This article is published in English.
Practical notes: Your First Real LangGraph Project: Building a Customer Support
Operable walkthrough of Practical notes: Your First Real LangGraph Project: Building a Customer Support: contracts, checks, and drop-in code slots for teams shipping this pattern.
The following notes reconstruct a practical path around “Your First Real LangGraph Project: Building a Customer Support Agent.”. Emphasis stays on contracts, checks, and drop-in code placeholders rather than motivational framing. When working through the Overview stage, write down the contract first: required inputs, success signal, and what happens on partial failure. That checklist keeps later code changes honest. Keep configuration outside application code. Environment files, secret stores, and feature flags belong in one place operators can audit without reading the whole graph.
Before We Write a Single Line: Understand the Plan
The Before We Write a stage works best when treated as a measurable surface. Capture one golden transcript, one failure case, and the rollback note before expanding scope. Document the happy path and the recovery path together. Retries, human gates, and dead-letter handling are part of the product, not later polish. Keep graph state flat and typed. Nested blobs hide which node wrote which field and break resume after interrupts.
The Setup
The Setup works best when treated as a measurable surface. Capture one golden transcript, one failure case, and the rollback note before expanding scope. Prefer small, testable units over sprawling scripts. When a step fails, the failure should point at a single responsibility rather than a tangled pipeline. Keep graph state flat and typed. Nested blobs hide which node wrote which field and break resume after interrupts.
pip install langgraph langchain langchain-openai langgraph-checkpoint-sqlite python-dotenv
OPENAI_API_KEY=your-key-here
Module 1: Imports & Configuration
Module 1: Imports & Configuration works best when treated as a measurable surface. Capture one golden transcript, one failure case, and the rollback note before expanding scope. Treat this stage as a contract between inputs and validated outputs. Name the artifacts, define success checks, and refuse silent partial completion. Keep graph state flat and typed. Nested blobs hide which node wrote which field and break resume after interrupts. Module 1: Imports & Configuration works best when treated as a measurable surface. Capture one golden transcript, one failure case, and the rollback note before expanding scope. Keep configuration outside application code. Environment files, secret stores, and feature flags belong in one place operators can audit without reading the whole graph.
# ============================================================
# MODULE 1: IMPORTS & CONFIGURATION
# ============================================================
import os
import sqlite3
from typing import Annotated, Literal
from datetime import datetime
from dotenv import load_dotenv
# LangChain - the AI layer
from langchain_openai import ChatOpenAI
from langchain_core.messages import (
HumanMessage,
AIMessage,
SystemMessage,
BaseMessage,
RemoveMessage,
)
from langchain_core.tools import tool
# LangGraph - the graph layer
from langgraph.graph import StateGraph, MessagesState, START, END
from langgraph.prebuilt import ToolNode
from langgraph.checkpoint.memory import MemorySaver
from langgraph.types import interrupt, Command
load_dotenv()
# ── The LLM ─────────────────────────────────────────────────
# temperature=0 means deterministic - the agent behaves
# consistently, which is what you want for a support bot.
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
Module 2: State
For the Module 2 State stage, define the inputs, the owner of the step, and the exit criteria before changing code. Operators should be able to re-run the step from a known checkpoint without guessing hidden state. Document the happy path and the recovery path together. Retries, human gates, and dead-letter handling are part of the product, not later polish. Put human approval on edges that spend money or change production data. Compile-time wiring does not equal business completeness.
# ============================================================
# MODULE 2: STATE
# ============================================================
class SupportState(MessagesState):
# MessagesState already gives us:
# messages: Annotated[list[BaseMessage], add_messages]
# We add three more fields for our specific needs:
# The running summary of the conversation (Part 2 pattern).
# Starts empty. Gets written by summarize_node when conversation gets long.
summary: str
# The name of the customer, extracted early in the conversation.
# Used to personalise every response. Starts empty.
customer_name: str
# Tracks the current ticket category, set by the agent.
# Helps the human reviewer understand context during escalation.
# Values: "order_inquiry" | "refund_request" | "complaint" | "general"
ticket_category: str
Why these three fields?
