LangGraph

• LangChain: A comprehensive framework for building LLM-powered applications. It excels at linear or simple sequential workflows (often called "chains"), providing high-level abstractions, components, and pre-built tools for common tasks like RAG, prompt templating, memory, and basic agents.
• LangGraph: A low-level orchestration framework and runtime specifically for building stateful, complex, controllable, and multi-actor agent workflows. It models applications as graphs (with cycles/loops allowed) for reliability in production-grade agents.

• Use LangChain alone for:
  • Straightforward RAG pipelines.
  • Simple sequential reasoning (e.g., summarize → extract → answer).
  • Rapid prototyping or when you want pre-built agents/chains.
• Use LangGraph (often with LangChain components inside nodes) for:
  • Agents that need to loop, self-correct, or iterate (ReAct-style or custom).
  • Multi-agent systems (researcher + critic + writer).
  • Long-running or interruptible workflows.
  • Anything requiring fine-grained control, persistence, or human oversight.

• Start node → Agent decides (LLM call) → Tool call or reflection loop → Conditional branch (if confident → END; else → revise) → Human interrupt possible at any checkpoint.

What We’ll Cover in This Series

In this series, we’ll take a deep dive into the core components and advanced capabilities of LangGraph, breaking things down step by step:

Core Components

• Graph
• Nodes
• Edges
• Conditional Edges
• START / END points
• Cycles and Self-Loops
• StateGraph vs MessageGraph
• Graph Compilation
• Graph Visualization

State Management

• State 
• Reducers
• State Channels
• Messages & Chat History
• Checkpoints
• Memory Systems
  • Short-term Memory
  • Long-term Memory
  • Conversation Memory
  • Vector Memory
• Time travel

Prompt Engineering

• System Prompts
• Prompt Templates
• Dynamic Prompts
• Context Injection
• Few-shot Prompting
• Structured Output Prompting
• Prompt Chaining
• Prompt + State Integration

Execution Control

• Streaming
• Parallel execution
• Async Workflows
• Interrupts
• Breakpoints
• Retry Logic
• Exception Handling
• Timeout Management
• Failure Recovery

LLMs & Tool Calling

• Model Integration
• Tool Calling
• ToolNode
• Structured Outputs
• JSON Schema
• Pydantic Outputs
• Validation & Parsing

Agent Architectures

• ReAct Agents
• Planner-Executor Pattern
• Multi-Agent Systems
• Supervisor Pattern
• Swarm Architectures
• Agent Delegation
• Reflection Agents
• Human-in-the-Loop Agents

RAG Systems

• Embeddings
• Vector Databases
• Retrievers
• Document Loaders
• RAG Pipelines
• Hybrid Retrieval

Advanced LangGraph

• Subgraphs (composition)
• Dynamic Graphs
• Runtime Configuration
• Conditional Graph Construction
• Recursive Workflows
• Dynamic Routing
• Runtime Graph Modification

Observability & Debugging

• Logging
• Tracing
• LangSmith Integration
• Metrics
• Graph Inspection
• Debugging Strategies

Deployment & Production

• FastAPI Integration
• API Serving
• Scaling Agents
• Production Architecture
• State Persistence
• Security Considerations
• Performance Optimization
• Deployment Strategies

By the end of this series, you’ll have a solid understanding of how to design, control, and scale complex LLM workflows using LangGraph.

Here's a simple graph that includes all components:

from langgraph.graph import StateGraph, START, END 
from langgraph.checkpoint.memory import MemorySaver 
from typing import TypedDict 
# STATE 
class AgentState(TypedDict): 
message: str 
retry_count: int 
passed: bool 
# NODES 
def process_node(state: AgentState): 
    print(f"Processing: {state['message']}") 
    return {"retry_count": state["retry_count"] + 1} 
 
def check_node(state: AgentState): 
    print(f"Checking... attempt {state['retry_count']}") 
    passed = state["retry_count"] >= 2 
    return {"passed": passed} 
 
def success_node(state: AgentState): 
    print("Success!") 
    return {} 
 
# CONDITIONAL EDGE function 
def should_retry(state: AgentState): 
    if state["passed"]: 
        return "success" 
    return "retry" 
 
# GRAPH 
builder = StateGraph(AgentState) 
 
# Adding Nodes 
builder.add_node("process", process_node) 
builder.add_node("check", check_node) 
builder.add_node("success", success_node) 
 
# Edges 
builder.add_edge(START, "process")        # Entry Point 
builder.add_edge("process", "check") 
 
# Conditional Edge (with Cycle if retry) 
builder.add_conditional_edges("check", should_retry, { 
    "retry": "process",   # CYCLE — loops back 
    "success": "success"  # moves forward 
}) 
 
builder.add_edge("success", END)          # End Point 
 
# CHECKPOINTER 
checkpointer = MemorySaver() 
graph = builder.compile(checkpointer=checkpointer) 
 
# Run 
config = {"configurable": {"thread_id": "1"}} 
result = graph.invoke({"message": "hello", "retry_count": 0, "passed": False}, config)