🎯📝⚙️ Session 3: LangGraph Multi-Agent Workflows Hub¶

When your petabyte-scale data lake spans multiple clouds and processing terabytes of streaming data requires coordinated work from validation agents, transformation engines, and quality monitors - rigid sequential pipelines become the bottleneck that kills performance. A single delayed data validation step can cascade through your entire pipeline, blocking critical downstream analytics and causing SLA violations that impact business decisions.

LangGraph transforms your data processing agents from sequential bottlenecks into intelligent orchestration networks where data validation, transformation, aggregation, and quality assurance work in parallel based on data characteristics, resource availability, and processing priorities in real-time.

🎯📝⚙️ Learning Path Overview¶

This session offers three distinct learning paths designed to match your goals and time investment:

🎯 Observer Path: LangGraph Architecture Foundations¶

Code Repository: src/session3/ Quick Start: cd src/session3 && python langgraph_basics_course.py Complete Demo: cd src/session3 && python demo_runner_course.py

Learning Outcomes¶

By completing the Observer Path, you will understand:

• Core LangGraph architecture and graph-based workflows
• Basic state management for multi-agent coordination
• When to choose graph-based vs sequential agent patterns

By completing the Participant Path, you will be able to:

• Design and implement graph-based data pipeline orchestration using LangGraph
• Build complex multi-agent systems with stateful coordination for data processing workflows
• Apply state management patterns for distributed data streaming coordination
• Implement production-grade tracing and observability for multi-agent data pipelines
• Evaluate when to choose graph-based architectures over simple chain-based data flows

The Graph Revolution: Beyond Linear Data Pipelines¶

Unlike sequential data pipelines where validation always precedes transformation which always precedes aggregation, LangGraph uses directed graphs with nodes (specialized processors) connected by conditional edges (intelligent routing). This architecture provides stateful coordination, dynamic decision-making, and production-grade observability for complex data processing workflows.

Think of it as the difference between traditional ETL pipelines and modern stream processing architectures:

• Sometimes data quality validation needs direct input from schema inference
• Sometimes ML feature engineering requires simultaneous input from multiple data sources
• Sometimes you need to route back to data ingestion when quality thresholds aren't met

Part 1: Graph Architecture Overview¶

Graph-Based Workflow Foundation¶

Building on our LangChain foundations, LangGraph transforms multi-agent data systems from linear pipelines into sophisticated graph structures that mirror real-world distributed data processing:

File: src/session3/langgraph_basics_course.py - Core workflow setup

Production-Grade State Management¶

LangGraph's StateGraph provides the foundation for distributed data processing coordination, solving the critical challenge of how multiple processing agents can work together while maintaining data lineage and pipeline observability:

from langgraph.graph import StateGraph, END
from langgraph.prebuilt import ToolNode
from typing import TypedDict, List, Optional

# Data processing workflow state with comprehensive tracking

class WorkflowState(TypedDict):
    messages: List[str]           # Processing status updates
    current_step: str            # Active processing stage
    completed_tasks: List[str]   # Processing audit trail
    data_context: dict          # Shared processing metadata
    error_state: Optional[str]  # Processing failure handling
    batch_id: str               # Current data batch identifier
    resource_usage: dict        # Cluster resource tracking

This state structure provides the observability and error handling required for production data processing - think of it as the shared processing context that all data agents can access and update. Each field serves a specific purpose in maintaining pipeline integrity and enabling debugging when data processing issues occur in your distributed system.

