Custom Tool Agent

In this section, we will see how to build an agent that uses custom tools to edit and save a document.

Objectives

Create custom tools using the @tool decorator.
Maintain global state to update content across loop executions.
Build a loop of execution (agent -> tools -> agent) that terminates only when a specific tool is called.

Agent V: Custom Tool Agent

Goal

Build a document drafting agent (Drafter) that loops using custom tools (update and save) to modify and save content to a local file.

Sample Input

Sequential instructions:

"Write a basic email draft saying I am sick."
"Save the document to email.txt."

Sample Output

"Document updated successfully. Current content is: Hi, I am unable to attend today due to sickness."
"Document has been saved successfully to 'email.txt'."

Plan

Define the AgentState schema using add_messages to append messages to history.
Create custom tools @tool for update (updates content in global variable) and save (saves content to a file).
Write the our_agent node function and should_continue conditional router function.
Compile and run the agent.

Code Implementation

1. Define the State Schema

We define a state schema using add_messages annotation:

from typing import Annotated, Sequence, TypedDict
from langchain_core.messages import BaseMessage
from langgraph.graph.message import add_messages

class AgentState(TypedDict):
    messages: Annotated[Sequence[BaseMessage], add_messages]

2. Create Custom Tools

We define two custom tools that operate on a global document content variable:

from langchain_core.tools import tool

document_content = ""

@tool
def update(content: str) -> str:
    \"\"\"Updates the document with the provided content.\"\"\"
    global document_content
    document_content = content
    return f"Document updated successfully. Current content is:\\n{document_content}"

@tool
def save(filename: str) -> str:
    \"\"\"Save the current document to a text file and finish.\n    \n    Args:\n        filename: Name for the text file.\n    \"\"\"
    global document_content
    if not filename.endswith('.txt'):
        filename = f"{filename}.txt"
    try:
        with open(filename, 'w') as file:
            file.write(document_content)
        return f"Document has been saved successfully to '{filename}'."
    except Exception as e:
        return f"Error saving document: {str(e)}"

3. Define Nodes and Routing Logic

We write the agent node (using a system prompt with current document content) and the router function that terminates execution when the save tool returns successfully:

from langchain_core.messages import SystemMessage, ToolMessage
from langchain_openai import ChatOpenAI

tools = [update, save]
model = ChatOpenAI(model="gpt-4o").bind_tools(tools)

def our_agent(state: AgentState) -> dict:
    sys = SystemMessage(content=f\"\"\"
    You are Drafter, a helpful writing assistant. Use 'update' to modify content, and 'save' to finish.
    The current document content is: {document_content}
    \"\"\")
    response = model.invoke([sys] + list(state["messages"]))
    return {"messages": [response]}

def should_continue(state: AgentState) -> str:
    messages = state["messages"]
    for msg in reversed(messages):
        if isinstance(msg, ToolMessage) and "saved successfully" in msg.content:
            return "end"
    return "continue"

4. Build and Compile the Graph

We hook up the loops:

from langgraph.graph import StateGraph, START, END
from langgraph.prebuilt import ToolNode

graph = StateGraph(AgentState)
graph.add_node("agent", our_agent)
graph.add_node("tools", ToolNode(tools))

graph.set_entry_point("agent")
graph.add_edge("agent", "tools")
graph.add_conditional_edges(
    "tools",
    should_continue,
    {
        "continue": "agent",
        "end": END
    }
)
app = graph.compile()

Exercise: Todo List Manager Agent 🗃

Goal

Build a Todo List Manager Agent with custom tools to add items, mark them as complete, and save the todo list to a text file.

Sample Input

Sequential inputs:

"Add buy milk to my todo list."
"Save my todo list to todos.txt."

Sample Output

"Todo list updated successfully. Current todos: ['buy milk']"
"Todo list has been saved successfully to 'todos.txt'."

Plan

Create an AgentState schema for messages and a global list todo_list = [] to store items.
Create @tool for add_todo(todo: str) (appends to list) and save_todos(filename: str) (writes list to file).
Initialize StateGraph, register agent and tool nodes, add loops, compile, and run.

