ChatModelIntegrationTests#

class langchain_tests.integration_tests.chat_models.ChatModelIntegrationTests[source]#

Base class for chat model integration tests.

Test subclasses must implement the chat_model_class and chat_model_params properties to specify what model to test and its initialization parameters.

Example:

from typing import Type

from langchain_tests.integration_tests import ChatModelIntegrationTests
from my_package.chat_models import MyChatModel


class TestMyChatModelIntegration(ChatModelIntegrationTests):
    @property
    def chat_model_class(self) -> Type[MyChatModel]:
        # Return the chat model class to test here
        return MyChatModel

    @property
    def chat_model_params(self) -> dict:
        # Return initialization parameters for the model.
        return {"model": "model-001", "temperature": 0}

Note

API references for individual test methods include troubleshooting tips.

Test subclasses must implement the following two properties:

chat_model_class

The chat model class to test, e.g., ChatParrotLink.

Example:

@property
def chat_model_class(self) -> Type[ChatParrotLink]:
    return ChatParrotLink
chat_model_params

Initialization parameters for the chat model.

Example:

@property
def chat_model_params(self) -> dict:
    return {"model": "bird-brain-001", "temperature": 0}

In addition, test subclasses can control what features are tested (such as tool calling or multi-modality) by selectively overriding the following properties. Expand to see details:

has_tool_calling

Boolean property indicating whether the chat model supports tool calling.

By default, this is determined by whether the chat model’s bind_tools method is overridden. It typically does not need to be overridden on the test class.

Example override:

@property
def has_tool_calling(self) -> bool:
    return True
tool_choice_value

Value to use for tool choice when used in tests.

Warning

Deprecated since version 0.3.15. This property will be removed in version 0.3.20. If a model supports tool_choice, it should accept tool_choice="any" and tool_choice=<string name of tool>. If a model does not support forcing tool calling, override the has_tool_choice property to return False.

Example:

@property
def tool_choice_value(self) -> Optional[str]:
    return "any"
has_tool_choice

Boolean property indicating whether the chat model supports forcing tool calling via a tool_choice parameter.

By default, this is determined by whether the parameter is included in the signature for the corresponding bind_tools method.

If True, the minimum requirement for this feature is that tool_choice="any" will force a tool call, and tool_choice=<tool name> will force a call to a specific tool.

Example override:

@property
def has_tool_choice(self) -> bool:
    return False
has_structured_output

Boolean property indicating whether the chat model supports structured output.

By default, this is determined by whether the chat model’s with_structured_output method is overridden. If the base implementation is intended to be used, this method should be overridden.

See: https://python.langchain.com/docs/concepts/structured_outputs/

Example:

@property
def has_structured_output(self) -> bool:
    return True
structured_output_kwargs

Dict property that can be used to specify additional kwargs for with_structured_output. Useful for testing different models.

Example:

@property
def structured_output_kwargs(self) -> dict:
    return {"method": "function_calling"}
supports_json_mode

Boolean property indicating whether the chat model supports JSON mode in with_structured_output.

See: https://python.langchain.com/docs/concepts/structured_outputs/#json-mode

Example:

@property
def supports_json_mode(self) -> bool:
    return True
supports_image_inputs

Boolean property indicating whether the chat model supports image inputs. Defaults to False.

If set to True, the chat model will be tested using content blocks of the form

{
    "type": "image",
    "source_type": "base64",
    "data": "<base64 image data>",
    "mime_type": "image/jpeg",  # or appropriate mime-type
}

In addition to OpenAI-style content blocks:

{
    "type": "image_url",
    "image_url": {"url": f"data:image/jpeg;base64,{image_data}"},
}

See https://python.langchain.com/docs/concepts/multimodality/

Example:

@property
def supports_image_inputs(self) -> bool:
    return True
supports_image_urls

Boolean property indicating whether the chat model supports image inputs from URLs. Defaults to False.

If set to True, the chat model will be tested using content blocks of the form

{
    "type": "image",
    "source_type": "url",
    "url": "https://...",
}

See https://python.langchain.com/docs/concepts/multimodality/

Example:

@property
def supports_image_urls(self) -> bool:
    return True
supports_pdf_inputs

Boolean property indicating whether the chat model supports PDF inputs. Defaults to False.

If set to True, the chat model will be tested using content blocks of the form

{
    "type": "file",
    "source_type": "base64",
    "data": "<base64 file data>",
    "mime_type": "application/pdf",
}

See https://python.langchain.com/docs/concepts/multimodality/

Example:

@property
def supports_pdf_inputs(self) -> bool:
    return True
supports_audio_inputs

Boolean property indicating whether the chat model supports audio inputs. Defaults to False.

If set to True, the chat model will be tested using content blocks of the form

{
    "type": "audio",
    "source_type": "base64",
    "data": "<base64 audio data>",
    "mime_type": "audio/wav",  # or appropriate mime-type
}

See https://python.langchain.com/docs/concepts/multimodality/

Example:

@property
def supports_audio_inputs(self) -> bool:
    return True
supports_video_inputs

Boolean property indicating whether the chat model supports image inputs. Defaults to False. No current tests are written for this feature.

returns_usage_metadata

Boolean property indicating whether the chat model returns usage metadata on invoke and streaming responses. Defaults to True.

usage_metadata is an optional dict attribute on ``AIMessage``s that track input and output tokens. See more.

Example:

@property
def returns_usage_metadata(self) -> bool:
    return False

Models supporting usage_metadata should also return the name of the underlying model in the response_metadata of the AIMessage.

supports_anthropic_inputs

Boolean property indicating whether the chat model supports Anthropic-style inputs.

These inputs might feature “tool use” and “tool result” content blocks, e.g.,

[
    {"type": "text", "text": "Hmm let me think about that"},
    {
        "type": "tool_use",
        "input": {"fav_color": "green"},
        "id": "foo",
        "name": "color_picker",
    },
]

If set to True, the chat model will be tested using content blocks of this form.

