Define and customize tools

You can create custom tools in Letta using the Python SDK, as well as via the ADE tool builder.

For your agent to call a tool, Letta constructs an OpenAI tool schema (contained in json_schema field) from the function you define. Letta can either parse this automatically from a properly formatting docstring, or you can pass in the schema explicitly by providing a Pydantic object that defines the argument schema.

Creating a custom tool

Specifying tools via Pydantic models

To create a custom tool, you can extend the BaseTool class and specify the following:

• name - The name of the tool
• args_schema - A Pydantic model that defines the arguments for the tool
• description - A description of the tool
• tags - (Optional) A list of tags for the tool to query You must also define a run(..) method for the tool code that takes in the fields from the args_schema.

Below is an example of how to create a tool by extending BaseTool:

python

from letta_client import Letta
from letta_client.client import BaseTool
from pydantic import BaseModel
from typing import List, Type
import os

class InventoryItem(BaseModel):
    sku: str  # Unique product identifier
    name: str  # Product name
    price: float  # Current price
    category: str  # Product category (e.g., "Electronics", "Clothing")

class InventoryEntry(BaseModel):
    timestamp: int  # Unix timestamp of the transaction
    item: InventoryItem  # The product being updated
    transaction_id: str  # Unique identifier for this inventory update

class InventoryEntryData(BaseModel):
    data: InventoryEntry
    quantity_change: int  # Change in quantity (positive for additions, negative for removals)


class ManageInventoryTool(BaseTool):
    name: str = "manage_inventory"
    args_schema: Type[BaseModel] = InventoryEntryData
    description: str = "Update inventory catalogue with a new data entry"
    tags: List[str] = ["inventory", "shop"]

    def run(self, data: InventoryEntry, quantity_change: int) -> bool:
        print(f"Updated inventory for {data.item.name} with a quantity change of {quantity_change}")
        return True

# create a client connected to Letta Cloud
# Get your API key at https://app.letta.com/api-keys
client = Letta(token=os.getenv("LETTA_API_KEY"))

# create the tool
tool_from_class = client.tools.add(
    tool=ManageInventoryTool(),
)

To add this tool using the SDK:

TypeScript

typescript

import { LettaClient } from "@letta-ai/letta-client";

// create a client to connect to your local Letta server
const client = new LettaClient({
  baseUrl: "http://localhost:8283",
});

// create the tool
const toolFromClass = await client.tools.add({
  tool: manageInventoryTool,
});

Python

python

from letta_client import Letta

# create a client to connect to your local Letta server
client = Letta(
  base_url="http://localhost:8283"
)

# create the tool
tool_from_class = client.tools.add(
    tool=ManageInventoryTool(),
)

Specifying tools via function docstrings

You can create a tool by passing in a function with a Google Style Python docstring specifying the arguments and description of the tool:

TypeScript

typescript

// install letta-client with `npm install @letta-ai/letta-client`
import { LettaClient } from '@letta-ai/letta-client';

// create a client connected to Letta Cloud
const client = new LettaClient({
token: process.env.LETTA_API_KEY
});

// define a function
function rollDice(): string {
const diceRoleOutcome = Math.floor(Math.random() \* 20) + 1;
const outputString = `You rolled a ${diceRoleOutcome}`;
return outputString;
}

// create the tool
const tool = await client.tools.createFromFunction({
func: rollDice
});

Python

python

# install letta_client with `pip install letta-client`
from letta_client import Letta
import os

# create a client connected to Letta Cloud
client = Letta(token=os.getenv("LETTA_API_KEY"))

# define a function with a docstring
def roll_dice() -> str:
    """
    Simulate the roll of a 20-sided die (d20).

    This function generates a random integer between 1 and 20, inclusive,
    which represents the outcome of a single roll of a d20.

    Returns:
        str: The result of the die roll.
    """
    import random

    dice_role_outcome = random.randint(1, 20)
    output_string = f"You rolled a {dice_role_outcome}"
    return output_string

# create the tool
tool = client.tools.create_from_function(
    func=roll_dice
)

The tool creation will return a Tool object. You can update the tool with client.tools.upsert_from_function(...).

Specifying arguments via Pydantic models

To specify the arguments for a complex tool, you can use the args_schema parameter.

python

# install letta_client with `pip install letta-client`
from letta_client import Letta

class Step(BaseModel):
    name: str = Field(
        ...,
        description="Name of the step.",
    )
    description: str = Field(
        ...,
        description="An exhaustic description of what this step is trying to achieve and accomplish.",
    )


class StepsList(BaseModel):
    steps: list[Step] = Field(
        ...,
        description="List of steps to add to the task plan.",
    )
    explanation: str = Field(
        ...,
        description="Explanation for the list of steps.",
    )

def create_task_plan(steps, explanation):
    """ Creates a task plan for the current task. """
    return steps


tool = client.tools.upsert_from_function(
    func=create_task_plan,
    args_schema=StepsList
)

Note: this path for updating tools is currently only supported in Python.

Creating a tool from a file

You can also define a tool from a file that contains source code. For example, you may have the following file:

custom_tool.py

from typing import List, Optional
from pydantic import BaseModel, Field


class Order(BaseModel):
    order_number: int = Field(
        ...,
        description="The order number to check on.",
    )
    customer_name: str = Field(
        ...,
        description="The customer name to check on.",
    )

def check_order_status(
    orders: List[Order]
):
    """
    Check status of a provided list of orders

    Args:
        orders (List[Order]): List of orders to check

    Returns:
        str: The status of the order (e.g. cancelled, refunded, processed, processing, shipping).
    """
    # TODO: implement
    return "ok"

Then, you can define the tool in Letta via the source_code parameter:

TypeScript

typescript

import * as fs from "fs";

const tool = await client.tools.create({
  sourceCode: fs.readFileSync("custom_tool.py", "utf-8"),
});

Python

python

tool = client.tools.create(
    source_code = open("custom_tool.py", "r").read()
)

Note that in this case, check_order_status will become the name of your tool, since it is the last Python function in the file. Make sure it includes a Google Style Python docstring to define the tool’s arguments and description.

(Advanced) Accessing Agent State

Danger

Tools that use agent_state currently do not work in the ADE live tool tester (they will error when you press “Run”), however if the tool is correct it will work once you attach it to an agent.

If you need to directly access the state of an agent inside a tool, you can use the reserved agent_state keyword argument, for example: ```python title=“python” def get_agent_id(agent_state: “AgentState”) -> str: """ A custom tool that returns the agent ID

Returns: str: The agent ID """ return agent_state.id