How to add standard tests to a tool

When creating either a custom tool or a new tool to publish in a LangChain integration, it is important to add standard tests to ensure the tool works as expected. This guide will show you how to add standard tests to a tool.

Setup

First, let's install 2 dependencies:

• langchain-core will define the interfaces we want to import to define our custom tool.
• langchain-tests==0.3.0 will provide the standard tests we want to use.

note

The langchain-tests package contains the module langchain_standard_tests. This name mistmatch is due to this package historically being called langchain_standard_tests and the name not being available on PyPi. This will either be reconciled by our PEP 541 request (we welcome upvotes!), or in a new release of langchain-tests.

Because added tests in new versions of langchain-tests will always break your CI/CD pipelines, we recommend pinning the version of langchain-tests==0.3.0 to avoid unexpected changes.

%pip install -U langchain-core langchain-tests==0.3.0 pytest pytest-socket

Let's say we're publishing a package, langchain_parrot_link, that exposes a tool called ParrotMultiplyTool:

langchain_parrot_link/tools.py

from langchain_core.tools import BaseTool


class ParrotMultiplyTool(BaseTool):
    name: str = "ParrotMultiplyTool"
    description: str = (
        "Multiply two numbers like a parrot. Parrots always add "
        "eighty for their matey."
    )

    def _run(self, a: int, b: int) -> int:
        return a * b + 80

And we'll assume you've structured your package the same way as the main LangChain packages:

/
├── langchain_parrot_link/
│   └── tools.py
└── tests/
    ├── unit_tests/
    │   └── test_tools.py
    └── integration_tests/
        └── test_tools.py

Add and configure standard tests

There are 2 namespaces in the langchain-tests package:

• unit tests (langchain_standard_tests.unit_tests): designed to be used to test the tool in isolation and without access to external services
• integration tests (langchain_standard_tests.integration_tests): designed to be used to test the tool with access to external services (in particular, the external service that the tool is designed to interact with).

note

Integration tests can also be run without access to external services, if they are properly mocked.

Both types of tests are implemented as pytest class-based test suites.

By subclassing the base classes for each type of standard test (see below), you get all of the standard tests for that type, and you can override the properties that the test suite uses to configure the tests.

Standard tools tests

Here's how you would configure the standard unit tests for the custom tool, e.g. in tests/test_tools.py:

tests/unit_tests/test_custom_tool.py

from typing import Type

from langchain_parrot_link.tools import ParrotMultiplyTool
from langchain_standard_tests.unit_tests import ToolsUnitTests


class MultiplyToolUnitTests(ToolsUnitTests):
    @property
    def tool_constructor(self) -> Type[ParrotMultiplyTool]:
        return ParrotMultiplyTool

    def tool_constructor_params(self) -> dict:
        # if your tool constructor instead required initialization arguments like
        # `def __init__(self, some_arg: int):`, you would return those here
        # as a dictionary, e.g.: `return {'some_arg': 42}`
        return {}

    def tool_invoke_params_example(self) -> dict:
        """
        Returns a dictionary representing the "args" of an example tool call.

        This should NOT be a ToolCall dict - i.e. it should not
        have {"name", "id", "args"} keys.
        """
        return {"a": 2, "b": 3}

tests/integration_tests/test_custom_tool.py

from typing import Type

from langchain_parrot_link.tools import ParrotMultiplyTool
from langchain_standard_tests.integration_tests import ToolsIntegrationTests


class MultiplyToolIntegrationTests(ToolsIntegrationTests):
    @property
    def tool_constructor(self) -> Type[ParrotMultiplyTool]:
        return ParrotMultiplyTool

    def tool_constructor_params(self) -> dict:
        # if your tool constructor instead required initialization arguments like
        # `def __init__(self, some_arg: int):`, you would return those here
        # as a dictionary, e.g.: `return {'some_arg': 42}`
        return {}

    def tool_invoke_params_example(self) -> dict:
        """
        Returns a dictionary representing the "args" of an example tool call.

        This should NOT be a ToolCall dict - i.e. it should not
        have {"name", "id", "args"} keys.
        """
        return {"a": 2, "b": 3}

and you would run these with the following commands from your project root

# run unit tests without network access
pytest --disable-socket --enable-unix-socket tests/unit_tests

# run integration tests
pytest tests/integration_tests