How to pass run time values to tools

📚Prerequisites

📦Compatibility

The code in this guide requires langchain-core>=0.2.21. Please ensure you have the correct packages installed.

You may need to bind values to a tool that are only known at runtime. For example, the tool logic may require using the ID of the user who made the request.

Most of the time, such values should not be controlled by the LLM. In fact, allowing the LLM to control the user ID may lead to a security risk.

Instead, the LLM should only control the parameters of the tool that are meant to be controlled by the LLM, while other parameters (such as user ID) should be fixed by the application logic.

This how-to guide shows you how to prevent the model from generating certain tool arguments and injecting them in directly at runtime.

Using with LangGraph

If you're using LangGraph, please refer to this how-to guide which shows how to create an agent that keeps track of a given user's favorite pets.

We can bind them to chat models as follows:

pip install -qU langchain-openai

import getpass
import os

os.environ["OPENAI_API_KEY"] = getpass.getpass()

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4o-mini")

pip install -qU langchain-anthropic

import getpass
import os

os.environ["ANTHROPIC_API_KEY"] = getpass.getpass()

from langchain_anthropic import ChatAnthropic

llm = ChatAnthropic(model="claude-3-5-sonnet-20240620")

pip install -qU langchain-openai

import getpass
import os

os.environ["AZURE_OPENAI_API_KEY"] = getpass.getpass()

from langchain_openai import AzureChatOpenAI

llm = AzureChatOpenAI(
    azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
    azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
    openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],
)

pip install -qU langchain-google-vertexai

# Ensure your VertexAI credentials are configured

from langchain_google_vertexai import ChatVertexAI

llm = ChatVertexAI(model="gemini-1.5-flash")

pip install -qU langchain-aws

# Ensure your AWS credentials are configured

from langchain_aws import ChatBedrock

llm = ChatBedrock(model="anthropic.claude-3-5-sonnet-20240620-v1:0",
    beta_use_converse_api=True)

pip install -qU langchain-cohere

import getpass
import os

os.environ["COHERE_API_KEY"] = getpass.getpass()

from langchain_cohere import ChatCohere

llm = ChatCohere(model="command-r-plus")

pip install -qU langchain-nvidia-ai-endpoints

import getpass
import os

os.environ["NVIDIA_API_KEY"] = getpass.getpass()

from langchain_nvidia_ai_endpoints import ChatNVIDIA

llm = ChatNVIDIA(model="meta/llama3-70b-instruct")

pip install -qU langchain-fireworks

import getpass
import os

os.environ["FIREWORKS_API_KEY"] = getpass.getpass()

from langchain_fireworks import ChatFireworks

llm = ChatFireworks(model="accounts/fireworks/models/firefunction-v1", temperature=0)

pip install -qU langchain-groq

import getpass
import os

os.environ["GROQ_API_KEY"] = getpass.getpass()

from langchain_groq import ChatGroq

llm = ChatGroq(model="llama3-8b-8192")

pip install -qU langchain-mistralai

import getpass
import os

os.environ["MISTRAL_API_KEY"] = getpass.getpass()

from langchain_mistralai import ChatMistralAI

llm = ChatMistralAI(model="mistral-large-latest")

pip install -qU langchain-openai

import getpass
import os

os.environ["TOGETHER_API_KEY"] = getpass.getpass()

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    base_url="https://api.together.xyz/v1",
    api_key=os.environ["TOGETHER_API_KEY"],
    model="mistralai/Mixtral-8x7B-Instruct-v0.1",
)

pip install -qU databricks-langchain

import getpass
import os

os.environ["DATABRICKS_TOKEN"] = getpass.getpass()

from databricks_langchain import ChatDatabricks

os.environ["DATABRICKS_HOST"] = "https://example.staging.cloud.databricks.com/serving-endpoints"

llm = ChatDatabricks(endpoint="databricks-meta-llama-3-1-70b-instruct")

Hiding arguments from the model

We can use the InjectedToolArg annotation to mark certain parameters of our Tool, like user_id as being injected at runtime, meaning they shouldn't be generated by the model

from typing import List

from langchain_core.tools import InjectedToolArg, tool
from typing_extensions import Annotated

user_to_pets = {}


@tool(parse_docstring=True)
def update_favorite_pets(
    pets: List[str], user_id: Annotated[str, InjectedToolArg]
) -> None:
    """Add the list of favorite pets.

