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langchain/libs/partners/anthropic/tests/unit_tests/test_chat_models.py

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"""Test chat model integration."""
from __future__ import annotations
import copy
import os
import warnings
from collections.abc import Callable
from typing import Any, Literal, cast
from unittest.mock import MagicMock, patch
import anthropic
import pytest
from anthropic.types import Message, TextBlock, Usage
from blockbuster import blockbuster_ctx
from langchain_core.exceptions import ContextOverflowError
from langchain_core.messages import AIMessage, HumanMessage, SystemMessage, ToolMessage
from langchain_core.runnables import RunnableBinding
from langchain_core.tools import BaseTool, tool
from langchain_core.tracers.base import BaseTracer
from langchain_core.tracers.schemas import Run
from pydantic import BaseModel, Field, SecretStr, ValidationError
from pytest import CaptureFixture, MonkeyPatch
from langchain_anthropic import ChatAnthropic
from langchain_anthropic.chat_models import (
_create_usage_metadata,
_format_image,
_format_messages,
_is_builtin_tool,
_merge_messages,
_thinking_in_params,
convert_to_anthropic_tool,
)
os.environ["ANTHROPIC_API_KEY"] = "foo"
MODEL_NAME = "claude-sonnet-4-5-20250929"
def test_initialization() -> None:
"""Test chat model initialization."""
for model in [
ChatAnthropic(model_name=MODEL_NAME, api_key="xyz", timeout=2), # type: ignore[arg-type, call-arg]
ChatAnthropic( # type: ignore[call-arg, call-arg, call-arg]
model=MODEL_NAME,
anthropic_api_key="xyz",
default_request_timeout=2,
base_url="https://api.anthropic.com",
),
]:
assert model.model == MODEL_NAME
assert cast("SecretStr", model.anthropic_api_key).get_secret_value() == "xyz"
assert model.default_request_timeout == 2.0
assert model.anthropic_api_url == "https://api.anthropic.com"
def test_user_agent_header_in_client_params() -> None:
"""Test that _client_params includes a User-Agent header."""
llm = ChatAnthropic(model=MODEL_NAME, api_key="test-key") # type: ignore[arg-type]
params = llm._client_params
assert "default_headers" in params
assert "User-Agent" in params["default_headers"]
assert params["default_headers"]["User-Agent"].startswith("langchain-anthropic/")
@pytest.mark.parametrize("async_api", [True, False])
def test_streaming_attribute_should_stream(async_api: bool) -> None: # noqa: FBT001
llm = ChatAnthropic(model=MODEL_NAME, streaming=True)
assert llm._should_stream(async_api=async_api)
def test_anthropic_client_caching() -> None:
"""Test that the OpenAI client is cached."""
llm1 = ChatAnthropic(model=MODEL_NAME)
llm2 = ChatAnthropic(model=MODEL_NAME)
assert llm1._client._client is llm2._client._client
llm3 = ChatAnthropic(model=MODEL_NAME, base_url="foo")
assert llm1._client._client is not llm3._client._client
llm4 = ChatAnthropic(model=MODEL_NAME, timeout=None)
assert llm1._client._client is llm4._client._client
llm5 = ChatAnthropic(model=MODEL_NAME, timeout=3)
assert llm1._client._client is not llm5._client._client
def test_anthropic_proxy_support() -> None:
"""Test that both sync and async clients support proxy configuration."""
proxy_url = "http://proxy.example.com:8080"
# Test sync client with proxy
llm_sync = ChatAnthropic(model=MODEL_NAME, anthropic_proxy=proxy_url)
sync_client = llm_sync._client
assert sync_client is not None
# Test async client with proxy - this should not raise TypeError
async_client = llm_sync._async_client
assert async_client is not None
# Test that clients with different proxy settings are not cached together
llm_no_proxy = ChatAnthropic(model=MODEL_NAME)
llm_with_proxy = ChatAnthropic(model=MODEL_NAME, anthropic_proxy=proxy_url)
# Different proxy settings should result in different cached clients
assert llm_no_proxy._client._client is not llm_with_proxy._client._client
def test_anthropic_proxy_from_environment() -> None:
"""Test that proxy can be set from ANTHROPIC_PROXY environment variable."""
proxy_url = "http://env-proxy.example.com:8080"
# Test with environment variable set
with patch.dict(os.environ, {"ANTHROPIC_PROXY": proxy_url}):
llm = ChatAnthropic(model=MODEL_NAME)
assert llm.anthropic_proxy == proxy_url
# Should be able to create clients successfully
sync_client = llm._client
async_client = llm._async_client
assert sync_client is not None
assert async_client is not None
# Test that explicit parameter overrides environment variable
with patch.dict(os.environ, {"ANTHROPIC_PROXY": "http://env-proxy.com"}):
explicit_proxy = "http://explicit-proxy.com"
llm = ChatAnthropic(model=MODEL_NAME, anthropic_proxy=explicit_proxy)
assert llm.anthropic_proxy == explicit_proxy
def test_set_default_max_tokens() -> None:
"""Test the set_default_max_tokens function."""
# Test claude-sonnet-4-5 models
llm = ChatAnthropic(model="claude-sonnet-4-5-20250929", anthropic_api_key="test")
assert llm.max_tokens == 64000
# Test claude-opus-4 models
llm = ChatAnthropic(model="claude-opus-4-20250514", anthropic_api_key="test")
assert llm.max_tokens == 32000
# Test claude-sonnet-4 models
llm = ChatAnthropic(model="claude-sonnet-4-20250514", anthropic_api_key="test")
assert llm.max_tokens == 64000
# Test claude-3-7-sonnet models
llm = ChatAnthropic(model="claude-3-7-sonnet-20250219", anthropic_api_key="test")
assert llm.max_tokens == 64000
# Test claude-3-5-haiku models
llm = ChatAnthropic(model="claude-3-5-haiku-20241022", anthropic_api_key="test")
assert llm.max_tokens == 8192
# Test claude-3-haiku models (should default to 4096)
llm = ChatAnthropic(model="claude-3-haiku-20240307", anthropic_api_key="test")
assert llm.max_tokens == 4096
# Test that existing max_tokens values are preserved
llm = ChatAnthropic(model=MODEL_NAME, max_tokens=2048, anthropic_api_key="test")
assert llm.max_tokens == 2048
# Test that explicitly set max_tokens values are preserved
llm = ChatAnthropic(model=MODEL_NAME, max_tokens=4096, anthropic_api_key="test")
assert llm.max_tokens == 4096
@pytest.mark.requires("anthropic")
def test_anthropic_model_name_param() -> None:
llm = ChatAnthropic(model_name=MODEL_NAME) # type: ignore[call-arg, call-arg]
assert llm.model == MODEL_NAME
@pytest.mark.requires("anthropic")
def test_anthropic_model_param() -> None:
llm = ChatAnthropic(model=MODEL_NAME) # type: ignore[call-arg]
assert llm.model == MODEL_NAME
@pytest.mark.requires("anthropic")
def test_anthropic_model_kwargs() -> None:
llm = ChatAnthropic(model_name=MODEL_NAME, model_kwargs={"foo": "bar"}) # type: ignore[call-arg, call-arg]
assert llm.model_kwargs == {"foo": "bar"}
@pytest.mark.requires("anthropic")
def test_anthropic_fields_in_model_kwargs() -> None:
"""Test that for backwards compatibility fields can be passed in as model_kwargs."""
llm = ChatAnthropic(model=MODEL_NAME, model_kwargs={"max_tokens_to_sample": 5}) # type: ignore[call-arg]
assert llm.max_tokens == 5
llm = ChatAnthropic(model=MODEL_NAME, model_kwargs={"max_tokens": 5}) # type: ignore[call-arg]
assert llm.max_tokens == 5
@pytest.mark.requires("anthropic")
def test_anthropic_incorrect_field() -> None:
with pytest.warns(match="not default parameter"):
llm = ChatAnthropic(model=MODEL_NAME, foo="bar") # type: ignore[call-arg, call-arg]
assert llm.model_kwargs == {"foo": "bar"}
@pytest.mark.requires("anthropic")
def test_anthropic_initialization() -> None:
"""Test anthropic initialization."""
# Verify that chat anthropic can be initialized using a secret key provided
# as a parameter rather than an environment variable.
ChatAnthropic(model=MODEL_NAME, anthropic_api_key="test") # type: ignore[call-arg, call-arg]
def test__format_output() -> None:
anthropic_msg = Message(
id="foo",
content=[TextBlock(type="text", text="bar")],
model="baz",
role="assistant",
stop_reason=None,
stop_sequence=None,
usage=Usage(input_tokens=2, output_tokens=1),
type="message",
)
expected = AIMessage( # type: ignore[misc]
"bar",
usage_metadata={
"input_tokens": 2,
"output_tokens": 1,
"total_tokens": 3,
"input_token_details": {},
},
response_metadata={"model_provider": "anthropic"},
)
llm = ChatAnthropic(model=MODEL_NAME, anthropic_api_key="test") # type: ignore[call-arg, call-arg]
actual = llm._format_output(anthropic_msg)
assert actual.generations[0].message == expected
def test__format_output_cached() -> None:
anthropic_msg = Message(
id="foo",
content=[TextBlock(type="text", text="bar")],
model="baz",
role="assistant",
stop_reason=None,
stop_sequence=None,
usage=Usage(
input_tokens=2,
output_tokens=1,
cache_creation_input_tokens=3,
cache_read_input_tokens=4,
),
type="message",
)
expected = AIMessage( # type: ignore[misc]
"bar",
usage_metadata={
"input_tokens": 9,
"output_tokens": 1,
"total_tokens": 10,
"input_token_details": {"cache_creation": 3, "cache_read": 4},
},
response_metadata={"model_provider": "anthropic"},
)
llm = ChatAnthropic(model=MODEL_NAME, anthropic_api_key="test") # type: ignore[call-arg, call-arg]
actual = llm._format_output(anthropic_msg)
assert actual.generations[0].message == expected
def test__merge_messages() -> None:
messages = [
SystemMessage("foo"), # type: ignore[misc]
HumanMessage("bar"), # type: ignore[misc]
AIMessage( # type: ignore[misc]
[
{"text": "baz", "type": "text"},
{
"tool_input": {"a": "b"},
"type": "tool_use",
"id": "1",
"text": None,
"name": "buz",
},
{"text": "baz", "type": "text"},
{
"tool_input": {"a": "c"},
"type": "tool_use",
"id": "2",
"text": None,
"name": "blah",
},
{
"tool_input": {"a": "c"},
"type": "tool_use",
"id": "3",
"text": None,
"name": "blah",
},
],
),
ToolMessage("buz output", tool_call_id="1", status="error"), # type: ignore[misc]
ToolMessage(
content=[
{
"type": "image",
"source": {
"type": "base64",
"media_type": "image/jpeg",
"data": "fake_image_data",
},
},
],
tool_call_id="2",
), # type: ignore[misc]
ToolMessage([], tool_call_id="3"), # type: ignore[misc]
HumanMessage("next thing"), # type: ignore[misc]
]
expected = [
SystemMessage("foo"), # type: ignore[misc]
HumanMessage("bar"), # type: ignore[misc]
AIMessage( # type: ignore[misc]
[
{"text": "baz", "type": "text"},
{
"tool_input": {"a": "b"},
"type": "tool_use",
"id": "1",
"text": None,
"name": "buz",
},
{"text": "baz", "type": "text"},
{
"tool_input": {"a": "c"},
"type": "tool_use",
"id": "2",
"text": None,
"name": "blah",
},
{
"tool_input": {"a": "c"},
"type": "tool_use",
"id": "3",
"text": None,
"name": "blah",
},
],
),
HumanMessage( # type: ignore[misc]
[
{
"type": "tool_result",
"content": "buz output",
"tool_use_id": "1",
"is_error": True,
},
{
"type": "tool_result",
"content": [
{
"type": "image",
"source": {
"type": "base64",
"media_type": "image/jpeg",
"data": "fake_image_data",
},
},
],
"tool_use_id": "2",
"is_error": False,
},
{
"type": "tool_result",
"content": [],
"tool_use_id": "3",
"is_error": False,
},
{"type": "text", "text": "next thing"},
],
),
]
actual = _merge_messages(messages)
assert expected == actual
# Test tool message case
messages = [
ToolMessage("buz output", tool_call_id="1"), # type: ignore[misc]
ToolMessage( # type: ignore[misc]
content=[
{"type": "tool_result", "content": "blah output", "tool_use_id": "2"},
],
tool_call_id="2",
),
]
expected = [
HumanMessage( # type: ignore[misc]
[
{
"type": "tool_result",
"content": "buz output",
"tool_use_id": "1",
"is_error": False,
},
{"type": "tool_result", "content": "blah output", "tool_use_id": "2"},
],
),
]
actual = _merge_messages(messages)
assert expected == actual
def test__merge_messages_mutation() -> None:
original_messages = [
HumanMessage([{"type": "text", "text": "bar"}]), # type: ignore[misc]
HumanMessage("next thing"), # type: ignore[misc]
]
messages = [
HumanMessage([{"type": "text", "text": "bar"}]), # type: ignore[misc]
HumanMessage("next thing"), # type: ignore[misc]
]
expected = [
HumanMessage( # type: ignore[misc]
[{"type": "text", "text": "bar"}, {"type": "text", "text": "next thing"}],
),
]
actual = _merge_messages(messages)
assert expected == actual
assert messages == original_messages
def test__merge_messages_tool_message_cache_control() -> None:
"""Test that cache_control is hoisted from content blocks to tool_result level."""
