Thinking in LangGraph

When you build an agent with LangGraph, you will first break it apart into discrete steps called nodes. Then, you will describe the different decisions and transitions from each of your nodes. Finally, you connect nodes together through a shared state that each node can read from and write to.

In this walkthrough, we'll guide you through the thought process of building a customer support email agent with LangGraph.

Start with the process you want to automate

Imagine that you need to build an AI agent that handles customer support emails. Your product team has given you these requirements:

The agent should:

- Read incoming customer emails
- Classify them by urgency and topic
- Search relevant documentation to answer questions
- Draft appropriate responses
- Escalate complex issues to human agents
- Schedule follow-ups when needed

Example scenarios to handle:

1. Simple product question: "How do I reset my password?"
2. Bug report: "The export feature crashes when I select PDF format"
3. Urgent billing issue: "I was charged twice for my subscription!"
4. Feature request: "Can you add dark mode to the mobile app?"
5. Complex technical issue: "Our API integration fails intermittently with 504 errors"

To implement an agent in LangGraph, you will usually follow the same five steps.

Step 1: Map out your workflow as discrete steps

Start by identifying the distinct steps in your process. Each step will become a node (a function that does one specific thing). Then, sketch how these steps connect to each other.

flowchart TD
    A[START] --> B[Read Email]
    B --> C[Classify Intent]
 
    C -.-> D[Doc Search]
    C -.-> E[Bug Track]
    C -.-> F[Human Review]
 
    D --> G[Draft Reply]
    E --> G
    F --> G
 
    G -.-> H[Human Review]
    G -.-> I[Send Reply]
 
    H --> J[END]
    I --> J[END]
 
    classDef process fill:#E5F4FF,stroke:#006DDD,stroke-width:2px,color:#030710
    class A,B,C,D,E,F,G,H,I,J process

The arrows in this diagram show possible paths, but the actual decision of which path to take happens inside each node.

Now that we've identified the components in our workflow, let's understand what each node needs to do:

Read Email: Extract and parse the email content
Classify Intent: Use an LLM to categorize urgency and topic, then route to appropriate action
Doc Search: Query your knowledge base for relevant information
Bug Track: Create or update issue in tracking system
Draft Reply: Generate an appropriate response
Human Review: Escalate to human agent for approval or handling
Send Reply: Dispatch the email response

Notice that some nodes make decisions about where to go next (Classify Intent, Draft Reply, Human Review), while others always proceed to the same next step (Read Email always goes to Classify Intent, Doc Search always goes to Draft Reply).

Step 2: Identify what each step needs to do

For each node in your graph, determine what type of operation it represents and what context it needs to work properly.

LLM steps:Use when you need to understand, analyze, generate text, or make reasoning decisions
Data steps:Use when you need to retrieve information from external sources
Action steps:Use when you need to perform external actions
User input steps:Use when you need human intervention

LLM steps

When a step needs to understand, analyze, generate text, or make reasoning decisions:

Classify intent

    - Static context (prompt): Classification categories, urgency definitions, response format
    - Dynamic context (from state): Email content, sender information
    - Desired outcome: Structured classification that determines routing

Draft reply

    - Static context (prompt): Tone guidelines, company policies, response templates
    - Dynamic context (from state): Classification results, search results, customer history
    - Desired outcome: Professional email response ready for review

Data steps

When a step needs to retrieve information from external sources:

Document search

    - Parameters: Query built from intent and topic
    - Retry strategy: Yes, with exponential backoff for transient failures
    - Caching: Could cache common queries to reduce API calls

Customer history lookup

    - Parameters: Customer email or ID from state
    - Retry strategy: Yes, but with fallback to basic info if unavailable
    - Caching: Yes, with time-to-live to balance freshness and performance

Action steps

When a step needs to perform an external action:

Send reply

    - When to execute node: After approval (human or automated)
    - Retry strategy: Yes, with exponential backoff for network issues
    - Should not cache: Each send is a unique action

Bug track

    - When to execute node: Always when intent is "bug"
    - Retry strategy: Yes, critical to not lose bug reports
    - Returns: Ticket ID to include in response

User input steps

When a step needs human intervention:

Human review node

    - Context for decision: Original email, draft response, urgency, classification
    - Expected input format: Approval boolean plus optional edited response
    - When triggered: High urgency, complex issues, or quality concerns

Step 3: Design your state

State is the shared memory (opens in a new tab) accessible to all nodes in your agent. Think of it as the notebook your agent uses to keep track of everything it learns and decides as it works through the process.

