fix: update type hints and imports for defaultdict

Co-Authored-By: Joe Moura <joao@crewai.com>

docs/examples/custom_storage_knowledge_source_example.py

@@ -1,123 +0,0 @@
-"""Example of using a custom storage with CrewAI."""
-
-from pathlib import Path
-
-import chromadb
-from chromadb.config import Settings
-
-from crewai import Agent, Crew, Task
-from crewai.knowledge.source.custom_storage_knowledge_source import (
-    CustomStorageKnowledgeSource,
-)
-from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
-
-
-class CustomKnowledgeStorage(KnowledgeStorage):
-    """Custom knowledge storage that uses a specific persistent directory.
-    
-    Args:
-        persist_directory (str): Path to the directory where ChromaDB will persist data.
-        embedder: Embedding function to use for the collection. Defaults to None.
-        collection_name (str, optional): Name of the collection. Defaults to None.
-    
-    Raises:
-        ValueError: If persist_directory is empty or invalid.
-    """
-    
-    def __init__(self, persist_directory: str, embedder=None, collection_name=None):
-        if not persist_directory:
-            raise ValueError("persist_directory cannot be empty")
-        self.persist_directory = persist_directory
-        super().__init__(embedder=embedder, collection_name=collection_name)
-    
-    def initialize_knowledge_storage(self):
-        """Initialize the knowledge storage with a custom persistent directory.
-        
-        Creates a ChromaDB PersistentClient with the specified directory and
-        initializes a collection with the provided name and embedding function.
-        
-        Raises:
-            Exception: If collection creation or retrieval fails.
-        """
-        try:
-            chroma_client = chromadb.PersistentClient(
-                path=self.persist_directory,
-                settings=Settings(allow_reset=True),
-            )
-            self.app = chroma_client
-            
-            collection_name = (
-                "knowledge" if not self.collection_name else self.collection_name
-            )
-            self.collection = self.app.get_or_create_collection(
-                name=collection_name,
-                embedding_function=self.embedder_config,
-            )
-        except Exception as e:
-            raise Exception(f"Failed to create or get collection: {e}")
-
-
-def get_knowledge_source_with_custom_storage(
-    folder_name: str, 
-    embedder=None
-) -> CustomStorageKnowledgeSource:
-    """Create a knowledge source with a custom storage.
-    
-    Args:
-        folder_name (str): Name of the folder to store embeddings and collection.
-        embedder: Embedding function to use. Defaults to None.
-        
-    Returns:
-        CustomStorageKnowledgeSource: Configured knowledge source with custom storage.
-        
-    Raises:
-        Exception: If storage initialization fails.
-    """
-    try:
-        persist_path = f"vectorstores/knowledge_{folder_name}"
-        storage = CustomKnowledgeStorage(
-            persist_directory=persist_path,
-            embedder=embedder,
-            collection_name=folder_name
-        )
-        
-        storage.initialize_knowledge_storage()
-        
-        source = CustomStorageKnowledgeSource(collection_name=folder_name)
-        source.storage = storage
-        
-        source.validate_content()
-        
-        return source
-    except Exception as e:
-        raise Exception(f"Failed to initialize knowledge source: {e}")
-
-
-def main() -> None:
-    """Example of using a custom storage with CrewAI.
-    
-    This function demonstrates how to:
-    1. Create a knowledge source with pre-existing embeddings
-    2. Use it with a Crew
-    3. Run the Crew to perform tasks
-    """
-    try:
-        knowledge_source = get_knowledge_source_with_custom_storage(folder_name="example")
-        
-        agent = Agent(role="test", goal="test", backstory="test")
-        task = Task(description="test", expected_output="test", agent=agent)
-        
-        crew = Crew(
-            agents=[agent],
-            tasks=[task],
-            knowledge_sources=[knowledge_source]
-        )
-        
-        result = crew.kickoff()
-        print(result)
-    except Exception as e:
-        print(f"Error running example: {e}")
-
-
-if __name__ == "__main__":
-    main()

src/crewai/crew.py

@@ -16,6 +16,8 @@ from pydantic import (
     field_validator,
     model_validator,
 )
+
+from crewai.llm import LLM
 from pydantic_core import PydanticCustomError
 
