Back to Blog

Getting Started with LangGraph for AI Agent Development

8 min read
By Hari Prakash Pokala
LangGraphAILLMPythonLangChain

LangGraph has revolutionized how we build AI agents by providing a structured approach to creating stateful, multi-actor applications. In this comprehensive guide, I'll share practical insights from building production AI systems with LangGraph.

What is LangGraph?

LangGraph is a framework for building stateful, multi-actor applications with Large Language Models (LLMs). It extends LangChain's capabilities by introducing graph-based orchestration, making it ideal for complex AI workflows that require state management, conditional logic, and cyclic behaviors.

Why LangGraph?

Traditional LLM chains are linear and stateless, which limits their ability to handle complex, real-world scenarios. LangGraph solves this by:

  • Maintaining State: Preserves context across multiple steps
  • Supporting Cycles: Enables iterative refinement and error recovery
  • Conditional Branching: Routes execution based on runtime conditions
  • Human-in-the-Loop: Integrates human oversight at critical decision points

Core Concepts

1. State Management

State is the foundation of LangGraph. It represents the current context of your agent's execution:

from typing import TypedDict, List

class AgentState(TypedDict):
    messages: List[str]
    current_step: str
    attempts: int
    results: dict

2. Graph Structure

LangGraph uses a directed graph where:

  • Nodes represent functions that process state
  • Edges define the flow between nodes
  • Conditional Edges enable dynamic routing

3. Execution Flow

The graph executor manages state transitions, handling retries, error recovery, and human intervention points.

Building Your First Agent

Let's build a research agent that can gather information, analyze it, and generate insights:

from langgraph.graph import StateGraph, END
from langchain_openai import ChatOpenAI

# Define state
class ResearchState(TypedDict):
    query: str
    research_data: List[str]
    analysis: str
    final_report: str

# Create nodes
def research_node(state: ResearchState):
    # Gather information
    llm = ChatOpenAI(model="gpt-4")
    research = llm.invoke(f"Research: {state['query']}")
    return {
        **state,
        "research_data": [research.content]
    }

def analyze_node(state: ResearchState):
    # Analyze gathered data
    llm = ChatOpenAI(model="gpt-4")
    analysis = llm.invoke(
        f"Analyze this research: {state['research_data']}"
    )
    return {
        **state,
        "analysis": analysis.content
    }

def report_node(state: ResearchState):
    # Generate final report
    llm = ChatOpenAI(model="gpt-4")
    report = llm.invoke(
        f"Create report from: {state['analysis']}"
    )
    return {
        **state,
        "final_report": report.content
    }

# Build the graph
workflow = StateGraph(ResearchState)

# Add nodes
workflow.add_node("research", research_node)
workflow.add_node("analyze", analyze_node)
workflow.add_node("report", report_node)

# Define edges
workflow.set_entry_point("research")
workflow.add_edge("research", "analyze")
workflow.add_edge("analyze", "report")
workflow.add_edge("report", END)

# Compile
app = workflow.compile()

# Execute
result = app.invoke({
    "query": "Latest trends in AI",
    "research_data": [],
    "analysis": "",
    "final_report": ""
})

Advanced Patterns

Conditional Routing

Route execution based on state conditions:

def should_continue(state: AgentState):
    if state["attempts"] < 3:
        return "retry"
    return "end"

workflow.add_conditional_edges(
    "process",
    should_continue,
    {
        "retry": "process",
        "end": END
    }
)

Error Handling

Implement robust error recovery:

def error_handler(state: AgentState):
    try:
        # Processing logic
        result = process_data(state)
        return {"status": "success", "result": result}
    except Exception as e:
        return {
            "status": "error",
            "error": str(e),
            "attempts": state.get("attempts", 0) + 1
        }

Human-in-the-Loop

Add human checkpoints:

from langgraph.checkpoint import MemorySaver

# Add checkpointer for persistence
memory = MemorySaver()
app = workflow.compile(checkpointer=memory)

# Interrupt for human review
workflow.add_node("human_review", human_review_node)
workflow.add_edge("analyze", "human_review")
workflow.add_edge("human_review", "report")

Production Best Practices

1. State Validation

Validate state at each step:

from pydantic import BaseModel, validator

class ValidatedState(BaseModel):
    query: str
    results: List[str]

    @validator('query')
    def query_not_empty(cls, v):
        if not v.strip():
            raise ValueError('Query cannot be empty')
        return v

2. Observability

Add logging and monitoring:

import logging

def logged_node(state: AgentState):
    logging.info(f"Processing state: {state['current_step']}")
    result = process(state)
    logging.info(f"Completed: {state['current_step']}")
    return result

3. Cost Optimization

Implement caching and batch processing:

from functools import lru_cache

@lru_cache(maxsize=100)
def cached_llm_call(prompt: str):
    return llm.invoke(prompt)

Performance Considerations

  • Async Execution: Use async nodes for I/O-bound operations
  • Streaming: Stream responses for better UX
  • Checkpointing: Save state periodically for long-running workflows
  • Timeout Handling: Set timeouts for each node

Common Use Cases

  1. Multi-Step Research Agents: Gather, analyze, and synthesize information
  2. Code Generation Workflows: Plan, write, test, and refine code
  3. Customer Support Bots: Route, research, and respond to queries
  4. Data Processing Pipelines: Extract, transform, validate, and load data

Conclusion

LangGraph provides a powerful foundation for building production-grade AI agents. Its graph-based approach enables complex workflows while maintaining clarity and debuggability.

Key Takeaways:

  • Use state to maintain context across steps
  • Leverage conditional edges for dynamic routing
  • Implement error handling and retries
  • Add human-in-the-loop for critical decisions
  • Monitor and optimize for production

Ready to build your first LangGraph agent? Start with a simple workflow and gradually add complexity as you learn the patterns.

Resources:

View All Posts