refactor: Add logging and improve error handling

- Add Logger class usage
- Add descriptive error messages
- Add edge case test for memory reset failure
- Add docstrings to RAGStorage methods

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

docs/concepts/knowledge.mdx

@@ -91,7 +91,7 @@ result = crew.kickoff(inputs={"question": "What city does John live in and how o
 ```
 
 
-Here's another example with the `CrewDoclingSource`. The CrewDoclingSource is actually quite versatile and can handle multiple file formats including TXT, PDF, DOCX, HTML, and more. 
+Here's another example with the `CrewDoclingSource`. The CrewDoclingSource is actually quite versatile and can handle multiple file formats including MD, PDF, DOCX, HTML, and more. 
 
 <Note>
   You need to install `docling` for the following example to work: `uv add docling`
@@ -152,10 +152,10 @@ Here are examples of how to use different types of knowledge sources:
 
 ### Text File Knowledge Source
 ```python
-from crewai.knowledge.source.crew_docling_source import CrewDoclingSource
+from crewai.knowledge.source.text_file_knowledge_source import TextFileKnowledgeSource
 
 # Create a text file knowledge source
-text_source = CrewDoclingSource(
+text_source = TextFileKnowledgeSource(
     file_paths=["document.txt", "another.txt"]
 )
 

docs/concepts/memory.mdx

@@ -282,6 +282,19 @@ my_crew = Crew(
 
 ### Using Google AI embeddings
 
+#### Prerequisites
+Before using Google AI embeddings, ensure you have:
+- Access to the Gemini API
+- The necessary API keys and permissions
+
+You will need to update your *pyproject.toml* dependencies:
+```YAML
+dependencies = [
+    "google-generativeai>=0.8.4", #main version in January/2025 - crewai v.0.100.0 and crewai-tools 0.33.0
+    "crewai[tools]>=0.100.0,<1.0.0"
+]
+```
+
 ```python Code
 from crewai import Crew, Agent, Task, Process
 
@@ -434,6 +447,38 @@ my_crew = Crew(
 )
 ```
 