 from crewai.agent import Agent
@@ -1075,19 +1077,41 @@ class Crew(BaseModel):
     def test(
         self,
         n_iterations: int,
-        openai_model_name: Optional[str] = None,
+        llm: Optional[Union[str, InstanceOf[LLM], Any]] = None,
+        openai_model_name: Optional[str] = None,  # For backward compatibility
         inputs: Optional[Dict[str, Any]] = None,
     ) -> None:
-        """Test and evaluate the Crew with the given inputs for n iterations concurrently using concurrent.futures."""
+        """Test and evaluate the Crew with the given inputs for n iterations.
+
+        This method runs tests to evaluate the performance of the crew using the specified
+        language model. It supports both string model names and LLM instances for flexibility.
+
+        Args:
+            n_iterations: Number of test iterations to run
+            llm: Language model configuration (preferred). Can be:
+                - A string model name (e.g., "gpt-4")
+                - An LLM instance
+                - Any object with model_name or deployment_name attributes
+            openai_model_name: Legacy parameter for backward compatibility.
+                Deprecated: Will be removed in future versions. Use `llm` instead.
+            inputs: Optional dictionary of inputs to be used during testing
+
+        Note:
+            The `openai_model_name` parameter is deprecated and will be removed in
+            future versions. Use the more flexible `llm` parameter instead, which
+            supports any LLM implementation.
+        """
         test_crew = self.copy()
 
+        # For backward compatibility, convert openai_model_name to llm
+        model_name = llm or openai_model_name or "gpt-4o-mini"
         self._test_execution_span = test_crew._telemetry.test_execution_span(
             test_crew,
             n_iterations,
             inputs,
-            openai_model_name,  # type: ignore[arg-type]
-        )  # type: ignore[arg-type]
-        evaluator = CrewEvaluator(test_crew, openai_model_name)  # type: ignore[arg-type]
+            model_name,
+        )
+        evaluator = CrewEvaluator(test_crew, llm=model_name)
 
         for i in range(1, n_iterations + 1):
             evaluator.set_iteration(i)

src/crewai/knowledge/source/custom_storage_knowledge_source.py

@@ -1,45 +0,0 @@
-import logging
-from typing import Optional
-
-from pydantic import Field
-
-from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
-from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
-
-logger = logging.getLogger(__name__)
-
-
-class CustomStorageKnowledgeSource(BaseKnowledgeSource):
-    """A knowledge source that uses a pre-existing storage with embeddings.
-    
-    This class allows users to use pre-existing vector embeddings without re-embedding
-    when using CrewAI. It acts as a bridge between BaseKnowledgeSource and KnowledgeStorage.
-    
-    Args:
-        collection_name (Optional[str]): Name of the collection in the vector database.
-            Defaults to None.
-    
-    Attributes:
-        storage (KnowledgeStorage): The underlying storage implementation that contains
-            the pre-existing embeddings.
-    """
-
-    collection_name: Optional[str] = Field(default=None)
-
-    def validate_content(self):
-        """Validates that the storage is properly initialized.
-        
-        Raises:
-            ValueError: If storage is not initialized before use.
-        """
-        if not hasattr(self, 'storage') or self.storage is None:
-            raise ValueError("Storage not initialized. Please set storage before use.")
-        logger.debug(f"Storage validated for collection: {self.collection_name}")
-
-    def add(self) -> None:
-        """No need to add content as we're using pre-existing storage.
-        
-        This method is intentionally empty as the embeddings already exist in the storage.
-        """
-        logger.debug(f"Skipping add operation for pre-existing storage: {self.collection_name}")
-        pass

src/crewai/utilities/evaluators/crew_evaluator_handler.py

@@ -1,11 +1,19 @@
 from collections import defaultdict
+from typing import Any, Dict, List, Union
 
-from pydantic import BaseModel, Field
+from pydantic import (
+    BaseModel,
+    Field,
+    InstanceOf,
+    PrivateAttr,
+    model_validator,
+)
 from rich.box import HEAVY_EDGE
 from rich.console import Console
 from rich.table import Table
 
 from crewai.agent import Agent
+from crewai.llm import LLM
 from crewai.task import Task
 from crewai.tasks.task_output import TaskOutput
 from crewai.telemetry import Telemetry
@@ -17,27 +25,74 @@ class TaskEvaluationPydanticOutput(BaseModel):
     )
 
 
-class CrewEvaluator:
+class CrewEvaluator(BaseModel):
     """
     A class to evaluate the performance of the agents in the crew based on the tasks they have performed.
 
