larkintuckerllc

from typing import Any

from langchain.agents import create_agent, AgentState
from langchain.agents.middleware import before_agent
from langchain.messages import HumanMessage, SystemMessage
from langgraph.runtime import Runtime

QUESTION = HumanMessage(content="What's the capital of the Moon?")
SYSTEM_PROMPT = """"
You are a science fiction writer, create a capital city at the users request.

larkintuckerllc

from langchain.agents import create_agent
from langchain.agents.middleware import SummarizationMiddleware
from langchain.messages import HumanMessage, AIMessage
from langgraph.checkpoint.memory import InMemorySaver

CONFIG = {"configurable": {"thread_id": "1"}}
CONVERSATION = [
    HumanMessage(content="What is the capital of the moon?"),
    AIMessage(content="The capital of the moon is Lunapolis."),
    HumanMessage(content="What is the weather in Lunapolis?"),

larkintuckerllc

import asyncio

from langchain.agents import create_agent, AgentState
from langchain.messages import HumanMessage, ToolMessage
from langchain.tools import tool, ToolRuntime
from langchain_community.utilities import SQLDatabase
from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.types import Command
from tavily import TavilyClient

larkintuckerllc

from langchain.agents import create_agent
from langchain.messages import HumanMessage
from langchain.tools import tool

QUESTION = HumanMessage(content="What's the square root of 456?")

@tool
def square_root(x: float) -> float:
    """
    Calculate the square root of a number.

larkintuckerllc

from langchain.agents import AgentState, create_agent
from langchain.messages import ToolMessage, HumanMessage
from langchain.tools import tool, ToolRuntime
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.types import Command

CONFIG = {"configurable": {"thread_id": "1"}}
QUESTION_1 = HumanMessage(content="My favorite color is green.")
QUESTION_2 = HumanMessage(content="What is my favorite color?")

larkintuckerllc

from dataclasses import dataclass;
from langchain.agents import create_agent
from langchain.messages import HumanMessage
from langchain.tools import tool, ToolRuntime

QUESTION = HumanMessage(content="What is my favorite color?")

@dataclass
class ColorContext:
    favorite_color: str = "blue"

larkintuckerllc

import asyncio

from langchain.agents import create_agent
from langchain.messages import HumanMessage
from langchain_mcp_adapters.client import MultiServerMCPClient

QUESTION = HumanMessage(content="What's the weather in San Francisco?")

client = MultiServerMCPClient(
    {

larkintuckerllc

from langchain.agents import create_agent
from langgraph.checkpoint.memory import InMemorySaver
from langchain.messages import HumanMessage
from langchain.tools import tool
from tavily import TavilyClient

tavily_client = TavilyClient();

CONFIG = {"configurable": {"thread_id": "1"}}
QUESTION = HumanMessage(content="I have some leftover chicken and rice in my fridge. What can I make?")

larkintuckerllc

from langchain.agents import create_agent
from langgraph.checkpoint.memory import InMemorySaver
from langchain.messages import HumanMessage
from langchain.tools import tool

CONFIG = {"configurable": {"thread_id": "1"}}
QUESTION_1 = HumanMessage(content="Hello my name is Sean and my favorite color is green.")
QUESTION_2 = HumanMessage(content="What is my favorite color?")

agent = create_agent(

larkintuckerllc

from langchain.agents import create_agent
from langchain.messages import HumanMessage
from langchain.tools import tool
from tavily import TavilyClient

tavily_client = TavilyClient();

QUESTION = HumanMessage(content="Who is the current mayor of San Francisco?")

@tool