For the Why these three fields stage, define the inputs, the owner of the step, and the exit criteria before changing code. Operators should be able to re-run the step from a known checkpoint without guessing hidden state. Prefer small, testable units over sprawling scripts. When a step fails, the failure should point at a single responsibility rather than a tangled pipeline. Put human approval on edges that spend money or change production data. Compile-time wiring does not equal business completeness.
Module 3: Tools
For the Module 3 Tools stage, define the inputs, the owner of the step, and the exit criteria before changing code. Operators should be able to re-run the step from a known checkpoint without guessing hidden state. Treat this stage as a contract between inputs and validated outputs. Name the artifacts, define success checks, and refuse silent partial completion. Authenticate at the gateway and re-authorize at the data plane. A bearer token alone is not a tenancy boundary. For the Module 3 Tools stage, define the inputs, the owner of the step, and the exit criteria before changing code. Operators should be able to re-run the step from a known checkpoint without guessing hidden state. Keep configuration outside application code. Environment files, secret stores, and feature flags belong in one place operators can audit without reading the whole graph.
# ============================================================
# MODULE 3: TOOLS
# ============================================================
# ── Fake Database ────────────────────────────────────────────
# In a real project, these would be database queries or API calls.
# For learning purposes, we use a simple Python dictionary.
ORDERS_DB = {
"ORD-001": {
"customer": "Alex",
"product": "Wireless Headphones",
"status": "Delivered",
"amount": 89.99,
"delivery_date": "2025-06-10",
},
"ORD-002": {
"customer": "Sam",
"product": "Phone Case",
"status": "In Transit",
"amount": 14.99,
"delivery_date": "Expected 2025-06-18",
},
"ORD-003": {
"customer": "Jordan",
"product": "Laptop Stand",
"status": "Processing",
"amount": 45.00,
"delivery_date": "Expected 2025-06-20",
},
}
@tool
def lookup_order(order_id: str) -> str:
"""Look up the details of a customer's order by order ID.
Use this when the customer provides an order number and wants
to know the status, product name, or delivery date of their order.
Args:
order_id: The order ID string, e.g. 'ORD-001'
Returns:
A formatted string with full order details, or an error message
if the order is not found.
"""
order = ORDERS_DB.get(order_id.upper())
if not order:
return f"No order found with ID '{order_id}'. Please double-check the order number."
return (
f"Order {order_id.upper()}: {order['product']} | "
f"Status: {order['status']} | "
f"Amount: ${order['amount']:.2f} | "
f"Delivery: {order['delivery_date']}"
)
@tool
def check_refund_eligibility(order_id: str) -> str:
"""Check whether an order is eligible for a refund.
Use this BEFORE processing any refund request. An order is eligible
for a refund only if its status is 'Delivered'. Orders Fthat are
'In Transit' or 'Processing' cannot be refunded yet.
Args:
order_id: The order ID string, e.g. 'ORD-001'
Returns:
A string stating whether the order is eligible and why.
"""
order = ORDERS_DB.get(order_id.upper())
if not order:
return f"Cannot check refund: order '{order_id}' not found."
if order["status"] == "Delivered":
return (
f"Order {order_id.upper()} IS eligible for a refund. "
f"Product: {order['product']}, Amount: ${order['amount']:.2f}. "
f"Proceed to refund processing."
)
else:
return (
f"Order {order_id.upper()} is NOT eligible for a refund yet. "
f"Current status: {order['status']}. Refunds are only available "
f"for delivered orders."
)
@tool
def process_refund(order_id: str, reason: str) -> str:
"""Process a refund for a delivered order.
IMPORTANT: This tool actually issues the refund. It should only be
called AFTER human approval has been obtained. Never call this tool
without prior confirmation.
Args:
order_id: The order ID to refund
reason: The customer's stated reason for the refund
Returns:
A confirmation string with the refund reference number.
"""
order = ORDERS_DB.get(order_id.upper())
if not order:
return f"Refund failed: order '{order_id}' not found."
# In a real system, this would hit your payments API.
refund_ref = f"REF-{order_id.upper()}-{datetime.now().strftime('%H%M%S')}"
return (
f"Refund APPROVED and PROCESSED. Reference: {refund_ref}. "
f"${order['amount']:.2f} will be returned to the original payment method "
f"within 3–5 business days. Reason logged: '{reason}'."