    Args:
        pets: List of favorite pets to set.
        user_id: User's ID.
    """
    user_to_pets[user_id] = pets


@tool(parse_docstring=True)
def delete_favorite_pets(user_id: Annotated[str, InjectedToolArg]) -> None:
    """Delete the list of favorite pets.

    Args:
        user_id: User's ID.
    """
    if user_id in user_to_pets:
        del user_to_pets[user_id]


@tool(parse_docstring=True)
def list_favorite_pets(user_id: Annotated[str, InjectedToolArg]) -> None:
    """List favorite pets if any.

    Args:
        user_id: User's ID.
    """
    return user_to_pets.get(user_id, [])

API Reference:InjectedToolArg | tool

If we look at the input schemas for these tools, we'll see that user_id is still listed:

update_favorite_pets.get_input_schema().schema()

{'description': 'Add the list of favorite pets.',
 'properties': {'pets': {'description': 'List of favorite pets to set.',
   'items': {'type': 'string'},
   'title': 'Pets',
   'type': 'array'},
  'user_id': {'description': "User's ID.",
   'title': 'User Id',
   'type': 'string'}},
 'required': ['pets', 'user_id'],
 'title': 'update_favorite_petsSchema',
 'type': 'object'}

But if we look at the tool call schema, which is what is passed to the model for tool-calling, user_id has been removed:

update_favorite_pets.tool_call_schema.schema()

{'description': 'Add the list of favorite pets.',
 'properties': {'pets': {'description': 'List of favorite pets to set.',
   'items': {'type': 'string'},
   'title': 'Pets',
   'type': 'array'}},
 'required': ['pets'],
 'title': 'update_favorite_pets',
 'type': 'object'}

So when we invoke our tool, we need to pass in user_id:

user_id = "123"
update_favorite_pets.invoke({"pets": ["lizard", "dog"], "user_id": user_id})
print(user_to_pets)
print(list_favorite_pets.invoke({"user_id": user_id}))

{'123': ['lizard', 'dog']}
['lizard', 'dog']

But when the model calls the tool, no user_id argument will be generated:

tools = [
    update_favorite_pets,
    delete_favorite_pets,
    list_favorite_pets,
]
llm_with_tools = llm.bind_tools(tools)
ai_msg = llm_with_tools.invoke("my favorite animals are cats and parrots")
ai_msg.tool_calls

[{'name': 'update_favorite_pets',
  'args': {'pets': ['cats', 'parrots']},
  'id': 'call_pZ6XVREGh1L0BBSsiGIf1xVm',
  'type': 'tool_call'}]

Injecting arguments at runtime

If we want to actually execute our tools using the model-generated tool call, we'll need to inject the user_id ourselves:

from copy import deepcopy

from langchain_core.runnables import chain


@chain
def inject_user_id(ai_msg):
    tool_calls = []
    for tool_call in ai_msg.tool_calls:
        tool_call_copy = deepcopy(tool_call)
        tool_call_copy["args"]["user_id"] = user_id
        tool_calls.append(tool_call_copy)
    return tool_calls


inject_user_id.invoke(ai_msg)

API Reference:chain

[{'name': 'update_favorite_pets',
  'args': {'pets': ['cats', 'parrots'], 'user_id': '123'},
  'id': 'call_pZ6XVREGh1L0BBSsiGIf1xVm',
  'type': 'tool_call'}]

And now we can chain together our model, injection code, and the actual tools to create a tool-executing chain:

tool_map = {tool.name: tool for tool in tools}


@chain
def tool_router(tool_call):
    return tool_map[tool_call["name"]]


chain = llm_with_tools | inject_user_id | tool_router.map()
chain.invoke("my favorite animals are cats and parrots")

