Address PR review feedback: Add error handling, type validation, and edge case tests

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

docs/multiple_model_config.md

@@ -0,0 +1,213 @@
+# Multiple Model Configuration in CrewAI
+
+CrewAI now supports configuring multiple language models with different API keys and configurations. This feature allows you to:
+
+1. Load-balance across multiple model deployments
+2. Set up fallback models in case of rate limits or errors
+3. Configure different routing strategies for model selection
+4. Maintain fine-grained control over model selection and usage
+
+## Basic Usage
+
+You can configure multiple models at the agent level:
+
+```python
+from crewai import Agent
+
+# Define model configurations
+model_list = [
+    {
+        "model_name": "gpt-4o-mini",
+        "litellm_params": {
+            "model": "gpt-4o-mini",  # Required: model name must be specified here
+            "api_key": "your-openai-api-key-1"
+        }
+    },
+    {
+        "model_name": "gpt-3.5-turbo",
+        "litellm_params": {
+            "model": "gpt-3.5-turbo",  # Required: model name must be specified here
+            "api_key": "your-openai-api-key-2"
+        }
+    },
+    {
+        "model_name": "claude-3-sonnet-20240229",
+        "litellm_params": {
+            "model": "claude-3-sonnet-20240229",  # Required: model name must be specified here
+            "api_key": "your-anthropic-api-key"
+        }
+    }
+]
+
+# Create an agent with multiple model configurations
+agent = Agent(
+    role="Data Analyst",
+    goal="Analyze the data and provide insights",
+    backstory="You are an expert data analyst with years of experience.",
+    model_list=model_list,
+    routing_strategy="simple-shuffle"  # Optional routing strategy
+)
+```
+
+## Routing Strategies
+
+CrewAI supports the following routing strategies for precise control over model selection:
+
+- `simple-shuffle`: Randomly selects a model from the list
+- `least-busy`: Routes to the model with the least number of ongoing requests
+- `usage-based`: Routes based on token usage across models
+- `latency-based`: Routes to the model with the lowest latency
+- `cost-based`: Routes to the model with the lowest cost
+
+Example with latency-based routing:
+
+```python
+agent = Agent(
+    role="Data Analyst",
+    goal="Analyze the data and provide insights",
+    backstory="You are an expert data analyst with years of experience.",
+    model_list=model_list,
+    routing_strategy="latency-based"
+)
+```
+
+## Direct LLM Configuration
+
+You can also configure multiple models directly with the LLM class for more flexibility:
+
+```python
+from crewai import LLM
+
+llm = LLM(
+    model="gpt-4o-mini",
+    model_list=model_list,
+    routing_strategy="simple-shuffle"
+)
+```
+
+## Advanced Configuration
+
+For more advanced configurations, you can specify additional parameters for each model to handle complex use cases:
+
+```python
+model_list = [
+    {
+        "model_name": "gpt-4o-mini",
+        "litellm_params": {
+            "model": "gpt-4o-mini",  # Required: model name must be specified here
+            "api_key": "your-openai-api-key-1",
+            "temperature": 0.7
+        },
+        "tpm": 100000,  # Tokens per minute limit
+        "rpm": 1000     # Requests per minute limit
+    },
+    {
+        "model_name": "gpt-3.5-turbo",
+        "litellm_params": {
+            "model": "gpt-3.5-turbo",  # Required: model name must be specified here
+            "api_key": "your-openai-api-key-2",
+            "temperature": 0.5
+        }
+    }
+]
+```
+
+## Error Handling and Troubleshooting
+
+When working with multiple model configurations, you may encounter various issues. Here are some common problems and their solutions:
+
+### Missing Required Parameters
+
+**Problem**: Router initialization fails with an error about missing parameters.
+
+**Solution**: Ensure each model configuration in `model_list` includes both `model_name` and `litellm_params` with the required `model` parameter:
+
+```python
+# Correct configuration
+model_config = {
+    "model_name": "gpt-4o-mini",  # Required
+    "litellm_params": {
+        "model": "gpt-4o-mini",   # Required
+        "api_key": "your-api-key"
+    }
+}
+```
+
+### Invalid Routing Strategy
+
+**Problem**: Error when specifying an unsupported routing strategy.
+
+**Solution**: Use only the supported routing strategies:
+
+```python
+# Valid routing strategies
+valid_strategies = [
+    "simple-shuffle", 
+    "least-busy", 
+    "usage-based", 
+    "latency-based", 
+    "cost-based"
+]
+```
+
+### API Key Authentication Errors
+
+**Problem**: Authentication errors when making API calls.
+
+**Solution**: Verify that all API keys are valid and have the necessary permissions:
+
+```python
+# Check environment variables first
+import os
+os.environ.get("OPENAI_API_KEY")  # Should be set if using OpenAI models
+
+# Or explicitly provide in the configuration
+model_list = [{
+    "model_name": "gpt-4o-mini",
+    "litellm_params": {
+        "model": "gpt-4o-mini",
+        "api_key": "valid-api-key-here"  # Ensure this is correct
+    }
+}]
+```
+
+### Rate Limit Handling
+
+**Problem**: Encountering rate limits with multiple models.
+
+**Solution**: Configure rate limits and implement fallback mechanisms:
+
+```python
+model_list = [
+    {
+        "model_name": "primary-model",
+        "litellm_params": {"model": "primary-model", "api_key": "key1"},
+        "rpm": 100  # Requests per minute
+    },
+    {
+        "model_name": "fallback-model",
+        "litellm_params": {"model": "fallback-model", "api_key": "key2"}
+    }
+]
+
+# Configure with fallback
+llm = LLM(
+    model="primary-model",
+    model_list=model_list,
+    routing_strategy="least-busy"  # Will route to fallback when primary is busy
+)
+```
+
+### Debugging Router Issues
+
+If you're experiencing issues with the router, you can enable verbose logging to get more information:
+
+```python
+import litellm
+litellm.set_verbose = True
+
+# Then initialize your LLM
+llm = LLM(model="gpt-4o-mini", model_list=model_list)
+```
+
+This feature leverages litellm's Router functionality under the hood, providing robust load balancing and fallback capabilities for your CrewAI agents. The implementation ensures predictability and consistency in model selection while maintaining security through proper API key management.

src/crewai/agent.py

@@ -1,9 +1,10 @@
 import os
 import shutil
 import subprocess
+from enum import Enum
 from typing import Any, Dict, List, Literal, Optional, Union
 
