# Copyright 2026 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import base64 import json import os import re import sys from unittest import mock from unittest.mock import AsyncMock from unittest.mock import MagicMock from anthropic import NOT_GIVEN from anthropic import types as anthropic_types from google.adk import version as adk_version from google.adk.models import anthropic_llm from google.adk.models import AnthropicGenerateContentConfig from google.adk.models.anthropic_llm import AnthropicLlm from google.adk.models.anthropic_llm import Claude from google.adk.models.anthropic_llm import content_to_message_param from google.adk.models.anthropic_llm import function_declaration_to_tool_param from google.adk.models.anthropic_llm import part_to_message_block from google.adk.models.llm_request import LlmRequest from google.adk.models.llm_response import LlmResponse from google.genai import types from google.genai import version as genai_version from google.genai.types import Content from google.genai.types import Part import pytest @pytest.fixture def generate_content_response(): return anthropic_types.Message( id="msg_vrtx_testid", content=[ anthropic_types.TextBlock( citations=None, text="Hi! How can I help you today?", type="text" ) ], model="claude-3-5-sonnet-v2-20241022", role="assistant", stop_reason="end_turn", stop_sequence=None, type="message", usage=anthropic_types.Usage( cache_creation_input_tokens=0, cache_read_input_tokens=0, input_tokens=13, output_tokens=12, server_tool_use=None, service_tier=None, ), ) @pytest.fixture def generate_llm_response(): return LlmResponse.create( types.GenerateContentResponse( candidates=[ types.Candidate( content=Content( role="model", parts=[Part.from_text(text="Hello, how can I help you?")], ), finish_reason=types.FinishReason.STOP, ) ] ) ) @pytest.fixture def claude_llm(): return Claude(model="claude-3-5-sonnet-v2@20241022") @pytest.fixture def llm_request(): return LlmRequest( model="claude-3-5-sonnet-v2@20241022", contents=[Content(role="user", parts=[Part.from_text(text="Hello")])], config=types.GenerateContentConfig( temperature=0.1, response_modalities=[types.Modality.TEXT], system_instruction="You are a helpful assistant", ), ) def test_claude_anthropic_client_creation(): # Test with environment variables with mock.patch.dict( os.environ, { "GOOGLE_CLOUD_PROJECT": "env-project", "GOOGLE_CLOUD_LOCATION": "env-location", }, ): model = Claude(model="claude-3-5-sonnet-v2@20241022") with mock.patch( "google.adk.models.anthropic_llm.AsyncAnthropicVertex", autospec=True ) as mock_client_class: _ = model._anthropic_client mock_client_class.assert_called_once() _, kwargs = mock_client_class.call_args assert kwargs["project_id"] == "env-project" assert kwargs["region"] == "env-location" def test_claude_anthropic_client_creation_with_full_resource_name(): # Test with full resource name in model string model = Claude( model="projects/test-project/locations/test-location/publishers/anthropic/models/claude-3-5-sonnet-v2@20241022" ) with mock.patch( "google.adk.models.anthropic_llm.AsyncAnthropicVertex", autospec=True ) as mock_client_class: _ = model._anthropic_client mock_client_class.assert_called_once() _, kwargs = mock_client_class.call_args assert kwargs["project_id"] == "test-project" assert kwargs["region"] == "test-location" def test_supported_models(): models = Claude.supported_models() assert len(models) == 2 assert models[0] == r"claude-3-.*" assert models[1] == r"claude-.*-4.*" function_declaration_test_cases = [ ( "function_with_no_parameters", types.FunctionDeclaration( name="get_current_time", description="Gets the current time.", ), anthropic_types.ToolParam( name="get_current_time", description="Gets the current time.", input_schema={"type": "object", "properties": {}}, ), ), ( "function_with_one_optional_parameter", types.FunctionDeclaration( name="get_weather", description="Gets weather information for a given location.", parameters=types.Schema( type=types.Type.OBJECT, properties={ "location": types.Schema( type=types.Type.STRING, description="City and state, e.g., San Francisco, CA", ) }, ), ), anthropic_types.ToolParam( name="get_weather", description="Gets weather information for a given location.", input_schema={ "type": "object", "properties": { "location": { "type": "string", "description": ( "City and state, e.g., San Francisco, CA" ), } }, }, ), ), ( "function_with_one_required_parameter", types.FunctionDeclaration( name="get_stock_price", description="Gets the current price for a stock ticker.", parameters=types.Schema( type=types.Type.OBJECT, properties={ "ticker": types.Schema( type=types.Type.STRING, description="The stock ticker, e.g., AAPL", ) }, required=["ticker"], ), ), anthropic_types.ToolParam( name="get_stock_price", description="Gets the current price for a stock ticker.", input_schema={ "type": "object", "properties": { "ticker": { "type": "string", "description": "The stock ticker, e.g., AAPL", } }, "required": ["ticker"], }, ), ), ( "function_with_multiple_mixed_parameters", types.FunctionDeclaration( name="submit_order", description="Submits a product order.", parameters=types.Schema( type=types.Type.OBJECT, properties={ "product_id": types.Schema( type=types.Type.STRING, description="The product ID" ), "quantity": types.Schema( type=types.Type.INTEGER, description="The order quantity", ), "notes": types.Schema( type=types.Type.STRING, description="Optional order notes", ), }, required=["product_id", "quantity"], ), ), anthropic_types.ToolParam( name="submit_order", description="Submits a product order.", input_schema={ "type": "object", "properties": { "product_id": { "type": "string", "description": "The product ID", }, "quantity": { "type": "integer", "description": "The order quantity", }, "notes": { "type": "string", "description": "Optional order notes", }, }, "required": ["product_id", "quantity"], }, ), ), ( "function_with_complex_nested_parameter", types.FunctionDeclaration( name="create_playlist", description="Creates a playlist from a list of songs.", parameters=types.Schema( type=types.Type.OBJECT, properties={ "playlist_name": types.Schema( type=types.Type.STRING, description="The name for the new playlist", ), "songs": types.Schema( type=types.Type.ARRAY, description="A list of songs to add to the playlist", items=types.Schema( type=types.Type.OBJECT, properties={ "title": types.Schema(type=types.Type.STRING), "artist": types.Schema(type=types.Type.STRING), }, required=["title", "artist"], ), ), }, required=["playlist_name", "songs"], ), ), anthropic_types.ToolParam( name="create_playlist", description="Creates a playlist from a list of songs.", input_schema={ "type": "object", "properties": { "playlist_name": { "type": "string", "description": "The name for the new playlist", }, "songs": { "type": "array", "description": "A list of songs to add to the playlist", "items": { "type": "object", "properties": { "title": {"type": "string"}, "artist": {"type": "string"}, }, "required": ["title", "artist"], }, }, }, "required": ["playlist_name", "songs"], }, ), ), ( "function_with_nested_object_parameter", types.FunctionDeclaration( name="update_profile", description="Updates a user profile.", parameters=types.Schema( type=types.Type.OBJECT, properties={ "profile": types.Schema( type=types.Type.OBJECT, description="The profile data", properties={ "name": types.Schema( type=types.Type.STRING, description="Full name", ), "address": types.Schema( type=types.Type.OBJECT, description="Mailing address", properties={ "city": types.Schema( type=types.Type.STRING, ), "state": types.Schema( type=types.Type.STRING, ), }, ), }, ), }, required=["profile"], ), ), anthropic_types.ToolParam( name="update_profile", description="Updates a user profile.", input_schema={ "type": "object", "properties": { "profile": { "type": "object", "description": "The profile data", "properties": { "name": { "type": "string", "description": "Full name", }, "address": { "type": "object", "description": "Mailing address", "properties": { "city": {"type": "string"}, "state": {"type": "string"}, }, }, }, }, }, "required": ["profile"], }, ), ), ( "function_with_any_of_parameter", types.FunctionDeclaration( name="set_value", description="Sets a value that can be a string or integer.", parameters=types.Schema( type=types.Type.OBJECT, properties={ "value": types.Schema( description="A string or integer value", any_of=[ types.Schema(type=types.Type.STRING), types.Schema(type=types.Type.INTEGER), ], ), }, required=["value"], ), ), anthropic_types.ToolParam( name="set_value", description="Sets a value that can be a string or integer.", input_schema={ "type": "object", "properties": { "value": { "description": "A string or integer value", "anyOf": [ {"type": "string"}, {"type": "integer"}, ], }, }, "required": ["value"], }, ), ), ( "function_with_additional_properties_parameter", types.FunctionDeclaration( name="store_metadata", description="Stores arbitrary key-value metadata.", parameters=types.Schema( type=types.Type.OBJECT, properties={ "metadata": types.Schema( type=types.Type.OBJECT, description="Arbitrary metadata", additional_properties=types.Schema( type=types.Type.STRING, ), ), }, required=["metadata"], ), ), anthropic_types.ToolParam( name="store_metadata", description="Stores arbitrary key-value metadata.", input_schema={ "type": "object", "properties": { "metadata": { "type": "object", "description": "Arbitrary metadata", "additionalProperties": {"type": "string"}, }, }, "required": ["metadata"], }, ), ), ( "function_with_parameters_json_schema_combinators", types.FunctionDeclaration( name="validate_payload", description="Validates a payload with schema combinators.", parameters_json_schema={ "type": "OBJECT", "properties": { "choice": { "oneOf": [ {"type": "STRING"}, {"type": "INTEGER"}, ], }, "config": { "allOf": [ { "type": "OBJECT", "properties": { "enabled": {"type": "BOOLEAN"}, }, }, ], }, "blocked": { "not": { "type": "NULL", }, }, "tuple_value": { "type": "ARRAY", "items": [ {"type": "STRING"}, {"type": "INTEGER"}, ], }, }, "required": ["choice"], }, ), anthropic_types.ToolParam( name="validate_payload", description="Validates a payload with schema combinators.", input_schema={ "type": "object", "properties": { "choice": { "oneOf": [ {"type": "string"}, {"type": "integer"}, ], }, "config": { "allOf": [ { "type": "object", "properties": { "enabled": {"type": "boolean"}, }, }, ], }, "blocked": { "not": { "type": "null", }, }, "tuple_value": { "type": "array", "items": [ {"type": "string"}, {"type": "integer"}, ], }, }, "required": ["choice"], }, ), ), ( "function_with_parameters_json_schema", types.FunctionDeclaration( name="search_database", description="Searches a database with given criteria.", parameters_json_schema={ "type": "object", "properties": { "query": { "type": "string", "description": "The search query", }, "limit": { "type": "integer", "description": "Maximum number of results", }, }, "required": ["query"], }, ), anthropic_types.ToolParam( name="search_database", description="Searches a database with given criteria.", input_schema={ "type": "object", "properties": { "query": { "type": "string", "description": "The search query", }, "limit": { "type": "integer", "description": "Maximum number of results", }, }, "required": ["query"], }, ), ), ] @pytest.mark.parametrize( "_, function_declaration, expected_tool_param", function_declaration_test_cases, ids=[case[0] for case in function_declaration_test_cases], ) def test_function_declaration_to_tool_param( _, function_declaration, expected_tool_param ): """Test function_declaration_to_tool_param.""" assert ( function_declaration_to_tool_param(function_declaration) == expected_tool_param ) @pytest.mark.asyncio async def test_generate_content_async( claude_llm, llm_request, generate_content_response, generate_llm_response ): with mock.patch.object(claude_llm, "_anthropic_client") as mock_client: with mock.patch.object( anthropic_llm, "message_to_generate_content_response", return_value=generate_llm_response, ): # Create a mock coroutine that returns the generate_content_response. async def mock_coro(): return generate_content_response # Assign the coroutine to the mocked method mock_client.messages.create.return_value = mock_coro() responses = [ resp async for resp in claude_llm.generate_content_async( llm_request, stream=False ) ] assert len(responses) == 1 assert isinstance(responses[0], LlmResponse) assert responses[0].content.parts[0].text == "Hello, how can I help you?" @pytest.mark.asyncio async def test_anthropic_llm_generate_content_async( llm_request, generate_content_response, generate_llm_response ): anthropic_llm_instance = AnthropicLlm(model="claude-sonnet-4-20250514") with mock.patch.object( anthropic_llm_instance, "_anthropic_client" ) as mock_client: with mock.patch.object( anthropic_llm, "message_to_generate_content_response", return_value=generate_llm_response, ): # Create a mock coroutine that returns the generate_content_response. async def mock_coro(): return generate_content_response # Assign the coroutine to the mocked method mock_client.messages.create.return_value = mock_coro() responses = [ resp async for resp in anthropic_llm_instance.generate_content_async( llm_request, stream=False ) ] assert len(responses) == 1 assert isinstance(responses[0], LlmResponse) assert responses[0].content.parts[0].text == "Hello, how can I help you?" def test_claude_vertex_client_uses_tracking_headers(): """Tests that Claude vertex client is called with tracking headers.""" with mock.patch.object( anthropic_llm, "AsyncAnthropicVertex", autospec=True ) as mock_anthropic_vertex: with mock.patch.dict( os.environ, { "GOOGLE_CLOUD_PROJECT": "test-project", "GOOGLE_CLOUD_LOCATION": "us-central1", }, ): instance = Claude(model="claude-3-5-sonnet-v2@20241022") _ = instance._anthropic_client mock_anthropic_vertex.assert_called_once() _, kwargs = mock_anthropic_vertex.call_args assert "default_headers" in kwargs assert "x-goog-api-client" in kwargs["default_headers"] assert "user-agent" in kwargs["default_headers"] assert ( f"google-adk/{adk_version.__version__}" in kwargs["default_headers"]["user-agent"] ) @pytest.mark.asyncio async def test_generate_content_async_with_max_tokens( llm_request, generate_content_response, generate_llm_response ): claude_llm = Claude(model="claude-3-5-sonnet-v2@20241022", max_tokens=4096) with mock.patch.object(claude_llm, "_anthropic_client") as mock_client: with mock.patch.object( anthropic_llm, "message_to_generate_content_response", return_value=generate_llm_response, ): # Create a mock coroutine that returns the generate_content_response. async def mock_coro(): return generate_content_response # Assign the coroutine to the mocked method mock_client.messages.create.return_value = mock_coro() _ = [ resp async for resp in claude_llm.generate_content_async( llm_request, stream=False ) ] mock_client.messages.create.assert_called_once() _, kwargs = mock_client.messages.create.call_args assert kwargs["max_tokens"] == 4096 def test_part_to_message_block_with_content(): """Test that part_to_message_block handles content format.""" from google.adk.models.anthropic_llm import part_to_message_block # Create a function response part with content array. mcp_response_part = types.Part.from_function_response( name="generate_sample_filesystem", response={ "content": [{ "type": "text", "text": '{"name":"root","node_type":"folder","children":[]}', }] }, ) mcp_response_part.function_response.id = "test_id_123" result = part_to_message_block(mcp_response_part) # ToolResultBlockParam is a TypedDict. assert isinstance(result, dict) assert result["tool_use_id"] == "test_id_123" assert result["type"] == "tool_result" assert not result["is_error"] # Verify the content was extracted from the content format. assert ( '{"name":"root","node_type":"folder","children":[]}' in result["content"] ) def test_part_to_message_block_with_traditional_result(): """Test that part_to_message_block handles traditional result format.""" from google.adk.models.anthropic_llm import part_to_message_block # Create a function response part with traditional result format traditional_response_part = types.Part.from_function_response( name="some_tool", response={ "result": "This is the result from the tool", }, ) traditional_response_part.function_response.id = "test_id_456" result = part_to_message_block(traditional_response_part) # ToolResultBlockParam is a TypedDict. assert isinstance(result, dict) assert result["tool_use_id"] == "test_id_456" assert result["type"] == "tool_result" assert not result["is_error"] # Verify the content was extracted