Example:

@property
def supports_anthropic_inputs(self) -> bool:
    return False
supports_image_tool_message

Boolean property indicating whether the chat model supports ToolMessages that include image content, e.g.,

ToolMessage(
    content=[
        {
            "type": "image_url",
            "image_url": {"url": f"data:image/jpeg;base64,{image_data}"},
        },
    ],
    tool_call_id="1",
    name="random_image",
)

(OpenAI Chat Completions format), as well as

ToolMessage(
    content=[
        {
            "type": "image",
            "source_type": "base64",
            "data": image_data,
            "mime_type": "image/jpeg",
        },
    ],
    tool_call_id="1",
    name="random_image",
)

(standard format).

If set to True, the chat model will be tested with message sequences that include ToolMessages of this form.

Example:

@property
def supports_image_tool_message(self) -> bool:
    return False
supports_pdf_tool_message

Boolean property indicating whether the chat model supports ToolMessages that include PDF content, i.e.,

ToolMessage(
    content=[
        {
            "type": "file",
            "source_type": "base64",
            "data": pdf_data,
            "mime_type": "application/pdf",
        },
    ],
    tool_call_id="1",
    name="random_pdf",
)

(standard format).

If set to True, the chat model will be tested with message sequences that include ToolMessages of this form.

Example:

@property
def supports_pdf_tool_message(self) -> bool:
    return False
supported_usage_metadata_details

Property controlling what usage metadata details are emitted in both invoke and stream.

usage_metadata is an optional dict attribute on ``AIMessage``s that track input and output tokens. See more.

It includes optional keys input_token_details and output_token_details that can track usage details associated with special types of tokens, such as cached, audio, or reasoning.

Only needs to be overridden if these details are supplied.

enable_vcr_tests

Property controlling whether to enable select tests that rely on VCR caching of HTTP calls, such as benchmarking tests.

To enable these tests, follow these steps:

  1. Override the enable_vcr_tests property to return True:

    @property
def enable_vcr_tests(self) -> bool:
    return True

  2. Configure VCR to exclude sensitive headers and other information from cassettes.

    Important

    VCR will by default record authentication headers and other sensitive information in cassettes. Read below for how to configure what information is recorded in cassettes.

    To add configuration to VCR, add a conftest.py file to the tests/ directory and implement the vcr_config fixture there.

    langchain-tests excludes the headers 'authorization', 'x-api-key', and 'api-key' from VCR cassettes. To pick up this configuration, you will need to add conftest.py as shown below. You can also exclude additional headers, override the default exclusions, or apply other customizations to the VCR configuration. See example below:

    tests/conftest.py#
    import pytest
from langchain_tests.conftest import (
    _base_vcr_config as _base_vcr_config,
)

_EXTRA_HEADERS = [
    # Specify additional headers to redact
    ("user-agent", "PLACEHOLDER"),
]


def remove_response_headers(response: dict) -> dict:
    # If desired, remove or modify headers in the response.
    response["headers"] = {}
    return response


@pytest.fixture(scope="session")
def vcr_config(_base_vcr_config: dict) -> dict:  # noqa: F811
    """Extend the default configuration from langchain_tests."""
    config = _base_vcr_config.copy()
    config.setdefault("filter_headers", []).extend(_EXTRA_HEADERS)
    config["before_record_response"] = remove_response_headers

    return config
    Compressing cassettes

    langchain-tests includes a custom VCR serializer that compresses cassettes using gzip. To use it, register the yaml.gz serializer to your VCR fixture and enable this serializer in the config. See example below:

    tests/conftest.py#
    import pytest
from langchain_tests.conftest import (
    CustomPersister,
    CustomSerializer,
)
from langchain_tests.conftest import (
    _base_vcr_config as _base_vcr_config,
)
from vcr import VCR

_EXTRA_HEADERS = [
    # Specify additional headers to redact
    ("user-agent", "PLACEHOLDER"),
]


def remove_response_headers(response: dict) -> dict:
    # If desired, remove or modify headers in the response.
    response["headers"] = {}
    return response


@pytest.fixture(scope="session")
def vcr_config(_base_vcr_config: dict) -> dict:  # noqa: F811
    """Extend the default configuration from langchain_tests."""
    config = _base_vcr_config.copy()
    config.setdefault("filter_headers", []).extend(_EXTRA_HEADERS)
    config["before_record_response"] = remove_response_headers
    # New: enable serializer and set file extension
    config["serializer"] = "yaml.gz"
    config["path_transformer"] = VCR.ensure_suffix(".yaml.gz")

    return config


def pytest_recording_configure(config: dict, vcr: VCR) -> None:
    vcr.register_persister(CustomPersister())
    vcr.register_serializer("yaml.gz", CustomSerializer())

    You can inspect the contents of the compressed cassettes (e.g., to ensure no sensitive information is recorded) using

    gunzip -k /path/to/tests/cassettes/TestClass_test.yaml.gz

    or by using the serializer:

    from langchain_tests.conftest import (
    CustomPersister,
    CustomSerializer,
)

cassette_path = "/path/to/tests/cassettes/TestClass_test.yaml.gz"
requests, responses = CustomPersister().load_cassette(
    path, CustomSerializer()
)

  3. Run tests to generate VCR cassettes.

    Example:

    uv run python -m pytest tests/integration_tests/test_chat_models.py::TestMyModel::test_stream_time

    This will generate a VCR cassette for the test in tests/integration_tests/cassettes/.

    Important

    You should inspect the generated cassette to ensure that it does not contain sensitive information. If it does, you can modify the vcr_config fixture to exclude headers or modify the response before it is recorded.

    You can then commit the cassette to your repository. Subsequent test runs will use the cassette instead of making HTTP calls.

Attributes

chat_model_class

The chat model class to test, e.g., ChatParrotLink.

chat_model_params

Initialization parameters for the chat model.

enable_vcr_tests

(bool) whether to enable VCR tests for the chat model.

has_structured_output

(bool) whether the chat model supports structured output.

has_tool_calling

(bool) whether the model supports tool calling.

has_tool_choice

(bool) whether the model supports tool calling.

returns_usage_metadata

Returns usage metadata.

structured_output_kwargs

If specified, additional kwargs for with_structured_output.

supported_usage_metadata_details

Supported usage metadata details.

supports_anthropic_inputs

(bool) whether the chat model supports Anthropic-style inputs.

supports_audio_inputs

Supports audio inputs.

supports_image_inputs

Supports image inputs.

supports_image_tool_message

Supports image ToolMessages.

supports_image_urls

Supports image inputs from URLs.

supports_json_mode

(bool) whether the chat model supports JSON mode.

supports_pdf_inputs

(bool) whether the chat model supports PDF inputs, defaults to False.

supports_pdf_tool_message

Supports PDF ToolMessages.

supports_video_inputs

Supports video inputs.

tool_choice_value

(None or str) to use for tool choice when used in tests.