     Attributes:
         crew (Crew): The crew of agents to evaluate.
-        openai_model_name (str): The model to use for evaluating the performance of the agents (for now ONLY OpenAI accepted).
+        llm (Union[str, InstanceOf[LLM], Any]): The language model to use for evaluating the performance of the agents.
         tasks_scores (defaultdict): A dictionary to store the scores of the agents for each task.
         iteration (int): The current iteration of the evaluation.
     """
 
-    tasks_scores: defaultdict = defaultdict(list)
-    run_execution_times: defaultdict = defaultdict(list)
-    iteration: int = 0
+    crew: Any = Field(description="The crew of agents to evaluate.")
+    llm: Union[str, InstanceOf[LLM], Any] = Field(
+        description="Language model that will run the evaluation."
+    )
+    tasks_scores: Dict[int, List[float]] = Field(
+        default_factory=lambda: defaultdict(list),
+        description="Dictionary to store the scores of the agents for each task."
+    )
+    run_execution_times: Dict[int, List[int]] = Field(
+        default_factory=lambda: defaultdict(list),
+        description="Dictionary to store execution times for each run."
+    )
+    iteration: int = Field(
+        default=0,
+        description="Current iteration of the evaluation."
+    )
 
-    def __init__(self, crew, openai_model_name: str):
-        self.crew = crew
-        self.openai_model_name = openai_model_name
-        self._telemetry = Telemetry()
+    @model_validator(mode="after")
+    def validate_llm(self):
+        """Validates that the LLM is properly configured."""
+        if not self.llm:
+            raise ValueError("LLM configuration is required")
+        return self
+
+    _telemetry: Telemetry = PrivateAttr(default_factory=Telemetry)
+
+    def __init__(self, crew, llm: Union[str, InstanceOf[LLM], Any]):
+        # Initialize Pydantic model with validated fields
+        super().__init__(crew=crew, llm=llm)
         self._setup_for_evaluating()
 
+    @model_validator(mode="before")
+    def init_llm(cls, values):
+        """Initialize LLM before Pydantic validation."""
+        llm = values.get("llm")
+        try:
+            if isinstance(llm, str):
+                values["llm"] = LLM(model=llm)
+            elif isinstance(llm, LLM):
+                values["llm"] = llm
+            else:
+                # For any other type, attempt to extract relevant attributes
+                llm_params = {
+                    "model": getattr(llm, "model_name", None)
+                    or getattr(llm, "deployment_name", None)
+                    or str(llm),
+                    "temperature": getattr(llm, "temperature", None),
+                    "max_tokens": getattr(llm, "max_tokens", None),
+                    "timeout": getattr(llm, "timeout", None),
+                }
+                # Remove None values
+                llm_params = {k: v for k, v in llm_params.items() if v is not None}
+                values["llm"] = LLM(**llm_params)
+        except Exception as e:
+            raise ValueError(f"Invalid LLM configuration: {str(e)}") from e
+        return values
+
     def _setup_for_evaluating(self) -> None:
         """Sets up the crew for evaluating."""
         for task in self.crew.tasks:
@@ -51,7 +106,7 @@ class CrewEvaluator:
             ),
             backstory="Evaluator agent for crew evaluation with precise capabilities to evaluate the performance of the agents in the crew based on the tasks they have performed",
             verbose=False,
-            llm=self.openai_model_name,
+            llm=self.llm,
         )
 
     def _evaluation_task(
@@ -181,7 +236,7 @@ class CrewEvaluator:
                 self.crew,
                 evaluation_result.pydantic.quality,
                 current_task._execution_time,
-                self.openai_model_name,
+                self.llm.model if isinstance(self.llm, LLM) else self.llm,
             )
             self.tasks_scores[self.iteration].append(evaluation_result.pydantic.quality)
             self.run_execution_times[self.iteration].append(