+### Using Amazon Bedrock embeddings
+
+```python Code
+# Note: Ensure you have installed `boto3` for Bedrock embeddings to work.
+
+import os
+import boto3
+from crewai import Crew, Agent, Task, Process
+
+boto3_session = boto3.Session(
+    region_name=os.environ.get("AWS_REGION_NAME"),
+    aws_access_key_id=os.environ.get("AWS_ACCESS_KEY_ID"),
+    aws_secret_access_key=os.environ.get("AWS_SECRET_ACCESS_KEY")
+)
+
+my_crew = Crew(
+    agents=[...],
+    tasks=[...],
+    process=Process.sequential,
+    memory=True,
+    embedder={
+    "provider": "bedrock",
+        "config":{
+            "session": boto3_session,
+            "model": "amazon.titan-embed-text-v2:0",
+            "vector_dimension": 1024
+        }
+    }
+    verbose=True
+)
+```
+
 ### Adding Custom Embedding Function
 
 ```python Code

docs/concepts/tasks.mdx

@@ -268,7 +268,7 @@ analysis_task = Task(
 
 Task guardrails provide a way to validate and transform task outputs before they
 are passed to the next task. This feature helps ensure data quality and provides
-efeedback to agents when their output doesn't meet specific criteria.
+feedback to agents when their output doesn't meet specific criteria.
 
 ### Using Task Guardrails
 

docs/how-to/langfuse-observability.mdx

@@ -0,0 +1,98 @@
+---
+title: Agent Monitoring with Langfuse
+description: Learn how to integrate Langfuse with CrewAI via OpenTelemetry using OpenLit
+icon: magnifying-glass-chart
+---
+
+# Integrate Langfuse with CrewAI
+
+This notebook demonstrates how to integrate **Langfuse** with **CrewAI** using OpenTelemetry via the **OpenLit** SDK. By the end of this notebook, you will be able to trace your CrewAI applications with Langfuse for improved observability and debugging.
+
+> **What is Langfuse?** [Langfuse](https://langfuse.com) is an open-source LLM engineering platform. It provides tracing and monitoring capabilities for LLM applications, helping developers debug, analyze, and optimize their AI systems. Langfuse integrates with various tools and frameworks via native integrations, OpenTelemetry, and APIs/SDKs.
+
+## Get Started
+
+We'll walk through a simple example of using CrewAI and integrating it with Langfuse via OpenTelemetry using OpenLit.
+
+### Step 1: Install Dependencies
+
+
+```python
+%pip install langfuse openlit crewai crewai_tools
+```
+
+### Step 2: Set Up Environment Variables
+
+Set your Langfuse API keys and configure OpenTelemetry export settings to send traces to Langfuse. Please refer to the [Langfuse OpenTelemetry Docs](https://langfuse.com/docs/opentelemetry/get-started) for more information on the Langfuse OpenTelemetry endpoint `/api/public/otel` and authentication.
+
+
+```python
+import os
+import base64
+
+LANGFUSE_PUBLIC_KEY="pk-lf-..."
+LANGFUSE_SECRET_KEY="sk-lf-..."
+LANGFUSE_AUTH=base64.b64encode(f"{LANGFUSE_PUBLIC_KEY}:{LANGFUSE_SECRET_KEY}".encode()).decode()
+
+os.environ["OTEL_EXPORTER_OTLP_ENDPOINT"] = "https://cloud.langfuse.com/api/public/otel" # EU data region
+# os.environ["OTEL_EXPORTER_OTLP_ENDPOINT"] = "https://us.cloud.langfuse.com/api/public/otel" # US data region
+os.environ["OTEL_EXPORTER_OTLP_HEADERS"] = f"Authorization=Basic {LANGFUSE_AUTH}"
+
+# your openai key
+os.environ["OPENAI_API_KEY"] = "sk-..."
+```
+
+### Step 3: Initialize OpenLit
+
+Initialize the OpenLit OpenTelemetry instrumentation SDK to start capturing OpenTelemetry traces.
+
+
+```python
+import openlit
+
+openlit.init()
+```
+
+### Step 4: Create a Simple CrewAI Application
+
+We'll create a simple CrewAI application where multiple agents collaborate to answer a user's question.
+
+
+```python
+from crewai import Agent, Task, Crew
+
+from crewai_tools import (
+    WebsiteSearchTool
+)
+
+web_rag_tool = WebsiteSearchTool()
+
+writer = Agent(
+        role="Writer",
+        goal="You make math engaging and understandable for young children through poetry",
+        backstory="You're an expert in writing haikus but you know nothing of math.",
+        tools=[web_rag_tool],  
+    )
+
+task = Task(description=("What is {multiplication}?"),
+            expected_output=("Compose a haiku that includes the answer."),