-from pydantic import Field, InstanceOf, PrivateAttr, model_validator
+from pydantic import Field, InstanceOf, PrivateAttr, model_validator, field_validator
 
 from crewai.agents import CacheHandler
 from crewai.agents.agent_builder.base_agent import BaseAgent
@@ -86,7 +87,20 @@ class Agent(BaseAgent):
         description="Language model that will run the agent.", default=None
     )
     function_calling_llm: Optional[Any] = Field(
-        description="Language model that will run the agent.", default=None
+        description="Language model that will handle function calling for the agent.", default=None
+    )
+    class RoutingStrategy(str, Enum):
+        SIMPLE_SHUFFLE = "simple-shuffle"
+        LEAST_BUSY = "least-busy"
+        USAGE_BASED = "usage-based"
+        LATENCY_BASED = "latency-based"
+        COST_BASED = "cost-based"
+        
+    model_list: Optional[List[Dict[str, Any]]] = Field(
+        default=None, description="List of model configurations for routing between multiple models."
+    )
+    routing_strategy: Optional[RoutingStrategy] = Field(
+        default=None, description="Strategy for routing between multiple models (e.g., 'simple-shuffle', 'least-busy', 'usage-based', 'latency-based', 'cost-based')."
     )
     system_template: Optional[str] = Field(
         default=None, description="System format for the agent."
@@ -148,10 +162,17 @@ class Agent(BaseAgent):
         # Handle different cases for self.llm
         if isinstance(self.llm, str):
             # If it's a string, create an LLM instance
-            self.llm = LLM(model=self.llm)
+            self.llm = LLM(
+                model=self.llm,
+                model_list=self.model_list,
+                routing_strategy=self.routing_strategy
+            )
         elif isinstance(self.llm, LLM):
             # If it's already an LLM instance, keep it as is
-            pass
+            if self.model_list and not getattr(self.llm, "model_list", None):
+                self.llm.model_list = self.model_list
+                self.llm.routing_strategy = self.routing_strategy
+                self.llm._initialize_router()
         elif self.llm is None:
             # Determine the model name from environment variables or use default
             model_name = (
@@ -159,7 +180,11 @@ class Agent(BaseAgent):
                 or os.environ.get("MODEL")
                 or "gpt-4o-mini"
             )
-            llm_params = {"model": model_name}
+            llm_params = {
+                "model": model_name,
+                "model_list": self.model_list,
+                "routing_strategy": self.routing_strategy
+            }
 
             api_base = os.environ.get("OPENAI_API_BASE") or os.environ.get(
                 "OPENAI_BASE_URL"
@@ -207,6 +232,8 @@ class Agent(BaseAgent):
                 "api_key": getattr(self.llm, "api_key", None),
                 "base_url": getattr(self.llm, "base_url", None),
                 "organization": getattr(self.llm, "organization", None),
+                "model_list": self.model_list,
+                "routing_strategy": self.routing_strategy,
             }
             # Remove None values to avoid passing unnecessary parameters
             llm_params = {k: v for k, v in llm_params.items() if v is not None}
@@ -294,7 +321,14 @@ class Agent(BaseAgent):
             )
 
         if self.crew and self.crew.memory:
-            memory = self.crew.contextual_memory.build_context_for_task(task, context)
+            contextual_memory = ContextualMemory(
+                self.crew.memory_config,
+                self.crew._short_term_memory,
+                self.crew._long_term_memory,
+                self.crew._entity_memory,
+                self.crew._user_memory,
+            )
+            memory = contextual_memory.build_context_for_task(task, context)
             if memory.strip() != "":
                 task_prompt += self.i18n.slice("memory").format(memory=memory)
 

src/crewai/agents/crew_agent_executor.py

@@ -358,9 +358,9 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             if self.crew is not None and hasattr(self.crew, "_train_iteration"):
                 train_iteration = self.crew._train_iteration
                 if agent_id in training_data and isinstance(train_iteration, int):
-                    training_data[agent_id][train_iteration]["improved_output"] = (
-                        result.output
-                    )
+                    training_data[agent_id][train_iteration][
+                        "improved_output"
+                    ] = result.output
                     training_handler.save(training_data)
                 else:
                     self._printer.print(

src/crewai/cli/tools/main.py

@@ -153,12 +153,8 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
         login_response_json = login_response.json()
 
         settings = Settings()
-        settings.tool_repository_username = login_response_json["credential"][
-            "username"
-        ]
-        settings.tool_repository_password = login_response_json["credential"][
-            "password"
-        ]
+        settings.tool_repository_username = login_response_json["credential"]["username"]
+        settings.tool_repository_password = login_response_json["credential"]["password"]
         settings.dump()
 
         console.print(
@@ -183,7 +179,7 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
             capture_output=False,
             env=self._build_env_with_credentials(repository_handle),
             text=True,
-            check=True,
+            check=True
         )
 
         if add_package_result.stderr:
@@ -208,11 +204,7 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
         settings = Settings()
 
         env = os.environ.copy()
-        env[f"UV_INDEX_{repository_handle}_USERNAME"] = str(
-            settings.tool_repository_username or ""
-        )
-        env[f"UV_INDEX_{repository_handle}_PASSWORD"] = str(
-            settings.tool_repository_password or ""
-        )
+        env[f"UV_INDEX_{repository_handle}_USERNAME"] = str(settings.tool_repository_username or "")
+        env[f"UV_INDEX_{repository_handle}_PASSWORD"] = str(settings.tool_repository_password or "")
 
         return env

src/crewai/crew.py

@@ -25,7 +25,6 @@ from crewai.crews.crew_output import CrewOutput
 from crewai.knowledge.knowledge import Knowledge
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.llm import LLM
-from crewai.memory.contextual.contextual_memory import ContextualMemory
 from crewai.memory.entity.entity_memory import EntityMemory
 from crewai.memory.long_term.long_term_memory import LongTermMemory
 from crewai.memory.short_term.short_term_memory import ShortTermMemory
@@ -279,13 +278,6 @@ class Crew(BaseModel):
                 )
             else:
                 self._user_memory = None
-            self.contextual_memory = ContextualMemory(
-                memory_config=self.memory_config,
-                stm=self._short_term_memory,
-                ltm=self._long_term_memory,
-                em=self._entity_memory,
-                um=self._user_memory,
-            )
         return self
 
     @model_validator(mode="after")

src/crewai/knowledge/knowledge.py

@@ -14,13 +14,13 @@ class Knowledge(BaseModel):
     Knowledge is a collection of sources and setup for the vector store to save and query relevant context.
     Args:
         sources: List[BaseKnowledgeSource] = Field(default_factory=list)
-        storage: Optional[KnowledgeStorage] = Field(default=None)
+        storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage)
         embedder_config: Optional[Dict[str, Any]] = None
     """
 
     sources: List[BaseKnowledgeSource] = Field(default_factory=list)
     model_config = ConfigDict(arbitrary_types_allowed=True)
-    storage: Optional[KnowledgeStorage] = Field(default=None)
+    storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage)
     embedder_config: Optional[Dict[str, Any]] = None
     collection_name: Optional[str] = None
 
@@ -49,13 +49,8 @@ class Knowledge(BaseModel):
         """
         Query across all knowledge sources to find the most relevant information.
         Returns the top_k most relevant chunks.
-        
-        Raises:
-            ValueError: If storage is not initialized.
         """
-        if self.storage is None:
-            raise ValueError("Storage is not initialized.")
-            
+
         results = self.storage.search(
             query,
             limit,

src/crewai/knowledge/source/base_file_knowledge_source.py

@@ -22,7 +22,7 @@ class BaseFileKnowledgeSource(BaseKnowledgeSource, ABC):
         default_factory=list, description="The path to the file"
     )
     content: Dict[Path, str] = Field(init=False, default_factory=dict)
-    storage: Optional[KnowledgeStorage] = Field(default=None)
+    storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage)
     safe_file_paths: List[Path] = Field(default_factory=list)
 
     @field_validator("file_path", "file_paths", mode="before")
@@ -62,10 +62,7 @@ class BaseFileKnowledgeSource(BaseKnowledgeSource, ABC):
 
     def _save_documents(self):
         """Save the documents to the storage."""
-        if self.storage:
-            self.storage.save(self.chunks)
-        else:
-            raise ValueError("No storage found to save documents.")
+        self.storage.save(self.chunks)
 
     def convert_to_path(self, path: Union[Path, str]) -> Path:
         """Convert a path to a Path object."""