What belongs in state?

Ask yourself these questions about each piece of data:

Include in state:Does it need to persist across steps? If yes, it goes in state.
Don't store:Can you derive it from other data? If yes, compute it when needed instead of storing it in state.

For our email agent, we need to track:

The original email and sender info (can't reconstruct these later)
Classification results (needed by multiple later/downstream nodes)
Search results and customer data (expensive to re-fetch)
The draft response (needs to persist through review)
Execution metadata (for debugging and recovery)

Keep state raw, format prompts on-demand

A key principle: your state should store raw data, not formatted text. Format prompts inside nodes when you need them.

This separation means:

Different nodes can format the same data differently for their needs
You can change prompt templates without modifying your state schema
Debugging is clearer—you see exactly what data each node received
Your agent can evolve without breaking existing state

Let's define our state:

from typing import TypedDict, Literal
 
# Define the structure for email classification
class EmailClassification(TypedDict):
    intent: Literal["question", "bug", "billing", "feature", "complex"]
    urgency: Literal["low", "medium", "high", "critical"]
    topic: str
    summary: str
 
class EmailAgentState(TypedDict):
    # Raw email data
    email_content: str
    sender_email: str
    email_id: str
 
    # Classification result
    classification: EmailClassification | None
 
    # Raw search/API results
    search_results: list[str] | None  # List of raw document chunks
    customer_history: dict | None  # Raw customer data from CRM
 
    # Generated content
    draft_response: str | None
    messages: list[str] | None

Notice that the state contains only raw data—no prompt templates, no formatted strings, no instructions. The classification output is stored as a single dictionary, straight from the LLM.

Step 4: Build your nodes

Now we implement each step as a function. A node in LangGraph is just a Python function that takes the current state and returns updates to it.

Handle errors appropriately

Different errors need different handling strategies:

Error Type	Who Fixes It	Strategy	When to Use
Transient errors (network issues, rate limits)	System (automatic)	Retry policy	Temporary failures that usually resolve on retry
LLM-recoverable errors (tool failures, parsing issues)	LLM	Store error in state and loop back	LLM can see the error and adjust its approach
User-fixable errors (missing information, unclear instructions)	Human	Pause with `interrupt()`	Need user input to proceed
Recoverable failure after retries	Developer (declarative)	`error_handler`	Run a compensation/recovery branch after retry exhaustion
Unexpected errors	Developer	Let them bubble up	Unknown issues that need debugging

Transient errors

    Add a retry policy to automatically retry network issues and rate limits.

:::python

Combine with `timeout=` to cap each attempt. See [Fault tolerance](https://docs.langchain.com/oss/langgraph/fault-tolerance) for the full lifecycle.

```python
from langgraph.types import RetryPolicy

workflow.add_node(
    "search_documentation",
    search_documentation,
    retry_policy=RetryPolicy(max_attempts=3, initial_interval=1.0)
)
```

:::

:::js

```typescript
import type { RetryPolicy } from "@langchain/langgraph";

workflow.addNode(
  "searchDocumentation",
  searchDocumentation,
  {
    retryPolicy: { maxAttempts: 3, initialInterval: 1.0 },
  },
);
```

:::

LLM-recoverable

    Store the error in state and loop back so the LLM can see what went wrong and try again:

:::python

```python
from langgraph.types import Command


def execute_tool(state: State) -> Command[Literal["agent", "execute_tool"]]:
    try:
        result = run_tool(state['tool_call'])
        return Command(update={"tool_result": result}, goto="agent")
    except ToolError as e:
        # Let the LLM see what went wrong and try again
        return Command(
            update={"tool_result": f"Tool error: {str(e)}"},
            goto="agent"
        )
```