Methods

test_abatch(model)

Test to verify that await model.abatch([messages]) works.

test_agent_loop(model)

Test that the model supports a simple ReAct agent loop.

test_ainvoke(model)

Test to verify that await model.ainvoke(simple_message) works.

test_anthropic_inputs(model)

Test that model can process Anthropic-style message histories.

test_astream(model)

Test to verify that await model.astream(simple_message) works.

test_audio_inputs(model)

Test that the model can process audio inputs.

test_batch(model)

Test to verify that model.batch([messages]) works.

test_bind_runnables_as_tools(model)

Test bind runnables as tools.

test_conversation(model)

Test to verify that the model can handle multi-turn conversations.

test_double_messages_conversation(model)

Test to verify that the model can handle double-message conversations.

test_image_inputs(model)

Test that the model can process image inputs.

test_image_tool_message(model)

Test that the model can process ToolMessages with image inputs.

test_invoke(model)

Test to verify that model.invoke(simple_message) works.

test_json_mode(model)

Test structured output via `JSON mode.

test_message_with_name(model)

Test that HumanMessage with values for the name field can be handled.

test_pdf_inputs(model)

Test that the model can process PDF inputs.

test_pdf_tool_message(model)

Test that the model can process ToolMessages with PDF inputs.

test_stop_sequence(model)

Test that model does not fail when invoked with the stop parameter.

test_stream(model)

Test to verify that model.stream(simple_message) works.

test_stream_time(model, benchmark, vcr)

Test that streaming does not introduce undue overhead.

test_structured_few_shot_examples(model, ...)

Test that the model can process few-shot examples with tool calls.

test_structured_output(model, schema_type)

Test to verify structured output is generated both on invoke and stream.

test_structured_output_async(model, schema_type)

Test to verify structured output is generated both on invoke and stream.

test_structured_output_optional_param(model)

Test structured output with optional parameters.

test_structured_output_pydantic_2_v1(model)

Test structured output using pydantic.v1.BaseModel.

test_tool_calling(model)

Test that the model generates tool calls.

test_tool_calling_async(model)

Test that the model generates tool calls.

test_tool_calling_with_no_arguments(model)

Test that the model generates tool calls for tools with no arguments.

test_tool_choice(model)

Test tool_choice parameter.

test_tool_message_error_status(model, ...)

Test that ToolMessage with status="error" can be handled.

test_tool_message_histories_list_content(...)

Test that message histories are compatible with list tool contents.

test_tool_message_histories_string_content(...)

Test that message histories are compatible with string tool contents.

test_unicode_tool_call_integration(model, *)

Generic integration test for Unicode characters in tool calls.

test_usage_metadata(model)

Test to verify that the model returns correct usage metadata.

test_usage_metadata_streaming(model)

Test usage metadata in streaming mode.
async test_abatch(
model: BaseChatModel,
) → None[source]#

Test to verify that await model.abatch([messages]) works.

This should pass for all integrations. Tests the model’s ability to process multiple prompts in a single batch asynchronously.

Troubleshooting

First, debug test_batch() and test_ainvoke() because abatch has a default implementation that calls ainvoke for each message in the batch.

If those tests pass but not this one, you should make sure your abatch method does not raise any exceptions, and that it returns a list of valid AIMessage objects.

Parameters:

model (BaseChatModel)

Return type:

None

test_agent_loop(
model: BaseChatModel,
) → None[source]#

Test that the model supports a simple ReAct agent loop.

This test is skipped if the has_tool_calling property on the test class is set to False.

This test is optional and should be skipped if the model does not support tool calling (see Configuration below).

Configuration

To disable tool calling tests, set has_tool_calling to False in your test class:

class TestMyChatModelIntegration(ChatModelIntegrationTests):
    @property
    def has_tool_calling(self) -> bool:
        return False
Troubleshooting

If this test fails, check that bind_tools is implemented to correctly translate LangChain tool objects into the appropriate schema for your chat model.

Check also that all required information (e.g., tool calling identifiers) from AIMessage objects is propagated correctly to model payloads.

This test may fail if the chat model does not consistently generate tool calls in response to an appropriate query. In these cases you can xfail the test:

@pytest.mark.xfail(reason=("Does not support tool_choice."))
def test_agent_loop(self, model: BaseChatModel) -> None:
    super().test_agent_loop(model)
Parameters:

model (BaseChatModel)

Return type:

None

async test_ainvoke(
model: BaseChatModel,
) → None[source]#

Test to verify that await model.ainvoke(simple_message) works.

This should pass for all integrations. Passing this test does not indicate a “natively async” implementation, but rather that the model can be used in an async context.

Troubleshooting

First, debug test_invoke(). because ainvoke has a default implementation that calls invoke in an async context.

If that test passes but not this one, you should make sure your _agenerate method does not raise any exceptions, and that it returns a valid ChatResult like so:

return ChatResult(
    generations=[
        ChatGeneration(message=AIMessage(content="Output text"))
    ]
)
Parameters:

model (BaseChatModel)

Return type:

None

test_anthropic_inputs(
model: BaseChatModel,
) → None[source]#

Test that model can process Anthropic-style message histories.

These message histories will include AIMessage objects with tool_use content blocks, e.g.,

AIMessage(
    [
        {"type": "text", "text": "Hmm let me think about that"},
        {
            "type": "tool_use",
            "input": {"fav_color": "green"},
            "id": "foo",
            "name": "color_picker",
        },
    ]
)

as well as HumanMessage objects containing tool_result content blocks:

HumanMessage(
    [
        {
            "type": "tool_result",
            "tool_use_id": "foo",
            "content": [
                {
                    "type": "text",
                    "text": "green is a great pick! "
                    "that's my sister's favorite color",
                }
            ],
            "is_error": False,
        },
        {"type": "text", "text": "what's my sister's favorite color"},
    ]
)

This test should be skipped if the model does not support messages of this form (or doesn’t support tool calling generally). See Configuration below.