src/crewai/knowledge/source/base_knowledge_source.py

@@ -16,7 +16,7 @@ class BaseKnowledgeSource(BaseModel, ABC):
     chunk_embeddings: List[np.ndarray] = Field(default_factory=list)
 
     model_config = ConfigDict(arbitrary_types_allowed=True)
-    storage: Optional[KnowledgeStorage] = Field(default=None)
+    storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage)
     metadata: Dict[str, Any] = Field(default_factory=dict)  # Currently unused
     collection_name: Optional[str] = Field(default=None)
 
@@ -46,7 +46,4 @@ class BaseKnowledgeSource(BaseModel, ABC):
         Save the documents to the storage.
         This method should be called after the chunks and embeddings are generated.
         """
-        if self.storage:
-            self.storage.save(self.chunks)
-        else:
-            raise ValueError("No storage found to save documents.")
+        self.storage.save(self.chunks)

src/crewai/llm.py

@@ -7,12 +7,17 @@ from contextlib import contextmanager
 from typing import Any, Dict, List, Optional, Union
 
 import litellm
+from litellm import Router as LiteLLMRouter
 from litellm import get_supported_openai_params
+from tenacity import retry, stop_after_attempt, wait_exponential
 
+from crewai.utilities.logger import Logger
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededException,
 )
 
+logger = Logger(verbose=True)
+
 
 class FilteredStream:
     def __init__(self, original_stream):
@@ -113,6 +118,8 @@ class LLM:
         api_version: Optional[str] = None,
         api_key: Optional[str] = None,
         callbacks: List[Any] = [],
+        model_list: Optional[List[Dict[str, Any]]] = None,
+        routing_strategy: Optional[str] = None,
         **kwargs,
     ):
         self.model = model
@@ -136,11 +143,50 @@ class LLM:
         self.callbacks = callbacks
         self.context_window_size = 0
         self.kwargs = kwargs
+        self.model_list = model_list
+        self.routing_strategy = routing_strategy
+        self.router = None
 
         litellm.drop_params = True
         litellm.set_verbose = False
         self.set_callbacks(callbacks)
         self.set_env_callbacks()
+        
+        if self.model_list:
+            self._initialize_router()
+
+    def _initialize_router(self):
+        """
+        Initialize the litellm Router with the provided model_list and routing_strategy.
+        """
+        try:
+            router_kwargs = {}
+            if self.routing_strategy:
+                valid_strategies = ["simple-shuffle", "least-busy", "usage-based", "latency-based", "cost-based"]
+                if self.routing_strategy not in valid_strategies:
+                    raise ValueError(f"Invalid routing strategy: {self.routing_strategy}. Valid options are: {', '.join(valid_strategies)}")
+                router_kwargs["routing_strategy"] = self.routing_strategy
+                
+            self.router = LiteLLMRouter(
+                model_list=self.model_list,
+                **router_kwargs
+            )
+        except Exception as e:
+            logger.log("error", f"Failed to initialize router: {str(e)}")
+            raise RuntimeError(f"Router initialization failed: {str(e)}")
+            
+    @retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10))
+    def _execute_router_call(self, params):
+        """
+        Execute a call to the router with retry logic for handling transient issues.
+        
+        Args:
+            params: Parameters to pass to the router completion method
+            
+        Returns:
+            The response from the router
+        """
+        return self.router.completion(model=self.model, **params)
 
     def call(self, messages: List[Dict[str, str]], callbacks: List[Any] = []) -> str:
         with suppress_warnings():
@@ -149,7 +195,6 @@ class LLM:
 
             try:
                 params = {
-                    "model": self.model,
                     "messages": messages,
                     "timeout": self.timeout,
                     "temperature": self.temperature,
@@ -164,9 +209,6 @@ class LLM:
                     "seed": self.seed,
                     "logprobs": self.logprobs,
                     "top_logprobs": self.top_logprobs,
-                    "api_base": self.base_url,
-                    "api_version": self.api_version,
-                    "api_key": self.api_key,
                     "stream": False,
                     **self.kwargs,
                 }
@@ -174,7 +216,17 @@ class LLM:
                 # Remove None values to avoid passing unnecessary parameters
                 params = {k: v for k, v in params.items() if v is not None}
 
-                response = litellm.completion(**params)
+                if self.router:
+                    response = self._execute_router_call(params)
+                else:
+                    params.update({
+                        "model": self.model,
+                        "api_base": self.base_url,
+                        "api_version": self.api_version,
+                        "api_key": self.api_key,
+                    })
+                    response = litellm.completion(**params)
+                
                 return response["choices"][0]["message"]["content"]
             except Exception as e:
                 if not LLMContextLengthExceededException(

src/crewai/memory/contextual/contextual_memory.py

@@ -1,5 +1,4 @@
 from typing import Any, Dict, Optional
-from crewai.task import Task
 
 from crewai.memory import EntityMemory, LongTermMemory, ShortTermMemory, UserMemory
 
@@ -11,7 +10,7 @@ class ContextualMemory:
         stm: ShortTermMemory,
         ltm: LongTermMemory,
         em: EntityMemory,
-        um: Optional[UserMemory],
+        um: UserMemory,
     ):
         if memory_config is not None:
             self.memory_provider = memory_config.get("provider")
@@ -22,7 +21,7 @@ class ContextualMemory:
         self.em = em
         self.um = um
 
-    def build_context_for_task(self, task: Task, context: str) -> str:
+    def build_context_for_task(self, task, context) -> str:
         """
         Automatically builds a minimal, highly relevant set of contextual information
         for a given task.
@@ -40,7 +39,7 @@ class ContextualMemory:
             context.append(self._fetch_user_context(query))
         return "\n".join(filter(None, context))
 
-    def _fetch_stm_context(self, query: str) -> str:
+    def _fetch_stm_context(self, query) -> str:
         """
         Fetches recent relevant insights from STM related to the task's description and expected_output,
         formatted as bullet points.
@@ -54,7 +53,7 @@ class ContextualMemory:
         )
         return f"Recent Insights:\n{formatted_results}" if stm_results else ""
 
-    def _fetch_ltm_context(self, task: str) -> Optional[str]:
+    def _fetch_ltm_context(self, task) -> Optional[str]:
         """
         Fetches historical data or insights from LTM that are relevant to the task's description and expected_output,
         formatted as bullet points.
@@ -73,7 +72,7 @@ class ContextualMemory:
 
         return f"Historical Data:\n{formatted_results}" if ltm_results else ""
 