# Test with cache_control in content block
messages = [
ToolMessage(
content=[
{
"type": "text",
"text": "tool output",
"cache_control": {"type": "ephemeral"},
}
],
tool_call_id="1",
)
]
original_messages = [copy.deepcopy(m) for m in messages]
expected = [
HumanMessage(
[
{
"type": "tool_result",
"content": [{"type": "text", "text": "tool output"}],
"tool_use_id": "1",
"is_error": False,
"cache_control": {"type": "ephemeral"},
}
]
)
]
actual = _merge_messages(messages)
assert expected == actual
# Verify no mutation
assert messages == original_messages
# Test with multiple content blocks, cache_control on last one
messages = [
ToolMessage(
content=[
{"type": "text", "text": "first output"},
{
"type": "text",
"text": "second output",
"cache_control": {"type": "ephemeral"},
},
],
tool_call_id="2",
)
]
expected = [
HumanMessage(
[
{
"type": "tool_result",
"content": [
{"type": "text", "text": "first output"},
{"type": "text", "text": "second output"},
],
"tool_use_id": "2",
"is_error": False,
"cache_control": {"type": "ephemeral"},
}
]
)
]
actual = _merge_messages(messages)
assert expected == actual
# Test without cache_control
messages = [ToolMessage(content="simple output", tool_call_id="3")]
expected = [
HumanMessage(
[
{
"type": "tool_result",
"content": "simple output",
"tool_use_id": "3",
"is_error": False,
}
]
)
]
actual = _merge_messages(messages)
assert expected == actual
def test__format_image() -> None:
url = "dummyimage.com/600x400/000/fff"
with pytest.raises(ValueError):
_format_image(url)
@pytest.fixture
def pydantic() -> type[BaseModel]:
class dummy_function(BaseModel): # noqa: N801
"""Dummy function."""
arg1: int = Field(..., description="foo")
arg2: Literal["bar", "baz"] = Field(..., description="one of 'bar', 'baz'")
return dummy_function
@pytest.fixture
def function() -> Callable:
def dummy_function(arg1: int, arg2: Literal["bar", "baz"]) -> None:
"""Dummy function.
Args:
arg1: foo
arg2: one of 'bar', 'baz'
"""
return dummy_function
@pytest.fixture
def dummy_tool() -> BaseTool:
class Schema(BaseModel):
arg1: int = Field(..., description="foo")
arg2: Literal["bar", "baz"] = Field(..., description="one of 'bar', 'baz'")
class DummyFunction(BaseTool): # type: ignore[override]
args_schema: type[BaseModel] = Schema
name: str = "dummy_function"
description: str = "Dummy function."
def _run(self, *args: Any, **kwargs: Any) -> Any:
pass
return DummyFunction()
@pytest.fixture
def json_schema() -> dict:
return {
"title": "dummy_function",
"description": "Dummy function.",
"type": "object",
"properties": {
"arg1": {"description": "foo", "type": "integer"},
"arg2": {
"description": "one of 'bar', 'baz'",
"enum": ["bar", "baz"],
"type": "string",
},
},
"required": ["arg1", "arg2"],
}
@pytest.fixture
def openai_function() -> dict:
return {
"name": "dummy_function",
"description": "Dummy function.",
"parameters": {
"type": "object",
"properties": {
"arg1": {"description": "foo", "type": "integer"},
"arg2": {
"description": "one of 'bar', 'baz'",
"enum": ["bar", "baz"],
"type": "string",
},
},
"required": ["arg1", "arg2"],
},
}
def test_convert_to_anthropic_tool(
pydantic: type[BaseModel],
function: Callable,
dummy_tool: BaseTool,
json_schema: dict,
openai_function: dict,
) -> None:
expected = {
"name": "dummy_function",
"description": "Dummy function.",
"input_schema": {
"type": "object",
"properties": {
"arg1": {"description": "foo", "type": "integer"},
"arg2": {
"description": "one of 'bar', 'baz'",
"enum": ["bar", "baz"],
"type": "string",
},
},
"required": ["arg1", "arg2"],
},
}
for fn in (pydantic, function, dummy_tool, json_schema, expected, openai_function):
actual = convert_to_anthropic_tool(fn)
assert actual == expected
def test__format_messages_with_tool_calls() -> None:
system = SystemMessage("fuzz") # type: ignore[misc]
human = HumanMessage("foo") # type: ignore[misc]
ai = AIMessage(
"", # with empty string
tool_calls=[{"name": "bar", "id": "1", "args": {"baz": "buzz"}}],
)
ai2 = AIMessage(
[], # with empty list
tool_calls=[{"name": "bar", "id": "2", "args": {"baz": "buzz"}}],
)
tool = ToolMessage(
"blurb",
tool_call_id="1",
)
tool_image_url = ToolMessage(
[{"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,...."}}],
tool_call_id="2",
)
tool_image = ToolMessage(
[
{
"type": "image",
"source": {
"data": "....",
"type": "base64",
"media_type": "image/jpeg",
},
},
],
tool_call_id="3",
)
messages = [system, human, ai, tool, ai2, tool_image_url, tool_image]
expected = (
"fuzz",
[
{"role": "user", "content": "foo"},
{
"role": "assistant",
"content": [
{
"type": "tool_use",
"name": "bar",
"id": "1",
"input": {"baz": "buzz"},
},
],
},
{
"role": "user",
"content": [
{
"type": "tool_result",
"content": "blurb",
"tool_use_id": "1",
"is_error": False,
},
],
},
{
"role": "assistant",
"content": [
{
"type": "tool_use",
"name": "bar",
"id": "2",
"input": {"baz": "buzz"},
},
],
},
{
"role": "user",
"content": [
{
"type": "tool_result",
"content": [
{
"type": "image",
"source": {
"data": "....",
"type": "base64",
"media_type": "image/jpeg",
},
},
],
"tool_use_id": "2",
"is_error": False,
},
{
"type": "tool_result",
"content": [
{
"type": "image",
"source": {
"data": "....",
"type": "base64",
"media_type": "image/jpeg",
},
},
],
"tool_use_id": "3",
"is_error": False,
},
],
},
],
)
actual = _format_messages(messages)
assert expected == actual
# Check handling of empty AIMessage
empty_contents: list[str | list[str | dict]] = ["", []]
for empty_content in empty_contents:
## Permit message in final position
_, anthropic_messages = _format_messages([human, AIMessage(empty_content)])
expected_messages = [
{"role": "user", "content": "foo"},
{"role": "assistant", "content": empty_content},
]
assert expected_messages == anthropic_messages
## Remove message otherwise
_, anthropic_messages = _format_messages(
[human, AIMessage(empty_content), human]
)
expected_messages = [
{"role": "user", "content": "foo"},
{"role": "user", "content": "foo"},
]
assert expected_messages == anthropic_messages
actual = _format_messages(
[system, human, ai, tool, AIMessage(empty_content), human]
)
assert actual[0] == "fuzz"
assert [message["role"] for message in actual[1]] == [
"user",
"assistant",
"user",
"user",
]
def test__format_tool_use_block() -> None:
# Test we correctly format tool_use blocks when there is no corresponding tool_call.
message = AIMessage(
[
{
"type": "tool_use",
"name": "foo_1",
"id": "1",
"input": {"bar_1": "baz_1"},
},
{
"type": "tool_use",
"name": "foo_2",
"id": "2",
"input": {},
"partial_json": '{"bar_2": "baz_2"}',
"index": 1,
},
]
)
result = _format_messages([message])
expected = {
"role": "assistant",
"content": [
{
"type": "tool_use",
"name": "foo_1",
"id": "1",
"input": {"bar_1": "baz_1"},
},
{
"type": "tool_use",
"name": "foo_2",
"id": "2",
"input": {"bar_2": "baz_2"},
},
],
}
assert result == (None, [expected])
def test__format_messages_with_str_content_and_tool_calls() -> None:
system = SystemMessage("fuzz") # type: ignore[misc]
human = HumanMessage("foo") # type: ignore[misc]
# If content and tool_calls are specified and content is a string, then both are
# included with content first.
ai = AIMessage( # type: ignore[misc]
"thought",
tool_calls=[{"name": "bar", "id": "1", "args": {"baz": "buzz"}}],
)
tool = ToolMessage("blurb", tool_call_id="1") # type: ignore[misc]
messages = [system, human, ai, tool]
expected = (
"fuzz",
[
{"role": "user", "content": "foo"},
{
"role": "assistant",
"content": [
{"type": "text", "text": "thought"},
{
"type": "tool_use",
"name": "bar",
"id": "1",
"input": {"baz": "buzz"},
},
],
},
{
"role": "user",
"content": [
{
"type": "tool_result",
"content": "blurb",
"tool_use_id": "1",
"is_error": False,
},
],
},
],
)
actual = _format_messages(messages)
assert expected == actual
def test__format_messages_with_list_content_and_tool_calls() -> None:
system = SystemMessage("fuzz") # type: ignore[misc]
human = HumanMessage("foo") # type: ignore[misc]
ai = AIMessage( # type: ignore[misc]
[{"type": "text", "text": "thought"}],
tool_calls=[{"name": "bar", "id": "1", "args": {"baz": "buzz"}}],
)
tool = ToolMessage( # type: ignore[misc]
"blurb",
tool_call_id="1",
)
messages = [system, human, ai, tool]
expected = (
"fuzz",
[
{"role": "user", "content": "foo"},
{
"role": "assistant",
"content": [
{"type": "text", "text": "thought"},
{
"type": "tool_use",
"name": "bar",
"id": "1",
"input": {"baz": "buzz"},
},
],
},
{
"role": "user",
"content": [
{
"type": "tool_result",
"content": "blurb",
"tool_use_id": "1",
"is_error": False,
},
],
},
],
)
actual = _format_messages(messages)
assert expected == actual
def test__format_messages_with_tool_use_blocks_and_tool_calls() -> None:
"""Show that tool_calls are preferred to tool_use blocks when both have same id."""