)
# ── Collect tools and bind to LLM ───────────────────────────
# All three tools in one list.
tools = [lookup_order, check_refund_eligibility, process_refund]
# llm_with_tools = the LLM that KNOWS about the tools and can decide to call them.
# This is what we use inside agent_node.
llm_with_tools = llm.bind_tools(tools)
# tool_node = the pre-built node that EXECUTES whatever tool the LLM chose.
# This is what we register in Module 6.
tool_node = ToolNode(tools)
The Docstring Rule — One More Time
When working through the The Docstring Rule One stage, write down the contract first: required inputs, success signal, and what happens on partial failure. That checklist keeps later code changes honest. Document the happy path and the recovery path together. Retries, human gates, and dead-letter handling are part of the product, not later polish. Checkpoint after expensive steps. Resume should not re-bill the same LLM call when an operator retries a later node.
Module 4: Nodes
When working through the Module 4 Nodes stage, write down the contract first: required inputs, success signal, and what happens on partial failure. That checklist keeps later code changes honest. Prefer small, testable units over sprawling scripts. When a step fails, the failure should point at a single responsibility rather than a tangled pipeline. Checkpoint after expensive steps. Resume should not re-bill the same LLM call when an operator retries a later node.
# ============================================================
# MODULE 4: NODES
# ============================================================
# ── The System Prompt ────────────────────────────────────────
# Written once, used in every call to the LLM from agent_node.
# This is the personality and rulebook of your agent.
SYSTEM_PROMPT = """You are ShopBot, a friendly and professional customer support \
agent for an e-commerce store.
Your capabilities:
- Look up order details using the lookup_order tool
- Check if an order qualifies for a refund using check_refund_eligibility
- Process approved refunds using the process_refund tool
Your rules:
- Always greet the customer by name once you know it
- Always check refund eligibility BEFORE attempting to process a refund
- For refund requests, set ticket_category to "refund_request" in your reasoning
- Be empathetic, clear, and concise
- If you cannot help, offer to escalate to a human agent
Important: The process_refund tool requires prior human approval. Do not call it \
unless the conversation shows that a human has already approved the refund."""
# ── Node 1: agent_node ──────────────────────────────────────
def agent_node(state: SupportState) -> dict:
"""The brain of the operation. Reads state, calls the LLM, and decides
whether to use a tool, give a final answer, or do something else.
This node handles two cases:
1. Normal conversation - just call the LLM and respond
2. Long conversation - if a summary exists, prepend it so the LLM
has context without seeing all the raw messages
"""
# Part 2 pattern: check for an existing summary
summary = state.get("summary", "")
if summary:
# Build context: system prompt + compressed history + recent messages
system_with_summary = SystemMessage(
content=f"{SYSTEM_PROMPT}\n\nSummary of conversation so far:\n{summary}"
)
messages_to_send = [system_with_summary] + state["messages"]
else:
# No summary yet - full history is short enough to send as-is
system_msg = SystemMessage(content=SYSTEM_PROMPT)
messages_to_send = [system_msg] + state["messages"]
# Call the LLM. It sees tools and can choose to call one.
response = llm_with_tools.invoke(messages_to_send)
# Detect ticket category from the response for routing purposes.
# A smarter version would have the LLM explicitly set this -
# for now, we scan for keywords.
content_lower = response.content.lower() if response.content else ""
updates: dict = {"messages": [response]}
if "refund" in content_lower or (
hasattr(response, "tool_calls")
and any("refund" in str(tc).lower() for tc in (response.tool_calls or []))
):
updates["ticket_category"] = "refund_request"
return updates
# ── Node 2: review_refund ───────────────────────────────────
def review_refund(state: SupportState) -> dict:
"""The human approval gate. Pauses execution, shows the pending refund
details to a human agent, and waits for their decision.
This implements the Part 3 interrupt() pattern. Execution stops here
until someone calls graph.invoke(Command(resume=...), config).