Solution

from typing import Annotated, Sequence, TypedDict
from langchain_core.messages import BaseMessage, SystemMessage, ToolMessage
from langgraph.graph.message import add_messages
from langchain_openai import ChatOpenAI
from langchain_core.tools import tool
from langgraph.graph import StateGraph, START, END
from langgraph.prebuilt import ToolNode

todo_list = []

# 1. State
class AgentState(TypedDict):
    messages: Annotated[Sequence[BaseMessage], add_messages]

# 2. Tools
@tool
def add_todo(todo: str) -> str:
    \"\"\"Adds a new todo item to the list.\"\"\"
    global todo_list
    todo_list.append(todo)
    return f"Todo list updated successfully. Current todos: {todo_list}"

@tool
def save_todos(filename: str) -> str:
    \"\"\"Saves the todo list to a file and finishes.\"\"\"
    global todo_list
    if not filename.endswith('.txt'):
        filename = f"{filename}.txt"
    try:
        with open(filename, 'w') as f:
            f.write('\\n'.join(todo_list))
        return f"Todo list has been saved successfully to '{filename}'."
    except Exception as e:
        return f"Error saving todos: {str(e)}"

tools = [add_todo, save_todos]
model = ChatOpenAI(model="gpt-4o").bind_tools(tools)

# 3. Nodes and Routing
def todo_agent(state: AgentState) -> dict:
    sys = SystemMessage(content=f\"\"\"
    You are a Todo List Manager. Use 'add_todo' to add items, and 'save_todos' to save and finish.
    Current todos: {todo_list}
    \"\"\")
    response = model.invoke([sys] + list(state["messages"]))
    return {"messages": [response]}

def check_continue(state: AgentState) -> str:
    messages = state["messages"]
    for msg in reversed(messages):
        if isinstance(msg, ToolMessage) and "saved successfully" in msg.content:
            return "end"
    return "continue"

# 4. Build Graph
graph = StateGraph(AgentState)
graph.add_node("agent", todo_agent)
graph.add_node("tools", ToolNode(tools))
graph.set_entry_point("agent")
graph.add_edge("agent", "tools")
graph.add_conditional_edges(
    "tools",
    check_continue,
    {
        "continue": "agent",
        "end": END
    }
)
app = graph.compile()

# Test
state = {"messages": [HumanMessage(content="Add buy milk to my todo list.")]}
state = app.invoke(state)
state["messages"].append(HumanMessage(content="Save my todo list to todos.txt."))
res = app.invoke(state)
print(res["messages"][-1].content)

Practice & Exercises

To reinforce what you’ve learned in this section, practice with the interactive notebook:

Practice & Exercises

Practice managing global variable states inside custom tools, executing loops until a termination response is triggered, and compiling custom tool workflows.💻 VS Code | 🚀 Colab | 📥 Download

LangGraph Basics

LangGraph Agents

Objectives

Agent V: Custom Tool Agent

Goal

Sample Input

Sample Output

Plan

Code Implementation

1. Define the State Schema

2. Create Custom Tools

3. Define Nodes and Routing Logic

4. Build and Compile the Graph

Exercise: Todo List Manager Agent 🗃

Goal

Sample Input

Sample Output

Plan

Practice & Exercises

Practice & Exercises

​Objectives

​Agent V: Custom Tool Agent

​Goal

​Sample Input

​Sample Output

​Plan

​Code Implementation

​1. Define the State Schema

​2. Create Custom Tools

​3. Define Nodes and Routing Logic

​4. Build and Compile the Graph

​Exercise: Todo List Manager Agent 🗃

​Goal

​Sample Input

​Sample Output

​Plan

​Practice & Exercises

Practice & Exercises

Objectives

Agent V: Custom Tool Agent

Goal

Sample Input

Sample Output

Plan

Code Implementation

1. Define the State Schema

2. Create Custom Tools

3. Define Nodes and Routing Logic

4. Build and Compile the Graph

Exercise: Todo List Manager Agent 🗃

Goal

Sample Input

Sample Output

Plan

Practice & Exercises