Configuration

To disable this test, set supports_anthropic_inputs to False in your test class:

class TestMyChatModelIntegration(ChatModelIntegrationTests):
    @property
    def supports_anthropic_inputs(self) -> bool:
        return False
Troubleshooting

If this test fails, check that:

  1. The model can correctly handle message histories that include message objects with list content.

  2. The tool_calls attribute on AIMessage objects is correctly handled and passed to the model in an appropriate format.

  3. ``HumanMessage``s with “tool_result” content blocks are correctly handled.

Otherwise, if Anthropic tool call and result formats are not supported, set the supports_anthropic_inputs property to False.

Parameters:

model (BaseChatModel)

Return type:

None

async test_astream(
model: BaseChatModel,
) → None[source]#

Test to verify that await model.astream(simple_message) works.

This should pass for all integrations. Passing this test does not indicate a “natively async” or “streaming” implementation, but rather that the model can be used in an async streaming context.

Troubleshooting

First, debug test_stream(). and test_ainvoke(). because astream has a default implementation that calls _stream in an async context if it is implemented, or ainvoke and yields the result as a single chunk if not.

If those tests pass but not this one, you should make sure your _astream method does not raise any exceptions, and that it yields valid ChatGenerationChunk objects like so:

yield ChatGenerationChunk(message=AIMessageChunk(content="chunk text"))
Parameters:

model (BaseChatModel)

Return type:

None

test_audio_inputs(
model: BaseChatModel,
) → None[source]#

Test that the model can process audio inputs.

This test should be skipped (see Configuration below) if the model does not support audio inputs. These will take the form:

{
    "type": "audio",
    "source_type": "base64",
    "data": "<base64 audio data>",
    "mime_type": "audio/wav",  # or appropriate mime-type
}

See https://python.langchain.com/docs/concepts/multimodality/

Configuration

To disable this test, set supports_audio_inputs to False in your test class:

class TestMyChatModelIntegration(ChatModelIntegrationTests):
    @property
    def supports_audio_inputs(self) -> bool:
        return False
Troubleshooting

If this test fails, check that the model can correctly handle messages with audio content blocks, specifically base64-encoded files. Otherwise, set the supports_audio_inputs property to False.

Parameters:

model (BaseChatModel)

Return type:

None

test_batch(
model: BaseChatModel,
) → None[source]#

Test to verify that model.batch([messages]) works.

This should pass for all integrations. Tests the model’s ability to process multiple prompts in a single batch.

Troubleshooting

First, debug test_invoke() because batch has a default implementation that calls invoke for each message in the batch.

If that test passes but not this one, you should make sure your batch method does not raise any exceptions, and that it returns a list of valid AIMessage objects.

Parameters:

model (BaseChatModel)

Return type:

None

test_bind_runnables_as_tools(
model: BaseChatModel,
) → None[source]#

Test bind runnables as tools.

Test that the model generates tool calls for tools that are derived from LangChain runnables. This test is skipped if the has_tool_calling property on the test class is set to False.

This test is optional and should be skipped if the model does not support tool calling (see Configuration below).

Configuration

To disable tool calling tests, set has_tool_calling to False in your test class:

class TestMyChatModelIntegration(ChatModelIntegrationTests):
    @property
    def has_tool_calling(self) -> bool:
        return False
Troubleshooting

If this test fails, check that bind_tools is implemented to correctly translate LangChain tool objects into the appropriate schema for your chat model.

This test may fail if the chat model does not support a tool_choice parameter. This parameter can be used to force a tool call. If tool_choice is not supported and the model consistently fails this test, you can xfail the test:

@pytest.mark.xfail(reason=("Does not support tool_choice."))
def test_bind_runnables_as_tools(self, model: BaseChatModel) -> None:
    super().test_bind_runnables_as_tools(model)

Otherwise, ensure that the tool_choice_value property is correctly specified on the test class.

Parameters:

model (BaseChatModel)

Return type:

None

test_conversation(
model: BaseChatModel,
) → None[source]#

Test to verify that the model can handle multi-turn conversations.

This should pass for all integrations. Tests the model’s ability to process a sequence of alternating human and AI messages as context for generating the next response.

Troubleshooting

First, debug test_invoke() because this test also uses model.invoke().

If that test passes but not this one, you should verify that: 1. Your model correctly processes the message history 2. The model maintains appropriate context from previous messages 3. The response is a valid AIMessage

Parameters:

model (BaseChatModel)

Return type:

None

test_double_messages_conversation(
model: BaseChatModel,
) → None[source]#

Test to verify that the model can handle double-message conversations.

This should pass for all integrations. Tests the model’s ability to process a sequence of double-system, double-human, and double-ai messages as context for generating the next response.

Troubleshooting

First, debug test_invoke() because this test also uses model.invoke().

Second, debug test_conversation() because this test is the “basic case” without double messages.

If that test passes those but not this one, you should verify that: 1. Your model API can handle double messages, or the integration should

merge messages before sending them to the API.

  1. The response is a valid AIMessage

Parameters:

model (BaseChatModel)

Return type:

None

test_image_inputs(
model: BaseChatModel,
) → None[source]#

Test that the model can process image inputs.

