LangGraph

LangGraph extends LangChain with a graph-based approach for building stateful, multi-actor applications with LLMs. It's perfect for creating complex AI agents and workflows that require sophisticated state management and decision-making.

🎯 What is LangGraph?

LangGraph is a library for building stateful, multi-actor applications with LLMs. It provides:

Graph-based workflows - Model complex processes as nodes and edges
State management - Maintain and update state throughout the workflow
Persistence - Save and resume workflows
Parallel execution - Run multiple branches simultaneously
Cycles and loops - Create iterative processes

📚 Key Sections

State Management 🗄️

Master state handling in LangGraph:

TypedDict State - Structured state definitions
State Updates - Managing state transitions
Checkpoints - Saving and restoring state
Persistence - Long-term state storage

Workflows ⚙️

Build sophisticated workflow patterns:

Sequential Workflows - Linear processing chains
Conditional Workflows - Dynamic path selection
Looping Workflows - Iterative and recursive processes
Parallel Workflows - Concurrent execution

Agents 🤖

Create intelligent AI agents:

Single Agents - Autonomous decision makers
Multi-Agent Systems - Collaborative agent teams
Tool-Using Agents - Agents with external capabilities
Human-in-the-Loop - Supervised agent workflows

🚀 Quick Start

Installation

pip install langgraph

Basic Setup

from langgraph.graph import StateGraph, END
from typing import TypedDict, List, Annotated
import operator
 
# Define state structure
class AgentState(TypedDict):
    messages: Annotated[List, operator.add]
    current_task: str
    completed_tasks: List[str]
 
# Initialize LLM
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0.7)

Simple Agent Example

# Define state
class SimpleState(TypedDict):
    question: str
    answer: str
    confidence: float
 
# Define processing node
def answer_question(state: SimpleState):
    prompt = f"Answer this question and provide a confidence score (0-1): {state['question']}"
    response = llm.invoke(prompt)
 
    return {
        "answer": response.content,
        "confidence": 0.8
    }
 
# Build workflow
workflow = StateGraph(SimpleState)
workflow.add_node("answer", answer_question)
workflow.set_entry_point("answer")
workflow.add_edge("answer", END)
 
# Compile and run
app = workflow.compile()
result = app.invoke({"question": "What is LangGraph?"})
print(f"Answer: {result['answer']}")

Choose a section above to dive deeper into specific LangGraph concepts!

Memory State Management