tests/cassettes/test_agent_human_input.yaml

@@ -1,4 +1,87 @@
 interactions:
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+      Accept-Encoding:
+      - gzip, deflate
+      Connection:
+      - keep-alive
+      Content-Length:
+      - '2986'
+      Content-Type:
+      - application/x-protobuf
+      User-Agent:
+      - OTel-OTLP-Exporter-Python/1.27.0
+    method: POST
+    uri: https://telemetry.crewai.com:4319/v1/traces
+  response:
+    body:
+      string: "\n\0"
+    headers:
+      Content-Length:
+      - '2'
+      Content-Type:
+      - application/x-protobuf
+      Date:
+      - Fri, 27 Dec 2024 22:14:53 GMT
+    status:
+      code: 200
+      message: OK
 - request:
     body: '{"messages": [{"role": "system", "content": "You are test role. test backstory\nYour
       personal goal is: test goal\nTo give my best complete final answer to the task
@@ -22,18 +105,20 @@ interactions:
       - '824'
       content-type:
       - application/json
+      cookie:
+      - _cfuvid=ePJSDFdHag2D8lj21_ijAMWjoA6xfnPNxN4uekvC728-1727226247743-0.0.1.1-604800000
       host:
       - api.openai.com
       user-agent:
       - OpenAI/Python 1.52.1
       x-stainless-arch:
-      - arm64
+      - x64
       x-stainless-async:
       - 'false'
       x-stainless-lang:
       - python
       x-stainless-os:
-      - MacOS
+      - Linux
       x-stainless-package-version:
       - 1.52.1
       x-stainless-raw-response:
@@ -47,8 +132,8 @@ interactions:
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     uri: https://api.openai.com/v1/chat/completions
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       \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
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tests/crew_test.py

@@ -300,6 +300,15 @@ def test_hierarchical_process():
     )
 
 
+@mock.patch("crewai.crew.CrewEvaluator")
+@mock.patch("crewai.crew.Crew.copy")
+def test_crew_test_backward_compatibility(mock_copy, mock_evaluator):
+    crew = Crew(agents=[researcher], tasks=[Task(description="test", expected_output="test output", agent=researcher)])
+    crew.test(2, openai_model_name="gpt-4")
+    mock_evaluator.assert_called_once()
+    _, kwargs = mock_evaluator.call_args
+    assert kwargs["llm"] == "gpt-4"
+
 def test_manager_llm_requirement_for_hierarchical_process():
     task = Task(
         description="Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes.",
@@ -1123,7 +1132,7 @@ def test_kickoff_for_each_empty_input():
     assert results == []
 
 
-@pytest.mark.vcr(filter_headers=["authorization"])
+@pytest.mark.vcr(filter_headeruvs=["authorization"])
 def test_kickoff_for_each_invalid_input():
     """Tests if kickoff_for_each raises TypeError for invalid input types."""
 
@@ -2837,7 +2846,7 @@ def test_crew_testing_function(kickoff_mock, copy_mock, crew_evaluator):
 
     crew_evaluator.assert_has_calls(
         [
-            mock.call(crew, "gpt-4o-mini"),
+            mock.call(crew, llm="gpt-4o-mini"),
             mock.call().set_iteration(1),
             mock.call().set_iteration(2),
             mock.call().print_crew_evaluation_result(),
@@ -3125,4 +3134,4 @@ def test_multimodal_agent_live_image_analysis():
     # Verify we got a meaningful response
     assert isinstance(result.raw, str)
     assert len(result.raw) > 100  # Expecting a detailed analysis
-    assert "error" not in result.raw.lower()  # No error messages in response
+    assert "error" not in result.raw.lower()  # No error messages in response