Three outcomes the human can choose:
- "approve" → let the refund tool call proceed unchanged
- "reject" → cancel the refund, send a message to the customer
- "escalate" → hand the entire ticket to a human support agent
"""
last_message = state["messages"][-1]
# Find the refund-related tool call in the last AI message.
# We look for process_refund specifically - the "real action" tool.
refund_tool_call = None
if hasattr(last_message, "tool_calls"):
for tc in last_message.tool_calls:
if "refund" in tc["name"].lower():
refund_tool_call = tc
break
# Surface the context to the human reviewer via interrupt().
# Everything in this dict is what the human sees before deciding.
human_decision = interrupt({
"message": "⚠️ Refund approval required",
"customer_name": state.get("customer_name", "Unknown"),
"tool_being_called": refund_tool_call["name"] if refund_tool_call else "refund tool",
"arguments": refund_tool_call["args"] if refund_tool_call else {},
"conversation_summary": state.get("summary", "No summary yet"),
"options": ["approve", "reject", "escalate"],
})
# ── Handle the human's decision ─────────────────────────
if human_decision == "approve":
# Do nothing to state - let tool_node execute the tool call as-is
return {}
elif human_decision == "reject":
# Cancel the tool call. The LLM will see a ToolMessage explaining why,
# and generate a polite response to the customer.
from langchain_core.messages import ToolMessage
return {
"messages": [
ToolMessage(
content=(
"Refund request was reviewed and declined by our support team. "
"Please inform the customer politely and offer alternatives."
),
tool_call_id=refund_tool_call["id"] if refund_tool_call else "unknown",
)
]
}
elif human_decision == "escalate":
# Signal escalation - in a real system you'd open a ticket,
# ping Slack, or transfer to a live agent queue.
from langchain_core.messages import ToolMessage
return {
"messages": [
ToolMessage(
content=(
"This ticket has been escalated to a senior support agent. "
"Inform the customer that a human agent will contact them "
"within 2 business hours."
),
tool_call_id=refund_tool_call["id"] if refund_tool_call else "unknown",
)
]
}
# Fallback - treat as approve
return {}
# ── Node 3: summarize_node ──────────────────────────────────
def summarize_node(state: SupportState) -> dict:
"""Triggered when the conversation exceeds 6 messages. Compresses the
full message history into a short summary, then deletes old raw messages.
This is the rolling summary pattern from Part 2. The summary grows
richer turn by turn. Token costs stay nearly flat no matter how long
the conversation runs.
"""
existing_summary = state.get("summary", "")
if existing_summary:
# Extend the existing summary with new messages
summary_instruction = (
f"Current summary:\n{existing_summary}\n\n"
"Extend this summary with the new messages above. "
"Keep it under 5 sentences. Focus on: the customer's name, "
"their issue, any orders mentioned, and what actions were taken."
)
else:
# First time summarising
summary_instruction = (
"Summarise this customer support conversation in under 5 sentences. "
"Include: the customer's name (if mentioned), their issue, "
"any order numbers discussed, and what actions were taken so far."
)
messages = state["messages"] + [HumanMessage(content=summary_instruction)]
response = llm.invoke(messages) # Plain llm, no tools needed here
# Delete all but the 2 most recent messages.
# The summary now holds everything that was in the deleted messages.
messages_to_delete = [
RemoveMessage(id=m.id) for m in state["messages"][:-2]
]
return {
"summary": response.content,
"messages": messages_to_delete,
}
Module 5: Edges & Routing
When working through the Module 5 Edges Routing stage, write down the contract first: required inputs, success signal, and what happens on partial failure. That checklist keeps later code changes honest. Treat this stage as a contract between inputs and validated outputs. Name the artifacts, define success checks, and refuse silent partial completion. Checkpoint after expensive steps. Resume should not re-bill the same LLM call when an operator retries a later node. When working through the Module 5 Edges Routing stage, write down the contract first: required inputs, success signal, and what happens on partial failure. That checklist keeps later code changes honest. Keep configuration outside application code. Environment files, secret stores, and feature flags belong in one place operators can audit without reading the whole graph.
# ============================================================
# MODULE 5: EDGES & ROUTING
# ============================================================
def route_after_agent(state: SupportState) -> Literal[
"review_refund", "tools", "summarize_node", "__end__"
]:
"""Called after agent_node runs. Decides what happens next.