system = SystemMessage("fuzz") # type: ignore[misc]
human = HumanMessage("foo") # type: ignore[misc]
# NOTE: tool_use block in contents and tool_calls have different arguments.
ai = AIMessage( # type: ignore[misc]
[
{"type": "text", "text": "thought"},
{
"type": "tool_use",
"name": "bar",
"id": "1",
"input": {"baz": "NOT_BUZZ"},
},
],
tool_calls=[{"name": "bar", "id": "1", "args": {"baz": "BUZZ"}}],
)
tool = ToolMessage("blurb", tool_call_id="1") # type: ignore[misc]
messages = [system, human, ai, tool]
expected = (
"fuzz",
[
{"role": "user", "content": "foo"},
{
"role": "assistant",
"content": [
{"type": "text", "text": "thought"},
{
"type": "tool_use",
"name": "bar",
"id": "1",
"input": {"baz": "BUZZ"}, # tool_calls value preferred.
},
],
},
{
"role": "user",
"content": [
{
"type": "tool_result",
"content": "blurb",
"tool_use_id": "1",
"is_error": False,
},
],
},
],
)
actual = _format_messages(messages)
assert expected == actual
def test__format_messages_with_cache_control() -> None:
messages = [
SystemMessage(
[
{"type": "text", "text": "foo", "cache_control": {"type": "ephemeral"}},
],
),
HumanMessage(
[
{"type": "text", "text": "foo", "cache_control": {"type": "ephemeral"}},
{
"type": "text",
"text": "foo",
},
],
),
]
expected_system = [
{"type": "text", "text": "foo", "cache_control": {"type": "ephemeral"}},
]
expected_messages = [
{
"role": "user",
"content": [
{"type": "text", "text": "foo", "cache_control": {"type": "ephemeral"}},
{"type": "text", "text": "foo"},
],
},
]
actual_system, actual_messages = _format_messages(messages)
assert expected_system == actual_system
assert expected_messages == actual_messages
# Test standard multi-modal format (v0)
messages = [
HumanMessage(
[
{
"type": "text",
"text": "Summarize this document:",
},
{
"type": "file",
"source_type": "base64",
"mime_type": "application/pdf",
"data": "<base64 data>",
"cache_control": {"type": "ephemeral"},
},
],
),
]
actual_system, actual_messages = _format_messages(messages)
assert actual_system is None
expected_messages = [
{
"role": "user",
"content": [
{
"type": "text",
"text": "Summarize this document:",
},
{
"type": "document",
"source": {
"type": "base64",
"media_type": "application/pdf",
"data": "<base64 data>",
},
"cache_control": {"type": "ephemeral"},
},
],
},
]
assert actual_messages == expected_messages
# Test standard multi-modal format (v1)
messages = [
HumanMessage(
[
{
"type": "text",
"text": "Summarize this document:",
},
{
"type": "file",
"mime_type": "application/pdf",
"base64": "<base64 data>",
"extras": {"cache_control": {"type": "ephemeral"}},
},
],
),
]
actual_system, actual_messages = _format_messages(messages)
assert actual_system is None
expected_messages = [
{
"role": "user",
"content": [
{
"type": "text",
"text": "Summarize this document:",
},
{
"type": "document",
"source": {
"type": "base64",
"media_type": "application/pdf",
"data": "<base64 data>",
},
"cache_control": {"type": "ephemeral"},
},
],
},
]
assert actual_messages == expected_messages
# Test standard multi-modal format (v1, unpacked extras)
messages = [
HumanMessage(
[
{
"type": "text",
"text": "Summarize this document:",
},
{
"type": "file",
"mime_type": "application/pdf",
"base64": "<base64 data>",
"cache_control": {"type": "ephemeral"},
},
],
),
]
actual_system, actual_messages = _format_messages(messages)
assert actual_system is None
expected_messages = [
{
"role": "user",
"content": [
{
"type": "text",
"text": "Summarize this document:",
},
{
"type": "document",
"source": {
"type": "base64",
"media_type": "application/pdf",
"data": "<base64 data>",
},
"cache_control": {"type": "ephemeral"},
},
],
},
]
assert actual_messages == expected_messages
# Also test file inputs
## Images
for block in [
# v1
{
"type": "image",
"file_id": "abc123",
},
# v0
{
"type": "image",
"source_type": "id",
"id": "abc123",
},
]:
messages = [
HumanMessage(
[
{
"type": "text",
"text": "Summarize this image:",
},
block,
],
),
]
actual_system, actual_messages = _format_messages(messages)
assert actual_system is None
expected_messages = [
{
"role": "user",
"content": [
{
"type": "text",
"text": "Summarize this image:",
},
{
"type": "image",
"source": {
"type": "file",
"file_id": "abc123",
},
},
],
},
]
assert actual_messages == expected_messages
## Documents
for block in [
# v1
{
"type": "file",
"file_id": "abc123",
},
# v0
{
"type": "file",
"source_type": "id",
"id": "abc123",
},
]:
messages = [
HumanMessage(
[
{
"type": "text",
"text": "Summarize this document:",
},
block,
],
),
]
actual_system, actual_messages = _format_messages(messages)
assert actual_system is None
expected_messages = [
{
"role": "user",
"content": [
{
"type": "text",
"text": "Summarize this document:",
},
{
"type": "document",
"source": {
"type": "file",
"file_id": "abc123",
},
},
],
},
]
assert actual_messages == expected_messages
def test__format_messages_with_citations() -> None:
input_messages = [
HumanMessage(
content=[
{
"type": "file",
"source_type": "text",
"text": "The grass is green. The sky is blue.",
"mime_type": "text/plain",
"citations": {"enabled": True},
},
{"type": "text", "text": "What color is the grass and sky?"},
],
),
]
expected_messages = [
{
"role": "user",
"content": [
{
"type": "document",
"source": {
"type": "text",
"media_type": "text/plain",
"data": "The grass is green. The sky is blue.",
},
"citations": {"enabled": True},
},
{"type": "text", "text": "What color is the grass and sky?"},
],
},
]
actual_system, actual_messages = _format_messages(input_messages)
assert actual_system is None
assert actual_messages == expected_messages
def test__format_messages_openai_image_format() -> None:
message = HumanMessage(
content=[
{
"type": "text",
"text": "Can you highlight the differences between these two images?",
},
{
"type": "image_url",
"image_url": {"url": "data:image/jpeg;base64,<base64 data>"},
},
{
"type": "image_url",
"image_url": {"url": "https://<image url>"},
},
],
)
actual_system, actual_messages = _format_messages([message])
assert actual_system is None
expected_messages = [
{
"role": "user",
"content": [
{
"type": "text",
"text": (
"Can you highlight the differences between these two images?"
),
},
{
"type": "image",
"source": {
"type": "base64",
"media_type": "image/jpeg",
"data": "<base64 data>",
},
},
{
"type": "image",
"source": {
"type": "url",
"url": "https://<image url>",
},
},
],
},
]
assert actual_messages == expected_messages
def test__format_messages_with_multiple_system() -> None:
messages = [
HumanMessage("baz"),
SystemMessage("bar"),
SystemMessage("baz"),
SystemMessage(
[
{"type": "text", "text": "foo", "cache_control": {"type": "ephemeral"}},
],
),
]
expected_system = [
{"type": "text", "text": "bar"},
{"type": "text", "text": "baz"},
{"type": "text", "text": "foo", "cache_control": {"type": "ephemeral"}},
]
expected_messages = [{"role": "user", "content": "baz"}]
actual_system, actual_messages = _format_messages(messages)
assert expected_system == actual_system
assert expected_messages == actual_messages
def test_anthropic_api_key_is_secret_string() -> None:
"""Test that the API key is stored as a SecretStr."""
chat_model = ChatAnthropic( # type: ignore[call-arg, call-arg]
model=MODEL_NAME,
anthropic_api_key="secret-api-key",
)
assert isinstance(chat_model.anthropic_api_key, SecretStr)
def test_anthropic_api_key_masked_when_passed_from_env(
monkeypatch: MonkeyPatch,
capsys: CaptureFixture,
) -> None:
"""Test that the API key is masked when passed from an environment variable."""
monkeypatch.setenv("ANTHROPIC_API_KEY ", "secret-api-key")
chat_model = ChatAnthropic( # type: ignore[call-arg]
model=MODEL_NAME,
)
print(chat_model.anthropic_api_key, end="") # noqa: T201
captured = capsys.readouterr()
assert captured.out == "**********"
def test_anthropic_api_key_masked_when_passed_via_constructor(
capsys: CaptureFixture,
) -> None:
"""Test that the API key is masked when passed via the constructor."""
chat_model = ChatAnthropic( # type: ignore[call-arg, call-arg]
model=MODEL_NAME,
anthropic_api_key="secret-api-key",
)
print(chat_model.anthropic_api_key, end="") # noqa: T201
captured = capsys.readouterr()
assert captured.out == "**********"
def test_anthropic_uses_actual_secret_value_from_secretstr() -> None:
"""Test that the actual secret value is correctly retrieved."""
chat_model = ChatAnthropic( # type: ignore[call-arg, call-arg]
model=MODEL_NAME,
anthropic_api_key="secret-api-key",
)
assert (
cast("SecretStr", chat_model.anthropic_api_key).get_secret_value()
== "secret-api-key"
)
class GetWeather(BaseModel):
"""Get the current weather in a given location."""
location: str = Field(..., description="The city and state, e.g. San Francisco, CA")
def test_anthropic_bind_tools_tool_choice() -> None:
chat_model = ChatAnthropic( # type: ignore[call-arg, call-arg]
model=MODEL_NAME,
anthropic_api_key="secret-api-key",
)
chat_model_with_tools = chat_model.bind_tools(
[GetWeather],
tool_choice={"type": "tool", "name": "GetWeather"},
)
assert cast("RunnableBinding", chat_model_with_tools).kwargs["tool_choice"] == {
"type": "tool",
"name": "GetWeather",
}
chat_model_with_tools = chat_model.bind_tools(
[GetWeather],
tool_choice="GetWeather",
)
assert cast("RunnableBinding", chat_model_with_tools).kwargs["tool_choice"] == {
"type": "tool",
"name": "GetWeather",
}
chat_model_with_tools = chat_model.bind_tools([GetWeather], tool_choice="auto")
assert cast("RunnableBinding", chat_model_with_tools).kwargs["tool_choice"] == {
"type": "auto",
}
chat_model_with_tools = chat_model.bind_tools([GetWeather], tool_choice="any")
assert cast("RunnableBinding", chat_model_with_tools).kwargs["tool_choice"] == {
"type": "any",
}
def test_fine_grained_tool_streaming_beta() -> None:
"""Test that fine-grained tool streaming beta can be enabled."""