This test should be skipped (see Configuration below) if the model does not support image inputs. These will take the form:

{
    "type": "image",
    "source_type": "base64",
    "data": "<base64 image data>",
    "mime_type": "image/jpeg",  # or appropriate mime-type
}

For backward-compatibility, we must also support OpenAI-style image content blocks:

[
    {"type": "text", "text": "describe the weather in this image"},
    {
        "type": "image_url",
        "image_url": {"url": f"data:image/jpeg;base64,{image_data}"},
    },
]

See https://python.langchain.com/docs/concepts/multimodality/

If the property supports_image_urls is set to True, the test will also check that we can process content blocks of the form:

{
    "type": "image",
    "source_type": "url",
    "url": "<url>",
}
Configuration

To disable this test, set supports_image_inputs to False in your test class:

class TestMyChatModelIntegration(ChatModelIntegrationTests):
    @property
    def supports_image_inputs(self) -> bool:
        return False

    # Can also explicitly disable testing image URLs:
    @property
    def supports_image_urls(self) -> bool:
        return False
Troubleshooting

If this test fails, check that the model can correctly handle messages with image content blocks, including base64-encoded images. Otherwise, set the supports_image_inputs property to False.

Parameters:

model (BaseChatModel)

Return type:

None

test_image_tool_message(
model: BaseChatModel,
) → None[source]#

Test that the model can process ToolMessages with image inputs.

This test should be skipped if the model does not support messages of the form:

ToolMessage(
    content=[
        {
            "type": "image_url",
            "image_url": {"url": f"data:image/jpeg;base64,{image_data}"},
        },
    ],
    tool_call_id="1",
    name="random_image",
)

containing image content blocks in OpenAI Chat Completions format, in addition to messages of the form:

ToolMessage(
    content=[
        {
            "type": "image",
            "source_type": "base64",
            "data": image_data,
            "mime_type": "image/jpeg",
        },
    ],
    tool_call_id="1",
    name="random_image",
)

containing image content blocks in standard format.

This test can be skipped by setting the supports_image_tool_message property to False (see Configuration below).

Configuration

To disable this test, set supports_image_tool_message to False in your test class:

class TestMyChatModelIntegration(ChatModelIntegrationTests):
    @property
    def supports_image_tool_message(self) -> bool:
        return False
Troubleshooting

If this test fails, check that the model can correctly handle messages with image content blocks in ToolMessages, including base64-encoded images. Otherwise, set the supports_image_tool_message property to False.

Parameters:

model (BaseChatModel)

Return type:

None

test_invoke(
model: BaseChatModel,
) → None[source]#

Test to verify that model.invoke(simple_message) works.

This should pass for all integrations.

Troubleshooting

If this test fails, you should make sure your _generate method does not raise any exceptions, and that it returns a valid ChatResult like so:

return ChatResult(
    generations=[
        ChatGeneration(message=AIMessage(content="Output text"))
    ]
)
Parameters:

model (BaseChatModel)

Return type:

None

test_json_mode(
model: BaseChatModel,
) → None[source]#

Test structured output via JSON mode..

This test is optional and should be skipped if the model does not support the JSON mode feature (see Configuration below).

Configuration

To disable this test, set supports_json_mode to False in your test class:

class TestMyChatModelIntegration(ChatModelIntegrationTests):
    @property
    def supports_json_mode(self) -> bool:
        return False
Troubleshooting

See example implementation of with_structured_output here: https://python.langchain.com/api_reference/_modules/langchain_openai/chat_models/base.html#BaseChatOpenAI.with_structured_output

Parameters:

model (BaseChatModel)

Return type:

None

test_message_with_name(
model: BaseChatModel,
) → None[source]#

Test that HumanMessage with values for the name field can be handled.

These messages may take the form:

HumanMessage("hello", name="example_user")

If possible, the name field should be parsed and passed appropriately to the model. Otherwise, it should be ignored.

Troubleshooting

If this test fails, check that the name field on HumanMessage objects is either ignored or passed to the model appropriately.

Parameters:

model (BaseChatModel)

Return type:

None

test_pdf_inputs(
model: BaseChatModel,
) → None[source]#

Test that the model can process PDF inputs.

This test should be skipped (see Configuration below) if the model does not support PDF inputs. These will take the form:

{
    "type": "image",
    "source_type": "base64",
    "data": "<base64 image data>",
    "mime_type": "application/pdf",
}

See https://python.langchain.com/docs/concepts/multimodality/

Configuration

To disable this test, set supports_pdf_inputs to False in your test class:

class TestMyChatModelIntegration(ChatModelIntegrationTests):
    @property
    def supports_pdf_inputs(self) -> bool:
        return False
Troubleshooting

If this test fails, check that the model can correctly handle messages with pdf content blocks, including base64-encoded files. Otherwise, set the supports_pdf_inputs property to False.

Parameters:

model (BaseChatModel)

Return type:

None

test_pdf_tool_message(
model: BaseChatModel,
) → None[source]#

Test that the model can process ToolMessages with PDF inputs.

This test should be skipped if the model does not support messages of the form:

ToolMessage(
    content=[
        {
            "type": "file",
            "source_type": "base64",
            "data": pdf_data,
            "mime_type": "application/pdf",
        },
    ],
    tool_call_id="1",
    name="random_pdf",
)

containing PDF content blocks in standard format.

This test can be skipped by setting the supports_pdf_tool_message property to False (see Configuration below).

Configuration

To disable this test, set supports_pdf_tool_message to False in your test class:

class TestMyChatModelIntegration(ChatModelIntegrationTests):
    @property
    def supports_pdf_tool_message(self) -> bool:
        return False
Troubleshooting

If this test fails, check that the model can correctly handle messages with PDF content blocks in ToolMessages, specifically base64-encoded PDFs. Otherwise, set the supports_pdf_tool_message property to False.

Parameters:

model (BaseChatModel)

Return type:

None

test_stop_sequence(
model: BaseChatModel,
) → None[source]#

Test that model does not fail when invoked with the stop parameter.

The stop parameter is a standard parameter for stopping generation at a certain token.

More on standard parameters

This should pass for all integrations.

Troubleshooting

If this test fails, check that the function signature for _generate (as well as _stream and async variants) accepts the stop parameter:

def _generate(
    self,
    messages: List[BaseMessage],
    stop: Optional[List[str]] = None,
    run_manager: Optional[CallbackManagerForLLMRun] = None,
    **kwargs: Any,
) -> ChatResult:
Parameters:

model (BaseChatModel)

Return type:

None

test_stream(
model: BaseChatModel,
) → None[source]#

Test to verify that model.stream(simple_message) works.

This should pass for all integrations. Passing this test does not indicate a “streaming” implementation, but rather that the model can be used in a streaming context.