+            agent=writer)
+
+crew = Crew(
+  agents=[writer],
+  tasks=[task],
+  share_crew=False
+)
+```
+
+### Step 5: See Traces in Langfuse
+
+After running the agent, you can view the traces generated by your CrewAI application in [Langfuse](https://cloud.langfuse.com). You should see detailed steps of the LLM interactions, which can help you debug and optimize your AI agent.
+
+![CrewAI example trace in Langfuse](https://langfuse.com/images/cookbook/integration_crewai/crewai-example-trace.png)
+
+_[Public example trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/e2cf380ffc8d47d28da98f136140642b?timestamp=2025-02-05T15%3A12%3A02.717Z&observation=3b32338ee6a5d9af)_
+
+## References
+
+- [Langfuse OpenTelemetry Docs](https://langfuse.com/docs/opentelemetry/get-started)

docs/how-to/portkey-observability-and-guardrails.mdx

@@ -1,211 +0,0 @@
-# Portkey Integration with CrewAI
-<img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/main/Portkey-CrewAI.png" alt="Portkey CrewAI Header Image" width="70%" />
-
-
-[Portkey](https://portkey.ai/?utm_source=crewai&utm_medium=crewai&utm_campaign=crewai) is a 2-line upgrade to make your CrewAI agents reliable, cost-efficient, and fast.
-
-Portkey adds 4 core production capabilities to any CrewAI agent:
-1. Routing to **200+ LLMs**
-2. Making each LLM call more robust
-3. Full-stack tracing & cost, performance analytics
-4. Real-time guardrails to enforce behavior
-
-
-
-
-
-## Getting Started
-
-1. **Install Required Packages:**
-
-```bash
-pip install -qU crewai portkey-ai
-```
-
-2. **Configure the LLM Client:**
-
-To build CrewAI Agents with Portkey, you'll need two keys:
-- **Portkey API Key**: Sign up on the [Portkey app](https://app.portkey.ai/?utm_source=crewai&utm_medium=crewai&utm_campaign=crewai) and copy your API key
-- **Virtual Key**: Virtual Keys securely manage your LLM API keys in one place. Store your LLM provider API keys securely in Portkey's vault
-
-```python
-from crewai import LLM
-from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL
-
-gpt_llm = LLM(
-    model="gpt-4",
-    base_url=PORTKEY_GATEWAY_URL,
-    api_key="dummy", # We are using Virtual key
-    extra_headers=createHeaders(
-        api_key="YOUR_PORTKEY_API_KEY",
-        virtual_key="YOUR_VIRTUAL_KEY", # Enter your Virtual key from Portkey
-    )
-)
-```
-
-3. **Create and Run Your First Agent:**
-
-```python
-from crewai import Agent, Task, Crew
-
-# Define your agents with roles and goals
-coder = Agent(
-    role='Software developer',
-    goal='Write clear, concise code on demand',
-    backstory='An expert coder with a keen eye for software trends.',
-    llm=gpt_llm
-)
-
-# Create tasks for your agents
-task1 = Task(
-    description="Define the HTML for making a simple website with heading- Hello World! Portkey is working!",
-    expected_output="A clear and concise HTML code",
-    agent=coder
-)
-
-# Instantiate your crew
-crew = Crew(
-    agents=[coder],
-    tasks=[task1],
-)
-
-result = crew.kickoff()
-print(result)
-```
-
-
-## Key Features
-
-| Feature | Description |
-|---------|-------------|
-| 🌐 Multi-LLM Support | Access OpenAI, Anthropic, Gemini, Azure, and 250+ providers through a unified interface |
-| 🛡️ Production Reliability | Implement retries, timeouts, load balancing, and fallbacks |
-| 📊 Advanced Observability | Track 40+ metrics including costs, tokens, latency, and custom metadata |
-| 🔍 Comprehensive Logging | Debug with detailed execution traces and function call logs |
-| 🚧 Security Controls | Set budget limits and implement role-based access control |
-| 🔄 Performance Analytics | Capture and analyze feedback for continuous improvement |
-| 💾 Intelligent Caching | Reduce costs and latency with semantic or simple caching |
-
-
-## Production Features with Portkey Configs
-
-All features mentioned below are through Portkey's Config system. Portkey's Config system allows you to define routing strategies using simple JSON objects in your LLM API calls. You can create and manage Configs directly in your code or through the Portkey Dashboard. Each Config has a unique ID for easy reference.
-
-<Frame>