:::

:::js

```typescript
import { Command, GraphNode } from "@langchain/langgraph";

const executeTool: GraphNode<typeof State> = async (state, config) => {
  try {
    const result = await runTool(state.toolCall);
    return new Command({
      update: { toolResult: result },
      goto: "agent",
    });
  } catch (error) {
    // Let the LLM see what went wrong and try again
    return new Command({
      update: { toolResult: `Tool error: ${error}` },
      goto: "agent"
    });
  }
}
```

:::

User-fixable

    Pause and collect information from the user when needed (like account IDs, order numbers, or clarifications):

:::python

```python
from langgraph.types import Command


def lookup_customer_history(
    state: State
) -> Command[Literal["lookup_customer_history", "draft_response"]]:
    if not state.get('customer_id'):
        user_input = interrupt({
            "message": "Customer ID needed",
            "request": "Please provide the customer's account ID to look up their subscription history"
        })
        return Command(
            update={"customer_id": user_input['customer_id']},
            goto="lookup_customer_history"
        )
    # Now proceed with the lookup
    customer_data = fetch_customer_history(state['customer_id'])
    return Command(update={"customer_history": customer_data}, goto="draft_response")
```

:::

:::js

```typescript
import { Command, GraphNode, interrupt } from "@langchain/langgraph";

const lookupCustomerHistory: GraphNode<typeof State> = async (state, config) => {
  if (!state.customerId) {
    const userInput = interrupt({
      message: "Customer ID needed",
      request: "Please provide the customer's account ID to look up their subscription history",
    });
    return new Command({
      update: { customerId: userInput.customerId },
      goto: "lookupCustomerHistory",
    });
  }
  // Now proceed with the lookup
  const customerData = await fetchCustomerHistory(state.customerId);
  return new Command({
    update: { customerHistory: customerData },
    goto: "draftResponse",
  });
}
```

:::

Unexpected

    Let them bubble up for debugging. Don't catch what you can't handle:

:::python

```python
def send_reply(state: EmailAgentState):
    try:
        email_service.send(state["draft_response"])
    except Exception:
        raise  # Surface unexpected errors
```

:::

:::js

```typescript
import { Command, GraphNode } from "@langchain/langgraph";

const sendReply: GraphNode<typeof EmailAgentState> = async (state, config) => {
  try {
    await emailService.send(state.responseText);
  } catch (error) {
    throw error;  // Surface unexpected errors
  }
}
```

:::

Saga / compensation

    After retries are exhausted, run a recovery function that updates state and routes to a compensation branch.

:::python

See [Fault tolerance](https://docs.langchain.com/oss/langgraph/fault-tolerance#error-handling) for the full pattern.

`error_handler` requires `langgraph>=1.2`.

```python
from langgraph.errors import NodeError
from langgraph.types import Command, RetryPolicy

def payment_error_handler(state: State, error: NodeError) -> Command:
    return Command(
        update={"status": f"compensated: {error.error}"},
        goto="finalize",
    )

workflow.add_node(
    "charge_payment",
    charge_payment,
    retry_policy=RetryPolicy(max_attempts=3, retry_on=ConnectionError),
    error_handler=payment_error_handler,
)
```

To apply the same `retry_policy`, `timeout`, or `error_handler` to every node in a graph without repeating them on each `add_node`, use `StateGraph.set_node_defaults(...)`. Per-node values still take precedence. See [Fault tolerance](https://docs.langchain.com/oss/langgraph/fault-tolerance#graph-defaults).

:::

Implementing our email agent nodes

We'll implement each node as a simple function. Remember: nodes take state, do work, and return updates.