[ToolMessage(content='null', name='update_favorite_pets', tool_call_id='call_oYCD0THSedHTbwNAY3NW6uUj')]

Looking at the user_to_pets dict, we can see that it's been updated to include cats and parrots:

user_to_pets

{'123': ['cats', 'parrots']}

Other ways of annotating args

Here are a few other ways of annotating our tool args:

from langchain_core.tools import BaseTool
from pydantic import BaseModel, Field

class UpdateFavoritePetsSchema(BaseModel):
    """Update list of favorite pets"""

    pets: List[str] = Field(..., description="List of favorite pets to set.")
    user_id: Annotated[str, InjectedToolArg] = Field(..., description="User's ID.")

@tool(args_schema=UpdateFavoritePetsSchema)
def update_favorite_pets(pets, user_id):
    user_to_pets[user_id] = pets

update_favorite_pets.get_input_schema().schema()

API Reference:BaseTool

{'description': 'Update list of favorite pets',
 'properties': {'pets': {'description': 'List of favorite pets to set.',
   'items': {'type': 'string'},
   'title': 'Pets',
   'type': 'array'},
  'user_id': {'description': "User's ID.",
   'title': 'User Id',
   'type': 'string'}},
 'required': ['pets', 'user_id'],
 'title': 'UpdateFavoritePetsSchema',
 'type': 'object'}

update_favorite_pets.tool_call_schema.schema()

{'description': 'Update list of favorite pets',
 'properties': {'pets': {'description': 'List of favorite pets to set.',
   'items': {'type': 'string'},
   'title': 'Pets',
   'type': 'array'}},
 'required': ['pets'],
 'title': 'update_favorite_pets',
 'type': 'object'}

from typing import Optional, Type


class UpdateFavoritePets(BaseTool):
    name: str = "update_favorite_pets"
    description: str = "Update list of favorite pets"
    args_schema: Optional[Type[BaseModel]] = UpdateFavoritePetsSchema

    def _run(self, pets, user_id):
        user_to_pets[user_id] = pets


UpdateFavoritePets().get_input_schema().schema()

{'description': 'Update list of favorite pets',
 'properties': {'pets': {'description': 'List of favorite pets to set.',
   'items': {'type': 'string'},
   'title': 'Pets',
   'type': 'array'},
  'user_id': {'description': "User's ID.",
   'title': 'User Id',
   'type': 'string'}},
 'required': ['pets', 'user_id'],
 'title': 'UpdateFavoritePetsSchema',
 'type': 'object'}

UpdateFavoritePets().tool_call_schema.schema()

{'description': 'Update list of favorite pets',
 'properties': {'pets': {'description': 'List of favorite pets to set.',
   'items': {'type': 'string'},
   'title': 'Pets',
   'type': 'array'}},
 'required': ['pets'],
 'title': 'update_favorite_pets',
 'type': 'object'}

class UpdateFavoritePets2(BaseTool):
    name: str = "update_favorite_pets"
    description: str = "Update list of favorite pets"

    def _run(self, pets: List[str], user_id: Annotated[str, InjectedToolArg]) -> None:
        user_to_pets[user_id] = pets


UpdateFavoritePets2().get_input_schema().schema()

{'description': 'Use the tool.\n\nAdd run_manager: Optional[CallbackManagerForToolRun] = None\nto child implementations to enable tracing.',
 'properties': {'pets': {'items': {'type': 'string'},
   'title': 'Pets',
   'type': 'array'},
  'user_id': {'title': 'User Id', 'type': 'string'}},
 'required': ['pets', 'user_id'],
 'title': 'update_favorite_petsSchema',
 'type': 'object'}

UpdateFavoritePets2().tool_call_schema.schema()

{'description': 'Update list of favorite pets',
 'properties': {'pets': {'items': {'type': 'string'},
   'title': 'Pets',
   'type': 'array'}},
 'required': ['pets'],
 'title': 'update_favorite_pets',
 'type': 'object'}

Hiding arguments from the model​

Injecting arguments at runtime​

Other ways of annotating args​

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Hiding arguments from the model

Injecting arguments at runtime

Other ways of annotating args