-    <img src="https://raw.githubusercontent.com/Portkey-AI/docs-core/refs/heads/main/images/libraries/libraries-3.avif"/>
-</Frame>
-
-
-### 1. Use 250+ LLMs
-Access various LLMs like Anthropic, Gemini, Mistral, Azure OpenAI, and more with minimal code changes. Switch between providers or use them together seamlessly. [Learn more about Universal API](https://portkey.ai/docs/product/ai-gateway/universal-api)
-
-
-Easily switch between different LLM providers:
-
-```python
-# Anthropic Configuration
-anthropic_llm = LLM(
-    model="claude-3-5-sonnet-latest",
-    base_url=PORTKEY_GATEWAY_URL,
-    api_key="dummy",
-    extra_headers=createHeaders(
-        api_key="YOUR_PORTKEY_API_KEY",
-        virtual_key="YOUR_ANTHROPIC_VIRTUAL_KEY", #You don't need provider when using Virtual keys
-        trace_id="anthropic_agent"
-    )
-)
-
-# Azure OpenAI Configuration
-azure_llm = LLM(
-    model="gpt-4",
-    base_url=PORTKEY_GATEWAY_URL,
-    api_key="dummy",
-    extra_headers=createHeaders(
-        api_key="YOUR_PORTKEY_API_KEY",
-        virtual_key="YOUR_AZURE_VIRTUAL_KEY", #You don't need provider when using Virtual keys
-        trace_id="azure_agent"
-    )
-)
-```
-
-
-### 2. Caching
-Improve response times and reduce costs with two powerful caching modes:
-- **Simple Cache**: Perfect for exact matches
-- **Semantic Cache**: Matches responses for requests that are semantically similar
-[Learn more about Caching](https://portkey.ai/docs/product/ai-gateway/cache-simple-and-semantic)
-
-```py
-config = {
-    "cache": {
-        "mode": "semantic",  # or "simple" for exact matching
-    }
-}
-```
-
-### 3. Production Reliability
-Portkey provides comprehensive reliability features:
-- **Automatic Retries**: Handle temporary failures gracefully
-- **Request Timeouts**: Prevent hanging operations
-- **Conditional Routing**: Route requests based on specific conditions
-- **Fallbacks**: Set up automatic provider failovers
-- **Load Balancing**: Distribute requests efficiently
-
-[Learn more about Reliability Features](https://portkey.ai/docs/product/ai-gateway/)
-
-
-
-### 4. Metrics
-
-Agent runs are complex. Portkey automatically logs **40+ comprehensive metrics** for your AI agents, including cost, tokens used, latency, etc. Whether you need a broad overview or granular insights into your agent runs, Portkey's customizable filters provide the metrics you need.
-
-
-- Cost per agent interaction
-- Response times and latency
-- Token usage and efficiency
-- Success/failure rates
-- Cache hit rates
-
-<img src="https://github.com/siddharthsambharia-portkey/Portkey-Product-Images/blob/main/Portkey-Dashboard.png?raw=true" width="70%" alt="Portkey Dashboard" />
-
-### 5. Detailed Logging
-Logs are essential for understanding agent behavior, diagnosing issues, and improving performance. They provide a detailed record of agent activities and tool use, which is crucial for debugging and optimizing processes.
-
-
-Access a dedicated section to view records of agent executions, including parameters, outcomes, function calls, and errors. Filter logs based on multiple parameters such as trace ID, model, tokens used, and metadata.
-
-<details>
-  <summary><b>Traces</b></summary>
-  <img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/main/Portkey-Traces.png" alt="Portkey Traces" width="70%" />
-</details>
-
-<details>
-  <summary><b>Logs</b></summary>
-  <img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/main/Portkey-Logs.png" alt="Portkey Logs" width="70%" />
-</details>
-
-### 6. Enterprise Security Features
-- Set budget limit and rate limts per Virtual Key (disposable API keys)
-- Implement role-based access control
-- Track system changes with audit logs
-- Configure data retention policies
-
-
-
-For detailed information on creating and managing Configs, visit the [Portkey documentation](https://docs.portkey.ai/product/ai-gateway/configs).
-
-## Resources
-
-- [📘 Portkey Documentation](https://docs.portkey.ai)
-- [📊 Portkey Dashboard](https://app.portkey.ai/?utm_source=crewai&utm_medium=crewai&utm_campaign=crewai)
-- [🐦 Twitter](https://twitter.com/portkeyai)