Troubleshooting

First, debug test_invoke(). because stream has a default implementation that calls invoke and yields the result as a single chunk.

If that test passes but not this one, you should make sure your _stream method does not raise any exceptions, and that it yields valid ChatGenerationChunk objects like so:

yield ChatGenerationChunk(message=AIMessageChunk(content="chunk text"))
Parameters:

model (BaseChatModel)

Return type:

None

test_stream_time(
model: BaseChatModel,
benchmark: BenchmarkFixture,
vcr: Cassette,
) → None[source]#

Test that streaming does not introduce undue overhead.

See enable_vcr_tests dropdown above for more information.

Configuration

This test can be enabled or disabled using the enable_vcr_tests property. For example, to disable the test, set this property to False:

@property
def enable_vcr_tests(self) -> bool:
    return False

Important

VCR will by default record authentication headers and other sensitive information in cassettes. See enable_vcr_tests dropdown above for how to configure what information is recorded in cassettes.

Parameters:

  • model (BaseChatModel)

  • benchmark (BenchmarkFixture)

  • vcr (Cassette)

Return type:

None

test_structured_few_shot_examples(
model: BaseChatModel,
my_adder_tool: BaseTool,
) → None[source]#

Test that the model can process few-shot examples with tool calls.

These are represented as a sequence of messages of the following form:

  • HumanMessage with string content;

  • AIMessage with the tool_calls attribute populated;

  • ToolMessage with string content;

  • AIMessage with string content (an answer);

  • HumanMessage with string content (a follow-up question).

This test should be skipped if the model does not support tool calling (see Configuration below).

Configuration

To disable tool calling tests, set has_tool_calling to False in your test class:

class TestMyChatModelIntegration(ChatModelIntegrationTests):
    @property
    def has_tool_calling(self) -> bool:
        return False
Troubleshooting

This test uses a utility function in langchain_core to generate a sequence of messages representing “few-shot” examples.

If this test fails, check that the model can correctly handle this sequence of messages.

You can xfail the test if tool calling is implemented but this format is not supported.

@pytest.mark.xfail(reason=("Not implemented."))
def test_structured_few_shot_examples(self, *args: Any) -> None:
    super().test_structured_few_shot_examples(*args)
Parameters:
Return type:

None

test_structured_output(
model: BaseChatModel,
schema_type: Literal['pydantic', 'typeddict', 'json_schema'],
) → None[source]#

Test to verify structured output is generated both on invoke and stream.

This test is optional and should be skipped if the model does not support structured output (see Configuration below).

Configuration

To disable structured output tests, set has_structured_output to False in your test class:

class TestMyChatModelIntegration(ChatModelIntegrationTests):
    @property
    def has_structured_output(self) -> bool:
        return False

By default, has_structured_output is True if a model overrides the with_structured_output or bind_tools methods.

Troubleshooting

If this test fails, ensure that the model’s bind_tools method properly handles both JSON Schema and Pydantic V2 models.

langchain_core implements a utility function that will accommodate most formats.

See example implementation of with_structured_output.

Parameters:

  • model (BaseChatModel)

  • schema_type (Literal['pydantic', 'typeddict', 'json_schema'])

Return type:

None

async test_structured_output_async(
model: BaseChatModel,
schema_type: Literal['pydantic', 'typeddict', 'json_schema'],
) → None[source]#

Test to verify structured output is generated both on invoke and stream.

This test is optional and should be skipped if the model does not support structured output (see Configuration below).

Configuration

To disable structured output tests, set has_structured_output to False in your test class:

class TestMyChatModelIntegration(ChatModelIntegrationTests):
    @property
    def has_structured_output(self) -> bool:
        return False

By default, has_structured_output is True if a model overrides the with_structured_output or bind_tools methods.

Troubleshooting

If this test fails, ensure that the model’s bind_tools method properly handles both JSON Schema and Pydantic V2 models.

langchain_core implements a utility function that will accommodate most formats.

See example implementation of with_structured_output.

Parameters:

  • model (BaseChatModel)

  • schema_type (Literal['pydantic', 'typeddict', 'json_schema'])

Return type:

None

test_structured_output_optional_param(
model: BaseChatModel,
) → None[source]#

Test structured output with optional parameters.

Test to verify we can generate structured output that includes optional parameters.

This test is optional and should be skipped if the model does not support structured output (see Configuration below).

Configuration

To disable structured output tests, set has_structured_output to False in your test class:

class TestMyChatModelIntegration(ChatModelIntegrationTests):
    @property
    def has_structured_output(self) -> bool:
        return False

By default, has_structured_output is True if a model overrides the with_structured_output or bind_tools methods.

Troubleshooting

If this test fails, ensure that the model’s bind_tools method properly handles Pydantic V2 models with optional parameters.

langchain_core implements a utility function that will accommodate most formats.

See example implementation of with_structured_output.

Parameters:

model (BaseChatModel)

Return type:

None

test_structured_output_pydantic_2_v1(
model: BaseChatModel,
) → None[source]#

Test structured output using pydantic.v1.BaseModel.

Verify we can generate structured output using pydantic.v1.BaseModel.

pydantic.v1.BaseModel is available in the Pydantic 2 package.

This test is optional and should be skipped if the model does not support structured output (see Configuration below).

Configuration

To disable structured output tests, set has_structured_output to False in your test class:

class TestMyChatModelIntegration(ChatModelIntegrationTests):
    @property
    def has_structured_output(self) -> bool:
        return False

By default, has_structured_output is True if a model overrides the with_structured_output or bind_tools methods.

Troubleshooting

If this test fails, ensure that the model’s bind_tools method properly handles both JSON Schema and Pydantic V1 models.

langchain_core implements a utility function that will accommodate most formats.

See example implementation of with_structured_output.

Parameters:

model (BaseChatModel)

Return type:

None

test_tool_calling(
model: BaseChatModel,
) → None[source]#

Test that the model generates tool calls.

This test is skipped if the has_tool_calling property on the test class is set to False.

This test is optional and should be skipped if the model does not support tool calling (see Configuration below).