-    def _fetch_entity_context(self, query: str) -> str:
+    def _fetch_entity_context(self, query) -> str:
         """
         Fetches relevant entity information from Entity Memory related to the task's description and expected_output,
         formatted as bullet points.
@@ -95,8 +94,6 @@ class ContextualMemory:
         Returns:
             str: Formatted user memories as bullet points, or an empty string if none found.
         """
-        if not self.um:
-            return ""
         user_memories = self.um.search(query)
         if not user_memories:
             return ""

src/crewai/memory/entity/entity_memory.py

@@ -11,7 +11,7 @@ class EntityMemory(Memory):
     """
 
     def __init__(self, crew=None, embedder_config=None, storage=None, path=None):
-        if crew and hasattr(crew, "memory_config") and crew.memory_config is not None:
+        if hasattr(crew, "memory_config") and crew.memory_config is not None:
             self.memory_provider = crew.memory_config.get("provider")
         else:
             self.memory_provider = None

src/crewai/memory/long_term/long_term_memory.py

@@ -15,17 +15,8 @@ class LongTermMemory(Memory):
     """
 
     def __init__(self, storage=None, path=None):
-        """Initialize long term memory.
-
-        Args:
-            storage: Optional custom storage instance
-            path: Optional custom path for storage location
-
-        Note:
-            If both storage and path are provided, storage takes precedence
-        """
         if not storage:
-            storage = LTMSQLiteStorage(storage_path=path) if path else LTMSQLiteStorage()
+            storage = LTMSQLiteStorage(db_path=path) if path else LTMSQLiteStorage()
         super().__init__(storage)
 
     def save(self, item: LongTermMemoryItem) -> None:  # type: ignore # BUG?: Signature of "save" incompatible with supertype "Memory"

src/crewai/memory/short_term/short_term_memory.py

@@ -15,7 +15,7 @@ class ShortTermMemory(Memory):
     """
 
     def __init__(self, crew=None, embedder_config=None, storage=None, path=None):
-        if crew and hasattr(crew, "memory_config") and crew.memory_config is not None:
+        if hasattr(crew, "memory_config") and crew.memory_config is not None:
             self.memory_provider = crew.memory_config.get("provider")
         else:
             self.memory_provider = None

src/crewai/memory/storage/base_rag_storage.py

@@ -1,11 +1,5 @@
 from abc import ABC, abstractmethod
-from pathlib import Path
-import os
-from typing import Any, Dict, List, Optional, TypeVar
-from abc import ABC, abstractmethod
-from pathlib import Path
-
-from crewai.utilities.paths import get_default_storage_path
+from typing import Any, Dict, List, Optional
 
 
 class BaseRAGStorage(ABC):
@@ -18,46 +12,17 @@ class BaseRAGStorage(ABC):
     def __init__(
         self,
         type: str,
-        storage_path: Optional[Path] = None,
         allow_reset: bool = True,
         embedder_config: Optional[Any] = None,
         crew: Any = None,
-    ) -> None:
-        """Initialize the BaseRAGStorage.
-
-        Args:
-            type: Type of storage being used
-            storage_path: Optional custom path for storage location
-            allow_reset: Whether storage can be reset
-            embedder_config: Optional configuration for the embedder
-            crew: Optional crew instance this storage belongs to
-        
-        Raises:
-            PermissionError: If storage path is not writable
-            OSError: If storage path cannot be created
-        """
+    ):
         self.type = type
-        self.storage_path = storage_path if storage_path else get_default_storage_path('rag')
-        
-        # Validate storage path
-        try:
-            self.storage_path.parent.mkdir(parents=True, exist_ok=True)
-            if not os.access(self.storage_path.parent, os.W_OK):
-                raise PermissionError(f"No write permission for storage path: {self.storage_path}")
-        except OSError as e:
-            raise OSError(f"Failed to initialize storage path: {str(e)}")
-            
         self.allow_reset = allow_reset
         self.embedder_config = embedder_config
         self.crew = crew
         self.agents = self._initialize_agents()
 
     def _initialize_agents(self) -> str:
-        """Initialize agent identifiers for storage.
-        
-        Returns:
-            str: Underscore-joined string of sanitized agent role names
-        """
         if self.crew:
             return "_".join(
                 [self._sanitize_role(agent.role) for agent in self.crew.agents]
@@ -66,27 +31,12 @@ class BaseRAGStorage(ABC):
 
     @abstractmethod
     def _sanitize_role(self, role: str) -> str:
-        """Sanitizes agent roles to ensure valid directory names.
-        
-        Args:
-            role: The agent role name to sanitize
-            
-        Returns:
-            str: Sanitized role name safe for use in paths
-        """
+        """Sanitizes agent roles to ensure valid directory names."""
         pass
 
     @abstractmethod
     def save(self, value: Any, metadata: Dict[str, Any]) -> None:
-        """Save a value with metadata to the storage.
-        
-        Args:
-            value: The value to store
-            metadata: Additional metadata to store with the value
-            
-        Raises:
-            OSError: If there is an error writing to storage
-        """
+        """Save a value with metadata to the storage."""
         pass
 
     @abstractmethod
@@ -96,55 +46,25 @@ class BaseRAGStorage(ABC):
         limit: int = 3,
         filter: Optional[dict] = None,
         score_threshold: float = 0.35,
-    ) -> List[Dict[str, Any]]:
-        """Search for entries in the storage.
-        
-        Args:
-            query: The search query string
-            limit: Maximum number of results to return
-            filter: Optional filter criteria
-            score_threshold: Minimum similarity score threshold
-            
-        Returns:
-            List[Dict[str, Any]]: List of matching entries with their metadata
-        """
+    ) -> List[Any]:
+        """Search for entries in the storage."""
         pass
 
     @abstractmethod
     def reset(self) -> None:
-        """Reset the storage.
-        
-        Raises:
-            OSError: If there is an error clearing storage
-            PermissionError: If reset is not allowed
-        """
+        """Reset the storage."""
         pass
 
     @abstractmethod
     def _generate_embedding(
         self, text: str, metadata: Optional[Dict[str, Any]] = None
-    ) -> List[float]:
-        """Generate an embedding for the given text and metadata.
-        
-        Args:
-            text: Text to generate embedding for
-            metadata: Optional metadata to include in embedding
-            
-        Returns:
-            List[float]: Vector embedding of the text
-            
-        Raises:
-            ValueError: If text is empty or invalid
-        """
+    ) -> Any:
+        """Generate an embedding for the given text and metadata."""
         pass
 
     @abstractmethod
-    def _initialize_app(self) -> None:
-        """Initialize the vector db.
-        
-        Raises:
-            OSError: If vector db initialization fails
-        """
+    def _initialize_app(self):
+        """Initialize the vector db."""
         pass
 
     def setup_config(self, config: Dict[str, Any]):

src/crewai/memory/storage/kickoff_task_outputs_storage.py

@@ -1,13 +1,11 @@
 import json
-import os
 import sqlite3
-from pathlib import Path
 from typing import Any, Dict, List, Optional
 
 from crewai.task import Task
 from crewai.utilities import Printer
 from crewai.utilities.crew_json_encoder import CrewJSONEncoder
-from crewai.utilities.paths import get_default_storage_path
+from crewai.utilities.paths import db_storage_path
 
 
 class KickoffTaskOutputsSQLiteStorage:
@@ -15,26 +13,10 @@ class KickoffTaskOutputsSQLiteStorage:
     An updated SQLite storage class for kickoff task outputs storage.
     """
 
-    def __init__(self, storage_path: Optional[Path] = None) -> None:
-        """Initialize kickoff task outputs storage.
-        
-        Args:
-            storage_path: Optional custom path for storage location
-            
-        Raises:
-            PermissionError: If storage path is not writable
-            OSError: If storage path cannot be created
-        """
-        self.storage_path = storage_path if storage_path else get_default_storage_path('kickoff')
-        
-        # Validate storage path
-        try:
-            self.storage_path.parent.mkdir(parents=True, exist_ok=True)
-            if not os.access(self.storage_path.parent, os.W_OK):
-                raise PermissionError(f"No write permission for storage path: {self.storage_path}")
-        except OSError as e:
-            raise OSError(f"Failed to initialize storage path: {str(e)}")
-            
+    def __init__(
+        self, db_path: str = f"{db_storage_path()}/latest_kickoff_task_outputs.db"
+    ) -> None:
+        self.db_path = db_path
         self._printer: Printer = Printer()
         self._initialize_db()
 