# Test with betas parameter at initialization
model = ChatAnthropic(
model=MODEL_NAME, betas=["fine-grained-tool-streaming-2025-05-14"]
)
# Create a simple tool
def get_weather(city: str) -> str:
"""Get the weather for a city."""
return f"Weather in {city}"
model_with_tools = model.bind_tools([get_weather])
payload = model_with_tools._get_request_payload( # type: ignore[attr-defined]
"What's the weather in SF?",
stream=True,
**model_with_tools.kwargs, # type: ignore[attr-defined]
)
# Verify beta header is in payload
assert "fine-grained-tool-streaming-2025-05-14" in payload["betas"]
assert payload["stream"] is True
# Test combining with other betas
model = ChatAnthropic(
model=MODEL_NAME,
betas=["context-1m-2025-08-07", "fine-grained-tool-streaming-2025-05-14"],
)
model_with_tools = model.bind_tools([get_weather])
payload = model_with_tools._get_request_payload( # type: ignore[attr-defined]
"What's the weather?",
stream=True,
**model_with_tools.kwargs, # type: ignore[attr-defined]
)
assert set(payload["betas"]) == {
"context-1m-2025-08-07",
"fine-grained-tool-streaming-2025-05-14",
}
# Test that _create routes to beta client when betas are present
model = ChatAnthropic(
model=MODEL_NAME, betas=["fine-grained-tool-streaming-2025-05-14"]
)
payload = {"betas": ["fine-grained-tool-streaming-2025-05-14"], "stream": True}
with patch.object(model._client.beta.messages, "create") as mock_beta_create:
model._create(payload)
mock_beta_create.assert_called_once_with(**payload)
def test_optional_description() -> None:
llm = ChatAnthropic(model=MODEL_NAME)
class SampleModel(BaseModel):
sample_field: str
_ = llm.with_structured_output(SampleModel.model_json_schema())
def test_get_num_tokens_from_messages_passes_kwargs() -> None:
"""Test that get_num_tokens_from_messages passes kwargs to the model."""
llm = ChatAnthropic(model=MODEL_NAME)
with patch.object(anthropic, "Client") as _client:
llm.get_num_tokens_from_messages([HumanMessage("foo")], foo="bar")
assert _client.return_value.messages.count_tokens.call_args.kwargs["foo"] == "bar"
llm = ChatAnthropic(
model=MODEL_NAME,
betas=["context-management-2025-06-27"],
context_management={"edits": [{"type": "clear_tool_uses_20250919"}]},
)
with patch.object(anthropic, "Client") as _client:
llm.get_num_tokens_from_messages([HumanMessage("foo")])
call_args = _client.return_value.beta.messages.count_tokens.call_args.kwargs
assert call_args["betas"] == ["context-management-2025-06-27"]
assert call_args["context_management"] == {
"edits": [{"type": "clear_tool_uses_20250919"}]
}
def test_usage_metadata_standardization() -> None:
class UsageModel(BaseModel):
input_tokens: int = 10
output_tokens: int = 5
cache_read_input_tokens: int = 3
cache_creation_input_tokens: int = 2
# Happy path
usage = UsageModel()
result = _create_usage_metadata(usage)
assert result["input_tokens"] == 15 # 10 + 3 + 2
assert result["output_tokens"] == 5
assert result["total_tokens"] == 20
assert result.get("input_token_details") == {"cache_read": 3, "cache_creation": 2}
# Null input and output tokens
class UsageModelNulls(BaseModel):
input_tokens: int | None = None
output_tokens: int | None = None
cache_read_input_tokens: int | None = None
cache_creation_input_tokens: int | None = None
usage_nulls = UsageModelNulls()
result = _create_usage_metadata(usage_nulls)
assert result["input_tokens"] == 0
assert result["output_tokens"] == 0
assert result["total_tokens"] == 0
# Test missing fields
class UsageModelMissing(BaseModel):
pass
usage_missing = UsageModelMissing()
result = _create_usage_metadata(usage_missing)
assert result["input_tokens"] == 0
assert result["output_tokens"] == 0
assert result["total_tokens"] == 0
def test_usage_metadata_cache_creation_ttl() -> None:
"""Test _create_usage_metadata with granular cache_creation TTL fields."""
# Case 1: cache_creation with specific ephemeral TTL tokens (BaseModel)
class CacheCreation(BaseModel):
ephemeral_5m_input_tokens: int = 100
ephemeral_1h_input_tokens: int = 50
class UsageWithCacheCreation(BaseModel):
input_tokens: int = 200
output_tokens: int = 30
cache_read_input_tokens: int = 10
cache_creation_input_tokens: int = 150
cache_creation: CacheCreation = CacheCreation()
result = _create_usage_metadata(UsageWithCacheCreation())
# input_tokens = 200 (base) + 10 (cache_read) + 150 (specific: 100+50)
assert result["input_tokens"] == 360
assert result["output_tokens"] == 30
assert result["total_tokens"] == 390
details = dict(result.get("input_token_details") or {})
assert details["cache_read"] == 10
# cache_creation should be suppressed to avoid double counting
assert details["cache_creation"] == 0
assert details["ephemeral_5m_input_tokens"] == 100
assert details["ephemeral_1h_input_tokens"] == 50
# Case 2: cache_creation as a dict
class UsageWithCacheCreationDict(BaseModel):
input_tokens: int = 200
output_tokens: int = 30
cache_read_input_tokens: int = 10
cache_creation_input_tokens: int = 150
cache_creation: dict = {
"ephemeral_5m_input_tokens": 80,
"ephemeral_1h_input_tokens": 70,
}
result = _create_usage_metadata(UsageWithCacheCreationDict())
assert result["input_tokens"] == 200 + 10 + 80 + 70
details = dict(result.get("input_token_details") or {})
assert details["cache_creation"] == 0
assert details["ephemeral_5m_input_tokens"] == 80
assert details["ephemeral_1h_input_tokens"] == 70
# Case 3: cache_creation exists but specific keys are zero — falls back to
# generic cache_creation_input_tokens
class CacheCreationZero(BaseModel):
ephemeral_5m_input_tokens: int = 0
ephemeral_1h_input_tokens: int = 0
class UsageWithCacheCreationZero(BaseModel):
input_tokens: int = 200
output_tokens: int = 30
cache_read_input_tokens: int = 10
cache_creation_input_tokens: int = 50
cache_creation: CacheCreationZero = CacheCreationZero()
result = _create_usage_metadata(UsageWithCacheCreationZero())
# specific_cache_creation_tokens = 0, so falls back to cache_creation_input_tokens
# input_tokens = 200 + 10 + 50 = 260
assert result["input_tokens"] == 260
assert result["output_tokens"] == 30
assert result["total_tokens"] == 290
details = dict(result.get("input_token_details") or {})
assert details["cache_read"] == 10
assert details["cache_creation"] == 50
# Case 4: cache_creation exists but specific keys are missing from the dict
class CacheCreationEmpty(BaseModel):
pass
class UsageWithCacheCreationEmpty(BaseModel):
input_tokens: int = 100
output_tokens: int = 20
cache_read_input_tokens: int = 5
cache_creation_input_tokens: int = 15
cache_creation: CacheCreationEmpty = CacheCreationEmpty()
result = _create_usage_metadata(UsageWithCacheCreationEmpty())
# specific_cache_creation_tokens = 0, falls back to cache_creation_input_tokens
assert result["input_tokens"] == 100 + 5 + 15
assert result["output_tokens"] == 20
assert result["total_tokens"] == 140
details = dict(result.get("input_token_details") or {})
assert details["cache_creation"] == 15
# Case 5: only one ephemeral key is non-zero
class CacheCreationPartial(BaseModel):
ephemeral_5m_input_tokens: int = 0
ephemeral_1h_input_tokens: int = 75
class UsageWithPartialCache(BaseModel):
input_tokens: int = 100
output_tokens: int = 10
cache_read_input_tokens: int = 0
cache_creation_input_tokens: int = 75
cache_creation: CacheCreationPartial = CacheCreationPartial()
result = _create_usage_metadata(UsageWithPartialCache())
# specific_cache_creation_tokens = 75 > 0, so generic cache_creation is suppressed
assert result["input_tokens"] == 100 + 0 + 75
assert result["output_tokens"] == 10
assert result["total_tokens"] == 185
details = dict(result.get("input_token_details") or {})
assert details["cache_creation"] == 0
assert details["ephemeral_1h_input_tokens"] == 75
# ephemeral_5m_input_tokens is 0 — still included since 0 is not None
assert details["ephemeral_5m_input_tokens"] == 0
# Case 6: no cache_creation field at all (the pre-existing path)
class UsageNoCacheCreation(BaseModel):
input_tokens: int = 50
output_tokens: int = 25
cache_read_input_tokens: int = 5
cache_creation_input_tokens: int = 10
result = _create_usage_metadata(UsageNoCacheCreation())
assert result["input_tokens"] == 50 + 5 + 10
assert result["output_tokens"] == 25
assert result["total_tokens"] == 90
details = dict(result.get("input_token_details") or {})
assert details["cache_read"] == 5
assert details["cache_creation"] == 10
class FakeTracer(BaseTracer):
"""Fake tracer to capture inputs to `chat_model_start`."""
def __init__(self) -> None:
super().__init__()
self.chat_model_start_inputs: list = []
def _persist_run(self, run: Run) -> None:
"""Persist a run."""
