Goal → Think → Act → Observe → Think → Act → ... → Done

def search_web(query: str) -> str:
    """Search the internet for information"""
    return results

def run_python(code: str) -> str:
    """Execute Python code and return output"""
    return output

Question: What is the population of Tokyo?

Thought: I need to search for the current population of Tokyo.
Action: search_web("Tokyo population 2024")
Observation: Tokyo's population is approximately 13.9 million.

Thought: I have the answer.
Answer: Tokyo's population is approximately 13.9 million people.

from langchain_openai import ChatOpenAI
from langchain.agents import create_react_agent, AgentExecutor
from langchain_community.tools import DuckDuckGoSearchRun
from langchain import hub

# Setup
llm = ChatOpenAI(model="gpt-4o", temperature=0)
tools = [DuckDuckGoSearchRun()]
prompt = hub.pull("hwchase17/react")

# Create agent
agent = create_react_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

# Run
result = agent_executor.invoke({"input": "What is the latest news about AI?"})

User Request
     ↓
 Orchestrator Agent
   /     |     \
Research  Code  Writer
Agent    Agent  Agent
   \     |     /
    Final Response

from langgraph.graph import StateGraph, END
from typing import TypedDict

class AgentState(TypedDict):
    messages: list
    next: str

def research_node(state: AgentState):
    return {"messages": state["messages"] + ["research done"]}

def write_node(state: AgentState):
    return {"messages": state["messages"] + ["writing done"]}

# Build graph
workflow = StateGraph(AgentState)
workflow.add_node("research", research_node)
workflow.add_node("write", write_node)
workflow.add_edge("research", "write")
workflow.add_edge("write", END)
workflow.set_entry_point("research")

app = workflow.compile()

from openai import OpenAI

client = OpenAI()
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get weather for a city",
            "parameters": {
                "type": "object",
                "properties": {
                    "city": {"type": "string", "description": "City name"}
                },
                "required": ["city"]
            }
        }
    }
]

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "What's the weather in Tokyo?"}],
    tools=tools
)