@@ -43,7 +25,7 @@ class KickoffTaskOutputsSQLiteStorage:
         Initializes the SQLite database and creates LTM table
         """
         try:
-            with sqlite3.connect(str(self.storage_path)) as conn:
+            with sqlite3.connect(self.db_path) as conn:
                 cursor = conn.cursor()
                 cursor.execute(
                     """
@@ -73,21 +55,9 @@ class KickoffTaskOutputsSQLiteStorage:
         task_index: int,
         was_replayed: bool = False,
         inputs: Dict[str, Any] = {},
-    ) -> None:
-        """Add a task output to storage.
-        
-        Args:
-            task: The task whose output is being stored
-            output: The output data from the task
-            task_index: Index of this task in the sequence
-            was_replayed: Whether this was from a replay
-            inputs: Optional input data that led to this output
-            
-        Raises:
-            sqlite3.Error: If there is an error saving to database
-        """
+    ):
         try:
-            with sqlite3.connect(str(self.storage_path)) as conn:
+            with sqlite3.connect(self.db_path) as conn:
                 cursor = conn.cursor()
                 cursor.execute(
                     """
@@ -120,7 +90,7 @@ class KickoffTaskOutputsSQLiteStorage:
         Updates an existing row in the latest_kickoff_task_outputs table based on task_index.
         """
         try:
-            with sqlite3.connect(str(self.storage_path)) as conn:
+            with sqlite3.connect(self.db_path) as conn:
                 cursor = conn.cursor()
 
                 fields = []
@@ -149,7 +119,7 @@ class KickoffTaskOutputsSQLiteStorage:
 
     def load(self) -> Optional[List[Dict[str, Any]]]:
         try:
-            with sqlite3.connect(str(self.storage_path)) as conn:
+            with sqlite3.connect(self.db_path) as conn:
                 cursor = conn.cursor()
                 cursor.execute("""
                 SELECT *
@@ -185,7 +155,7 @@ class KickoffTaskOutputsSQLiteStorage:
         Deletes all rows from the latest_kickoff_task_outputs table.
         """
         try:
-            with sqlite3.connect(str(self.storage_path)) as conn:
+            with sqlite3.connect(self.db_path) as conn:
                 cursor = conn.cursor()
                 cursor.execute("DELETE FROM latest_kickoff_task_outputs")
                 conn.commit()

src/crewai/memory/storage/ltm_sqlite_storage.py

@@ -1,11 +1,9 @@
 import json
-import os
 import sqlite3
-from pathlib import Path
 from typing import Any, Dict, List, Optional, Union
 
 from crewai.utilities import Printer
-from crewai.utilities.paths import get_default_storage_path
+from crewai.utilities.paths import db_storage_path
 
 
 class LTMSQLiteStorage:
@@ -13,26 +11,10 @@ class LTMSQLiteStorage:
     An updated SQLite storage class for LTM data storage.
     """
 
-    def __init__(self, storage_path: Optional[Path] = None) -> None:
-        """Initialize LTM SQLite storage.
-        
-        Args:
-            storage_path: Optional custom path for storage location
-            
-        Raises:
-            PermissionError: If storage path is not writable
-            OSError: If storage path cannot be created
-        """
-        self.storage_path = storage_path if storage_path else get_default_storage_path('ltm')
-        
-        # Validate storage path
-        try:
-            self.storage_path.parent.mkdir(parents=True, exist_ok=True)
-            if not os.access(self.storage_path.parent, os.W_OK):
-                raise PermissionError(f"No write permission for storage path: {self.storage_path}")
-        except OSError as e:
-            raise OSError(f"Failed to initialize storage path: {str(e)}")
-            
+    def __init__(
+        self, db_path: str = f"{db_storage_path()}/long_term_memory_storage.db"
+    ) -> None:
+        self.db_path = db_path
         self._printer: Printer = Printer()
         self._initialize_db()
 
@@ -41,7 +23,7 @@ class LTMSQLiteStorage:
         Initializes the SQLite database and creates LTM table
         """
         try:
-            with sqlite3.connect(str(self.storage_path)) as conn:
+            with sqlite3.connect(self.db_path) as conn:
                 cursor = conn.cursor()
                 cursor.execute(
                     """
@@ -69,20 +51,9 @@ class LTMSQLiteStorage:
         datetime: str,
         score: Union[int, float],
     ) -> None:
-        """Save a memory entry to long-term memory.
-        
-        Args:
-            task_description: Description of the task this memory relates to
-            metadata: Additional data to store with the memory
-            datetime: Timestamp for when this memory was created
-            score: Relevance score for this memory (higher is more relevant)
-            
-        Raises:
-            sqlite3.Error: If there is an error saving to the database
-        """
         """Saves data to the LTM table with error handling."""
         try:
-            with sqlite3.connect(str(self.storage_path)) as conn:
+            with sqlite3.connect(self.db_path) as conn:
                 cursor = conn.cursor()
                 cursor.execute(
                     """
@@ -103,7 +74,7 @@ class LTMSQLiteStorage:
     ) -> Optional[List[Dict[str, Any]]]:
         """Queries the LTM table by task description with error handling."""
         try:
-            with sqlite3.connect(str(self.storage_path)) as conn:
+            with sqlite3.connect(self.db_path) as conn:
                 cursor = conn.cursor()
                 cursor.execute(
                     f"""
@@ -138,7 +109,7 @@ class LTMSQLiteStorage:
     ) -> None:
         """Resets the LTM table with error handling."""
         try:
-            with sqlite3.connect(str(self.storage_path)) as conn:
+            with sqlite3.connect(self.db_path) as conn:
                 cursor = conn.cursor()
                 cursor.execute("DELETE FROM long_term_memories")
                 conn.commit()

src/crewai/memory/storage/mem0_storage.py

@@ -19,7 +19,7 @@ class Mem0Storage(Storage):
 
         self.memory_type = type
         self.crew = crew
-        self.memory_config = crew.memory_config if crew else None
+        self.memory_config = crew.memory_config
 
         # User ID is required for user memory type "user" since it's used as a unique identifier for the user.
         user_id = self._get_user_id()
@@ -27,10 +27,9 @@ class Mem0Storage(Storage):
             raise ValueError("User ID is required for user memory type")
 
         # API key in memory config overrides the environment variable
-        if self.memory_config and self.memory_config.get("config"):
-            mem0_api_key = self.memory_config.get("config").get("api_key")
-        else:
-            mem0_api_key = os.getenv("MEM0_API_KEY")
+        mem0_api_key = self.memory_config.get("config", {}).get("api_key") or os.getenv(
+            "MEM0_API_KEY"
+        )
         self.memory = MemoryClient(api_key=mem0_api_key)
 
     def _sanitize_role(self, role: str) -> str:

src/crewai/memory/storage/rag_storage.py

@@ -11,6 +11,7 @@ from chromadb.api import ClientAPI
 from crewai.memory.storage.base_rag_storage import BaseRAGStorage
 from crewai.utilities import EmbeddingConfigurator
 from crewai.utilities.constants import MAX_FILE_NAME_LENGTH
+from crewai.utilities.paths import db_storage_path
 
