> ## Documentation Index
> Fetch the complete documentation index at: https://docs.langchain.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Thinking in LangGraph
> Learn how to think about building agents with 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:
```txt theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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.
```mermaid theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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.
Use when you need to understand, analyze, generate text, or make reasoning decisions
Use when you need to retrieve information from external sources
Use when you need to perform external actions
Use when you need human intervention
### LLM steps
When a step needs to understand, analyze, generate text, or make reasoning decisions:
* Static context (prompt): Classification categories, urgency definitions, response format
* Dynamic context (from state): Email content, sender information
* Desired outcome: Structured classification that determines routing
* 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:
* 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
* 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:
* 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
* 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:
* 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](/oss/python/concepts/memory) 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:
Does it need to persist across steps? If yes, it goes in state.
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:
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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 |
Add a retry policy to automatically retry network issues and rate limits.
Combine with `timeout=` to cap each attempt. See [Fault tolerance](/oss/python/langgraph/fault-tolerance) for the full lifecycle.
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langgraph.types import RetryPolicy
workflow.add_node(
"search_documentation",
search_documentation,
retry_policy=RetryPolicy(max_attempts=3, initial_interval=1.0)
)
```
Store the error in state and loop back so the LLM can see what went wrong and try again:
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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"
)
```
Pause and collect information from the user when needed (like account IDs, order numbers, or clarifications):
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langgraph.types import Command
def lookup_customer_history(state: State) -> Command[Literal["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")
```
Let them bubble up for debugging. Don't catch what you can't handle:
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
def send_reply(state: EmailAgentState):
try:
email_service.send(state["draft_response"])
except Exception:
raise # Surface unexpected errors
```
After retries are exhausted, run a recovery function that updates state and routes to a compensation branch.
See [Fault tolerance](/oss/python/langgraph/fault-tolerance#error-handling) for the full pattern.
`error_handler` requires `langgraph>=1.2`.
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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,
)
```
### Implementing our email agent nodes
We'll implement each node as a simple function. Remember: nodes take state, do work, and return updates.
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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
)
```
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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"
)
```
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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 {}
```
## 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](/oss/python/langgraph/interrupts) with `interrupt()`, we need to compile with a [checkpointer](/oss/python/langgraph/persistence) to save state between runs:
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
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`](https://reference.langchain.com/python/langgraph/types/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:
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# Test with an urgent billing issue
initial_state = {
"email_content": "I was charged twice for my subscription! This is urgent!",
"sender_email": "customer@example.com",
"email_id": "email_123",
"messages": []
}
# Run with a thread_id for persistence
config = {"configurable": {"thread_id": "customer_123"}}
result = app.invoke(initial_state, config)
# The graph will pause at human_review
print(f"human review interrupt:{result['__interrupt__']}")
# When ready, provide human input to resume
from langgraph.types import Command
human_response = Command(
resume={
"approved": True,
"edited_response": "We sincerely apologize for the double charge. I've initiated an immediate refund..."
}
)
# Resume execution
final_result = app.invoke(human_response, config)
print(f"Email sent successfully!")
```
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:
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.
Store raw data, not formatted text. This lets different nodes use the same information in different ways.
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.
Transient failures get retries, LLM-recoverable errors loop back with context, user-fixable problems pause for input, and unexpected errors bubble up for debugging.
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.
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 [durable execution](/oss/python/langgraph/durable-execution) 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](/oss/python/langgraph/durable-execution#durability-modes)), 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:
Learn how to add tool approval before execution, batch approval, and other patterns
Create subgraphs for complex multi-step operations
Add streaming to show real-time progress to users
Add observability with LangSmith for debugging and monitoring
Integrate more tools for web search, database queries, and API calls
Implement retry logic with exponential backoff for failed operations
***
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[Edit this page on GitHub](https://github.com/langchain-ai/docs/edit/main/src/oss/langgraph/thinking-in-langgraph.mdx) or [file an issue](https://github.com/langchain-ai/docs/issues/new/choose).