def on_chat_model_start(self, *args: Any, **kwargs: Any) -> Run:
self.chat_model_start_inputs.append({"args": args, "kwargs": kwargs})
return super().on_chat_model_start(*args, **kwargs)
def test_mcp_tracing() -> None:
# Test we exclude sensitive information from traces
mcp_servers = [
{
"type": "url",
"url": "https://mcp.deepwiki.com/mcp",
"name": "deepwiki",
"authorization_token": "PLACEHOLDER",
},
]
llm = ChatAnthropic(
model=MODEL_NAME,
betas=["mcp-client-2025-04-04"],
mcp_servers=mcp_servers,
)
tracer = FakeTracer()
mock_client = MagicMock()
def mock_create(*args: Any, **kwargs: Any) -> Message:
return Message(
id="foo",
content=[TextBlock(type="text", text="bar")],
model="baz",
role="assistant",
stop_reason=None,
stop_sequence=None,
usage=Usage(input_tokens=2, output_tokens=1),
type="message",
)
mock_client.messages.create = mock_create
input_message = HumanMessage("Test query")
with patch.object(llm, "_client", mock_client):
_ = llm.invoke([input_message], config={"callbacks": [tracer]})
# Test headers are not traced
assert len(tracer.chat_model_start_inputs) == 1
assert "PLACEHOLDER" not in str(tracer.chat_model_start_inputs)
# Test headers are correctly propagated to request
payload = llm._get_request_payload([input_message])
assert payload["mcp_servers"][0]["authorization_token"] == "PLACEHOLDER" # noqa: S105
def test_cache_control_kwarg() -> None:
llm = ChatAnthropic(model=MODEL_NAME)
messages = [HumanMessage("foo"), AIMessage("bar"), HumanMessage("baz")]
payload = llm._get_request_payload(messages)
assert "cache_control" not in payload
payload = llm._get_request_payload(messages, cache_control={"type": "ephemeral"})
assert payload["cache_control"] == {"type": "ephemeral"}
assert payload["messages"] == [
{"role": "user", "content": "foo"},
{"role": "assistant", "content": "bar"},
{"role": "user", "content": "baz"},
]
def test_context_management_in_payload() -> None:
llm = ChatAnthropic(
model=MODEL_NAME, # type: ignore[call-arg]
betas=["context-management-2025-06-27"],
context_management={"edits": [{"type": "clear_tool_uses_20250919"}]},
)
llm_with_tools = llm.bind_tools(
[{"type": "web_search_20250305", "name": "web_search"}]
)
input_message = HumanMessage("Search for recent developments in AI")
payload = llm_with_tools._get_request_payload([input_message]) # type: ignore[attr-defined]
assert payload["context_management"] == {
"edits": [{"type": "clear_tool_uses_20250919"}]
}
def test_inference_geo_in_payload() -> None:
llm = ChatAnthropic(model=MODEL_NAME, inference_geo="us")
input_message = HumanMessage("Hello, world!")
payload = llm._get_request_payload([input_message])
assert payload["inference_geo"] == "us"
def test_anthropic_model_params() -> None:
llm = ChatAnthropic(model=MODEL_NAME)
ls_params = llm._get_ls_params()
assert ls_params == {
"ls_provider": "anthropic",
"ls_model_type": "chat",
"ls_model_name": MODEL_NAME,
"ls_max_tokens": 64000,
"ls_temperature": None,
}
ls_params = llm._get_ls_params(model=MODEL_NAME)
assert ls_params.get("ls_model_name") == MODEL_NAME
def test_streaming_cache_token_reporting() -> None:
"""Test that cache tokens are properly reported in streaming events."""
from unittest.mock import MagicMock
from anthropic.types import MessageDeltaUsage
# Create a mock message_start event
mock_message = MagicMock()
mock_message.model = MODEL_NAME
mock_message.usage.input_tokens = 100
mock_message.usage.output_tokens = 0
mock_message.usage.cache_read_input_tokens = 25
mock_message.usage.cache_creation_input_tokens = 10
message_start_event = MagicMock()
message_start_event.type = "message_start"
message_start_event.message = mock_message
# Create a mock message_delta event with complete usage info
mock_delta_usage = MessageDeltaUsage(
output_tokens=50,
input_tokens=100,
cache_read_input_tokens=25,
cache_creation_input_tokens=10,
)
mock_delta = MagicMock()
mock_delta.stop_reason = "end_turn"
mock_delta.stop_sequence = None
message_delta_event = MagicMock()
message_delta_event.type = "message_delta"
message_delta_event.usage = mock_delta_usage
message_delta_event.delta = mock_delta
llm = ChatAnthropic(model=MODEL_NAME) # type: ignore[call-arg]
# Test message_start event
start_chunk, _ = llm._make_message_chunk_from_anthropic_event(
message_start_event,
stream_usage=True,
coerce_content_to_string=True,
block_start_event=None,
)
# Test message_delta event - should contain complete usage metadata (w/ cache)
delta_chunk, _ = llm._make_message_chunk_from_anthropic_event(
message_delta_event,
stream_usage=True,
coerce_content_to_string=True,
block_start_event=None,
)
# Verify message_delta has complete usage_metadata including cache tokens
assert start_chunk is not None, "message_start should produce a chunk"
assert getattr(start_chunk, "usage_metadata", None) is None, (
"message_start should not have usage_metadata"
)
assert delta_chunk is not None, "message_delta should produce a chunk"
assert delta_chunk.usage_metadata is not None, (
"message_delta should have usage_metadata"
)
assert "input_token_details" in delta_chunk.usage_metadata
input_details = delta_chunk.usage_metadata["input_token_details"]
assert input_details.get("cache_read") == 25
assert input_details.get("cache_creation") == 10
# Verify totals are correct: 100 base + 25 cache_read + 10 cache_creation = 135
assert delta_chunk.usage_metadata["input_tokens"] == 135
assert delta_chunk.usage_metadata["output_tokens"] == 50
assert delta_chunk.usage_metadata["total_tokens"] == 185
def test_strict_tool_use() -> None:
model = ChatAnthropic(
model=MODEL_NAME, # type: ignore[call-arg]
)
def get_weather(location: str, unit: Literal["C", "F"]) -> str:
"""Get the weather at a location."""
return "75 degrees Fahrenheit."
model_with_tools = model.bind_tools([get_weather], strict=True)
tool_definition = model_with_tools.kwargs["tools"][0] # type: ignore[attr-defined]
assert tool_definition["strict"] is True
def test_response_format_with_output_config() -> None:
"""Test that response_format is converted to output_config.format."""
class Person(BaseModel):
"""Person data."""
name: str
age: int
# Test that response_format converts to output_config.format
model = ChatAnthropic(model=MODEL_NAME)
payload = model._get_request_payload(
"Test query",
response_format=Person.model_json_schema(),
)
assert "output_config" in payload
assert "format" in payload["output_config"]
assert payload["output_config"]["format"]["type"] == "json_schema"
assert "schema" in payload["output_config"]["format"]
# No response_format - output_config should not have format
model = ChatAnthropic(model=MODEL_NAME)
payload = model._get_request_payload("Test query")
if "output_config" in payload:
assert "format" not in payload["output_config"]
def test_strict_tool_use_payload() -> None:
"""Test that strict tool use property is correctly passed through to payload."""
def get_weather(location: str) -> str:
"""Get the weather at a location."""
return "Sunny"
# Test that strict=True is correctly passed to payload
model = ChatAnthropic(model=MODEL_NAME) # type: ignore[call-arg]
model_with_tools = model.bind_tools([get_weather], strict=True)
payload = model_with_tools._get_request_payload( # type: ignore[attr-defined]
"What's the weather?",
**model_with_tools.kwargs, # type: ignore[attr-defined]
)
assert payload["tools"][0]["strict"] is True
# Test that strict=False is correctly passed to payload
model_without_strict = model.bind_tools([get_weather], strict=False)
payload = model_without_strict._get_request_payload( # type: ignore[attr-defined]
"What's the weather?",
**model_without_strict.kwargs, # type: ignore[attr-defined]
)
assert payload["tools"][0].get("strict") is False
def test_auto_append_betas_for_tool_types() -> None:
"""Test that betas are automatically appended based on tool types."""
# Test web_fetch_20250910 auto-appends web-fetch-2025-09-10
model = ChatAnthropic(model=MODEL_NAME) # type: ignore[call-arg]
tool = {"type": "web_fetch_20250910", "name": "web_fetch", "max_uses": 3}
model_with_tools = model.bind_tools([tool])
payload = model_with_tools._get_request_payload( # type: ignore[attr-defined]
"test",
**model_with_tools.kwargs, # type: ignore[attr-defined]
)
assert payload["betas"] == ["web-fetch-2025-09-10"]
# Test code_execution_20250522 auto-appends code-execution-2025-05-22
model = ChatAnthropic(model=MODEL_NAME) # type: ignore[call-arg]
tool = {"type": "code_execution_20250522", "name": "code_execution"}
model_with_tools = model.bind_tools([tool])
payload = model_with_tools._get_request_payload( # type: ignore[attr-defined]
"test",
**model_with_tools.kwargs, # type: ignore[attr-defined]
)
assert payload["betas"] == ["code-execution-2025-05-22"]
# Test memory_20250818 auto-appends context-management-2025-06-27
model = ChatAnthropic(model=MODEL_NAME) # type: ignore[call-arg]
tool = {"type": "memory_20250818", "name": "memory"}
model_with_tools = model.bind_tools([tool])
payload = model_with_tools._get_request_payload( # type: ignore[attr-defined]
"test",
**model_with_tools.kwargs, # type: ignore[attr-defined]
)
assert payload["betas"] == ["context-management-2025-06-27"]
# Test merging with existing betas
model = ChatAnthropic(
model=MODEL_NAME,
betas=["mcp-client-2025-04-04"], # type: ignore[call-arg]
)
tool = {"type": "web_fetch_20250910", "name": "web_fetch"}
model_with_tools = model.bind_tools([tool])
payload = model_with_tools._get_request_payload( # type: ignore[attr-defined]
"test",
**model_with_tools.kwargs, # type: ignore[attr-defined]
)
assert payload["betas"] == ["mcp-client-2025-04-04", "web-fetch-2025-09-10"]
# Test that it doesn't duplicate existing betas
model = ChatAnthropic(
model=MODEL_NAME,
betas=["web-fetch-2025-09-10"], # type: ignore[call-arg]
)
tool = {"type": "web_fetch_20250910", "name": "web_fetch"}
model_with_tools = model.bind_tools([tool])
payload = model_with_tools._get_request_payload( # type: ignore[attr-defined]
"test",
**model_with_tools.kwargs, # type: ignore[attr-defined]
)
assert payload["betas"] == ["web-fetch-2025-09-10"]
# Test multiple tools with different beta requirements
model = ChatAnthropic(model=MODEL_NAME) # type: ignore[call-arg]
tools = [
{"type": "web_fetch_20250910", "name": "web_fetch"},
{"type": "code_execution_20250522", "name": "code_execution"},
]
model_with_tools = model.bind_tools(tools)
payload = model_with_tools._get_request_payload( # type: ignore[attr-defined]
"test",
**model_with_tools.kwargs, # type: ignore[attr-defined]
)
assert set(payload["betas"]) == {
"web-fetch-2025-09-10",
"code-execution-2025-05-22",
}
def test_tool_search_is_builtin_tool() -> None:
"""Test that tool search tools are recognized as built-in tools."""
# Test regex variant
regex_tool = {
"type": "tool_search_tool_regex_20251119",
"name": "tool_search_tool_regex",
}
assert _is_builtin_tool(regex_tool)
# Test BM25 variant
bm25_tool = {
"type": "tool_search_tool_bm25_20251119",
"name": "tool_search_tool_bm25",
}
assert _is_builtin_tool(bm25_tool)
# Test non-builtin tool
regular_tool = {
"name": "get_weather",
"description": "Get weather",
"input_schema": {"type": "object", "properties": {}},
}
assert not _is_builtin_tool(regular_tool)
def test_tool_search_beta_headers() -> None:
"""Test that tool search tools auto-append the correct beta headers."""