 
 @contextlib.contextmanager
@@ -39,15 +40,9 @@ class RAGStorage(BaseRAGStorage):
     app: ClientAPI | None = None
 
     def __init__(
-        self,
-        type,
-        storage_path=None,
-        allow_reset=True,
-        embedder_config=None,
-        crew=None,
-        path=None,
+        self, type, allow_reset=True, embedder_config=None, crew=None, path=None
     ):
-        super().__init__(type, storage_path, allow_reset, embedder_config, crew)
+        super().__init__(type, allow_reset, embedder_config, crew)
         agents = crew.agents if crew else []
         agents = [self._sanitize_role(agent.role) for agent in agents]
         agents = "_".join(agents)
@@ -95,7 +90,7 @@ class RAGStorage(BaseRAGStorage):
         """
         Ensures file name does not exceed max allowed by OS
         """
-        base_path = f"{self.storage_path}/{type}"
+        base_path = f"{db_storage_path()}/{type}"
 
         if len(file_name) > MAX_FILE_NAME_LENGTH:
             logging.warning(
@@ -157,7 +152,7 @@ class RAGStorage(BaseRAGStorage):
         try:
             if self.app:
                 self.app.reset()
-                shutil.rmtree(f"{self.storage_path}/{self.type}")
+                shutil.rmtree(f"{db_storage_path()}/{self.type}")
                 self.app = None
                 self.collection = None
         except Exception as e:

src/crewai/project/annotations.py

@@ -66,6 +66,7 @@ def cache_handler(func):
 
 
 def crew(func) -> Callable[..., Crew]:
+
     @wraps(func)
     def wrapper(self, *args, **kwargs) -> Crew:
         instantiated_tasks = []

src/crewai/project/crew_base.py

@@ -216,5 +216,5 @@ def CrewBase(cls: T) -> T:
     # Include base class (qual)name in the wrapper class (qual)name.
     WrappedClass.__name__ = CrewBase.__name__ + "(" + cls.__name__ + ")"
     WrappedClass.__qualname__ = CrewBase.__qualname__ + "(" + cls.__name__ + ")"
-
+  
     return cast(T, WrappedClass)

src/crewai/task.py

@@ -373,9 +373,7 @@ class Task(BaseModel):
             content = (
                 json_output
                 if json_output
-                else pydantic_output.model_dump_json()
-                if pydantic_output
-                else result
+                else pydantic_output.model_dump_json() if pydantic_output else result
             )
             self._save_file(content)
 

src/crewai/utilities/embedding_configurator.py

@@ -27,7 +27,7 @@ class EmbeddingConfigurator:
         if embedder_config is None:
             return self._create_default_embedding_function()
 
-        provider = embedder_config.get("provider", "")
+        provider = embedder_config.get("provider")
         config = embedder_config.get("config", {})
         model_name = config.get("model")
 
@@ -38,13 +38,12 @@ class EmbeddingConfigurator:
             except Exception as e:
                 raise ValueError(f"Invalid custom embedding function: {str(e)}")
 
-        embedding_function = self.embedding_functions.get(provider, None)
-        if not embedding_function:
+        if provider not in self.embedding_functions:
             raise Exception(
                 f"Unsupported embedding provider: {provider}, supported providers: {list(self.embedding_functions.keys())}"
             )
 
-        return embedding_function(config, model_name)
+        return self.embedding_functions[provider](config, model_name)
 
     @staticmethod
     def _create_default_embedding_function():

src/crewai/utilities/paths.py

@@ -22,26 +22,3 @@ def get_project_directory_name():
         cwd = Path.cwd()
         project_directory_name = cwd.name
         return project_directory_name
-
-def get_default_storage_path(storage_type: str) -> Path:
-    """Returns the default storage path for a given storage type.
-    
-    Args:
-        storage_type: Type of storage ('ltm', 'kickoff', 'rag')
-        
-    Returns:
-        Path: Default storage path for the specified type
-        
-    Raises:
-        ValueError: If storage_type is not recognized
-    """
-    base_path = db_storage_path()
-    
-    if storage_type == 'ltm':
-        return base_path / 'latest_long_term_memories.db'
-    elif storage_type == 'kickoff':
-        return base_path / 'latest_kickoff_task_outputs.db'
-    elif storage_type == 'rag':
-        return base_path
-    else:
-        raise ValueError(f"Unknown storage type: {storage_type}")

tests/cli/tools/test_main.py

@@ -28,10 +28,9 @@ def test_create_success(mock_subprocess):
     with in_temp_dir():
         tool_command = ToolCommand()
 
-        with (
-            patch.object(tool_command, "login") as mock_login,
-            patch("sys.stdout", new=StringIO()) as fake_out,
-        ):
+        with patch.object(tool_command, "login") as mock_login, patch(
+            "sys.stdout", new=StringIO()
+        ) as fake_out:
             tool_command.create("test-tool")
             output = fake_out.getvalue()
 
@@ -83,7 +82,7 @@ def test_install_success(mock_get, mock_subprocess_run):
         capture_output=False,
         text=True,
         check=True,
-        env=unittest.mock.ANY,
+        env=unittest.mock.ANY
     )
 
     assert "Successfully installed sample-tool" in output

tests/memory/test_storage_paths.py

@@ -1,83 +0,0 @@
-import os
-import tempfile
-from pathlib import Path
-import pytest
-from unittest.mock import patch
-
-from crewai.memory.storage.ltm_sqlite_storage import LTMSQLiteStorage
-from crewai.memory.storage.kickoff_task_outputs_storage import KickoffTaskOutputsSQLiteStorage
-from crewai.memory.storage.base_rag_storage import BaseRAGStorage
-from crewai.utilities.paths import get_default_storage_path
-
-class MockRAGStorage(BaseRAGStorage):
-    """Mock implementation of BaseRAGStorage for testing."""
-    def _sanitize_role(self, role: str) -> str:
-        return role.lower()
-    
-    def save(self, value, metadata):
-        pass
-    
-    def search(self, query, limit=3, filter=None, score_threshold=0.35):
-        return []
-    
-    def reset(self):
-        pass
-    
-    def _generate_embedding(self, text, metadata=None):
-        return []
-    
-    def _initialize_app(self):
-        pass
-
-def test_default_storage_paths():
-    """Test that default storage paths are created correctly."""
-    ltm_path = get_default_storage_path('ltm')
-    kickoff_path = get_default_storage_path('kickoff')
-    rag_path = get_default_storage_path('rag')
-    
-    assert str(ltm_path).endswith('latest_long_term_memories.db')
-    assert str(kickoff_path).endswith('latest_kickoff_task_outputs.db')
-    assert isinstance(rag_path, Path)
-
-def test_custom_storage_paths():
-    """Test that custom storage paths are respected."""
-    with tempfile.TemporaryDirectory() as temp_dir:
-        custom_path = Path(temp_dir) / 'custom.db'
-        
-        ltm = LTMSQLiteStorage(storage_path=custom_path)
-        assert ltm.storage_path == custom_path
-        
-        kickoff = KickoffTaskOutputsSQLiteStorage(storage_path=custom_path)
-        assert kickoff.storage_path == custom_path
-        
-        rag = MockRAGStorage('test', storage_path=custom_path)
-        assert rag.storage_path == custom_path
-
-def test_directory_creation():
-    """Test that storage directories are created automatically."""
-    with tempfile.TemporaryDirectory() as temp_dir:
-        test_dir = Path(temp_dir) / 'test_storage'
-        storage_path = test_dir / 'test.db'
-        
-        assert not test_dir.exists()
-        LTMSQLiteStorage(storage_path=storage_path)
-        assert test_dir.exists()
-
-def test_permission_error():
-    """Test that permission errors are handled correctly."""
-    with tempfile.TemporaryDirectory() as temp_dir:
-        test_dir = Path(temp_dir) / 'readonly'
-        test_dir.mkdir()
-        os.chmod(test_dir, 0o444)  # Read-only
-        
-        storage_path = test_dir / 'test.db'
-        with pytest.raises((PermissionError, OSError)) as exc_info:
-            LTMSQLiteStorage(storage_path=storage_path)
-        # Verify that the error message mentions permission
-        assert "permission" in str(exc_info.value).lower()
-
-def test_invalid_path():
-    """Test that invalid paths raise appropriate errors."""
-    with pytest.raises(OSError):
-        # Try to create storage in a non-existent root directory
-        LTMSQLiteStorage(storage_path=Path('/nonexistent/dir/test.db'))