Four possible routes:
1. The LLM wants to call process_refund → must go through human review first
2. The LLM wants to call any other tool → go directly to tool_node
3. The LLM gave a plain text answer AND the conversation is long → summarise
4. The LLM gave a plain text answer and conversation is short → we're done
"""
last_message = state["messages"][-1]
has_tool_calls = hasattr(last_message, "tool_calls") and bool(last_message.tool_calls)
if has_tool_calls:
# Check if ANY of the tool calls is the sensitive process_refund tool
tool_names = [tc["name"] for tc in last_message.tool_calls]
if "process_refund" in tool_names:
return "review_refund" # → Pause for human approval first
return "tools" # → Safe tool, run it directly
# No tool call - the LLM gave a plain response.
# Check if the conversation is long enough to need summarisation.
if len(state["messages"]) > 6:
return "summarize_node"
return "__end__" # → Conversation turn is complete
def route_after_review(state: SupportState) -> Literal["tools", "agent_node"]:
"""Called after review_refund runs (i.e., after the human has decided).
Two routes:
1. Human approved or escalated → run the tool (tool_node handles the call)
2. Human rejected → the review node already added a ToolMessage cancelling
the tool call, so skip tool_node and go back to agent_node to respond
"""
last_message = state["messages"][-1]
# If the last message is a ToolMessage, the review node cancelled the call.
# Go back to agent_node so it can generate a customer-facing response.
from langchain_core.messages import ToolMessage
if isinstance(last_message, ToolMessage):
return "agent_node"
# Otherwise, the review node returned {} (approved) - proceed to tools.
return "tools"
Module 6: Graph Assembly
Module 6: Graph Assembly works best when treated as a measurable surface. Capture one golden transcript, one failure case, and the rollback note before expanding scope. Document the happy path and the recovery path together. Retries, human gates, and dead-letter handling are part of the product, not later polish. Keep graph state flat and typed. Nested blobs hide which node wrote which field and break resume after interrupts.
# ============================================================
# MODULE 6: GRAPH ASSEMBLY
# ============================================================
# ── Step 1: Initialize ──────────────────────────────────────
graph_builder = StateGraph(SupportState)
# ── Step 2: Register All Nodes ──────────────────────────────
# Format: add_node("string_name", function)
# The string name is what you use in every edge definition below.
graph_builder.add_node("agent_node", agent_node)
graph_builder.add_node("tools", tool_node) # Pre-built from Module 3
graph_builder.add_node("review_refund", review_refund)
graph_builder.add_node("summarize_node", summarize_node)
# ── Step 3: Set Entry Point ─────────────────────────────────
# The first node that runs when a user sends a message.
graph_builder.add_edge(START, "agent_node")
# ── Step 4: Wire the Edges ──────────────────────────────────
# After agent_node: conditional - depends on what the LLM decided
graph_builder.add_conditional_edges(
"agent_node", # Source
route_after_agent, # Router function from Module 5
{
"review_refund": "review_refund", # Refund tool → human review first
"tools": "tools", # Other tools → run directly
"summarize_node": "summarize_node", # Long conversation → summarise
"__end__": END, # Plain answer → done
}
)
# After review_refund: conditional - depends on human's decision
graph_builder.add_conditional_edges(
"review_refund",
route_after_review,
{
"tools": "tools", # Approved → execute the tool
"agent_node": "agent_node", # Rejected → back to agent to respond
}
)
# After tools run: always go back to agent_node
# (the ReAct loop - agent sees tool result, decides what to do next)
graph_builder.add_edge("tools", "agent_node")
# After summarization: conversation turn is done
graph_builder.add_edge("summarize_node", END)
# ── Step 5: Compile ─────────────────────────────────────────
# Using MemorySaver for development.
# For production, swap this one line to SqliteSaver or PostgresSaver.
memory = MemorySaver()
shopbot = graph_builder.compile(checkpointer=memory)
Visualising the Graph (Optional but Recommended)
The Visualising the Graph Optional stage works best when treated as a measurable surface. Capture one golden transcript, one failure case, and the rollback note before expanding scope. Prefer small, testable units over sprawling scripts. When a step fails, the failure should point at a single responsibility rather than a tangled pipeline. Keep graph state flat and typed. Nested blobs hide which node wrote which field and break resume after interrupts.
from IPython.display import display, Image
from langchain_core.runnables.graph import MermaidDrawMethod
display(Image(
shopbot.get_graph().draw_mermaid_png(
draw_method=MermaidDrawMethod.API
)
))
Module 7: Entrypoint
Module 7: Entrypoint works best when treated as a measurable surface. Capture one golden transcript, one failure case, and the rollback note before expanding scope. Treat this stage as a contract between inputs and validated outputs. Name the artifacts, define success checks, and refuse silent partial completion. Keep graph state flat and typed. Nested blobs hide which node wrote which field and break resume after interrupts. Module 7: Entrypoint works best when treated as a measurable surface. Capture one golden transcript, one failure case, and the rollback note before expanding scope. Keep configuration outside application code. Environment files, secret stores, and feature flags belong in one place operators can audit without reading the whole graph.