tests/knowledge/custom_storage_knowledge_source_test.py

@@ -1,125 +0,0 @@
-"""Test CustomStorageKnowledgeSource functionality."""
-
-import os
-import shutil
-import tempfile
-from pathlib import Path
-from unittest.mock import MagicMock, patch
-
-import pytest
-
-from crewai.knowledge.knowledge import Knowledge
-from crewai.knowledge.source.custom_storage_knowledge_source import (
-    CustomStorageKnowledgeSource,
-)
-from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
-
-
-@pytest.fixture
-def custom_storage():
-    """Create a custom KnowledgeStorage instance."""
-    storage = KnowledgeStorage(collection_name="test_collection")
-    return storage
-
-
-@pytest.fixture
-def temp_dir():
-    """Create a temporary directory for test files."""
-    temp_dir = tempfile.mkdtemp()
-    yield temp_dir
-    if os.path.exists(temp_dir):
-        shutil.rmtree(temp_dir)
-
-
-def test_custom_storage_knowledge_source(custom_storage):
-    """Test that a CustomStorageKnowledgeSource can be created with a pre-existing storage."""
-    source = CustomStorageKnowledgeSource(collection_name="test_collection")
-    
-    assert source is not None
-    assert source.collection_name == "test_collection"
-
-
-def test_custom_storage_knowledge_source_validation():
-    """Test that validation fails when storage is not properly initialized."""
-    source = CustomStorageKnowledgeSource(collection_name="test_collection")
-    
-    source.storage = None
-    
-    with pytest.raises(ValueError, match="Storage not initialized"):
-        source.validate_content()
-
-
-def test_custom_storage_knowledge_source_with_knowledge(custom_storage):
-    """Test that a CustomStorageKnowledgeSource can be used with Knowledge."""
-    source = CustomStorageKnowledgeSource(collection_name="test_collection")
-    source.storage = custom_storage
-    
-    with patch.object(KnowledgeStorage, 'initialize_knowledge_storage'):
-        with patch.object(CustomStorageKnowledgeSource, 'add'):
-            knowledge = Knowledge(
-                sources=[source],
-                storage=custom_storage,
-                collection_name="test_collection"
-            )
-    
-    assert knowledge is not None
-    assert knowledge.sources[0] == source
-    assert knowledge.storage == custom_storage
-
-
-def test_custom_storage_knowledge_source_with_crew():
-    """Test that a CustomStorageKnowledgeSource can be used with Crew."""
-    from crewai.agent import Agent
-    from crewai.crew import Crew
-    from crewai.task import Task
-    
-    storage = KnowledgeStorage(collection_name="test_collection")
-    
-    source = CustomStorageKnowledgeSource(collection_name="test_collection")
-    source.storage = storage
-    
-    agent = Agent(role="test", goal="test", backstory="test")
-    task = Task(description="test", expected_output="test", agent=agent)
-    
-    with patch.object(KnowledgeStorage, 'initialize_knowledge_storage'):
-        with patch.object(CustomStorageKnowledgeSource, 'add'):
-            crew = Crew(
-                agents=[agent],
-                tasks=[task],
-                knowledge_sources=[source]
-            )
-    
-    assert crew is not None
-    assert crew.knowledge_sources[0] == source
-
-
-def test_custom_storage_knowledge_source_add_method():
-    """Test that the add method doesn't modify the storage."""
-    source = CustomStorageKnowledgeSource(collection_name="test_collection")
-    storage = MagicMock(spec=KnowledgeStorage)
-    source.storage = storage
-    
-    source.add()
-    
-    storage.assert_not_called()
-
-
-def test_integration_with_existing_storage(temp_dir):
-    """Test integration with an existing storage directory."""
-    storage_path = os.path.join(temp_dir, "test_storage")
-    os.makedirs(storage_path, exist_ok=True)
-    
-    class MockStorage(KnowledgeStorage):
-        def initialize_knowledge_storage(self):
-            self.initialized = True
-    
-    storage = MockStorage(collection_name="test_integration")
-    storage.initialize_knowledge_storage()
-    
-    source = CustomStorageKnowledgeSource(collection_name="test_integration")
-    source.storage = storage
-    
-    source.validate_content()
-    
-    assert hasattr(storage, "initialized")
-    assert storage.initialized is True

tests/utilities/evaluators/test_crew_evaluator_handler.py

@@ -4,6 +4,7 @@ import pytest
 
 from crewai.agent import Agent
 from crewai.crew import Crew
+from crewai.llm import LLM
 from crewai.task import Task
 from crewai.tasks.task_output import TaskOutput
 from crewai.utilities.evaluators.crew_evaluator_handler import (
@@ -23,7 +24,7 @@ class TestCrewEvaluator:
         )
         crew = Crew(agents=[agent], tasks=[task])
 
-        return CrewEvaluator(crew, openai_model_name="gpt-4o-mini")
+        return CrewEvaluator(crew, llm="gpt-4o-mini")
 
     def test_setup_for_evaluating(self, crew_planner):
         crew_planner._setup_for_evaluating()
@@ -46,6 +47,7 @@ class TestCrewEvaluator:
         )
         assert agent.verbose is False
         assert agent.llm.model == "gpt-4o-mini"
+        assert isinstance(agent.llm, LLM)
 
     def test_evaluation_task(self, crew_planner):
         evaluator_agent = Agent(
@@ -131,6 +133,17 @@ class TestCrewEvaluator:
         # Ensure the console prints the table
         console.assert_has_calls([mock.call(), mock.call().print(table())])
 