tests/multiple_model_config_test.py

@@ -0,0 +1,246 @@
+import pytest
+from unittest.mock import patch, MagicMock
+
+from crewai.llm import LLM
+from crewai.agent import Agent
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+@patch("litellm.Router")
+@patch.object(LLM, '_initialize_router')
+def test_llm_with_model_list(mock_initialize_router, mock_router):
+    """Test that LLM can be initialized with a model_list for multiple model configurations."""
+    mock_initialize_router.return_value = None
+    
+    mock_router_instance = MagicMock()
+    mock_router.return_value = mock_router_instance
+    
+    model_list = [
+        {
+            "model_name": "gpt-4o-mini",
+            "litellm_params": {
+                "model": "gpt-4o-mini",
+                "api_key": "test-key-1"
+            }
+        },
+        {
+            "model_name": "gpt-3.5-turbo",
+            "litellm_params": {
+                "model": "gpt-3.5-turbo",
+                "api_key": "test-key-2"
+            }
+        }
+    ]
+    
+    llm = LLM(model="gpt-4o-mini", model_list=model_list)
+    llm.router = mock_router_instance
+    
+    assert llm.model == "gpt-4o-mini"
+    assert llm.model_list == model_list
+    assert llm.router is not None
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+@patch("litellm.Router")
+@patch.object(LLM, '_initialize_router')
+def test_llm_with_routing_strategy(mock_initialize_router, mock_router):
+    """Test that LLM can be initialized with a routing strategy."""
+    mock_initialize_router.return_value = None
+    
+    mock_router_instance = MagicMock()
+    mock_router.return_value = mock_router_instance
+    
+    model_list = [
+        {
+            "model_name": "gpt-4o-mini",
+            "litellm_params": {
+                "model": "gpt-4o-mini",
+                "api_key": "test-key-1"
+            }
+        },
+        {
+            "model_name": "gpt-3.5-turbo",
+            "litellm_params": {
+                "model": "gpt-3.5-turbo",
+                "api_key": "test-key-2"
+            }
+        }
+    ]
+    
+    llm = LLM(
+        model="gpt-4o-mini", 
+        model_list=model_list, 
+        routing_strategy="simple-shuffle"
+    )
+    llm.router = mock_router_instance
+    
+    assert llm.routing_strategy == "simple-shuffle"
+    assert llm.router is not None
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+@patch("litellm.Router")
+@patch.object(LLM, '_initialize_router')
+def test_agent_with_model_list(mock_initialize_router, mock_router):
+    """Test that Agent can be initialized with a model_list for multiple model configurations."""
+    mock_initialize_router.return_value = None
+    
+    mock_router_instance = MagicMock()
+    mock_router.return_value = mock_router_instance
+    
+    model_list = [
+        {
+            "model_name": "gpt-4o-mini",
+            "litellm_params": {
+                "model": "gpt-4o-mini",
+                "api_key": "test-key-1"
+            }
+        },
+        {
+            "model_name": "gpt-3.5-turbo",
+            "litellm_params": {
+                "model": "gpt-3.5-turbo",
+                "api_key": "test-key-2"
+            }
+        }
+    ]
+    
+    with patch.object(Agent, 'post_init_setup', wraps=Agent.post_init_setup) as mock_post_init:
+        agent = Agent(
+            role="test",
+            goal="test",
+            backstory="test",
+            model_list=model_list
+        )
+        
+        agent.llm.router = mock_router_instance
+        
+        assert agent.model_list == model_list
+        assert agent.llm.model_list == model_list
+        assert agent.llm.router is not None
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+@patch("litellm.Router")
+@patch.object(LLM, '_initialize_router')
+def test_llm_call_with_router(mock_initialize_router, mock_router):
+    """Test that LLM.call uses the router when model_list is provided."""
+    mock_initialize_router.return_value = None
+    
+    mock_router_instance = MagicMock()
+    mock_router.return_value = mock_router_instance
+    
+    mock_response = {
+        "choices": [{"message": {"content": "Test response"}}]
+    }
+    mock_router_instance.completion.return_value = mock_response
+    
+    model_list = [
+        {
+            "model_name": "gpt-4o-mini",
+            "litellm_params": {
+                "model": "gpt-4o-mini",
+                "api_key": "test-key-1"
+            }
+        }
+    ]
+    
+    # Create LLM with model_list
+    llm = LLM(model="gpt-4o-mini", model_list=model_list)
+    
+    llm.router = mock_router_instance
+    
+    messages = [{"role": "user", "content": "Hello"}]
+    response = llm.call(messages)
+    
+    mock_router_instance.completion.assert_called_once()
+    assert response == "Test response"
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+@patch("litellm.completion")
+def test_llm_call_without_router(mock_completion):
+    """Test that LLM.call uses litellm.completion when no model_list is provided."""
+    mock_response = {
+        "choices": [{"message": {"content": "Test response"}}]
+    }
+    mock_completion.return_value = mock_response
+    
+    llm = LLM(model="gpt-4o-mini")
+    
+    messages = [{"role": "user", "content": "Hello"}]
+    response = llm.call(messages)
+    
+    mock_completion.assert_called_once()
+    assert response == "Test response"
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_llm_with_invalid_routing_strategy():
+    """Test that LLM initialization raises an error with an invalid routing strategy."""
+    model_list = [
+        {
+            "model_name": "gpt-4o-mini",
+            "litellm_params": {
+                "model": "gpt-4o-mini",
+                "api_key": "test-key-1"
+            }
+        }
+    ]
+    
+    with pytest.raises(RuntimeError) as exc_info:
+        LLM(
+            model="gpt-4o-mini", 
+            model_list=model_list, 
+            routing_strategy="invalid-strategy"
+        )
+    
+    assert "Invalid routing strategy" in str(exc_info.value)
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_agent_with_invalid_routing_strategy():
+    """Test that Agent initialization raises an error with an invalid routing strategy."""
+    model_list = [
+        {
+            "model_name": "gpt-4o-mini",
+            "litellm_params": {
+                "model": "gpt-4o-mini",
+                "api_key": "test-key-1"
+            }
+        }
+    ]
+    
+    with pytest.raises(Exception) as exc_info:
+        Agent(
+            role="test",
+            goal="test",
+            backstory="test",
+            model_list=model_list,
+            routing_strategy="invalid-strategy"
+        )
+    
+    assert "Input should be" in str(exc_info.value)
+    assert "simple-shuffle" in str(exc_info.value)
+    assert "least-busy" in str(exc_info.value)
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+@patch.object(LLM, '_initialize_router')
+def test_llm_with_missing_model_in_litellm_params(mock_initialize_router):
+    """Test that LLM initialization raises an error when model is missing in litellm_params."""
+    mock_initialize_router.side_effect = RuntimeError("Router initialization failed: Missing required 'model' in litellm_params")
+    
+    model_list = [
+        {
+            "model_name": "gpt-4o-mini",
+            "litellm_params": {
+                "api_key": "test-key-1"
+            }
+        }
+    ]
+    
+    with pytest.raises(RuntimeError) as exc_info:
+        LLM(model="gpt-4o-mini", model_list=model_list)
+    
+    assert "Router initialization failed" in str(exc_info.value)

uv.lock

@@ -1,18 +1,10 @@
 version = 1
 requires-python = ">=3.10, <3.13"
 resolution-markers = [
-    "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')",
+    "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'",
 ]
 