# ============================================================
# MODULE 7: ENTRYPOINT
# ============================================================
def run_shopbot():
"""
Interactive command-line session with ShopBot.
Demonstrates: multi-turn conversation, tool use, and human-in-the-loop.
"""
print("=" * 55)
print(" ShopBot - Customer Support Agent")
print(" Powered by LangGraph")
print("=" * 55)
print("Type your message below. Type 'exit' to quit.")
print("Type 'state' to inspect what ShopBot currently remembers.\n")
# One config per session.
# thread_id is the session key - same ID = same memory thread.
# Change the ID to start a completely fresh conversation.
config = {"configurable": {"thread_id": "customer-session-001"}}
while True:
user_input = input("You: ").strip()
if not user_input:
continue
if user_input.lower() == "exit":
print("ShopBot: Thank you for contacting support. Have a great day!")
break
# ── Debug: inspect current state ────────────────────
if user_input.lower() == "state":
snapshot = shopbot.get_state(config)
print("\n[DEBUG] Current State:")
print(f" Messages in state : {len(snapshot.values.get('messages', []))}")
print(f" Customer name : {snapshot.values.get('customer_name', '(not set)')}")
print(f" Ticket category : {snapshot.values.get('ticket_category', '(not set)')}")
print(f" Summary : {snapshot.values.get('summary', '(none yet)')}")
print(f" Next node(s) : {snapshot.next}\n")
continue
# ── Normal message: invoke the graph ─────────────────
result = shopbot.invoke(
{"messages": [HumanMessage(content=user_input)]},
config=config,
)
# ── Check if graph paused for human approval ─────────
# This is how you detect that interrupt() was called inside review_refund.
while "__interrupt__" in result:
interrupt_data = result["__interrupt__"][0].value
print("\n" + "=" * 55)
print(" HUMAN APPROVAL REQUIRED")
print("=" * 55)
print(f" Customer : {interrupt_data.get('customer_name', 'Unknown')}")
print(f" Action : {interrupt_data.get('tool_being_called', 'refund')}")
print(f" Arguments : {interrupt_data.get('arguments', {})}")
print(f" Context : {interrupt_data.get('conversation_summary', 'N/A')}")
print("=" * 55)
print("Options: [a] Approve [r] Reject [e] Escalate")
human_choice = input("Your decision: ").strip().lower()
if human_choice == "a":
resume_value = "approve"
elif human_choice == "r":
resume_value = "reject"
elif human_choice == "e":
resume_value = "escalate"
else:
print("Invalid choice. Defaulting to reject.")
resume_value = "reject"
# Resume the graph with the human's decision.
# Command(resume=...) answers the pending interrupt() call.
result = shopbot.invoke(
Command(resume=resume_value),
config=config,
)
# ── Print the agent's final response ─────────────────
last_message = result["messages"][-1]
print(f"\nShopBot: {last_message.content}\n")
if __name__ == "__main__":
run_shopbot()
Running It: What a Real Conversation Looks Like
For the Running It What a stage, define the inputs, the owner of the step, and the exit criteria before changing code. Operators should be able to re-run the step from a known checkpoint without guessing hidden state. Document the happy path and the recovery path together. Retries, human gates, and dead-letter handling are part of the product, not later polish. Put human approval on edges that spend money or change production data. Compile-time wiring does not equal business completeness.
=======================================================
ShopBot — Customer Support Agent
Powered by LangGraph
=======================================================
Type your message below. Type 'exit' to quit.
Type 'state' to inspect what ShopBot currently remembers.
You: Hi, my name is Alex and I need help with order ORD-001
ShopBot: Hi Alex! I'd be happy to help you with order ORD-001.
I can see your order:
- Product: Wireless Headphones
- Status: Delivered on 2025-06-10
- Amount: $89.99
What can I help you with regarding this order?