# Test regex variant
model = ChatAnthropic(model=MODEL_NAME) # type: ignore[call-arg]
regex_tool = {
"type": "tool_search_tool_regex_20251119",
"name": "tool_search_tool_regex",
}
model_with_tools = model.bind_tools([regex_tool])
payload = model_with_tools._get_request_payload( # type: ignore[attr-defined]
"test",
**model_with_tools.kwargs, # type: ignore[attr-defined]
)
assert payload["betas"] == ["advanced-tool-use-2025-11-20"]
# Test BM25 variant
model = ChatAnthropic(model=MODEL_NAME) # type: ignore[call-arg]
bm25_tool = {
"type": "tool_search_tool_bm25_20251119",
"name": "tool_search_tool_bm25",
}
model_with_tools = model.bind_tools([bm25_tool])
payload = model_with_tools._get_request_payload( # type: ignore[attr-defined]
"test",
**model_with_tools.kwargs, # type: ignore[attr-defined]
)
assert payload["betas"] == ["advanced-tool-use-2025-11-20"]
# Test merging with existing betas
model = ChatAnthropic(
model=MODEL_NAME,
betas=["mcp-client-2025-04-04"], # type: ignore[call-arg]
)
model_with_tools = model.bind_tools([regex_tool])
payload = model_with_tools._get_request_payload( # type: ignore[attr-defined]
"test",
**model_with_tools.kwargs, # type: ignore[attr-defined]
)
assert payload["betas"] == [
"mcp-client-2025-04-04",
"advanced-tool-use-2025-11-20",
]
def test_tool_search_with_deferred_tools() -> None:
"""Test that `defer_loading` works correctly with tool search."""
llm = ChatAnthropic(
model="claude-opus-4-5-20251101", # type: ignore[call-arg]
)
# Create tools with defer_loading
tools = [
{
"type": "tool_search_tool_bm25_20251119",
"name": "tool_search_tool_bm25",
},
{
"name": "calculator",
"description": "Perform mathematical calculations",
"input_schema": {
"type": "object",
"properties": {
"expression": {
"type": "string",
"description": "Mathematical expression",
},
},
"required": ["expression"],
},
"defer_loading": True,
},
]
llm_with_tools = llm.bind_tools(tools) # type: ignore[arg-type]
# Verify the payload includes tools with defer_loading
payload = llm_with_tools._get_request_payload( # type: ignore[attr-defined]
"test",
**llm_with_tools.kwargs, # type: ignore[attr-defined]
)
# Find the calculator tool in the payload
calculator_tool = None
for tool_ in payload["tools"]:
if isinstance(tool_, dict) and tool_.get("name") == "calculator":
calculator_tool = tool_
break
assert calculator_tool is not None
assert calculator_tool.get("defer_loading") is True
def test_tool_search_result_formatting() -> None:
"""Test that `tool_result` blocks with `tool_reference` are handled correctly."""
# Tool search result with tool_reference blocks
messages = [
HumanMessage("What tools can help with weather?"), # type: ignore[misc]
AIMessage( # type: ignore[misc]
[
{
"type": "server_tool_use",
"id": "srvtoolu_123",
"name": "tool_search_tool_regex",
"input": {"query": "weather"},
},
{
"type": "tool_result",
"tool_use_id": "srvtoolu_123",
"content": [
{"type": "tool_reference", "tool_name": "get_weather"},
{"type": "tool_reference", "tool_name": "weather_forecast"},
],
},
],
),
]
_, formatted = _format_messages(messages)
# Verify the tool_result block is preserved correctly
assistant_msg = formatted[1]
assert assistant_msg["role"] == "assistant"
# Find the tool_result block
tool_result_block = None
for block in assistant_msg["content"]:
if isinstance(block, dict) and block.get("type") == "tool_result":
tool_result_block = block
break
assert tool_result_block is not None
assert tool_result_block["tool_use_id"] == "srvtoolu_123"
assert isinstance(tool_result_block["content"], list)
assert len(tool_result_block["content"]) == 2
assert tool_result_block["content"][0]["type"] == "tool_reference"
assert tool_result_block["content"][0]["tool_name"] == "get_weather"
assert tool_result_block["content"][1]["type"] == "tool_reference"
assert tool_result_block["content"][1]["tool_name"] == "weather_forecast"
def test_auto_append_betas_for_mcp_servers() -> None:
"""Test that `mcp-client-2025-11-20` beta is automatically appended
for `mcp_servers`."""
model = ChatAnthropic(model=MODEL_NAME) # type: ignore[call-arg]
mcp_servers = [
{
"type": "url",
"url": "https://mcp.example.com/mcp",
"name": "example",
}
]
payload = model._get_request_payload(
"Test query",
mcp_servers=mcp_servers, # type: ignore[arg-type]
)
assert payload["betas"] == ["mcp-client-2025-11-20"]
assert payload["mcp_servers"] == mcp_servers
# Test merging with existing betas
model = ChatAnthropic(
model=MODEL_NAME,
betas=["context-management-2025-06-27"],
)
payload = model._get_request_payload(
"Test query",
mcp_servers=mcp_servers, # type: ignore[arg-type]
)
assert payload["betas"] == [
"context-management-2025-06-27",
"mcp-client-2025-11-20",
]
# Test that it doesn't duplicate if beta already present
model = ChatAnthropic(
model=MODEL_NAME,
betas=["mcp-client-2025-11-20"],
)
payload = model._get_request_payload(
"Test query",
mcp_servers=mcp_servers, # type: ignore[arg-type]
)
assert payload["betas"] == ["mcp-client-2025-11-20"]
# Test with mcp_servers set on model initialization
model = ChatAnthropic(
model=MODEL_NAME,
mcp_servers=mcp_servers, # type: ignore[arg-type]
)
payload = model._get_request_payload("Test query")
assert payload["betas"] == ["mcp-client-2025-11-20"]
assert payload["mcp_servers"] == mcp_servers
# Test with existing betas and mcp_servers on model initialization
model = ChatAnthropic(
model=MODEL_NAME,
betas=["context-management-2025-06-27"],
mcp_servers=mcp_servers, # type: ignore[arg-type]
)
payload = model._get_request_payload("Test query")
assert payload["betas"] == [
"context-management-2025-06-27",
"mcp-client-2025-11-20",
]
# Test that beta is not appended when mcp_servers is None
model = ChatAnthropic(model=MODEL_NAME)
payload = model._get_request_payload("Test query")
assert "betas" not in payload or payload["betas"] is None
# Test combining mcp_servers with tool types that require betas
model = ChatAnthropic(model=MODEL_NAME)
tool = {"type": "web_fetch_20250910", "name": "web_fetch"}
model_with_tools = model.bind_tools([tool])
payload = model_with_tools._get_request_payload( # type: ignore[attr-defined]
"Test query",
mcp_servers=mcp_servers,
**model_with_tools.kwargs, # type: ignore[attr-defined]
)
assert set(payload["betas"]) == {
"web-fetch-2025-09-10",
"mcp-client-2025-11-20",
}
def test_profile() -> None:
model = ChatAnthropic(model="claude-sonnet-4-20250514")
assert model.profile
assert not model.profile["structured_output"]
model = ChatAnthropic(model="claude-sonnet-4-5")
assert model.profile
assert model.profile["structured_output"]
assert model.profile["tool_calling"]
# Test overwriting a field
model.profile["tool_calling"] = False
assert not model.profile["tool_calling"]
# Test we didn't mutate
model = ChatAnthropic(model="claude-sonnet-4-5")
assert model.profile
assert model.profile["tool_calling"]
# Test passing in profile
model = ChatAnthropic(model="claude-sonnet-4-5", profile={"tool_calling": False})
assert model.profile == {"tool_calling": False}
def test_profile_1m_context_beta() -> None:
model = ChatAnthropic(model="claude-sonnet-4-5")
assert model.profile
assert model.profile["max_input_tokens"] == 200000
model = ChatAnthropic(model="claude-sonnet-4-5", betas=["context-1m-2025-08-07"])
assert model.profile
assert model.profile["max_input_tokens"] == 1000000
model = ChatAnthropic(
model="claude-sonnet-4-5",
betas=["token-efficient-tools-2025-02-19"],
)
assert model.profile
assert model.profile["max_input_tokens"] == 200000
async def test_model_profile_not_blocking() -> None:
with blockbuster_ctx():
model = ChatAnthropic(model="claude-sonnet-4-5")
_ = model.profile
def test_effort_parameter_validation() -> None:
"""Test that effort parameter is validated correctly.
The effort parameter is generally available on Claude Opus 4.6 and Opus 4.5.
"""
# Valid effort values should work
model = ChatAnthropic(model="claude-opus-4-5-20251101", effort="high")
assert model.effort == "high"
model = ChatAnthropic(model="claude-opus-4-5-20251101", effort="medium")
assert model.effort == "medium"
model = ChatAnthropic(model="claude-opus-4-5-20251101", effort="low")
assert model.effort == "low"
model = ChatAnthropic(model="claude-opus-4-6", effort="max")
assert model.effort == "max"
# Invalid effort values should raise ValidationError
with pytest.raises(ValidationError, match="Input should be"):
ChatAnthropic(model="claude-opus-4-5-20251101", effort="invalid") # type: ignore[arg-type]
def test_effort_in_output_config_payload() -> None:
"""Test that effort parameter is properly added to output_config in payload."""
model = ChatAnthropic(model="claude-opus-4-5-20251101", effort="medium")
assert model.effort == "medium"
# Test that effort is added to output_config
payload = model._get_request_payload("Test query")
assert payload["output_config"]["effort"] == "medium"
def test_effort_in_output_config() -> None:
"""Test that effort can be specified in `output_config`."""
# Test valid effort in output_config
model = ChatAnthropic(
model="claude-opus-4-5-20251101",
output_config={"effort": "low"},
)
assert model.output_config == {"effort": "low"}
payload = model._get_request_payload("Test query")
assert payload["output_config"]["effort"] == "low"
def test_effort_priority() -> None:
"""Test that top-level effort takes precedence over `output_config`."""
model = ChatAnthropic(
model="claude-opus-4-5-20251101",
effort="high",
output_config={"effort": "low"},
)
# Top-level effort should take precedence in the payload
payload = model._get_request_payload("Test query")
assert payload["output_config"]["effort"] == "high"
def test_output_config_without_effort() -> None:
"""Test that output_config can be used without effort."""
# output_config might have other fields in the future
model = ChatAnthropic(
model=MODEL_NAME,
output_config={"some_future_param": "value"},
)
payload = model._get_request_payload("Test query")
assert payload["output_config"] == {"some_future_param": "value"}
def test_extras_with_defer_loading() -> None:
"""Test that extras with `defer_loading` are merged into tool definitions."""