 [[package]]
@@ -308,7 +300,7 @@ name = "build"
 version = "1.2.2.post1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { 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')" },
+    { name = "colorama", marker = "os_name == 'nt'" },
     { name = "importlib-metadata", marker = "python_full_version < '3.10.2'" },
     { name = "packaging" },
     { name = "pyproject-hooks" },
@@ -543,7 +535,7 @@ name = "click"
 version = "8.1.7"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "colorama", marker = "sys_platform == 'win32'" },
+    { name = "colorama", marker = "platform_system == 'Windows'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/96/d3/f04c7bfcf5c1862a2a5b845c6b2b360488cf47af55dfa79c98f6a6bf98b5/click-8.1.7.tar.gz", hash = "sha256:ca9853ad459e787e2192211578cc907e7594e294c7ccc834310722b41b9ca6de", size = 336121 }
 wheels = [
@@ -650,6 +642,7 @@ tools = [
 [package.dev-dependencies]
 dev = [
     { name = "cairosvg" },
+    { name = "crewai-tools" },
     { name = "mkdocs" },
     { name = "mkdocs-material" },
     { name = "mkdocs-material-extensions" },
@@ -703,6 +696,7 @@ 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" },
@@ -2468,7 +2462,7 @@ version = "1.6.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "click" },
-    { name = "colorama", marker = "sys_platform == 'win32'" },
+    { name = "colorama", marker = "platform_system == 'Windows'" },
     { name = "ghp-import" },
     { name = "jinja2" },
     { name = "markdown" },
@@ -2649,7 +2643,7 @@ version = "2.10.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "pygments" },
-    { name = "pywin32", marker = "sys_platform == 'win32'" },
+    { name = "pywin32", marker = "platform_system == 'Windows'" },
     { name = "tqdm" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/3a/93/80ac75c20ce54c785648b4ed363c88f148bf22637e10c9863db4fbe73e74/mpire-2.10.2.tar.gz", hash = "sha256:f66a321e93fadff34585a4bfa05e95bd946cf714b442f51c529038eb45773d97", size = 271270 }
@@ -2896,7 +2890,7 @@ 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 sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
+    { name = "nvidia-cublas-cu12", marker = "(platform_machine != 'aarch64' and platform_system != 'Darwin') or (platform_system != 'Darwin' and platform_system != 'Linux')" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/9f/fd/713452cd72343f682b1c7b9321e23829f00b842ceaedcda96e742ea0b0b3/nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl", hash = "sha256:165764f44ef8c61fcdfdfdbe769d687e06374059fbb388b6c89ecb0e28793a6f", size = 664752741 },
@@ -2923,9 +2917,9 @@ name = "nvidia-cusolver-cu12"
 version = "11.4.5.107"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { 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')" },
+    { name = "nvidia-cublas-cu12", marker = "(platform_machine != 'aarch64' and platform_system != 'Darwin') or (platform_system != 'Darwin' and platform_system != 'Linux')" },
+    { 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')" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/bc/1d/8de1e5c67099015c834315e333911273a8c6aaba78923dd1d1e25fc5f217/nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl", hash = "sha256:8a7ec542f0412294b15072fa7dab71d31334014a69f953004ea7a118206fe0dd", size = 124161928 },
@@ -2936,7 +2930,7 @@ 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 sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
+    { name = "nvidia-nvjitlink-cu12", marker = "(platform_machine != 'aarch64' and platform_system != 'Darwin') or (platform_system != 'Darwin' and platform_system != 'Linux')" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/65/5b/cfaeebf25cd9fdec14338ccb16f6b2c4c7fa9163aefcf057d86b9cc248bb/nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl", hash = "sha256:f3b50f42cf363f86ab21f720998517a659a48131e8d538dc02f8768237bd884c", size = 195958278 },
@@ -3486,7 +3480,7 @@ name = "portalocker"
 version = "2.10.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "pywin32", marker = "sys_platform == 'win32'" },
+    { name = "pywin32", marker = "platform_system == 'Windows'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/ed/d3/c6c64067759e87af98cc668c1cc75171347d0f1577fab7ca3749134e3cd4/portalocker-2.10.1.tar.gz", hash = "sha256:ef1bf844e878ab08aee7e40184156e1151f228f103aa5c6bd0724cc330960f8f", size = 40891 }
 wheels = [
@@ -5028,19 +5022,19 @@ dependencies = [
     { name = "fsspec" },
     { name = "jinja2" },
     { name = "networkx" },
-    { 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 = "nvidia-cublas-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" },
+    { name = "nvidia-cuda-cupti-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" },
+    { name = "nvidia-cuda-nvrtc-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" },
+    { name = "nvidia-cuda-runtime-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" },
+    { name = "nvidia-cudnn-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" },
+    { name = "nvidia-cufft-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" },
+    { name = "nvidia-curand-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" },
+    { name = "nvidia-cusolver-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" },
+    { name = "nvidia-cusparse-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" },
+    { 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 = "sympy" },
-    { name = "triton", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
+    { name = "triton", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" },
     { name = "typing-extensions" },
 ]
 wheels = [
@@ -5087,7 +5081,7 @@ name = "tqdm"
 version = "4.66.5"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "colorama", marker = "sys_platform == 'win32'" },
+    { name = "colorama", marker = "platform_system == 'Windows'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/58/83/6ba9844a41128c62e810fddddd72473201f3eacde02046066142a2d96cc5/tqdm-4.66.5.tar.gz", hash = "sha256:e1020aef2e5096702d8a025ac7d16b1577279c9d63f8375b63083e9a5f0fcbad", size = 169504 }
 wheels = [
@@ -5130,7 +5124,7 @@ version = "0.27.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "attrs" },
-    { 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')" },
+    { name = "cffi", marker = "implementation_name != 'pypy' and os_name == 'nt'" },
     { name = "exceptiongroup", marker = "python_full_version < '3.11'" },
     { name = "idna" },
     { name = "outcome" },
@@ -5161,7 +5155,7 @@ name = "triton"
 version = "3.0.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "filelock", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
+    { name = "filelock", marker = "(platform_machine != 'aarch64' and platform_system != 'Darwin') or (platform_system != 'Darwin' and platform_system != 'Linux')" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/45/27/14cc3101409b9b4b9241d2ba7deaa93535a217a211c86c4cc7151fb12181/triton-3.0.0-1-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:e1efef76935b2febc365bfadf74bcb65a6f959a9872e5bddf44cc9e0adce1e1a", size = 209376304 },