from the traditional format assert "This is the result from the tool" in result["content"] def test_part_to_message_block_with_multiple_content_items(): """Test content with multiple items.""" from google.adk.models.anthropic_llm import part_to_message_block # Create a function response with multiple content items multi_content_part = types.Part.from_function_response( name="multi_response_tool", response={ "content": [ {"type": "text", "text": "First part"}, {"type": "text", "text": "Second part"}, ] }, ) multi_content_part.function_response.id = "test_id_789" result = part_to_message_block(multi_content_part) # ToolResultBlockParam is a TypedDict. assert isinstance(result, dict) # Multiple text items should be joined with newlines assert result["content"] == "First part\nSecond part" def test_part_to_message_block_with_pdf_document(): """Test that part_to_message_block handles PDF document parts.""" pdf_data = b"%PDF-1.4 fake pdf content" part = Part( inline_data=types.Blob(mime_type="application/pdf", data=pdf_data) ) result = part_to_message_block(part) assert isinstance(result, dict) assert result["type"] == "document" assert result["source"]["type"] == "base64" assert result["source"]["media_type"] == "application/pdf" assert result["source"]["data"] == base64.b64encode(pdf_data).decode() def test_part_to_message_block_with_pdf_mime_type_parameters(): """Test that PDF parts with MIME type parameters are handled correctly.""" pdf_data = b"%PDF-1.4 fake pdf content" part = Part( inline_data=types.Blob( mime_type="application/pdf; name=doc.pdf", data=pdf_data ) ) result = part_to_message_block(part) assert isinstance(result, dict) assert result["type"] == "document" assert result["source"]["type"] == "base64" assert result["source"]["media_type"] == "application/pdf; name=doc.pdf" assert result["source"]["data"] == base64.b64encode(pdf_data).decode() content_to_message_param_test_cases = [ ( "user_role_with_text_and_image", Content( role="user", parts=[ Part.from_text(text="What's in this image?"), Part( inline_data=types.Blob( mime_type="image/jpeg", data=b"fake_image_data" ) ), ], ), "user", 2, # Expected content length None, # No warning expected ), ( "model_role_with_text_and_image", Content( role="model", parts=[ Part.from_text(text="I see a cat."), Part( inline_data=types.Blob( mime_type="image/png", data=b"fake_image_data" ) ), ], ), "assistant", 1, # Image filtered out, only text remains "Image data is not supported in Claude for assistant turns.", ), ( "assistant_role_with_text_and_image", Content( role="assistant", parts=[ Part.from_text(text="Here's what I found."), Part( inline_data=types.Blob( mime_type="image/webp", data=b"fake_image_data" ) ), ], ), "assistant", 1, # Image filtered out, only text remains "Image data is not supported in Claude for assistant turns.", ), ( "user_role_with_text_and_document", Content( role="user", parts=[ Part.from_text(text="Summarize this document."), Part( inline_data=types.Blob( mime_type="application/pdf", data=b"fake_pdf_data" ) ), ], ), "user", 2, # Both text and document included None, # No warning expected ), ( "model_role_with_text_and_document", Content( role="model", parts=[ Part.from_text(text="Here is the summary."), Part( inline_data=types.Blob( mime_type="application/pdf", data=b"fake_pdf_data" ) ), ], ), "assistant", 1, # Document filtered out, only text remains "PDF data is not supported in Claude for assistant turns.", ), ] @pytest.mark.parametrize( "_, content, expected_role, expected_content_length, expected_warning", content_to_message_param_test_cases, ids=[case[0] for case in content_to_message_param_test_cases], ) def test_content_to_message_param( _, content, expected_role, expected_content_length, expected_warning ): """Test content_to_message_param handles images and documents based on role.""" with mock.patch("google.adk.models.anthropic_llm.logger") as mock_logger: result = content_to_message_param(content) assert result["role"] == expected_role assert len(result["content"]) == expected_content_length if expected_warning: mock_logger.warning.assert_called_once_with(expected_warning) else: mock_logger.warning.assert_not_called() # --- Tests for Bug #2: json.dumps for dict/list function results --- def test_part_to_message_block_dict_result_serialized_as_json(): """Dict results should be serialized with json.dumps, not str().""" response_part = types.Part.from_function_response( name="get_topic", response={"result": {"topic": "travel", "active": True, "count": None}}, ) response_part.function_response.id = "test_id" result = part_to_message_block(response_part) content = result["content"] # Must be valid JSON (json.dumps produces "true"/"null", not "True"/"None") parsed = json.loads(content) assert parsed["topic"] == "travel" assert parsed["active"] is True assert parsed["count"] is None def test_part_to_message_block_list_result_serialized_as_json(): """List results should be serialized with json.dumps.""" response_part = types.Part.from_function_response( name="get_items", response={"result": ["item1", "item2", "item3"]}, ) response_part.function_response.id = "test_id" result = part_to_message_block(response_part) content = result["content"] parsed = json.loads(content) assert parsed == ["item1", "item2", "item3"] def test_part_to_message_block_empty_dict_result_not_dropped(): """Empty dict results should produce '{}', not empty string.""" response_part = types.Part.from_function_response( name="some_tool", response={"result": {}}, ) response_part.function_response.id = "test_id" result = part_to_message_block(response_part) assert result["content"] == "{}" def test_part_to_message_block_empty_list_result_not_dropped(): """Empty list results should produce '[]', not empty string.""" response_part = types.Part.from_function_response( name="some_tool", response={"result": []}, ) response_part.function_response.id = "test_id" result = part_to_message_block(response_part) assert result["content"] == "[]" def test_part_to_message_block_string_result_unchanged(): """String results should still work as before (backward compat).""" response_part = types.Part.from_function_response( name="simple_tool", response={"result": "plain text result"}, ) response_part.function_response.id = "test_id" result = part_to_message_block(response_part) assert result["content"] == "plain text result" def test_part_to_message_block_nested_dict_result(): """Nested dict with arrays should produce valid JSON.""" response_part = types.Part.from_function_response( name="search", response={ "result": { "results": [ {"id": 1, "tags": ["a", "b"]}, {"id": 2, "meta": {"key": "val"}}, ], "has_more": False, } }, ) response_part.function_response.id = "test_id" result = part_to_message_block(response_part) parsed = json.loads(result["content"]) assert parsed["has_more"] is False assert parsed["results"][0]["tags"] == ["a", "b"] # --- Tests for arbitrary dict fallback (e.g. SkillToolset load_skill) --- def test_part_to_message_block_arbitrary_dict_serialized_as_json(): """Dicts with keys other than 'content'/'result' should be JSON-serialized. This covers tools like load_skill that return arbitrary key structures such as {"skill_name": ..., "instructions": ..., "frontmatter": ...}. """ response_part = types.Part.from_function_response( name="load_skill", response={ "skill_name": "my_skill", "instructions": "Step 1: do this. Step 2: do that.", "frontmatter": {"version": "1.0", "tags": ["a", "b"]}, }, ) response_part.function_response.id = "test_id" result = part_to_message_block(response_part) assert result["type"] == "tool_result" assert result["tool_use_id"] == "test_id" assert not result["is_error"] parsed = json.loads(result["content"]) assert parsed["skill_name"] == "my_skill" assert parsed["instructions"] == "Step 1: do this. Step 2: do that." assert parsed["frontmatter"]["version"] == "1.0" def test_part_to_message_block_run_skill_script_response(): """run_skill_script response keys (stdout/stderr/status) should not be dropped.""" response_part = types.Part.from_function_response( name="run_skill_script", response={ "skill_name": "my_skill", "file_path": "scripts/setup.py", "stdout": "Done.", "stderr": "", "status": "success", }, ) response_part.function_response.id = "test_id_2" result = part_to_message_block(response_part) parsed = json.loads(result["content"]) assert parsed["status"] == "success" assert parsed["stdout"] == "Done." def test_part_to_message_block_error_response_not_dropped(): """Error dicts like {"error": ..., "error_code": ...