:::python

```python
from typing import Literal
from langgraph.graph import StateGraph, START, END
from langgraph.types import interrupt, Command, RetryPolicy
from langchain_openai import ChatOpenAI
from langchain.messages import HumanMessage

llm = ChatOpenAI(model="gpt-5-nano")

def read_email(state: EmailAgentState) -> dict:
    """Extract and parse email content"""
    # In production, this would connect to your email service
    return {
        "messages": [HumanMessage(content=f"Processing email: {state['email_content']}")]
    }

def classify_intent(state: EmailAgentState) -> Command[Literal["search_documentation", "human_review", "draft_response", "bug_tracking"]]:
    """Use LLM to classify email intent and urgency, then route accordingly"""

    # Create structured LLM that returns EmailClassification dict
    structured_llm = llm.with_structured_output(EmailClassification)

    # Format the prompt on-demand, not stored in state
    classification_prompt = f"""
    Analyze this customer email and classify it:

    Email: {state['email_content']}
    From: {state['sender_email']}

    Provide classification including intent, urgency, topic, and summary.
    """

    # Get structured response directly as dict
    classification = structured_llm.invoke(classification_prompt)

    # Determine next node based on classification
    if classification['intent'] == 'billing' or classification['urgency'] == 'critical':
        goto = "human_review"
    elif classification['intent'] in ['question', 'feature']:
        goto = "search_documentation"
    elif classification['intent'] == 'bug':
        goto = "bug_tracking"
    else:
        goto = "draft_response"

    # Store classification as a single dict in state
    return Command(
        update={"classification": classification},
        goto=goto
    )
```

:::

:::js

```typescript
import { StateGraph, START, END, GraphNode, Command } from "@langchain/langgraph";
import { HumanMessage } from "@langchain/core/messages";
import { ChatAnthropic } from "@langchain/anthropic";

const llm = new ChatAnthropic({ model: "claude-sonnet-4-6" });

const readEmail: GraphNode<typeof EmailAgentState> = async (state, config) => {
  // Extract and parse email content
  // In production, this would connect to your email service
  console.log(`Processing email: ${state.emailContent}`);
  return {};
}

const classifyIntent: GraphNode<typeof EmailAgentState> = async (state, config) => {
  // Use LLM to classify email intent and urgency, then route accordingly

  // Create structured LLM that returns EmailClassification object
  const structuredLlm = llm.withStructuredOutput(EmailClassificationSchema);

  // Format the prompt on-demand, not stored in state
  const classificationPrompt = `
  Analyze this customer email and classify it:

  Email: ${state.emailContent}
  From: ${state.senderEmail}

  Provide classification including intent, urgency, topic, and summary.
  `;

  // Get structured response directly as object
  const classification = await structuredLlm.invoke(classificationPrompt);

  // Determine next node based on classification
  let nextNode: "searchDocumentation" | "humanReview" | "draftResponse" | "bugTracking";

  if (classification.intent === "billing" || classification.urgency === "critical") {
    nextNode = "humanReview";
  } else if (classification.intent === "question" || classification.intent === "feature") {
    nextNode = "searchDocumentation";
  } else if (classification.intent === "bug") {
    nextNode = "bugTracking";
  } else {
    nextNode = "draftResponse";
  }

  // Store classification as a single object in state
  return new Command({
    update: { classification },
    goto: nextNode,
  });
}
```

:::





:::python

```python
def search_documentation(state: EmailAgentState) -> Command[Literal["draft_response"]]:
    """Search knowledge base for relevant information"""

    # Build search query from classification
    classification = state.get('classification', {})
    query = f"{classification.get('intent', '')} {classification.get('topic', '')}"

    try:
        # Implement your search logic here
        # Store raw search results, not formatted text
        search_results = [
            "Reset password via Settings > Security > Change Password",
            "Password must be at least 12 characters",
            "Include uppercase, lowercase, numbers, and symbols"
        ]
    except SearchAPIError as e:
        # For recoverable search errors, store error and continue
        search_results = [f"Search temporarily unavailable: {str(e)}"]

    return Command(
        update={"search_results": search_results},  # Store raw results or error
        goto="draft_response"
    )

def bug_tracking(state: EmailAgentState) -> Command[Literal["draft_response"]]:
    """Create or update bug tracking ticket"""

    # Create ticket in your bug tracking system
    ticket_id = "BUG-12345"  # Would be created via API

    return Command(
        update={
            "search_results": [f"Bug ticket {ticket_id} created"],
            "current_step": "bug_tracked"
        },
        goto="draft_response"
    )
```

