actions

- name: send_notification
  type: action
  action: notify_team
  inputs: ["message", "priority"]

State Management

Persistent State: Maintains conversation context across workflow steps
Variable Passing: Share data between nodes through state variables
Session Storage: Keep track of user sessions and conversation history

Conditional Logic

Simple conditions:

condition: "issue_type == 'billing'"

Complex conditions:

condition: "priority == 'high' and customer_tier == 'premium'"

Complex Use Cases

1. Multi-Step Customer Service

Handle complex support scenarios that require multiple data lookups and decision points:

langgraphConfig:
  graphType: conditional
  
  nodes:
  - name: classify_issue
    type: llm
    prompt: |
      Classify this customer request:
      Request: {user_input}
      
      Determine issue type, priority, and required data.
    outputs: ["issue_type", "priority", "customer_id"]
  
  - name: fetch_customer_data
    type: tool
    tool: customer_lookup
    condition: "customer_id is not None"
    
  - name: check_policies
    type: tool
    tool: policy_engine
    inputs: ["issue_type", "customer_data"]
    
  - name: escalate_or_resolve
    type: llm
    condition: "policy_decision == 'escalate'"
    prompt: "Escalate this case: {customer_data}"
  
  edges:
  - from: classify_issue
    to: fetch_customer_data
  - from: fetch_customer_data  
    to: check_policies
  - from: check_policies
    to: escalate_or_resolve

2. Research and Analysis Pipeline

Multi-stage research that gathers information from multiple sources:

langgraphConfig:
  graphType: sequential
  
  nodes:
  - name: decompose_query
    type: llm
    prompt: "Break down research question: {user_input}"
    outputs: ["search_terms", "research_strategy"]
  
  - name: gather_sources
    type: tool
    tool: web_search
    inputs: ["search_terms"]
  
  - name: validate_sources  
    type: llm
    prompt: "Validate source credibility: {web_results}"
    outputs: ["validated_facts"]
  
  - name: synthesize_findings
    type: llm
    prompt: "Create comprehensive analysis: {validated_facts}"
    outputs: ["final_report"]

3. Decision Making Engine

Complex business decision analysis with multiple criteria:

langgraphConfig:
  graphType: hierarchical
  
  nodes:
  - name: define_decision
    type: llm
    prompt: "Structure decision problem: {user_input}"
    
  - name: gather_market_data
    type: tool
    tool: market_research
    
  - name: financial_analysis
    type: tool  
    tool: financial_calculator
    
  - name: risk_assessment
    type: tool
    tool: risk_analyzer
    
  - name: multi_criteria_evaluation
    type: llm
    prompt: "Evaluate options using gathered data"
    
  - name: final_recommendation
    type: llm
    prompt: "Generate executive recommendation"

Configuration Best Practices

Resource Planning

LangGraph workflows require more resources than direct agents:

# Typical LangGraph resource allocation
resources:
  requests:
    cpu: 500m        # Baseline for workflow processing
    memory: 1Gi      # State storage and LLM responses
  limits:
    cpu: 1.5         # Burst capacity for complex workflows
    memory: 3Gi      # Handle large state objects

State Design

Keep state minimal and well-structured:

state:
  # Core workflow data
  user_request: {type: string}
  current_step: {type: string}
  
  # Domain-specific data  
  customer_id: {type: string}
  order_data: {type: object}
  
  # Control flow
  workflow_status: {type: string}
  errors: {type: array}

Error Handling

Include error handling in your workflows:

nodes:
- name: error_handler
  type: llm
  condition: "errors is not None"
  prompt: |
    Handle workflow errors: {errors}
    Provide user-friendly error message.

Performance Optimization

Parallelize independent operations where possible
Cache frequently accessed data in state
Implement smart retry logic for external services
Monitor state size to prevent memory issues
Use circuit breakers for unreliable external dependencies

Monitoring and Debugging

Key metrics for LangGraph agents:

Workflow completion rate
Average workflow duration
Node execution times
State size over time
Error rates by node
Resource utilization patterns

Debugging strategies:

Add logging nodes for state inspection
Use conditional nodes for error handling
Monitor workflow paths taken
Track state changes between nodes

Migration from Direct Framework

When to migrate:

✅ Upgrade to LangGraph when you need:

Multi-step conditional logic
State persistence across interactions
Complex tool orchestration
Decision trees and branching
Workflow visibility and debugging

Migration example:

Before (Direct) - Limited coordination:

framework: direct
tools:
- name: lookup_customer
- name: process_refund
# Independent tool calls

After (LangGraph) - Systematic workflow:

framework: langgraph
langgraphConfig:
  nodes:
  - name: lookup_customer
    type: tool
    tool: lookup_customer
  - name: validate_refund
    type: llm  
    condition: "customer_data.tier == 'premium'"
  - name: process_refund
    type: tool
    tool: process_refund
    condition: "refund_approved == true"
  edges:
  - from: lookup_customer
    to: validate_refund
  - from: validate_refund
    to: process_refund

Advanced Features

Workflow Types

Sequential: Linear progression through steps
Parallel: Concurrent execution of independent operations
Conditional: Branch based on logic and state
Hierarchical: Nested workflows and sub-processes

State Persistence

LangGraph maintains state across conversation turns:

state:
  conversation_history: {type: array}
  user_preferences: {type: object}
  task_progress: {type: object}
  context_data: {type: object}

Dynamic Routing

Runtime decision making for workflow paths:

edges:
- from: analyze_issue
  to: high_priority_path
  condition: "priority == 'urgent'"
- from: analyze_issue  
  to: standard_path
  condition: "priority != 'urgent'"

Complete Examples

Explore comprehensive examples in our repository:

langgraph-workflow-agent.yaml - Advanced customer service workflow
Research Agent - Multi-step research pipeline
Decision Engine - Business decision analysis

Next Steps

Compare with Direct Framework for simpler use cases
View API Reference for complete configuration options
Try Local Testing to experiment with workflows
Check Examples for more complex scenarios

The LangGraph Framework excels when you need sophisticated reasoning, complex workflows, and stateful interactions that go beyond simple request-response patterns.