} should be serialized.""" response_part = types.Part.from_function_response( name="load_skill", response={ "error": "Skill 'missing' not found.", "error_code": "SKILL_NOT_FOUND", }, ) response_part.function_response.id = "test_id_3" result = part_to_message_block(response_part) parsed = json.loads(result["content"]) assert parsed["error_code"] == "SKILL_NOT_FOUND" def test_part_to_message_block_empty_response_stays_empty(): """An empty response dict should still produce an empty content string.""" response_part = types.Part.from_function_response( name="some_tool", response={}, ) response_part.function_response.id = "test_id_4" result = part_to_message_block(response_part) assert result["content"] == "" def test_part_to_message_block_string_content_passes_through(): """A scalar string `content` value must not be iterated char-by-char.""" response_part = types.Part.from_function_response( name="some_tool", response={"content": "Hello"}, ) response_part.function_response.id = "test_id_str_content" result = part_to_message_block(response_part) assert result["content"] == "Hello" def test_part_to_message_block_load_skill_resource_response(): """LoadSkillResourceTool returns {content: } as a string.""" file_text = "Line one\nLine two\nLine three" response_part = types.Part.from_function_response( name="load_skill_resource", response={ "skill_name": "my-skill", "file_path": "references/doc.md", "content": file_text, }, ) response_part.function_response.id = "test_id_load_skill" result = part_to_message_block(response_part) assert result["content"] == file_text def test_part_to_message_block_empty_string_content_falls_through(): """`{"content": ""}` falls through to the JSON-dump fallback, not a crash.""" response_part = types.Part.from_function_response( name="some_tool", response={"content": ""}, ) response_part.function_response.id = "test_id_empty_content_only" result = part_to_message_block(response_part) assert json.loads(result["content"]) == {"content": ""} def test_part_to_message_block_empty_content_with_metadata_keeps_metadata(): """`content: ""` is falsy; sibling keys still reach the model via JSON dump.""" response_part = types.Part.from_function_response( name="some_tool", response={"content": "", "extra": "keep me"}, ) response_part.function_response.id = "test_id_empty_content_with_meta" result = part_to_message_block(response_part) parsed = json.loads(result["content"]) assert parsed["content"] == "" assert parsed["extra"] == "keep me" # --- Tests for Bug #1: Streaming support --- def _make_mock_stream_events(events): """Helper to create an async iterable from a list of events.""" async def _stream(): for event in events: yield event return _stream() @pytest.mark.asyncio async def test_streaming_text_yields_partial_and_final(): """Streaming text should yield partial chunks then a final response.""" llm = AnthropicLlm(model="claude-sonnet-4-20250514") events = [ MagicMock( type="message_start", message=MagicMock(usage=MagicMock(input_tokens=10, output_tokens=0)), ), MagicMock( type="content_block_start", index=0, content_block=anthropic_types.TextBlock(text="", type="text"), ), MagicMock( type="content_block_delta", index=0, delta=anthropic_types.TextDelta(text="Hello ", type="text_delta"), ), MagicMock( type="content_block_delta", index=0, delta=anthropic_types.TextDelta(text="world!", type="text_delta"), ), MagicMock(type="content_block_stop", index=0), MagicMock( type="message_delta", delta=MagicMock(stop_reason="end_turn"), usage=MagicMock(output_tokens=5), ), MagicMock(type="message_stop"), ] mock_client = MagicMock() mock_client.messages.create = AsyncMock( return_value=_make_mock_stream_events(events) ) llm_request = LlmRequest( model="claude-sonnet-4-20250514", contents=[Content(role="user", parts=[Part.from_text(text="Hi")])], config=types.GenerateContentConfig( system_instruction="You are helpful", ), ) with mock.patch.object(llm, "_anthropic_client", mock_client): responses = [ r async for r in llm.generate_content_async(llm_request, stream=True) ] # 2 partial text chunks + 1 final aggregated assert len(responses) == 3 assert responses[0].partial is True assert responses[0].content.parts[0].text == "Hello " assert responses[1].partial is True assert responses[1].content.parts[0].text == "world!" assert responses[2].partial is False assert responses[2].content.parts[0].text == "Hello world!" assert responses[2].usage_metadata.prompt_token_count == 10 assert responses[2].usage_metadata.candidates_token_count == 5 @pytest.mark.asyncio async def test_streaming_tool_use_yields_function_call(): """Streaming tool_use should accumulate args and yield in final.""" llm = AnthropicLlm(model="claude-sonnet-4-20250514") events = [ MagicMock( type="message_start", message=MagicMock(usage=MagicMock(input_tokens=20, output_tokens=0)), ), MagicMock( type="content_block_start", index=0, content_block=anthropic_types.TextBlock(text="", type="text"), ), MagicMock( type="content_block_delta", index=0, delta=anthropic_types.TextDelta(text="Checking.", type="text_delta"), ), MagicMock(type="content_block_stop", index=0), MagicMock( type="content_block_start", index=1, content_block=anthropic_types.ToolUseBlock( id="toolu_abc", name="get_weather", input={}, type="tool_use", ), ), MagicMock( type="content_block_delta", index=1, delta=anthropic_types.InputJSONDelta( partial_json='{"city": "Paris"}', type="input_json_delta", ), ), MagicMock(type="content_block_stop", index=1), MagicMock( type="message_delta", delta=MagicMock(stop_reason="tool_use"), usage=MagicMock(output_tokens=12), ), MagicMock(type="message_stop"), ] mock_client = MagicMock() mock_client.messages.create = AsyncMock( return_value=_make_mock_stream_events(events) ) llm_request = LlmRequest( model="claude-sonnet-4-20250514", contents=[ Content( role="user", parts=[Part.from_text(text="Weather?")], ) ], config=types.GenerateContentConfig( system_instruction="You are helpful", ), ) with mock.patch.object(llm, "_anthropic_client", mock_client): responses = [ r async for r in llm.generate_content_async(llm_request, stream=True) ] # 1 text partial + 1 final assert len(responses) == 2 final = responses[-1] assert final.partial is False assert len(final.content.parts) == 2 assert final.content.parts[0].text == "Checking." assert final.content.parts[1].function_call.name == "get_weather" assert final.content.parts[1].function_call.args == {"city": "Paris"} assert final.content.parts[1].function_call.id == "toolu_abc" @pytest.mark.asyncio async def test_streaming_passes_stream_true_to_create(): """When stream=True, messages.create should be called with stream=True.""" llm = AnthropicLlm(model="claude-sonnet-4-20250514") events = [ MagicMock( type="message_start", message=MagicMock(usage=MagicMock(input_tokens=5, output_tokens=0)), ), MagicMock( type="content_block_start", index=0, content_block=anthropic_types.TextBlock(text="", type="text"), ), MagicMock( type="content_block_delta", index=0, delta=anthropic_types.TextDelta(text="Hi", type="text_delta"), ), MagicMock(type="content_block_stop", index=0), MagicMock( type="message_delta", delta=MagicMock(stop_reason="end_turn"), usage=MagicMock(output_tokens=1), ), MagicMock(type="message_stop"), ] mock_client = MagicMock() mock_client.messages.create = AsyncMock( return_value=_make_mock_stream_events(events) ) llm_request = LlmRequest( model="claude-sonnet-4-20250514", contents=[Content(role="user", parts=[Part.from_text(text="Hi")])], config=types.GenerateContentConfig( system_instruction="Test", ), ) with mock.patch.object(llm, "_anthropic_client", mock_client): _ = [r async for r in llm.generate_content_async(llm_request, stream=True)] mock_client.messages.create.assert_called_once() _, kwargs = mock_client.messages.create.call_args assert kwargs["stream"] is True @pytest.mark.asyncio async def test_non_streaming_does_not_pass_stream_param(): """When stream=False, messages.create should NOT get stream param.""" llm = AnthropicLlm(model="claude-sonnet-4-20250514") mock_message = anthropic_types.Message( id="msg_test", content=[ anthropic_types.TextBlock(text="Hello!", type="text", citations=None) ], model="claude-sonnet-4-20250514", role="assistant", stop_reason="end_turn", stop_sequence=None, type="message", usage=anthropic_types.Usage( input_tokens=5, output_tokens=2, cache_creation_input_tokens=0, cache_read_input_tokens=0, server_tool_use=None, service_tier=None, ), ) mock_client = MagicMock() mock_client.messages.create = AsyncMock(return_value=mock_message) llm_request = LlmRequest( model="claude-sonnet-4-20250514", contents=[Content(role="user", parts=[Part.from_text(text="Hi")])], config=types.GenerateContentConfig( system_instruction="Test", ), ) with mock.patch.object(llm, "_anthropic_client", mock_client): responses = [ r async for r in llm.generate_content_async(llm_request, stream=False) ] assert len(responses) == 1 mock_client.messages.create.assert_called_once() _, kwargs = mock_client.messages.create.call_args assert "stream" not in kwargs def test_part_to_message_block_function_call_preserves_valid_id(): """Valid Anthropic ids must round-trip byte-for-byte.""" part = types.Part.from_function_call(name="test_tool", args={"k": "v"}) part.function_call.id = "toolu_01abc" result = part_to_message_block(part) assert result["id"] == "toolu_01abc" def test_part_to_message_block_function_response_preserves_valid_id(): """function_response ids must round-trip byte-for-byte to tool_use_id.""" part = types.Part.from_function_response( name="test_tool", response={"result": "ok"} ) part.function_response.id = "toolu_01abc" result = part_to_message_block(part) assert result["tool_use_id"] == "toolu_01abc" def test_part_to_message_block_preserves_adk_fallback_id(): """ADK-generated ``adk-`` ids match Anthropic's regex and round-trip. This is the path exercised by the contents.py fix: when Vertex Claude returns id=None, ``populate_client_function_call_id`` writes ``adk-``, and contents.py preserves it through replay. ``part_to_message_block`` must pass it through to Anthropic unchanged so call/response stay paired. """ call_part = types.Part.from_function_call(name="t", args={"a": 1}) call_part.function_call.id = "adk-12345678-1234-1234-1234-123456789012" response_part = types.Part.from_function_response( name="t", response={"result": "ok"} ) response_part.function_response.id = ( "adk-12345678-1234-1234-1234-123456789012" ) call_result = part_to_message_block(call_part) response_result = part_to_message_block(response_part) assert call_result["id"] == "adk-12345678-1234-1234-1234-123456789012" assert ( response_result["tool_use_id"] == "adk-12345678-1234-1234-1234-123456789012" ) # The pair must remain matched after conversion. assert call_result["id"] == response_result["tool_use_id"] # --- Tests for extended thinking support --- def test_build_anthropic_thinking_param_with_config(): """When thinking_config has a positive budget, return ThinkingConfigEnabledParam.""" from google.adk.models.anthropic_llm import _build_anthropic_thinking_param config = types.GenerateContentConfig( thinking_config=types.ThinkingConfig(thinking_budget=5000), ) result = _build_anthropic_thinking_param(config) assert result == anthropic_types.ThinkingConfigEnabledParam( type="enabled", budget_tokens=5000 ) def test_build_anthropic_thinking_param_zero_budget_disabled(): """thinking_budget=0 maps to ThinkingConfigDisabledParam (genai DISABLED).""" from google.adk.models.anthropic_llm import _build_anthropic_thinking_param config = types.GenerateContentConfig( thinking_config=types.ThinkingConfig(thinking_budget=0), ) result = _build_anthropic_thinking_param(config) assert result == anthropic_types.ThinkingConfigDisabledParam(type="disabled") def test_build_anthropic_thinking_param_none_budget_raises(): """thinking_budget=None must be set explicitly; raises ValueError.""" from google.adk.models.anthropic_llm import _build_anthropic_thinking_param config = types.GenerateContentConfig( thinking_config=types.ThinkingConfig(), ) with pytest.raises( ValueError, match="thinking_budget must be set explicitly" ): _build_anthropic_thinking_param(config) def test_build_anthropic_thinking_param_automatic_budget_uses_adaptive(): """thinking_budget=-1 (genai AUTOMATIC) maps to Anthropic adaptive thinking. Required for Claude Opus 4.7 (which rejects ``"enabled"`` with a 400 error) and recommended for Opus 4.6 / Sonnet 4.6 where ``"enabled"`` is deprecated. """ from google.adk.models.anthropic_llm import _build_anthropic_thinking_param config = types.GenerateContentConfig( thinking_config=types.ThinkingConfig(thinking_budget=-1), ) result = _build_anthropic_thinking_param(config) assert result == anthropic_types.ThinkingConfigAdaptiveParam(type="adaptive") def test_build_anthropic_thinking_param_other_negative_uses_adaptive(): """Any negative thinking_budget (not just -1) maps to adaptive thinking.""" from google.adk.models.anthropic_llm import _build_anthropic_thinking_param config = types.GenerateContentConfig( thinking_config=types.ThinkingConfig(thinking_budget=-5), ) result = _build_anthropic_thinking_param(config) assert result == anthropic_types.ThinkingConfigAdaptiveParam(type="adaptive") def test_build_anthropic_thinking_param_no_config(): """Returns NOT_GIVEN when no thinking config is set.""" from anthropic import NOT_GIVEN from google.adk.models.anthropic_llm import _build_anthropic_thinking_param result_none = _build_anthropic_thinking_param(None) assert result_none is NOT_GIVEN config_no_thinking = types.GenerateContentConfig( system_instruction="test", ) result_no_thinking = _build_anthropic_thinking_param(config_no_thinking) assert result_no_thinking is NOT_GIVEN def test_content_block_to_part_thinking_block(): """ThinkingBlock should produce Part with thought=True and signature.""" from google.adk.models.anthropic_llm import content_block_to_part block = anthropic_types.ThinkingBlock( thinking="Let me reason about this.", signature="sig_abc123", type="thinking", ) part = content_block_to_part(block) assert part is not None assert part.text == "Let me reason about this." assert part.thought is True assert part.thought_signature == b"sig_abc123" def test_content_block_to_part_redacted_thinking(): """RedactedThinkingBlock should preserve the encrypted blob for round-trip.""" from google.adk.models.anthropic_llm import content_block_to_part block = anthropic_types.RedactedThinkingBlock( data="redacted_data", type="redacted_thinking", ) part = content_block_to_part(block) assert part.thought is True assert part.text is None assert part.thought_signature == b"redacted_data" def test_message_to_generate_content_response_with_thinking(): """Message with ThinkingBlock + TextBlock yields both parts.""" from google.adk.models.anthropic_llm import message_to_generate_content_response message = anthropic_types.Message( id="msg_test_thinking", content=[ anthropic_types.ThinkingBlock( thinking="I need to think about this.", signature="sig_xyz", type="thinking", ), anthropic_types.RedactedThinkingBlock( data="hidden", type="redacted_thinking", ), anthropic_types.TextBlock( text="Here is my answer.", type="text", citations=None, ), ], model="claude-sonnet-4-20250514", role="assistant", stop_reason="end_turn", stop_sequence=None, type="message", usage=anthropic_types.Usage( input_tokens=10, output_tokens=20, cache_creation_input_tokens=0, cache_read_input_tokens=0, server_tool_use=None, service_tier=None, ), ) response = message_to_generate_content_response(message) assert len(response.content.parts) == 3 thinking_part = response.content.parts[0] assert thinking_part.text == "I need to think about this." assert thinking_part.thought is True assert thinking_part.thought_signature == b"sig_xyz" redacted_part = response.content.parts[1] assert redacted_part.thought is True assert redacted_part.text is None assert redacted_part.thought_signature == b"hidden" text_part = response.content.parts[2] assert text_part.text == "Here is my answer." assert text_part.thought is not True def test_message_to_generate_content_response_reports_cache_read_tokens(): """cache_read_input_tokens maps to usage_metadata.cached_content_token_count.""" from google.adk.models.anthropic_llm import message_to_generate_content_response message = anthropic_types.Message( id="msg_cache_read", content=[ anthropic_types.TextBlock(text="hi", type="text", citations=None) ], model="claude-sonnet-4-20250514", role="assistant", stop_reason="end_turn", stop_sequence=None, type="message", usage=anthropic_types.Usage( input_tokens=100, output_tokens=20, cache_creation_input_tokens=0, cache_read_input_tokens=75, server_tool_use=None, service_tier=None, ), ) response = message_to_generate_content_response(message) assert response.usage_metadata.cached_content_token_count == 75 def test_message_to_generate_content_response_no_cache_read_tokens(): """Absent cache_read_input_tokens yields cached_content_token_count=None.""" from google.adk.models.anthropic_llm import message_to_generate_content_response message = anthropic_types.Message( id="msg_no_cache", content=[ anthropic_types.TextBlock(text="hi", type="text", citations=None) ], model="claude-sonnet-4-20250514", role="assistant", stop_reason="end_turn", stop_sequence=None, type="message", usage=anthropic_types.Usage( input_tokens=100, output_tokens=20, cache_creation_input_tokens=0, cache_read_input_tokens=None, server_tool_use=None, service_tier=None, ), ) response = message_to_generate_content_response(message) assert response.usage_metadata.cached_content_token_count is None def test_part_to_message_block_thinking_roundtrip(): """Part with thought=True and signature creates ThinkingBlockParam.""" part = Part( text="My reasoning steps.", thought=True, thought_signature=b"roundtrip_sig", ) result = part_to_message_block(part) assert isinstance(result, dict) assert result["type"] == "thinking" assert result["thinking"] == "My reasoning steps." assert result["signature"] == "roundtrip_sig" def test_part_to_message_block_redacted_thinking_roundtrip(): """Part with thought=True, no text, signature -> RedactedThinkingBlockParam.""" part = Part(thought=True, thought_signature=b"encrypted_blob") result = part_to_message_block(part) assert isinstance(result, dict) assert result["type"] == "redacted_thinking" assert result["data"] == "encrypted_blob" @pytest.mark.asyncio async def test_non_streaming_passes_thinking_param(): """When thinking_config is set, messages.create gets thinking kwarg.""" llm = AnthropicLlm(model="claude-sonnet-4-20250514") mock_message = anthropic_types.Message( id="msg_think", content=[ anthropic_types.TextBlock(text="Answer.", type="text", citations=None) ], model="claude-sonnet-4-20250514", role="assistant", stop_reason="end_turn", stop_sequence=None, type="message", usage=anthropic_types.Usage( input_tokens=5, output_tokens=2, cache_creation_input_tokens=0, cache_read_input_tokens=0, server_tool_use=None, service_tier=None, ), ) mock_client = MagicMock() mock_client.messages.create = AsyncMock(return_value=mock_message) request = LlmRequest( model="claude-sonnet-4-20250514", contents=[Content(role="user", parts=[Part.from_text(text="Think")])], config=types.GenerateContentConfig( system_instruction="Test", thinking_config=types.ThinkingConfig(thinking_budget=8000), ), ) with mock.patch.object(llm, "_anthropic_client", mock_client): _ = [r async for r in llm.generate_content_async(request, stream=False)] mock_client.messages.create.assert_called_once() _, kwargs = mock_client.messages.create.call_args assert kwargs["thinking"] == anthropic_types.ThinkingConfigEnabledParam( type="enabled", budget_tokens=8000 ) @pytest.mark.asyncio async def test_non_streaming_no_thinking_param_without_config(): """Without thinking_config, thinking kwarg should be NOT_GIVEN.""" from anthropic import NOT_GIVEN llm = AnthropicLlm(model="claude-sonnet-4-20250514") mock_message = anthropic_types.Message( id="msg_no_think", content=[ anthropic_types.TextBlock(text="Hello!", type="text", citations=None) ], model="claude-sonnet-4-20250514", role="assistant", stop_reason="end_turn", stop_sequence=None, type="message", usage=anthropic_types.Usage( input_tokens=5, output_tokens=2, cache_creation_input_tokens=0, cache_read_input_tokens=0, server_tool_use=None, service_tier=None, ), ) mock_client = MagicMock() mock_client.messages.create = AsyncMock(return_value=mock_message) request = LlmRequest( model="claude-sonnet-4-20250514", contents=[Content(role="user", parts=[Part.from_text(text="Hi")])], config=types.GenerateContentConfig( system_instruction="Test", ), ) with mock.patch.object(llm, "_anthropic_client", mock_client): _ = [r async for r in llm.generate_content_async(request, stream=False)] mock_client.messages.create.assert_called_once() _, kwargs = mock_client.messages.create.call_args assert kwargs["thinking"] is NOT_GIVEN @pytest.mark.asyncio async def test_streaming_thinking_yields_partial_and_final(): """Streaming with thinking blocks yields partial thought then final.""" llm = AnthropicLlm(model="claude-sonnet-4-20250514") events = [ MagicMock( type="message_start", message=MagicMock(usage=MagicMock(input_tokens=15, output_tokens=0)), ), # Thinking block start MagicMock( type="content_block_start", index=0, content_block=anthropic_types.ThinkingBlock( thinking="", signature="", type="thinking" ), ), # Thinking deltas MagicMock( type="content_block_delta", index=0, delta=anthropic_types.ThinkingDelta( thinking="Step 1: ", type="thinking_delta" ), ), MagicMock( type="content_block_delta", index=0, delta=anthropic_types.ThinkingDelta( thinking="analyze.", type="thinking_delta" ), ), MagicMock(type="content_block_stop", index=0), # Text block start MagicMock( type="content_block_start", index=1, content_block=anthropic_types.TextBlock(text="", type="text"), ), MagicMock( type="content_block_delta", index=1, delta=anthropic_types.TextDelta( text="The answer is 42.", type="text_delta" ), ), MagicMock(type="content_block_stop", index=1), MagicMock( type="message_delta", delta=MagicMock(stop_reason="end_turn"), usage=MagicMock(output_tokens=10), ), MagicMock(type="message_stop"), ] mock_client = MagicMock() mock_client.messages.create = AsyncMock( return_value=_make_mock_stream_events(events) ) request = LlmRequest( model="claude-sonnet-4-20250514", contents=[Content(role="user", parts=[Part.from_text(text="What?")])], config=types.GenerateContentConfig( system_instruction="Think carefully", thinking_config=types.ThinkingConfig(thinking_budget=5000), ), ) with mock.patch.object(llm, "_anthropic_client", mock_client): responses = [ r async for r in llm.generate_content_async(request, stream=True) ] # 2 thinking partials + 1 text partial + 1 final = 4 responses assert len(responses) == 4 # First two partials are thinking chunks. assert responses[0].partial is True assert responses[0].content.parts[0].thought is True assert responses[0].content.parts[0].text == "Step 1: " assert responses[1].partial is True assert responses[1].content.parts[0].thought is True assert responses[1].content.parts[0].text == "analyze." # Third partial is text. assert responses[2].partial is True assert responses[2].content.parts[0].text == "The answer is 42." # Final aggregated response has both thinking and text parts. final = responses[3] assert final.partial is False assert len(final.content.parts) == 2 thinking_part = final.content.parts[0] assert thinking_part.thought is True assert thinking_part.text == "Step 1: analyze." text_part = final.content.parts[1] assert text_part.text == "The answer is 42." assert final.usage_metadata.prompt_token_count == 15 assert final.usage_metadata.candidates_token_count == 10 @pytest.mark.asyncio async def test_streaming_passes_thinking_param(): """When thinking_config is set and stream=True, thinking kwarg is passed.""" llm = AnthropicLlm(model="claude-sonnet-4-20250514") events = [ MagicMock( type="message_start", message=MagicMock(usage=MagicMock(input_tokens=5, output_tokens=0)), ), MagicMock( type="content_block_start", index=0, content_block=anthropic_types.TextBlock(text="", type="text"), ), MagicMock( type="content_block_delta", index=0, delta=anthropic_types.TextDelta(text="Ok", type="text_delta"), ), MagicMock(type="content_block_stop", index=0), MagicMock( type="message_delta", delta=MagicMock(stop_reason="end_turn"), usage=MagicMock(output_tokens=1), ), MagicMock(type="message_stop"), ] mock_client = MagicMock() mock_client.messages.create = AsyncMock( return_value=_make_mock_stream_events(events) ) request = LlmRequest( model="claude-sonnet-4-20250514", contents=[Content(role="user", parts=[Part.from_text(text="Hi")])], config=types.GenerateContentConfig( system_instruction="Test", thinking_config=types.ThinkingConfig(thinking_budget=3000), ), ) with