:::

:::js

```typescript
import { Command, GraphNode } from "@langchain/langgraph";

const searchDocumentation: GraphNode<typeof EmailAgentState> = async (state, config) => {
  // Search knowledge base for relevant information

  // Build search query from classification
  const classification = state.classification!;
  const query = `${classification.intent} ${classification.topic}`;

  let searchResults: string[];

  try {
    // Implement your search logic here
    // Store raw search results, not formatted text
    searchResults = [
      "Reset password via Settings > Security > Change Password",
      "Password must be at least 12 characters",
      "Include uppercase, lowercase, numbers, and symbols",
    ];
  } catch (error) {
    // For recoverable search errors, store error and continue
    searchResults = [`Search temporarily unavailable: ${error}`];
  }

  return new Command({
    update: { searchResults },  // Store raw results or error
    goto: "draftResponse",
  });
}

const bugTracking: GraphNode<typeof EmailAgentState> = async (state, config) => {
  // Create or update bug tracking ticket

  // Create ticket in your bug tracking system
  const ticketId = "BUG-12345";  // Would be created via API

  return new Command({
    update: { searchResults: [`Bug ticket ${ticketId} created`] },
    goto: "draftResponse",
  });
}
```
:::




:::python

```python
def draft_response(state: EmailAgentState) -> Command[Literal["human_review", "send_reply"]]:
    """Generate response using context and route based on quality"""

    classification = state.get('classification', {})

    # Format context from raw state data on-demand
    context_sections = []

    if state.get('search_results'):
        # Format search results for the prompt
        formatted_docs = "\n".join([f"- {doc}" for doc in state['search_results']])
        context_sections.append(f"Relevant documentation:\n{formatted_docs}")

    if state.get('customer_history'):
        # Format customer data for the prompt
        context_sections.append(f"Customer tier: {state['customer_history'].get('tier', 'standard')}")

    # Build the prompt with formatted context
    draft_prompt = f"""
    Draft a response to this customer email:
    {state['email_content']}

    Email intent: {classification.get('intent', 'unknown')}
    Urgency level: {classification.get('urgency', 'medium')}

    {chr(10).join(context_sections)}

    Guidelines:
    - Be professional and helpful
    - Address their specific concern
    - Use the provided documentation when relevant
    """

    response = llm.invoke(draft_prompt)

    # Determine if human review needed based on urgency and intent
    needs_review = (
        classification.get('urgency') in ['high', 'critical'] or
        classification.get('intent') == 'complex'
    )

    # Route to appropriate next node
    goto = "human_review" if needs_review else "send_reply"

    return Command(
        update={"draft_response": response.content},  # Store only the raw response
        goto=goto
    )

def human_review(state: EmailAgentState) -> Command[Literal["send_reply", END]]:
    """Pause for human review using interrupt and route based on decision"""

    classification = state.get('classification', {})

    # interrupt() must come first - any code before it will re-run on resume
    human_decision = interrupt({
        "email_id": state.get('email_id',''),
        "original_email": state.get('email_content',''),
        "draft_response": state.get('draft_response',''),
        "urgency": classification.get('urgency'),
        "intent": classification.get('intent'),
        "action": "Please review and approve/edit this response"
    })

    # Now process the human's decision
    if human_decision.get("approved"):
        return Command(
            update={"draft_response": human_decision.get("edited_response", state.get('draft_response',''))},
            goto="send_reply"
        )
    else:
        # Rejection means human will handle directly
        return Command(update={}, goto=END)

def send_reply(state: EmailAgentState) -> dict:
    """Send the email response"""
    # Integrate with email service
    print(f"Sending reply: {state['draft_response'][:100]}...")
    return {}
```

