What is LangGraph?
LangGraph is a tool that helps you build smart apps where multiple AI agents can work together. Think of it like a team of workers who can talk to each other and share information to get things done.
Instead of having one AI that does everything, LangGraph lets you create
multiple AI agents that each have a specific job. They can work together, share information, and make decisions based on what's happening.
Main Ideas
Keeping Track of Information
LangGraph uses something called
state to remember what's happening in your app. This is like a notebook where you write down important information that your AI agents need to remember.
from typing import TypedDict, Annotated
from langgraph.graph import StateGraph
class AgentState(TypedDict):
messages: Annotated[list, "The messages in the conversation"]
next: Annotated[str, "The next helper to call"]
current_step: Annotated[int, "Which step we are on"]
# Create a workflow
workflow = StateGraph(AgentState)
Workers and Connections
Workers (called nodes) are the individual steps in your app.
Connections (called edges) show how information flows between these workers.
# Define workers (functions that do specific jobs)
def research_helper(state: AgentState) -> AgentState:
# This helper does research
return state
def writing_helper(state: AgentState) -> AgentState:
# This helper does writing
return state
# Add workers to the workflow
workflow.add_node("research", research_helper)
workflow.add_node("writing", writing_helper)
# Connect the workers
workflow.add_edge("research", "writing")
Building Your First LangGraph App
Let's create a simple example that shows how LangGraph works:
import os
from typing import TypedDict, Annotated
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, END
from langchain_core.tools import tool
# Define what information we want to remember
class AgentState(TypedDict):
messages: Annotated[list, "The messages in the conversation"]
next: Annotated[str, "The next helper to call"]
# Create helpful tools
@tool
def search_web(query: str) -> str:
"""Search the internet for current information."""
return f"Search results for: {query}"
@tool
def calculate(expression: str) -> str:
"""Do math calculations."""
return f"Result: {eval(expression)}"
# Set up our AI helper
llm = ChatOpenAI(model="gpt-3.5-turbo")
# Define what our helper does
def helper(state: AgentState) -> AgentState:
messages = state["messages"]
# Get the last message
last_message = messages[-1]
# Get a response from the AI
response = llm.invoke(messages)
# Save the response
messages.append(response)
return {"messages": messages, "next": "continue"}
# Create the workflow
workflow = StateGraph(AgentState)
# Add our helper
workflow.add_node("helper", helper)
# Set the starting point
workflow.set_entry_point("helper")
# Decide when to stop
def should_continue(state: AgentState) -> str:
# Stop if the last message says goodbye
last_message = state["messages"][-1].content
if "goodbye" in last_message.lower():
return END
return "helper"
workflow.add_conditional_edges("helper", should_continue)
# Put everything together
app = workflow.compile()
# Run the app
config = {"configurable": {"thread_id": "1"}}
for event in app.stream({"messages": [{"role": "user", "content": "Hello!"}]}, config):
print(event)
Advanced Features
Multiple Agents Working Together
LangGraph is great for creating systems where different agents each have a specific job:
def research_helper(state: AgentState) -> AgentState:
"""Helper that finds information."""
# Research work here
return state
def writing_helper(state: AgentState) -> AgentState:
"""Helper that writes content."""
# Writing work here
return state
def review_helper(state: AgentState) -> AgentState:
"""Helper that checks the work."""
# Review work here
return state
# Create a team of agents
workflow = StateGraph(AgentState)
workflow.add_node("research", research_helper)
workflow.add_node("writing", writing_helper)
workflow.add_node("review", review_helper)
# Set up the work flow
workflow.add_edge("research", "writing")
workflow.add_edge("writing", "review")
workflow.add_edge("review", END)
Smart Decision Making
You can add logic to decide which helper should work next:
def choose_helper(state: AgentState) -> str:
"""Choose which helper should work next."""
last_message = state["messages"][-1].content.lower()
if "research" in last_message:
return "research_helper"
elif "write" in last_message:
return "writing_helper"
elif "review" in last_message:
return "review_helper"
else:
return "general_helper"
# Add smart routing
workflow.add_conditional_edges("choose", choose_helper)
Good Practices
1. Keep It Simple
•
Start small: Begin with simple workflows and add more later
•
Use clear names: Give your agents and information clear, simple names
•
Write comments: Explain what each part does
2. Handle Problems
def safe_helper(state: AgentState) -> AgentState:
try:
# Your helper's work here
return state
except Exception as e:
# Handle problems gracefully
error_message = {"role": "system", "content": f"Error: {str(e)}"}
state["messages"].append(error_message)
return state
3. Test Your Work
•
Test each agent: Make sure each agent works on its own
•
Test the whole system: Make sure all agents work together
•
Use fake data: Test with simple examples first
Real Examples
Content Creation System
# Content creation workflow
workflow = StateGraph(ContentState)
workflow.add_node("research", research_topic)
workflow.add_node("outline", create_outline)
workflow.add_node("write", write_content)
workflow.add_node("edit", edit_content)
workflow.add_node("publish", publish_content)
# Set up the work flow
workflow.add_edge("research", "outline")
workflow.add_edge("outline", "write")
workflow.add_edge("write", "edit")
workflow.add_edge("edit", "publish")
Customer Help System
# Customer help workflow
workflow = StateGraph(SupportState)
workflow.add_node("understand", understand_problem)
workflow.add_node("find_solution", find_solution)
workflow.add_node("respond", give_response)
workflow.add_node("escalate", get_human_help)
# Choose what to do based on the problem
def choose_action(state: SupportState) -> str:
problem_type = state["problem_type"]
if problem_type == "simple":
return "respond"
elif problem_type == "complex":
return "find_solution"
else:
return "escalate"
Getting Started
Installation
pip install langgraph langchain-openai
Set Up Your Environment
export OPENAI_API_KEY="your-api-key-here"
Summary
LangGraph is a powerful tool for building smart apps where multiple AI agents work together. By understanding the basic ideas of information tracking, workers, and connections, you can create sophisticated workflows that coordinate multiple AI agents.
The key to success with LangGraph is:
•
Start simple: Begin with basic workflows and add more later
•
Plan your information: Think about what data your agents need to share
•
Test everything: Make sure your workflows work as expected
•
Watch performance: Keep track of how your apps perform
Whether you're building content creation systems, customer help systems, or research assistants, LangGraph gives you the tools to create intelligent, multi-step AI applications.
Ready to start building? Check out the official documentation and join the LangGraph community to learn from others and share your experiences!
