Thinking in LangGraph
Status: ACTIVE (pulled from docs.langchain.com) Source: https://docs.langchain.com/oss/python/langgraph/thinking-in-langgraph Timestamp: 2026-05-11
The 5-Step Methodology
When building an agent with LangGraph, follow these five steps:
Step 1: Map out your workflow as discrete steps
Start by identifying the distinct steps in your process. Each step becomes a node (a function that does one specific thing). Sketch how they connect.
START -> Read Email -> Classify Intent -> (Doc Search | Bug Track | Human Review)
-> Draft Reply -> (Human Review | Send Reply) -> END
Step 2: Identify what each step needs to do
| Step Type | When to Use | Example |
|---|---|---|
| LLM steps | Understand, analyze, generate text, make decisions | Classify intent, draft reply |
| Data steps | Retrieve information from external sources | Document search, customer history lookup |
| Action steps | Perform external actions | Send reply, bug track |
| User input steps | Need human intervention | Human review node |
Step 3: Design your state
State is the shared memory accessible to all nodes.
What belongs in state? - Data that needs to persist across steps (email content, classification, search results) - DON'T store: data derivable from other state
Key principle: Keep state raw, format prompts on-demand - Different nodes format the same data differently - Change prompt templates without modifying state schema - Clearer debugging
class EmailAgentState(TypedDict):
email_content: str
sender_email: str
classification: dict | None
search_results: list[str] | None
draft_response: str | None
Step 4: Build your nodes
Nodes are Python functions: state in -> state update out.
Error handling strategies:
| Error Type | Who Fixes | Strategy |
|---|---|---|
| Transient (network, rate limits) | System | Retry policy |
| LLM-recoverable | LLM | Store error, loop back |
| User-fixable | Human | interrupt() |
| Recoverable after retries | Developer | error_handler |
| Unexpected | Developer | Let bubble up |
Step 5: Wire it together
Connect nodes into a working graph. Since nodes handle their own routing via Command, only essential edges are needed. Compile with a checkpointer for persistence:
from langgraph.checkpoint.memory import MemorySaver
workflow = StateGraph(EmailAgentState)
# ... add nodes ...
memory = MemorySaver()
app = workflow.compile(checkpointer=memory)
Key Insights
- Break into discrete steps: Each node does one thing. Enables streaming, durable execution, debugging.
- State is shared memory: Store raw data, not formatted text.
- Nodes are functions: Take state, do work, return updates + routing.
- Errors are part of the flow: Different errors get different handling.
- Human input is first-class:
interrupt()pauses indefinitely, resumes exactly where it left off. - Graph structure emerges naturally: Define essential connections; nodes handle their own routing.