+    def test_custom_llm_support(self):
+        agent = Agent(role="Agent 1", goal="Goal 1", backstory="Backstory 1")
+        task = Task(description="Task 1", expected_output="Output 1", agent=agent)
+        crew = Crew(agents=[agent], tasks=[task])
+        
+        custom_llm = LLM(model="custom-model")
+        evaluator = CrewEvaluator(crew, llm=custom_llm)
+        
+        assert evaluator.llm.model == "custom-model"
+        assert isinstance(evaluator.llm, LLM)
+
     def test_evaluate(self, crew_planner):
         task_output = TaskOutput(
             description="Task 1", agent=str(crew_planner.crew.agents[0])

uv.lock

@@ -1,10 +1,18 @@
 version = 1
 requires-python = ">=3.10, <3.13"
 resolution-markers = [
-    "python_full_version < '3.11'",
-    "python_full_version == '3.11.*'",
-    "python_full_version >= '3.12' and python_full_version < '3.12.4'",
-    "python_full_version >= '3.12.4'",
+    "python_full_version < '3.11' and sys_platform == 'darwin'",
+    "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "python_full_version == '3.11.*' and sys_platform == 'darwin'",
+    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "python_full_version >= '3.12' and python_full_version < '3.12.4' and sys_platform == 'darwin'",
+    "python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "(python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12' and python_full_version < '3.12.4' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "python_full_version >= '3.12.4' and sys_platform == 'darwin'",
+    "python_full_version >= '3.12.4' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "(python_full_version >= '3.12.4' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12.4' and sys_platform != 'darwin' and sys_platform != 'linux')",
 ]
 