mock.patch.object(llm, "_anthropic_client", mock_client): _ = [r async for r in llm.generate_content_async(request, stream=True)] mock_client.messages.create.assert_called_once() _, kwargs = mock_client.messages.create.call_args assert kwargs["thinking"] == anthropic_types.ThinkingConfigEnabledParam( type="enabled", budget_tokens=3000 ) assert kwargs["stream"] is True @pytest.mark.asyncio async def test_streaming_redacted_thinking_block_preserved_in_final(): """Streaming RedactedThinkingBlock arrives at start and ends up in final.""" llm = AnthropicLlm(model="claude-sonnet-4-20250514") events = [ MagicMock( type="message_start", message=MagicMock(usage=MagicMock(input_tokens=8, output_tokens=0)), ), MagicMock( type="content_block_start", index=0, content_block=anthropic_types.RedactedThinkingBlock( data="encrypted_blob", type="redacted_thinking" ), ), MagicMock(type="content_block_stop", index=0), MagicMock( type="content_block_start", index=1, content_block=anthropic_types.TextBlock(text="", type="text"), ), MagicMock( type="content_block_delta", index=1, delta=anthropic_types.TextDelta(text="Done.", type="text_delta"), ), MagicMock(type="content_block_stop", index=1), MagicMock( type="message_delta", delta=MagicMock(stop_reason="end_turn"), usage=MagicMock(output_tokens=4), ), MagicMock(type="message_stop"), ] mock_client = MagicMock() mock_client.messages.create = AsyncMock( return_value=_make_mock_stream_events(events) ) request = LlmRequest( model="claude-sonnet-4-20250514", contents=[Content(role="user", parts=[Part.from_text(text="Hi")])], config=types.GenerateContentConfig( system_instruction="Test", thinking_config=types.ThinkingConfig(thinking_budget=3000), ), ) with mock.patch.object(llm, "_anthropic_client", mock_client): responses = [ r async for r in llm.generate_content_async(request, stream=True) ] final = responses[-1] assert final.partial is False assert len(final.content.parts) == 2 redacted_part = final.content.parts[0] assert redacted_part.thought is True assert redacted_part.text is None assert redacted_part.thought_signature == b"encrypted_blob" text_part = final.content.parts[1] assert text_part.text == "Done." def test_part_to_message_block_function_call_none_id(): """Function call with None ID should get a valid generated ID.""" part = types.Part.from_function_call(name="test_tool", args={"key": "value"}) part.function_call.id = None result = part_to_message_block(part) assert result["id"].startswith("toolu_") assert re.fullmatch(r"[a-zA-Z0-9_-]+", result["id"]) def test_part_to_message_block_function_call_empty_id(): """Function call with empty string ID should get a valid generated ID.""" part = types.Part.from_function_call(name="test_tool", args={"key": "value"}) part.function_call.id = "" result = part_to_message_block(part) assert result["id"].startswith("toolu_") assert re.fullmatch(r"[a-zA-Z0-9_-]+", result["id"]) def test_part_to_message_block_function_call_invalid_chars_id(): """Function call with invalid chars in ID should get a valid generated ID.""" part = types.Part.from_function_call(name="test_tool", args={"key": "value"}) part.function_call.id = "invalid id with spaces!" result = part_to_message_block(part) assert result["id"].startswith("toolu_") assert re.fullmatch(r"[a-zA-Z0-9_-]+", result["id"]) def test_part_to_message_block_function_response_none_id(): """Function response with None ID should get a valid generated ID.""" part = types.Part.from_function_response( name="test_tool", response={"result": "ok"} ) part.function_response.id = None result = part_to_message_block(part) assert result["tool_use_id"].startswith("toolu_") assert re.fullmatch(r"[a-zA-Z0-9_-]+", result["tool_use_id"]) def test_part_to_message_block_function_response_empty_id(): """Function response with empty ID should get a valid generated ID.""" part = types.Part.from_function_response( name="test_tool", response={"result": "ok"} ) part.function_response.id = "" result = part_to_message_block(part) assert result["tool_use_id"].startswith("toolu_") assert re.fullmatch(r"[a-zA-Z0-9_-]+", result["tool_use_id"]) def _make_tool_call_part(name: str, call_id: str | None) -> Part: part = types.Part.from_function_call(name=name, args={}) part.function_call.id = call_id return part def _make_tool_response_part(name: str, response_id: str | None) -> Part: part = types.Part.from_function_response(name=name, response={"result": "ok"}) part.function_response.id = response_id return part async def _capture_anthropic_messages( llm: AnthropicLlm, contents: list[Content], generate_content_response, generate_llm_response, ) -> list[dict]: llm_request = LlmRequest( model="claude-sonnet-4-20250514", contents=contents, config=types.GenerateContentConfig(system_instruction="You are helpful"), ) with mock.patch.object(llm, "_anthropic_client") as mock_client: with mock.patch.object( anthropic_llm, "message_to_generate_content_response", return_value=generate_llm_response, ): async def mock_coro(): return generate_content_response mock_client.messages.create.return_value = mock_coro() _ = [ r async for r in llm.generate_content_async(llm_request, stream=False) ] _, kwargs = mock_client.messages.create.call_args return kwargs["messages"] @pytest.mark.parametrize( "case_id,call_ids,response_ids,expected_unique", [ ( "distinct_invalid_pair_uniquely", ["bad A!", "bad B!"], ["bad A!", "bad B!"], 2, ), ("matching_empty_ids_pair", [""], [""], 1), ("none_and_empty_collapse", [None], [""], 1), ("repeated_invalid_id_consistent", ["bad!"], ["bad!"], 1), ], ids=lambda v: v if isinstance(v, str) else None, ) @pytest.mark.asyncio async def test_generate_content_async_pairs_invalid_tool_ids( case_id, call_ids, response_ids, expected_unique, generate_content_response, generate_llm_response, ): """Anthropic requests have matching, properly-counted tool_use/tool_result IDs.""" llm = AnthropicLlm(model="claude-sonnet-4-20250514") contents = [ Content(role="user", parts=[Part.from_text(text="Hi")]), Content( role="model", parts=[ _make_tool_call_part(f"tool_{i}", cid) for i, cid in enumerate(call_ids) ], ), Content( role="user", parts=[ _make_tool_response_part(f"tool_{i}", rid) for i, rid in enumerate(response_ids) ], ), ] messages = await _capture_anthropic_messages( llm, contents, generate_content_response, generate_llm_response ) use_ids = [b["id"] for b in messages[1]["content"] if b["type"] == "tool_use"] result_ids = [ b["tool_use_id"] for b in messages[2]["content"] if b["type"] == "tool_result" ] assert len(set(use_ids)) == expected_unique assert set(use_ids) == set(result_ids) @pytest.mark.asyncio async def test_non_streaming_no_system_instruction_passes_not_given(): """system=NOT_GIVEN when LlmRequest has no system_instruction.""" llm = AnthropicLlm(model="claude-sonnet-4-20250514") mock_message = anthropic_types.Message( id="msg_test", content=[ anthropic_types.TextBlock(text="ok", type="text", citations=None) ], model="claude-sonnet-4-20250514", role="assistant", stop_reason="end_turn", stop_sequence=None, type="message", usage=anthropic_types.Usage( input_tokens=1, output_tokens=1, cache_creation_input_tokens=0, cache_read_input_tokens=0, server_tool_use=None, service_tier=None, ), ) mock_client = MagicMock() mock_client.messages.create = AsyncMock(return_value=mock_message) request = LlmRequest( model="claude-sonnet-4-20250514", contents=[Content(role="user", parts=[Part.from_text(text="Hi")])], ) assert request.config.system_instruction is None with mock.patch.object(llm, "_anthropic_client", mock_client): _ = [r async for r in llm.generate_content_async(request, stream=False)] mock_client.messages.create.assert_called_once() _, kwargs = mock_client.messages.create.call_args assert kwargs["system"] is NOT_GIVEN @pytest.mark.asyncio async def test_streaming_no_system_instruction_passes_not_given(): """system=NOT_GIVEN on the streaming path when no system_instruction.""" llm = AnthropicLlm(model="claude-sonnet-4-20250514") events = [ MagicMock( type="message_start", message=MagicMock(usage=MagicMock(input_tokens=1, output_tokens=0)), ), MagicMock( type="content_block_start", index=0, content_block=anthropic_types.TextBlock(text="", type="text"), ), MagicMock( type="content_block_delta", index=0, delta=anthropic_types.TextDelta(text="ok", type="text_delta"), ), MagicMock(type="content_block_stop", index=0), MagicMock( type="message_delta", delta=MagicMock(stop_reason="end_turn"), usage=MagicMock(output_tokens=1), ), MagicMock(type="message_stop"), ] mock_client = MagicMock() mock_client.messages.create = AsyncMock( return_value=_make_mock_stream_events(events) ) request = LlmRequest( model="claude-sonnet-4-20250514", contents=[Content(role="user", parts=[Part.from_text(text="Hi")])], ) assert request.config.system_instruction is None with mock.patch.object(llm, "_anthropic_client", mock_client): _ = [r async for r in llm.generate_content_async(request, stream=True)] mock_client.messages.create.assert_called_once() _, kwargs = mock_client.messages.create.call_args assert kwargs["system"] is NOT_GIVEN @pytest.mark.asyncio async def test_generate_content_async_with_generation_config( generate_content_response, generate_llm_response ): claude_llm = Claude(model="claude-3-5-sonnet-v2@20241022") llm_request = LlmRequest( model="claude-3-5-sonnet-v2@20241022", contents=[Content(role="user", parts=[Part.from_text(text="Hello")])], config=types.GenerateContentConfig( temperature=0.7, top_p=0.9, top_k=50, stop_sequences=["##"], max_output_tokens=1024, ), ) with mock.patch.object(claude_llm, "_anthropic_client") as mock_client: with mock.patch.object( anthropic_llm, "message_to_generate_content_response", return_value=generate_llm_response, ): async def mock_coro(): return generate_content_response mock_client.messages.create.return_value = mock_coro() _ = [ resp async for resp in claude_llm.generate_content_async( llm_request, stream=False ) ] mock_client.messages.create.assert_called_once() _, kwargs = mock_client.messages.create.call_args assert kwargs["temperature"] == 0.7 assert kwargs["top_p"] == 0.9 assert kwargs["top_k"] == 50 assert kwargs["stop_sequences"] == ["##"] assert kwargs["max_tokens"] == 1024 @pytest.mark.asyncio async def test_generate_content_streaming_with_generation_config( generate_content_response, ): claude_llm = Claude(model="claude-3-5-sonnet-v2@20241022") llm_request = LlmRequest( model="claude-3-5-sonnet-v2@20241022", contents=[Content(role="user", parts=[Part.from_text(text="Hello")])], config=types.GenerateContentConfig( temperature=0.7, top_p=0.9, top_k=50, stop_sequences=["##"], max_output_tokens=1024, ), ) with mock.patch.object(claude_llm, "_anthropic_client") as mock_client: async def mock_coro(*args, **kwargs): async def async_gen(): if False: yield None return async_gen() mock_client.messages.create.side_effect = mock_coro _ = [ resp async for resp in claude_llm.generate_content_async( llm_request, stream=True ) ] mock_client.messages.create.assert_called_once() _, kwargs = mock_client.messages.create.call_args assert kwargs["temperature"] == 0.7 assert kwargs["top_p"] == 0.9 assert kwargs["top_k"] == 50 assert kwargs["stop_sequences"] == ["##"] assert kwargs["max_tokens"] == 1024 assert kwargs["stream"] @pytest.mark.asyncio async def test_generate_content_async_with_thinking_level_warns_and_ignores( generate_content_response, generate_llm_response, ): """Tests that generate_content_async with standard thinking_level warns and ignores it.""" claude_llm = AnthropicLlm(model="claude-sonnet-4-20250514") llm_request = LlmRequest( model="claude-sonnet-4-20250514", contents=[Content(role="user", parts=[Part.from_text(text="Hello")])], config=types.GenerateContentConfig( thinking_config=types.ThinkingConfig( thinking_budget=-1, thinking_level=types.ThinkingLevel.MINIMAL, ) ), ) with mock.patch.object(claude_llm, "_anthropic_client") as mock_client: with mock.patch.object( anthropic_llm, "message_to_generate_content_response", return_value=generate_llm_response, ): async def mock_coro(): return generate_content_response mock_client.messages.create.return_value = mock_coro() with pytest.warns( UserWarning, match="Standard thinking_config.thinking_level is not supported", ): _ = [ resp async for resp in claude_llm.generate_content_async( llm_request, stream=False ) ] mock_client.messages.create.assert_called_once() _, kwargs = mock_client.messages.create.call_args # Verify that thinking_level was ignored (but budget -1 still enabled adaptive thinking). assert kwargs["thinking"] == {"type": "adaptive"} assert "output_config" not in kwargs @pytest.mark.asyncio async def test_generate_content_async_anthropic_config_with_thinking_level_raises_error(): """Tests that AnthropicGenerateContentConfig with standard thinking_level raises ValueError.""" with pytest.raises( ValueError, match="thinking_level is not supported in AnthropicGenerateContentConfig", ): _ = AnthropicGenerateContentConfig( effort="xhigh", thinking_config=types.ThinkingConfig( thinking_budget=-1, thinking_level=types.ThinkingLevel.MINIMAL, ), ) @pytest.mark.asyncio async def test_generate_content_async_with_anthropic_config_effort( generate_content_response, generate_llm_response, ): """Tests generate_content_async with Anthropic-specific effort configuration.""" claude_llm = AnthropicLlm(model="claude-sonnet-4-20250514") llm_request = LlmRequest( model="claude-sonnet-4-20250514", contents=[Content(role="user", parts=[Part.from_text(text="Hello")])], config=AnthropicGenerateContentConfig( effort="xhigh", ), ) with mock.patch.object(claude_llm, "_anthropic_client") as mock_client: with mock.patch.object( anthropic_llm, "message_to_generate_content_response", return_value=generate_llm_response, ): async def mock_coro(): return generate_content_response mock_client.messages.create.return_value = mock_coro() _ = [ resp async for resp in claude_llm.generate_content_async( llm_request, stream=False ) ] mock_client.messages.create.assert_called_once() _, kwargs = mock_client.messages.create.call_args assert kwargs["output_config"] == {"effort": "xhigh"} assert kwargs["thinking"] is NOT_GIVEN @pytest.mark.asyncio async def test_generate_content_async_excludes_sampling_when_thinking( generate_content_response, generate_llm_response, ): """Tests that sampling parameters are excluded when thinking is enabled.""" claude_llm = AnthropicLlm(model="claude-sonnet-4-20250514") llm_request = LlmRequest( model="claude-sonnet-4-20250514", contents=[Content(role="user", parts=[Part.from_text(text="Hello")])], config=types.GenerateContentConfig( temperature=0.7, top_p=0.9, top_k=50, thinking_config=types.ThinkingConfig( thinking_budget=1024, ), ), ) with mock.patch.object(claude_llm, "_anthropic_client") as mock_client: with mock.patch.object( anthropic_llm, "message_to_generate_content_response", return_value=generate_llm_response, ): async def mock_coro(): return generate_content_response mock_client.messages.create.return_value = mock_coro() with pytest.warns( UserWarning, match="Sampling parameters .* are ignored" ): _ = [ resp async for resp in claude_llm.generate_content_async( llm_request, stream=False ) ] mock_client.messages.create.assert_called_once() _, kwargs = mock_client.messages.create.call_args assert "temperature" not in kwargs assert "top_p" not in kwargs assert "top_k" not in kwargs assert kwargs["max_tokens"] == 8192 assert kwargs["thinking"] == {"type": "enabled", "budget_tokens": 1024} @pytest.mark.asyncio async def test_generate_content_async_excludes_sampling_when_effort( generate_content_response, generate_llm_response, ): """Tests that sampling parameters are excluded when effort is enabled.""" claude_llm = AnthropicLlm(model="claude-sonnet-4-20250514") llm_request = LlmRequest( model="claude-sonnet-4-20250514", contents=[Content(role="user", parts=[Part.from_text(text="Hello")])], config=AnthropicGenerateContentConfig( temperature=0.7, top_p=0.9, top_k=50, effort="xhigh", ), ) with mock.patch.object(claude_llm, "_anthropic_client") as mock_client: with mock.patch.object( anthropic_llm, "message_to_generate_content_response", return_value=generate_llm_response, ): async def mock_coro(): return generate_content_response mock_client.messages.create.return_value = mock_coro() with pytest.warns( UserWarning, match="Sampling parameters .* are ignored" ): _ = [ resp async for resp in claude_llm.generate_content_async( llm_request, stream=False ) ] mock_client.messages.create.assert_called_once() _, kwargs = mock_client.messages.create.call_args assert "temperature" not in kwargs assert "top_p" not in kwargs assert "top_k" not in kwargs assert kwargs["output_config"] == {"effort": "xhigh"}