You: I want to return it and get a refund
ShopBot: I've checked your order ORD-001 and it's eligible for a refund
since it's been delivered. Let me process that for you right away.
=======================================================
HUMAN APPROVAL REQUIRED
=======================================================
Customer : Alex
Action : process_refund
Arguments : {'order_id': 'ORD-001', 'reason': 'Customer requested return'}
Context : No summary yet
=======================================================
Options: [a] Approve [r] Reject [e] Escalate
Your decision: a
ShopBot: Great news, Alex! Your refund has been successfully processed.
Refund Reference: REF-ORD-001-143022
Amount: $89.99 will be returned to your original payment method within
3–5 business days.
Is there anything else I can help you with?
You: state
[DEBUG] Current State:
Messages in state : 6
Customer name : (not set)
Ticket category : refund_request
Summary : (none yet)
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What You Just Built and Why It Matters
For the What You Just Built stage, define the inputs, the owner of the step, and the exit criteria before changing code. Operators should be able to re-run the step from a known checkpoint without guessing hidden state. Prefer small, testable units over sprawling scripts. When a step fails, the failure should point at a single responsibility rather than a tangled pipeline. Put human approval on edges that spend money or change production data. Compile-time wiring does not equal business completeness.
Extending This Project (Your Next Steps)
For the Extending This Project Your stage, define the inputs, the owner of the step, and the exit criteria before changing code. Operators should be able to re-run the step from a known checkpoint without guessing hidden state. Treat this stage as a contract between inputs and validated outputs. Name the artifacts, define success checks, and refuse silent partial completion. Put human approval on edges that spend money or change production data. Compile-time wiring does not equal business completeness. For the Extending This Project Your stage, define the inputs, the owner of the step, and the exit criteria before changing code. Operators should be able to re-run the step from a known checkpoint without guessing hidden state. Keep configuration outside application code. Environment files, secret stores, and feature flags belong in one place operators can audit without reading the whole graph.
The Keyword Summary for This Project
When working through the The Keyword Summary for stage, write down the contract first: required inputs, success signal, and what happens on partial failure. That checklist keeps later code changes honest. Document the happy path and the recovery path together. Retries, human gates, and dead-letter handling are part of the product, not later polish. Checkpoint after expensive steps. Resume should not re-bill the same LLM call when an operator retries a later node.
Conclusion: The Map Is Not the Territory
When working through the Conclusion The Map Is stage, write down the contract first: required inputs, success signal, and what happens on partial failure. That checklist keeps later code changes honest. Prefer small, testable units over sprawling scripts. When a step fails, the failure should point at a single responsibility rather than a tangled pipeline. Checkpoint after expensive steps. Resume should not re-bill the same LLM call when an operator retries a later node.
FOR THE SECOND PART OF THIS PROJECT : Improving Our LangGraph Agent for Real-World E-Commerce
When working through the FOR THE SECOND PART stage, write down the contract first: required inputs, success signal, and what happens on partial failure. That checklist keeps later code changes honest. Treat this stage as a contract between inputs and validated outputs. Name the artifacts, define success checks, and refuse silent partial completion. Checkpoint after expensive steps. Resume should not re-bill the same LLM call when an operator retries a later node.
Operational checklist
For the Operational checklist stage, define the inputs, the owner of the step, and the exit criteria before changing code. Operators should be able to re-run the step from a known checkpoint without guessing hidden state.
Record timings and token or query cost next to functional results. Cost visibility early prevents surprise bills when the path moves from demo to shared environments.
Put human approval on edges that spend money or change production data. Compile-time wiring does not equal business completeness.
Write a short runbook: how to rotate keys, how to drain the queue, how to roll back the last ingest.
Keep configuration outside application code. Environment files, secret stores, and feature flags belong in one place operators can audit without reading the whole graph.
Put human approval on edges that spend money or change production data. Compile-time wiring does not equal business completeness.
Before promoting the stack, freeze versions, capture a golden transcript for the critical path, and confirm rollback steps. Shared environments need rate limits, tenancy checks, and a clear owner for secret rotation. Favor boring reliability over clever one-off demos.
Batch note for ac5eb00f923a: keep provider keys out of the repo, set a per-session token ceiling, and store transcripts next to the eval fixtures so later model swaps stay comparable.
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