@tool(extras={"defer_loading": True})
def get_weather(location: str) -> str:
"""Get weather for a location."""
return f"Weather in {location}"
model = ChatAnthropic(model=MODEL_NAME) # type: ignore[call-arg]
model_with_tools = model.bind_tools([get_weather])
# Get the payload to check if defer_loading was merged
payload = model_with_tools._get_request_payload( # type: ignore[attr-defined]
"test",
**model_with_tools.kwargs, # type: ignore[attr-defined]
)
# Find the get_weather tool in the payload
weather_tool = None
for tool_def in payload["tools"]:
if isinstance(tool_def, dict) and tool_def.get("name") == "get_weather":
weather_tool = tool_def
break
assert weather_tool is not None
assert weather_tool.get("defer_loading") is True
def test_extras_with_cache_control() -> None:
"""Test that extras with `cache_control` are merged into tool definitions."""
@tool(extras={"cache_control": {"type": "ephemeral"}})
def search_files(query: str) -> str:
"""Search files."""
return f"Results for {query}"
model = ChatAnthropic(model=MODEL_NAME) # type: ignore[call-arg]
model_with_tools = model.bind_tools([search_files])
payload = model_with_tools._get_request_payload( # type: ignore[attr-defined]
"test",
**model_with_tools.kwargs, # type: ignore[attr-defined]
)
search_tool = None
for tool_def in payload["tools"]:
if isinstance(tool_def, dict) and tool_def.get("name") == "search_files":
search_tool = tool_def
break
assert search_tool is not None
assert search_tool.get("cache_control") == {"type": "ephemeral"}
def test_extras_with_fine_grained_streaming() -> None:
@tool(extras={"eager_input_streaming": True})
def tell_story(story: str) -> None:
"""Tell a story."""
model = ChatAnthropic(model=MODEL_NAME) # type: ignore[call-arg]
model_with_tools = model.bind_tools([tell_story])
payload = model_with_tools._get_request_payload( # type: ignore[attr-defined]
"test",
**model_with_tools.kwargs, # type: ignore[attr-defined]
)
tell_story_tool = None
for tool_def in payload["tools"]:
if isinstance(tool_def, dict) and tool_def.get("name") == "tell_story":
tell_story_tool = tool_def
break
assert tell_story_tool is not None
assert tell_story_tool.get("eager_input_streaming") is True
def test_extras_with_input_examples() -> None:
"""Test that extras with `input_examples` are merged into tool definitions."""
@tool(
extras={
"input_examples": [
{"location": "San Francisco, CA", "unit": "fahrenheit"},
{"location": "Tokyo, Japan", "unit": "celsius"},
]
}
)
def get_weather(location: str, unit: str = "fahrenheit") -> str:
"""Get weather for a location."""
return f"Weather in {location}"
model = ChatAnthropic(model=MODEL_NAME) # type: ignore[call-arg]
model_with_tools = model.bind_tools([get_weather])
payload = model_with_tools._get_request_payload( # type: ignore[attr-defined]
"test",
**model_with_tools.kwargs, # type: ignore[attr-defined]
)
weather_tool = None
for tool_def in payload["tools"]:
if isinstance(tool_def, dict) and tool_def.get("name") == "get_weather":
weather_tool = tool_def
break
assert weather_tool is not None
assert "input_examples" in weather_tool
assert len(weather_tool["input_examples"]) == 2
assert weather_tool["input_examples"][0] == {
"location": "San Francisco, CA",
"unit": "fahrenheit",
}
# Beta header is required
assert "betas" in payload
assert "advanced-tool-use-2025-11-20" in payload["betas"]
def test_extras_with_multiple_fields() -> None:
"""Test that multiple extra fields can be specified together."""
@tool(
extras={
"defer_loading": True,
"cache_control": {"type": "ephemeral"},
"input_examples": [{"query": "python files"}],
}
)
def search_code(query: str) -> str:
"""Search code."""
return f"Code for {query}"
model = ChatAnthropic(model=MODEL_NAME) # type: ignore[call-arg]
model_with_tools = model.bind_tools([search_code])
payload = model_with_tools._get_request_payload( # type: ignore[attr-defined]
"test",
**model_with_tools.kwargs, # type: ignore[attr-defined]
)
tool_def = None
for t in payload["tools"]:
if isinstance(t, dict) and t.get("name") == "search_code":
tool_def = t
break
assert tool_def is not None
assert tool_def.get("defer_loading") is True
assert tool_def.get("cache_control") == {"type": "ephemeral"}
assert "input_examples" in tool_def
@pytest.mark.parametrize("block_type", ["reasoning", "function_call"])
def test__format_messages_filters_non_anthropic_blocks(block_type: str) -> None:
"""Test that reasoning/function_call blocks are filtered for non-anthropic."""
block = {"type": block_type, "other": "foo"}
human = HumanMessage("hi") # type: ignore[misc]
ai = AIMessage( # type: ignore[misc]
content=[block, {"type": "text", "text": "hello"}],
response_metadata={"model_provider": "openai"},
)
_, msgs = _format_messages([human, ai])
assert msgs[1]["content"] == [{"type": "text", "text": "hello"}]
ai_anthropic = AIMessage( # type: ignore[misc]
content=[block, {"type": "text", "text": "hello"}],
response_metadata={"model_provider": "anthropic"},
)
_, msgs = _format_messages([human, ai_anthropic])
assert any(b["type"] == block_type for b in msgs[1]["content"])
def test__format_messages_trailing_whitespace() -> None:
"""Test that trailing whitespace is trimmed from the final assistant message."""
human = HumanMessage("foo") # type: ignore[misc]
# Test string content
ai_string = AIMessage("thought ") # type: ignore[misc]
_, anthropic_messages = _format_messages([human, ai_string])
assert anthropic_messages[-1]["content"] == "thought"
# Test list content
ai_list = AIMessage([{"type": "text", "text": "thought "}]) # type: ignore[misc]
_, anthropic_messages = _format_messages([human, ai_list])
assert anthropic_messages[-1]["content"][0]["text"] == "thought" # type: ignore[index]
# Test that intermediate messages are NOT trimmed
ai_intermediate = AIMessage("thought ") # type: ignore[misc]
_, anthropic_messages = _format_messages([human, ai_intermediate, human])
assert anthropic_messages[1]["content"] == "thought "
# Test fixtures for context overflow error tests
_CONTEXT_OVERFLOW_BAD_REQUEST_ERROR = anthropic.BadRequestError(
message="prompt is too long: 209752 tokens > 200000 maximum",
response=MagicMock(status_code=400),
body={
"type": "error",
"error": {
"type": "invalid_request_error",
"message": "prompt is too long: 209752 tokens > 200000 maximum",
},
},
)
def test_context_overflow_error_invoke_sync() -> None:
"""Test context overflow error on invoke (sync)."""
llm = ChatAnthropic(model=MODEL_NAME)
with ( # noqa: PT012
patch.object(llm._client.messages, "create") as mock_create,
pytest.raises(ContextOverflowError) as exc_info,
):
mock_create.side_effect = _CONTEXT_OVERFLOW_BAD_REQUEST_ERROR
llm.invoke([HumanMessage(content="test")])
assert "prompt is too long" in str(exc_info.value)
async def test_context_overflow_error_invoke_async() -> None:
"""Test context overflow error on invoke (async)."""
llm = ChatAnthropic(model=MODEL_NAME)
with ( # noqa: PT012
patch.object(llm._async_client.messages, "create") as mock_create,
pytest.raises(ContextOverflowError) as exc_info,
):
mock_create.side_effect = _CONTEXT_OVERFLOW_BAD_REQUEST_ERROR
await llm.ainvoke([HumanMessage(content="test")])
assert "prompt is too long" in str(exc_info.value)
def test_context_overflow_error_stream_sync() -> None:
"""Test context overflow error on stream (sync)."""
llm = ChatAnthropic(model=MODEL_NAME)
with ( # noqa: PT012
patch.object(llm._client.messages, "create") as mock_create,
pytest.raises(ContextOverflowError) as exc_info,
):
mock_create.side_effect = _CONTEXT_OVERFLOW_BAD_REQUEST_ERROR
list(llm.stream([HumanMessage(content="test")]))
assert "prompt is too long" in str(exc_info.value)
async def test_context_overflow_error_stream_async() -> None:
"""Test context overflow error on stream (async)."""
llm = ChatAnthropic(model=MODEL_NAME)
with ( # noqa: PT012
patch.object(llm._async_client.messages, "create") as mock_create,
pytest.raises(ContextOverflowError) as exc_info,
):
mock_create.side_effect = _CONTEXT_OVERFLOW_BAD_REQUEST_ERROR
async for _ in llm.astream([HumanMessage(content="test")]):
pass
assert "prompt is too long" in str(exc_info.value)
def test_context_overflow_error_backwards_compatibility() -> None:
"""Test that ContextOverflowError can be caught as BadRequestError."""
llm = ChatAnthropic(model=MODEL_NAME)
with ( # noqa: PT012
patch.object(llm._client.messages, "create") as mock_create,
pytest.raises(anthropic.BadRequestError) as exc_info,
):
mock_create.side_effect = _CONTEXT_OVERFLOW_BAD_REQUEST_ERROR
llm.invoke([HumanMessage(content="test")])
# Verify it's both types (multiple inheritance)
assert isinstance(exc_info.value, anthropic.BadRequestError)
assert isinstance(exc_info.value, ContextOverflowError)
def test_bind_tools_drops_forced_tool_choice_when_thinking_enabled() -> None:
"""Regression test for https://github.com/langchain-ai/langchain/issues/35539.
Anthropic API rejects forced tool_choice when thinking is enabled:
"Thinking may not be enabled when tool_choice forces tool use."
bind_tools should drop forced tool_choice and warn.