Configuration

To disable tool calling tests, set has_tool_calling to False in your test class:

class TestMyChatModelIntegration(ChatModelIntegrationTests):
    @property
    def has_tool_calling(self) -> bool:
        return False
Troubleshooting

If this test fails, check that bind_tools is implemented to correctly translate LangChain tool objects into the appropriate schema for your chat model.

This test may fail if the chat model does not support a tool_choice parameter. This parameter can be used to force a tool call. If tool_choice is not supported and the model consistently fails this test, you can xfail the test:

@pytest.mark.xfail(reason=("Does not support tool_choice."))
def test_tool_calling(self, model: BaseChatModel) -> None:
    super().test_tool_calling(model)

Otherwise, in the case that only one tool is bound, ensure that tool_choice supports the string 'any' to force calling that tool.

Parameters:

model (BaseChatModel)

Return type:

None

async test_tool_calling_async(
model: BaseChatModel,
) → None[source]#

Test that the model generates tool calls.

This test is skipped if the has_tool_calling property on the test class is set to False.

This test is optional and should be skipped if the model does not support tool calling (see Configuration below).

Configuration

To disable tool calling tests, set has_tool_calling to False in your test class:

class TestMyChatModelIntegration(ChatModelIntegrationTests):
    @property
    def has_tool_calling(self) -> bool:
        return False
Troubleshooting

If this test fails, check that bind_tools is implemented to correctly translate LangChain tool objects into the appropriate schema for your chat model.

This test may fail if the chat model does not support a tool_choice parameter. This parameter can be used to force a tool call. If tool_choice is not supported and the model consistently fails this test, you can xfail the test:

@pytest.mark.xfail(reason=("Does not support tool_choice."))
async def test_tool_calling_async(self, model: BaseChatModel) -> None:
    await super().test_tool_calling_async(model)

Otherwise, in the case that only one tool is bound, ensure that tool_choice supports the string 'any' to force calling that tool.

Parameters:

model (BaseChatModel)

Return type:

None

test_tool_calling_with_no_arguments(
model: BaseChatModel,
) → None[source]#

Test that the model generates tool calls for tools with no arguments.

This test is skipped if the has_tool_calling property on the test class is set to False.

This test is optional and should be skipped if the model does not support tool calling (see Configuration below).

Configuration

To disable tool calling tests, set has_tool_calling to False in your test class:

class TestMyChatModelIntegration(ChatModelIntegrationTests):
    @property
    def has_tool_calling(self) -> bool:
        return False
Troubleshooting

If this test fails, check that bind_tools is implemented to correctly translate LangChain tool objects into the appropriate schema for your chat model. It should correctly handle the case where a tool has no arguments.

This test may fail if the chat model does not support a tool_choice parameter. This parameter can be used to force a tool call. It may also fail if a provider does not support this form of tool. In these cases, you can xfail the test:

@pytest.mark.xfail(reason=("Does not support tool_choice."))
def test_tool_calling_with_no_arguments(
    self, model: BaseChatModel
) -> None:
    super().test_tool_calling_with_no_arguments(model)

Otherwise, in the case that only one tool is bound, ensure that tool_choice supports the string 'any' to force calling that tool.

Parameters:

model (BaseChatModel)

Return type:

None

test_tool_choice(
model: BaseChatModel,
) → None[source]#

Test tool_choice parameter.

Test that the model can force tool calling via the tool_choice parameter. This test is skipped if the has_tool_choice property on the test class is set to False.

This test is optional and should be skipped if the model does not support tool calling (see Configuration below).

Configuration

To disable tool calling tests, set has_tool_choice to False in your test class:

class TestMyChatModelIntegration(ChatModelIntegrationTests):
    @property
    def has_tool_choice(self) -> bool:
        return False
Troubleshooting

If this test fails, check whether the test_tool_calling test is passing. If it is not, refer to the troubleshooting steps in that test first.

If test_tool_calling is passing, check that the underlying model supports forced tool calling. If it does, bind_tools should accept a tool_choice parameter that can be used to force a tool call.

It should accept (1) the string 'any' to force calling the bound tool, and (2) the string name of the tool to force calling that tool.

Parameters:

model (BaseChatModel)

Return type:

None

test_tool_message_error_status(
model: BaseChatModel,
my_adder_tool: BaseTool,
) → None[source]#

Test that ToolMessage with status="error" can be handled.

These messages may take the form:

ToolMessage(
    "Error: Missing required argument 'b'.",
    name="my_adder_tool",
    tool_call_id="abc123",
    status="error",
)

If possible, the status field should be parsed and passed appropriately to the model.

This test is optional and should be skipped if the model does not support tool calling (see Configuration below).

Configuration

To disable tool calling tests, set has_tool_calling to False in your test class:

class TestMyChatModelIntegration(ChatModelIntegrationTests):
    @property
    def has_tool_calling(self) -> bool:
        return False
Troubleshooting

If this test fails, check that the status field on ToolMessage objects is either ignored or passed to the model appropriately.

Parameters:
Return type:

None

test_tool_message_histories_list_content(
model: BaseChatModel,
my_adder_tool: BaseTool,
) → None[source]#

Test that message histories are compatible with list tool contents.

For instance with Anthropic format contents.

These message histories will include AIMessage objects with “tool use” and content blocks, e.g.,

[
    {"type": "text", "text": "Hmm let me think about that"},
    {
        "type": "tool_use",
        "input": {"fav_color": "green"},
        "id": "foo",
        "name": "color_picker",
    },
]

This test should be skipped if the model does not support tool calling (see Configuration below).

Configuration

To disable tool calling tests, set has_tool_calling to False in your test class:

class TestMyChatModelIntegration(ChatModelIntegrationTests):
    @property
    def has_tool_calling(self) -> bool:
        return False
Troubleshooting

If this test fails, check that:

  1. The model can correctly handle message histories that include AIMessage objects with list content.

  2. The tool_calls attribute on AIMessage objects is correctly handled and passed to the model in an appropriate format.

  3. The model can correctly handle ToolMessage objects with string content and arbitrary string values for tool_call_id.

You can xfail the test if tool calling is implemented but this format is not supported.