:::

:::js

```typescript
import { Command, interrupt } from "@langchain/langgraph";

const draftResponse: GraphNode<typeof EmailAgentState> = async (state, config) => {
  // Generate response using context and route based on quality

  const classification = state.classification!;

  // Format context from raw state data on-demand
  const contextSections: string[] = [];

  if (state.searchResults) {
    // Format search results for the prompt
    const formattedDocs = state.searchResults.map(doc => `- ${doc}`).join("\n");
    contextSections.push(`Relevant documentation:\n${formattedDocs}`);
  }

  if (state.customerHistory) {
    // Format customer data for the prompt
    contextSections.push(`Customer tier: ${state.customerHistory.tier ?? "standard"}`);
  }

  // Build the prompt with formatted context
  const draftPrompt = `
  Draft a response to this customer email:
  ${state.emailContent}

  Email intent: ${classification.intent}
  Urgency level: ${classification.urgency}

  ${contextSections.join("\n\n")}

  Guidelines:
  - Be professional and helpful
  - Address their specific concern
  - Use the provided documentation when relevant
  `;

  const response = await llm.invoke([new HumanMessage(draftPrompt)]);

  // Determine if human review needed based on urgency and intent
  const needsReview = (
    classification.urgency === "high" ||
    classification.urgency === "critical" ||
    classification.intent === "complex"
  );

  // Route to appropriate next node
  const nextNode = needsReview ? "humanReview" : "sendReply";

  return new Command({
    update: { responseText: response.content.toString() },  // Store only the raw response
    goto: nextNode,
  });
}

const humanReview: GraphNode<typeof EmailAgentState> = async (state, config) => {
  // Pause for human review using interrupt and route based on decision
  const classification = state.classification!;

  // interrupt() must come first - any code before it will re-run on resume
  const humanDecision = interrupt({
    emailId: state.emailId,
    originalEmail: state.emailContent,
    draftResponse: state.responseText,
    urgency: classification.urgency,
    intent: classification.intent,
    action: "Please review and approve/edit this response",
  });

  // Now process the human's decision
  if (humanDecision.approved) {
    return new Command({
      update: { responseText: humanDecision.editedResponse || state.responseText },
      goto: "sendReply",
    });
  } else {
    // Rejection means human will handle directly
    return new Command({ update: {}, goto: END });
  }
}

const sendReply: GraphNode<typeof EmailAgentState> = async (state, config) => {
  // Send the email response
  // Integrate with email service
  console.log(`Sending reply: ${state.responseText!.substring(0, 100)}...`);
  return {};
}
```

:::

Step 5: Wire it together

Now we connect our nodes into a working graph. Since our nodes handle their own routing decisions, we only need a few essential edges.

To enable human-in-the-loop (opens in a new tab) with interrupt(), we need to compile with a checkpointer (opens in a new tab) to save state between runs:

from langgraph.checkpoint.memory import MemorySaver
from langgraph.types import RetryPolicy
 
# Create the graph
workflow = StateGraph(EmailAgentState)
 
# Add nodes with appropriate error handling
workflow.add_node("read_email", read_email)
workflow.add_node("classify_intent", classify_intent)
 
# Add retry policy for nodes that might have transient failures
workflow.add_node(
    "search_documentation",
    search_documentation,
    retry_policy=RetryPolicy(max_attempts=3)
)
workflow.add_node("bug_tracking", bug_tracking)
workflow.add_node("draft_response", draft_response)
workflow.add_node("human_review", human_review)
workflow.add_node("send_reply", send_reply)
 
# Add only the essential edges
workflow.add_edge(START, "read_email")
workflow.add_edge("read_email", "classify_intent")
workflow.add_edge("send_reply", END)
 
# Compile with checkpointer for persistence, in case run graph with Local_Server --> Please compile without checkpointer
memory = MemorySaver()
app = workflow.compile(checkpointer=memory)

The graph structure is minimal because routing happens inside nodes through @[Command] objects. Each node declares where it can go using type hints like Command[Literal["node1", "node2"]], making the flow explicit and traceable.