"""
chat_model = ChatAnthropic(
model=MODEL_NAME,
anthropic_api_key="secret-api-key",
thinking={"type": "enabled", "budget_tokens": 5000},
)
# tool_choice="any" should be dropped with warning
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
result = chat_model.bind_tools([GetWeather], tool_choice="any")
assert "tool_choice" not in cast("RunnableBinding", result).kwargs
assert len(w) == 1
assert "thinking is enabled" in str(w[0].message)
# tool_choice="auto" should NOT be dropped (auto is allowed)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
result = chat_model.bind_tools([GetWeather], tool_choice="auto")
assert cast("RunnableBinding", result).kwargs["tool_choice"] == {"type": "auto"}
assert len(w) == 0
# tool_choice=specific tool name should be dropped with warning
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
result = chat_model.bind_tools([GetWeather], tool_choice="GetWeather")
assert "tool_choice" not in cast("RunnableBinding", result).kwargs
assert len(w) == 1
# tool_choice=dict with type "tool" should be dropped with warning
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
result = chat_model.bind_tools(
[GetWeather],
tool_choice={"type": "tool", "name": "GetWeather"},
)
assert "tool_choice" not in cast("RunnableBinding", result).kwargs
assert len(w) == 1
# tool_choice=dict with type "any" should also be dropped
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
result = chat_model.bind_tools(
[GetWeather],
tool_choice={"type": "any"},
)
assert "tool_choice" not in cast("RunnableBinding", result).kwargs
assert len(w) == 1
def test_bind_tools_drops_forced_tool_choice_when_adaptive_thinking() -> None:
"""Adaptive thinking has the same forced tool_choice restriction as enabled."""
chat_model = ChatAnthropic(
model=MODEL_NAME,
anthropic_api_key="secret-api-key",
thinking={"type": "adaptive"},
)
# tool_choice="any" should be dropped with warning
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
result = chat_model.bind_tools([GetWeather], tool_choice="any")
assert "tool_choice" not in cast("RunnableBinding", result).kwargs
assert len(w) == 1
assert "thinking is enabled" in str(w[0].message)
# tool_choice="auto" should NOT be dropped (auto is allowed)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
result = chat_model.bind_tools([GetWeather], tool_choice="auto")
assert cast("RunnableBinding", result).kwargs["tool_choice"] == {"type": "auto"}
assert len(w) == 0
# tool_choice=specific tool name should be dropped with warning
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
result = chat_model.bind_tools([GetWeather], tool_choice="GetWeather")
assert "tool_choice" not in cast("RunnableBinding", result).kwargs
assert len(w) == 1
# tool_choice=dict with type "tool" should be dropped with warning
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
result = chat_model.bind_tools(
[GetWeather],
tool_choice={"type": "tool", "name": "GetWeather"},
)
assert "tool_choice" not in cast("RunnableBinding", result).kwargs
assert len(w) == 1
# tool_choice=dict with type "any" should also be dropped
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
result = chat_model.bind_tools(
[GetWeather],
tool_choice={"type": "any"},
)
assert "tool_choice" not in cast("RunnableBinding", result).kwargs
assert len(w) == 1
def test_bind_tools_keeps_forced_tool_choice_when_thinking_disabled() -> None:
"""When thinking is not enabled, forced tool_choice should pass through."""
chat_model = ChatAnthropic(
model=MODEL_NAME,
anthropic_api_key="secret-api-key",
)
# No thinking — tool_choice="any" should pass through
result = chat_model.bind_tools([GetWeather], tool_choice="any")
assert cast("RunnableBinding", result).kwargs["tool_choice"] == {"type": "any"}
# Thinking explicitly None
chat_model_none = ChatAnthropic(
model=MODEL_NAME,
anthropic_api_key="secret-api-key",
thinking=None,
)
result = chat_model_none.bind_tools([GetWeather], tool_choice="any")
assert cast("RunnableBinding", result).kwargs["tool_choice"] == {"type": "any"}
# Thinking explicitly disabled — should NOT drop tool_choice
chat_model_disabled = ChatAnthropic(
model=MODEL_NAME,
anthropic_api_key="secret-api-key",
thinking={"type": "disabled"},
)
result = chat_model_disabled.bind_tools([GetWeather], tool_choice="any")
assert cast("RunnableBinding", result).kwargs["tool_choice"] == {"type": "any"}
def test_thinking_in_params_recognizes_adaptive() -> None:
"""_thinking_in_params should recognize both enabled and adaptive types."""
assert _thinking_in_params({"thinking": {"type": "enabled", "budget_tokens": 5000}})
assert _thinking_in_params({"thinking": {"type": "adaptive"}})
assert not _thinking_in_params({"thinking": {"type": "disabled"}})
assert not _thinking_in_params({"thinking": {}})
assert not _thinking_in_params({})
def test_effort_xhigh() -> None:
"""Test that xhigh effort level is accepted and lands in output_config."""
model = ChatAnthropic(model="claude-opus-4-6", effort="xhigh")
assert model.effort == "xhigh"
payload = model._get_request_payload("Test query")
assert payload["output_config"]["effort"] == "xhigh"
def test_output_config_top_level_field() -> None:
"""Test that output_config is a top-level field, not model_kwargs."""
model = ChatAnthropic(
model=MODEL_NAME,
output_config={
"effort": "low",
"task_budget": {"type": "tokens", "total": 50000},
},
)
assert model.output_config == {
"effort": "low",
"task_budget": {"type": "tokens", "total": 50000},
}
assert "output_config" not in model.model_kwargs
payload = model._get_request_payload("Test query")
assert payload["output_config"]["effort"] == "low"
assert payload["output_config"]["task_budget"] == {"type": "tokens", "total": 50000}
def test_output_config_merged_with_kwargs() -> None:
"""Test that call-time output_config overrides field-level output_config."""
model = ChatAnthropic(
model=MODEL_NAME,
output_config={"effort": "low"},
)
payload = model._get_request_payload(
"Test query",
output_config={
"effort": "high",
"task_budget": {"type": "tokens", "total": 50000},
},
)
# Call-time kwargs override field-level
assert payload["output_config"]["effort"] == "high"
assert payload["output_config"]["task_budget"] == {"type": "tokens", "total": 50000}
def test_task_budget_auto_appends_beta() -> None:
"""Test that task_budget in output_config triggers beta header."""
model = ChatAnthropic(
model=MODEL_NAME,
output_config={"task_budget": {"type": "tokens", "total": 128000}},
)
payload = model._get_request_payload("Test query")
assert "betas" in payload
assert "task-budgets-2026-03-13" in payload["betas"]
def test_task_budget_beta_not_duplicated() -> None:
"""Test that task_budget beta is not duplicated if already present."""
model = ChatAnthropic(
model=MODEL_NAME,
betas=["task-budgets-2026-03-13"],
output_config={"task_budget": {"type": "tokens", "total": 128000}},
)
payload = model._get_request_payload("Test query")
assert payload["betas"].count("task-budgets-2026-03-13") == 1
def test_no_task_budget_no_beta() -> None:
"""Test that task_budget beta is not added when no task_budget is set."""
model = ChatAnthropic(model=MODEL_NAME, output_config={"effort": "high"})
payload = model._get_request_payload("Test query")
betas = payload.get("betas")
if betas:
assert "task-budgets-2026-03-13" not in betas
def test_anthropic_stream_v2_lifecycle() -> None:
"""Validate lifecycle events across a thinking + text + tool_use stream.
Anthropic emits raw `content_block_start` / `content_block_delta` /
`content_block_stop` events with integer `index` fields, interleaved
with `message_start` and `message_delta`. This test threads a
realistic event sequence through `_stream` via a mocked raw client
and asserts that `stream_v2` produces a spec-conformant event
stream: paired start/finish per block, no interleaving, sequential
`uint` wire indices.
"""
from unittest.mock import patch
from anthropic.types import (
InputJSONDelta,
RawContentBlockDeltaEvent,
RawContentBlockStartEvent,
RawContentBlockStopEvent,
RawMessageDeltaEvent,
RawMessageStartEvent,
RawMessageStopEvent,
TextDelta,
ThinkingBlock,
ThinkingDelta,
ToolUseBlock,
)
from anthropic.types.raw_message_delta_event import Delta as RawMessageDelta
from anthropic.types.raw_message_delta_event import (
MessageDeltaUsage as RawMessageDeltaUsage,
)
from langchain_tests.utils.stream_lifecycle import assert_valid_event_stream
msg = Message(
id="msg_1",
content=[],
model=MODEL_NAME,
role="assistant",
stop_reason=None,
stop_sequence=None,
usage=Usage(input_tokens=10, output_tokens=0),
type="message",
)
events = [
RawMessageStartEvent(message=msg, type="message_start"),
# thinking block (index=0)
RawContentBlockStartEvent(
content_block=ThinkingBlock(signature="", thinking="", type="thinking"),
index=0,
type="content_block_start",
),
RawContentBlockDeltaEvent(
delta=ThinkingDelta(thinking="Let me ", type="thinking_delta"),
index=0,
type="content_block_delta",
),
RawContentBlockDeltaEvent(
delta=ThinkingDelta(thinking="think.", type="thinking_delta"),
index=0,
type="content_block_delta",
),
RawContentBlockStopEvent(index=0, type="content_block_stop"),
# text block (index=1)
RawContentBlockStartEvent(
content_block=TextBlock(text="", type="text"),
index=1,
type="content_block_start",
),
RawContentBlockDeltaEvent(
delta=TextDelta(text="The answer ", type="text_delta"),
index=1,
type="content_block_delta",
),
RawContentBlockDeltaEvent(
delta=TextDelta(text="is 42.", type="text_delta"),
index=1,
type="content_block_delta",
),
RawContentBlockStopEvent(index=1, type="content_block_stop"),
# tool_use block (index=2)
RawContentBlockStartEvent(
content_block=ToolUseBlock(
id="toolu_1",
input={},
name="search",
type="tool_use",
),
index=2,
type="content_block_start",
),
RawContentBlockDeltaEvent(
delta=InputJSONDelta(partial_json='{"q":', type="input_json_delta"),
index=2,
type="content_block_delta",
),
RawContentBlockDeltaEvent(
delta=InputJSONDelta(partial_json=' "weather"}', type="input_json_delta"),
index=2,
type="content_block_delta",
),
RawContentBlockStopEvent(index=2, type="content_block_stop"),
# message_delta with final usage and stop_reason
RawMessageDeltaEvent(
delta=RawMessageDelta(stop_reason="tool_use", stop_sequence=None),
type="message_delta",
usage=RawMessageDeltaUsage(
output_tokens=50,
input_tokens=10,
cache_read_input_tokens=0,
cache_creation_input_tokens=0,
),
),
RawMessageStopEvent(type="message_stop"),
]
# Enable thinking so `coerce_content_to_string=False` in `_stream`,
# which gives every content block an integer `index` field — the
# structured path the protocol bridge actually exercises. Default
# (no tools / thinking / documents) coerces text to a plain string,
# which strips indices and is a separate code path not covered here.
llm = ChatAnthropic(
model=MODEL_NAME,
thinking={"type": "enabled", "budget_tokens": 1024},
)
def mock_create(_payload: Any) -> list:
return events
with patch.object(llm, "_create", mock_create):
stream_events = list(llm.stream_v2("Test query"))
assert_valid_event_stream(stream_events)
finishes = [e for e in stream_events if e["event"] == "content-block-finish"]
types = [f["content_block"]["type"] for f in finishes]
assert types == ["reasoning", "text", "tool_call"]
wire_indices = [f["index"] for f in finishes]
assert wire_indices == [0, 1, 2]
# Content accumulation reaches content-block-finish intact.
reasoning_block = cast("dict[str, Any]", finishes[0]["content_block"])
text_block = cast("dict[str, Any]", finishes[1]["content_block"])
tool_block = cast("dict[str, Any]", finishes[2]["content_block"])
assert reasoning_block["reasoning"] == "Let me think."
assert text_block["text"] == "The answer is 42."
assert tool_block["args"] == {"q": "weather"}
assert tool_block["name"] == "search"
# message-finish carries the tool_use stop reason inside metadata
# (protocol 0.0.9 moved the finish reason off the top-level event
# and into `metadata`, where the bridge deposits the provider's raw
# `stop_reason` alongside other response metadata).
message_finish = stream_events[-1]
assert message_finish["event"] == "message-finish"
assert message_finish["metadata"]["stop_reason"] == "tool_use"
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