# Initialize the data processing workflow graph

workflow = StateGraph(WorkflowState)

Core Architecture Principles¶

Understanding these principles is like grasping the fundamental laws that govern distributed data processing systems:

1. Directed Graph Structure: Nodes (specialized processors) connected by conditional edges (intelligent routing) - like having clear data flow paths between ingestion, validation, transformation, and storage layers
2. Immutable State Flow: State evolves through nodes without mutation, ensuring data lineage traceability - every processing step is recorded and auditable for compliance and debugging
3. Conditional Decision Points: Dynamic routing based on data characteristics and resource availability - like having intelligent load balancers that route data batches to optimal processing clusters

Nodes and Edges¶

Building blocks of LangGraph workflows - the data processing agents (nodes) and their coordination patterns (edges):

File: src/session3/workflow_nodes_course.py - Node implementations

def data_validation_node(state: WorkflowState):
    """Data quality validation phase of the processing workflow"""
    print(f"🔍 Validating: {state['current_step']} for batch {state['batch_id']}")
    # Add data validation logic here
    return {
        **state,
        "messages": state["messages"] + ["Data validation completed"],
        "completed_tasks": state["completed_tasks"] + ["validation"]
    }

Each node function receives the current processing state and returns an updated state - like a specialized data processing service receiving a data batch, performing its transformation, and updating the pipeline status. The **state syntax preserves existing processing context while updating specific fields, ensuring data lineage is never lost in the handoff.

def transformation_node(state: WorkflowState):
    """Data transformation phase of the workflow"""
    print(f"📊 Transforming: Processing validated data batch")
    return {
        **state,
        "messages": state["messages"] + ["Data transformation completed"],
        "completed_tasks": state["completed_tasks"] + ["transformation"]
    }

Now we connect these nodes to create our data processing structure - establishing the coordination patterns that enable effective data pipeline orchestration:

# Add nodes to workflow

workflow.add_node("validation", data_validation_node)
workflow.add_node("transformation", transformation_node)
workflow.add_edge("validation", "transformation")

Basic Graph Creation¶

Putting it all together into a functioning multi-agent data processing system:

# Set entry point and compile

workflow.set_entry_point("validation")
workflow.add_edge("transformation", END)

# Compile the workflow

app = workflow.compile()

# Run the workflow

result = app.invoke({
    "messages": [],
    "current_step": "start",
    "completed_tasks": []
})

📝 Participant Path: Multi-Agent Implementation¶

Prerequisites: Complete the 🎯 Observer Path content above

Ready to build complete multi-agent systems with proper coordination? The Participant Path provides comprehensive implementation guidance with working examples, error handling, and production patterns.

Continue to: 📝 Multi-Agent Implementation Guide →

What you'll build:
- Specialized data processing agent teams
- Sophisticated message passing and coordination
- Error handling and workflow validation
- Complete testing and integration patterns

⚙️ Implementer Path: Advanced Orchestration¶

Prerequisites: Complete 🎯 Observer and 📝 Participant paths above

Advanced Content: For complex workflow patterns and enterprise deployment:

• ⚙️ Advanced Orchestration Patterns - Complex workflow coordination & dynamic agent generation for large-scale data processing
• ⚙️ Enterprise State Management - Production state handling & sophisticated routing for enterprise data pipelines

📝 Multiple Choice Test - Session 3¶

Test your understanding of LangGraph workflows and multi-agent coordination:

Question 1: What is the primary advantage of LangGraph over sequential data pipeline agents?
A) Better performance
B) Lower cost
C) Graph-based workflows with conditional routing and parallel execution
D) Simpler implementation

Question 2: In LangGraph, what component defines the data that flows between nodes?
A) State (TypedDict)
B) Edges
C) Memory
D) Tools

Question 3: What determines the flow between nodes in a LangGraph workflow?
A) Random selection
B) User input
C) Sequential execution only
D) Conditional edges and decision functions

Question 4: How does LangGraph handle parallel agent execution?
A) Through parallel nodes with state merging
B) It doesn't support parallel execution
C) Through external orchestration
D) Using threading only

Question 5: What happens when a LangGraph node updates state?
A) State is reset to default
B) The entire state is replaced
C) Previous state is archived
D) Only specified fields are updated/merged

View Solutions →

🧭 Navigation¶

Previous: Session 2 - Implementation →
Next: Session 4 - Team Orchestration →