Try out your agent

Let's run our agent with an urgent billing issue that needs human review:

The graph pauses when it hits interrupt(), saves everything to the checkpointer, and waits. It can resume days later, picking up exactly where it left off. The thread_id ensures all state for this conversation is preserved together.

Summary and next steps

Key Insights

Building this email agent has shown us the LangGraph way of thinking:

Break into discrete steps:Each node does one thing well. This decomposition enables streaming progress updates, durable execution that can pause and resume, and clear debugging since you can inspect state between steps.
State is shared memory:Store raw data, not formatted text. This lets different nodes use the same information in different ways.
Nodes are functions:They take state, do work, and return updates. When they need to make routing decisions, they specify both the state updates and the next destination.
Errors are part of the flow:Transient failures get retries, LLM-recoverable errors loop back with context, user-fixable problems pause for input, and unexpected errors bubble up for debugging.
Human input is first-class (opens in a new tab):The interrupt() function pauses execution indefinitely, saves all state, and resumes exactly where it left off when you provide input. When combined with other operations in a node, it must come first.
Graph structure emerges naturally:You define the essential connections, and your nodes handle their own routing logic. This keeps control flow explicit and traceable - you can always understand what your agent will do next by looking at the current node.

Advanced considerations

This section explores the trade-offs in node granularity design. Most applications can skip this and use the patterns shown above.

You might wonder: why not combine Read Email and Classify Intent into one node?

Or why separate Doc Search from Draft Reply?

The answer involves trade-offs between resilience and observability.

The resilience consideration: LangGraph's persistence layer (opens in a new tab) creates checkpoints at node boundaries. When a workflow resumes after an interruption or failure, it starts from the beginning of the node where execution stopped. Smaller nodes mean more frequent checkpoints, which means less work to repeat if something goes wrong. If you combine multiple operations into one large node, a failure near the end means re-executing everything from the start of that node.

Why we chose this breakdown for the email agent:

Isolation of external services: Doc Search and Bug Track are separate nodes because they call external APIs. If the search service is slow or fails, we want to isolate that from the LLM calls. We can add retry policies to these specific nodes without affecting others.
Intermediate visibility: Having Classify Intent as its own node lets us inspect what the LLM decided before taking action. This is valuable for debugging and monitoring—you can see exactly when and why the agent routes to human review.
Different failure modes: LLM calls, database lookups, and email sending have different retry strategies. Separate nodes let you configure these independently.
Reusability and testing: Smaller nodes are easier to test in isolation and reuse in other workflows.

A different valid approach: You could combine Read Email and Classify Intent into a single node. You'd lose the ability to inspect the raw email before classification and would repeat both operations on any failure in that node. For most applications, the observability and debugging benefits of separate nodes are worth the trade-off.

Application-level concerns: The caching discussion in Step 2 (whether to cache search results) is an application-level decision, not a LangGraph framework feature. You implement caching within your node functions based on your specific requirements—LangGraph doesn't prescribe this.

Performance considerations: More nodes doesn't mean slower execution. LangGraph writes checkpoints in the background by default (async durability mode (opens in a new tab)), so your graph continues running without waiting for checkpoints to complete. This means you get frequent checkpoints with minimal performance impact. You can adjust this behavior if needed—use "exit" mode to checkpoint only at completion, or "sync" mode to block execution until each checkpoint is written.

Where to go from here

This was an introduction to thinking about building agents with LangGraph. You can extend this foundation with:

Human-in-the-loop patterns (opens in a new tab):Learn how to add tool approval before execution, batch approval, and other patterns
Subgraphs (opens in a new tab):Create subgraphs for complex multi-step operations
Streaming (opens in a new tab):Add streaming to show real-time progress to users
Observability (opens in a new tab):Add observability with LangSmith for debugging and monitoring
Tool Integration (opens in a new tab):Integrate more tools for web search, database queries, and API calls
Retry Logic (opens in a new tab):Implement retry logic with exponential backoff for failed operations

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