@pytest.mark.xfail(reason=("Not implemented."))
def test_tool_message_histories_list_content(self, *args: Any) -> None:
    super().test_tool_message_histories_list_content(*args)
Parameters:
Return type:

None

test_tool_message_histories_string_content(
model: BaseChatModel,
my_adder_tool: BaseTool,
) → None[source]#

Test that message histories are compatible with string tool contents.

For instance with OpenAI format contents. If a model passes this test, it should be compatible with messages generated from providers following OpenAI format.

This test should be skipped if the model does not support tool calling (see Configuration below).

Configuration

To disable tool calling tests, set has_tool_calling to False in your test class:

class TestMyChatModelIntegration(ChatModelIntegrationTests):
    @property
    def has_tool_calling(self) -> bool:
        return False
Troubleshooting

If this test fails, check that:

  1. The model can correctly handle message histories that include AIMessage objects with "" content.

  2. The tool_calls attribute on AIMessage objects is correctly handled and passed to the model in an appropriate format.

  3. The model can correctly handle ToolMessage objects with string content and arbitrary string values for tool_call_id.

You can xfail the test if tool calling is implemented but this format is not supported.

@pytest.mark.xfail(reason=("Not implemented."))
def test_tool_message_histories_string_content(
    self, *args: Any
) -> None:
    super().test_tool_message_histories_string_content(*args)
Parameters:
Return type:

None

test_unicode_tool_call_integration(
model: BaseChatModel,
*,
tool_choice: str | None = None,
force_tool_call: bool = True,
) → None[source]#

Generic integration test for Unicode characters in tool calls.

Parameters:

  • model (BaseChatModel) – The chat model to test

  • tool_choice (str | None) – Tool choice parameter to pass to bind_tools() (provider-specific)

  • force_tool_call (bool) – Whether to force a tool call (use tool_choice=True if None)

Return type:

None

Tests that Unicode characters in tool call arguments are preserved correctly, not escaped as \\uXXXX sequences.

test_usage_metadata(
model: BaseChatModel,
) → None[source]#

Test to verify that the model returns correct usage metadata.

This test is optional and should be skipped if the model does not return usage metadata (see Configuration below).

Changed in version 0.3.17: Additionally check for the presence of model_name in the response metadata, which is needed for usage tracking in callback handlers.

Configuration

By default, this test is run.

To disable this feature, set returns_usage_metadata to False in your test class:

class TestMyChatModelIntegration(ChatModelIntegrationTests):
    @property
    def returns_usage_metadata(self) -> bool:
        return False

This test can also check the format of specific kinds of usage metadata based on the supported_usage_metadata_details property. This property should be configured as follows with the types of tokens that the model supports tracking:

class TestMyChatModelIntegration(ChatModelIntegrationTests):
    @property
    def supported_usage_metadata_details(self) -> dict:
        return {
            "invoke": [
                "audio_input",
                "audio_output",
                "reasoning_output",
                "cache_read_input",
                "cache_creation_input",
            ],
            "stream": [
                "audio_input",
                "audio_output",
                "reasoning_output",
                "cache_read_input",
                "cache_creation_input",
            ],
        }
Troubleshooting

If this test fails, first verify that your model returns UsageMetadata dicts attached to the returned AIMessage object in _generate:

return ChatResult(
    generations=[
        ChatGeneration(
            message=AIMessage(
                content="Output text",
                usage_metadata={
                    "input_tokens": 350,
                    "output_tokens": 240,
                    "total_tokens": 590,
                    "input_token_details": {
                        "audio": 10,
                        "cache_creation": 200,
                        "cache_read": 100,
                    },
                    "output_token_details": {
                        "audio": 10,
                        "reasoning": 200,
                    },
                },
            )
        )
    ]
)

Check also that the response includes a 'model_name' key in its usage_metadata.

Parameters:

model (BaseChatModel)

Return type:

None

test_usage_metadata_streaming(
model: BaseChatModel,
) → None[source]#

Test usage metadata in streaming mode.

Test to verify that the model returns correct usage metadata in streaming mode.

Changed in version 0.3.17: Additionally check for the presence of model_name in the response metadata, which is needed for usage tracking in callback handlers.

Configuration

By default, this test is run. To disable this feature, set returns_usage_metadata to False in your test class:

class TestMyChatModelIntegration(ChatModelIntegrationTests):
    @property
    def returns_usage_metadata(self) -> bool:
        return False

This test can also check the format of specific kinds of usage metadata based on the supported_usage_metadata_details property. This property should be configured as follows with the types of tokens that the model supports tracking:

class TestMyChatModelIntegration(ChatModelIntegrationTests):
    @property
    def supported_usage_metadata_details(self) -> dict:
        return {
            "invoke": [
                "audio_input",
                "audio_output",
                "reasoning_output",
                "cache_read_input",
                "cache_creation_input",
            ],
            "stream": [
                "audio_input",
                "audio_output",
                "reasoning_output",
                "cache_read_input",
                "cache_creation_input",
            ],
        }
Troubleshooting

If this test fails, first verify that your model yields UsageMetadata dicts attached to the returned AIMessage object in _stream that sum up to the total usage metadata.

Note that input_tokens should only be included on one of the chunks (typically the first or the last chunk), and the rest should have 0 or None to avoid counting input tokens multiple times.

output_tokens typically count the number of tokens in each chunk, not the sum. This test will pass as long as the sum of output_tokens across all chunks is not 0.

yield ChatResult(
    generations=[
        ChatGeneration(
            message=AIMessage(
                content="Output text",
                usage_metadata={
                    "input_tokens": (
                        num_input_tokens if is_first_chunk else 0
                    ),
                    "output_tokens": 11,
                    "total_tokens": (
                        11 + num_input_tokens if is_first_chunk else 11
                    ),
                    "input_token_details": {
                        "audio": 10,
                        "cache_creation": 200,
                        "cache_read": 100,
                    },
                    "output_token_details": {
                        "audio": 10,
                        "reasoning": 200,
                    },
                },
            )
        )
    ]
)

Check also that the aggregated response includes a 'model_name' key in its usage_metadata.

Parameters:

model (BaseChatModel)

Return type:

None