 [[package]]
@@ -300,7 +308,7 @@ name = "build"
 version = "1.2.2.post1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "colorama", marker = "os_name == 'nt'" },
+    { name = "colorama", marker = "(os_name == 'nt' and platform_machine != 'aarch64' and sys_platform == 'linux') or (os_name == 'nt' and sys_platform != 'darwin' and sys_platform != 'linux')" },
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     { name = "packaging" },
     { name = "pyproject-hooks" },
@@ -535,7 +543,7 @@ name = "click"
 version = "8.1.7"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "colorama", marker = "platform_system == 'Windows'" },
+    { name = "colorama", marker = "sys_platform == 'win32'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/96/d3/f04c7bfcf5c1862a2a5b845c6b2b360488cf47af55dfa79c98f6a6bf98b5/click-8.1.7.tar.gz", hash = "sha256:ca9853ad459e787e2192211578cc907e7594e294c7ccc834310722b41b9ca6de", size = 336121 }
 wheels = [
@@ -642,7 +650,6 @@ tools = [
 [package.dev-dependencies]
 dev = [
     { name = "cairosvg" },
-    { name = "crewai-tools" },
     { name = "mkdocs" },
     { name = "mkdocs-material" },
     { name = "mkdocs-material-extensions" },
@@ -696,7 +703,6 @@ requires-dist = [
 [package.metadata.requires-dev]
 dev = [
     { name = "cairosvg", specifier = ">=2.7.1" },
-    { name = "crewai-tools", specifier = ">=0.17.0" },
     { name = "mkdocs", specifier = ">=1.4.3" },
     { name = "mkdocs-material", specifier = ">=9.5.7" },
     { name = "mkdocs-material-extensions", specifier = ">=1.3.1" },
@@ -2462,7 +2468,7 @@ version = "1.6.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "click" },
-    { name = "colorama", marker = "platform_system == 'Windows'" },
+    { name = "colorama", marker = "sys_platform == 'win32'" },
     { name = "ghp-import" },
     { name = "jinja2" },
     { name = "markdown" },
@@ -2643,7 +2649,7 @@ version = "2.10.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "pygments" },
-    { name = "pywin32", marker = "platform_system == 'Windows'" },
+    { name = "pywin32", marker = "sys_platform == 'win32'" },
     { name = "tqdm" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/3a/93/80ac75c20ce54c785648b4ed363c88f148bf22637e10c9863db4fbe73e74/mpire-2.10.2.tar.gz", hash = "sha256:f66a321e93fadff34585a4bfa05e95bd946cf714b442f51c529038eb45773d97", size = 271270 }
@@ -2890,7 +2896,7 @@ name = "nvidia-cudnn-cu12"
 version = "9.1.0.70"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-cublas-cu12", marker = "(platform_machine != 'aarch64' and platform_system != 'Darwin') or (platform_system != 'Darwin' and platform_system != 'Linux')" },
+    { name = "nvidia-cublas-cu12", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
 ]
 wheels = [
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@@ -2917,9 +2923,9 @@ name = "nvidia-cusolver-cu12"
 version = "11.4.5.107"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
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-    { name = "nvidia-cusparse-cu12", marker = "(platform_machine != 'aarch64' and platform_system != 'Darwin') or (platform_system != 'Darwin' and platform_system != 'Linux')" },
-    { name = "nvidia-nvjitlink-cu12", marker = "(platform_machine != 'aarch64' and platform_system != 'Darwin') or (platform_system != 'Darwin' and platform_system != 'Linux')" },
+    { name = "nvidia-cublas-cu12", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
+    { name = "nvidia-cusparse-cu12", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
+    { name = "nvidia-nvjitlink-cu12", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
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 wheels = [
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@@ -2930,7 +2936,7 @@ name = "nvidia-cusparse-cu12"
 version = "12.1.0.106"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-nvjitlink-cu12", marker = "(platform_machine != 'aarch64' and platform_system != 'Darwin') or (platform_system != 'Darwin' and platform_system != 'Linux')" },
+    { name = "nvidia-nvjitlink-cu12", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
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 wheels = [
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@@ -3480,7 +3486,7 @@ name = "portalocker"
 version = "2.10.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "pywin32", marker = "platform_system == 'Windows'" },
+    { name = "pywin32", marker = "sys_platform == 'win32'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/ed/d3/c6c64067759e87af98cc668c1cc75171347d0f1577fab7ca3749134e3cd4/portalocker-2.10.1.tar.gz", hash = "sha256:ef1bf844e878ab08aee7e40184156e1151f228f103aa5c6bd0724cc330960f8f", size = 40891 }
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@@ -5022,19 +5028,19 @@ dependencies = [
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     { name = "jinja2" },
     { name = "networkx" },
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-    { name = "nvidia-cuda-cupti-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" },
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-    { name = "nvidia-nccl-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" },
-    { name = "nvidia-nvtx-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" },
+    { name = "nvidia-cublas-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
+    { name = "nvidia-cuda-cupti-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
+    { name = "nvidia-cuda-nvrtc-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
+    { name = "nvidia-cuda-runtime-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
+    { name = "nvidia-cudnn-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
+    { name = "nvidia-cufft-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
+    { name = "nvidia-curand-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
+    { name = "nvidia-cusolver-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
+    { name = "nvidia-cusparse-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
+    { name = "nvidia-nccl-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
+    { name = "nvidia-nvtx-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
     { name = "sympy" },
-    { name = "triton", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" },
+    { name = "triton", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
     { name = "typing-extensions" },
 ]
 wheels = [
@@ -5081,7 +5087,7 @@ name = "tqdm"
 version = "4.66.5"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "colorama", marker = "platform_system == 'Windows'" },
+    { name = "colorama", marker = "sys_platform == 'win32'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/58/83/6ba9844a41128c62e810fddddd72473201f3eacde02046066142a2d96cc5/tqdm-4.66.5.tar.gz", hash = "sha256:e1020aef2e5096702d8a025ac7d16b1577279c9d63f8375b63083e9a5f0fcbad", size = 169504 }
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 dependencies = [
     { name = "attrs" },
-    { name = "cffi", marker = "implementation_name != 'pypy' and os_name == 'nt'" },
+    { name = "cffi", marker = "(implementation_name != 'pypy' and os_name == 'nt' and platform_machine != 'aarch64' and sys_platform == 'linux') or (implementation_name != 'pypy' and os_name == 'nt' and sys_platform != 'darwin' and sys_platform != 'linux')" },
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@@ -5155,7 +5161,7 @@ name = "triton"
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 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "filelock", marker = "(platform_machine != 'aarch64' and platform_system != 'Darwin') or (platform_system != 'Darwin' and platform_system != 'Linux')" },
+    { name = "filelock", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
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 wheels = [
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