-- [💬 Discord Community](https://discord.gg/DD7vgKK299)
-
-
-
-
-
-
-
-
-

docs/how-to/portkey-observability.mdx

@@ -1,5 +1,5 @@
 ---
-title: Portkey Observability and Guardrails
+title: Agent Monitoring with Portkey
 description: How to use Portkey with CrewAI
 icon: key
 ---

docs/memory.md

@@ -1,45 +0,0 @@
-# Memory in CrewAI
-
-CrewAI provides a robust memory system that allows agents to retain and recall information from previous interactions.
-
-## Configuring Embedding Providers
-
-CrewAI supports multiple embedding providers for memory functionality:
-
-- OpenAI (default) - Requires `OPENAI_API_KEY`
-- Ollama - Requires `CREWAI_OLLAMA_URL` (defaults to "http://localhost:11434/api/embeddings")
-
-### Environment Variables
-
-Configure the embedding provider using these environment variables:
-
-- `CREWAI_EMBEDDING_PROVIDER`: Provider name (default: "openai")
-- `CREWAI_EMBEDDING_MODEL`: Model name (default: "text-embedding-3-small")
-- `CREWAI_OLLAMA_URL`: URL for Ollama API (when using Ollama provider)
-
-### Example Configuration
-
-```python
-# Using OpenAI (default)
-os.environ["OPENAI_API_KEY"] = "your-api-key"
-
-# Using Ollama
-os.environ["CREWAI_EMBEDDING_PROVIDER"] = "ollama"
-os.environ["CREWAI_EMBEDDING_MODEL"] = "llama2"  # or any other model supported by your Ollama instance
-os.environ["CREWAI_OLLAMA_URL"] = "http://localhost:11434/api/embeddings"  # optional, this is the default
-```
-
-## Memory Usage
-
-When an agent has memory enabled, it can access and store information from previous interactions:
-
-```python
-agent = Agent(
-    role="Researcher",
-    goal="Research AI topics",
-    backstory="You're an AI researcher",
-    memory=True  # Enable memory for this agent
-)
-```
-
-The memory system uses embeddings to store and retrieve relevant information, allowing agents to maintain context across multiple interactions and tasks.

docs/mint.json

@@ -103,7 +103,8 @@
         "how-to/langtrace-observability",
         "how-to/mlflow-observability",
         "how-to/openlit-observability",
-        "how-to/portkey-observability"
+        "how-to/portkey-observability",
+        "how-to/langfuse-observability"
       ]
     },
     {

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "crewai"
-version = "0.100.1"
+version = "0.102.0"
 description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks."
 readme = "README.md"
 requires-python = ">=3.10,<3.13"
@@ -45,7 +45,7 @@ Documentation = "https://docs.crewai.com"
 Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
-tools = ["crewai-tools>=0.32.1"]
+tools = ["crewai-tools>=0.36.0"]
 embeddings = [
     "tiktoken~=0.7.0"
 ]

src/crewai/__init__.py

@@ -14,7 +14,7 @@ warnings.filterwarnings(
     category=UserWarning,
     module="pydantic.main",
 )
-__version__ = "0.100.1"
+__version__ = "0.102.0"
 __all__ = [
     "Agent",
     "Crew",

src/crewai/agent.py

@@ -1,14 +1,13 @@
-import os
+import re
 import shutil
 import subprocess
-from typing import Any, Dict, List, Literal, Optional, Union
+from typing import Any, Dict, List, Literal, Optional, Sequence, Union
 
 from pydantic import Field, InstanceOf, PrivateAttr, model_validator
 
 from crewai.agents import CacheHandler
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.crew_agent_executor import CrewAgentExecutor
-from crewai.cli.constants import ENV_VARS, LITELLM_PARAMS
 from crewai.knowledge.knowledge import Knowledge
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.knowledge.utils.knowledge_utils import extract_knowledge_context
@@ -17,10 +16,10 @@ from crewai.memory.contextual.contextual_memory import ContextualMemory
 from crewai.task import Task
 from crewai.tools import BaseTool
 from crewai.tools.agent_tools.agent_tools import AgentTools
-from crewai.tools.base_tool import Tool
 from crewai.utilities import Converter, Prompts
 from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_FILE
 from crewai.utilities.converter import generate_model_description
+from crewai.utilities.llm_utils import create_llm
 from crewai.utilities.token_counter_callback import TokenCalcHandler
 from crewai.utilities.training_handler import CrewTrainingHandler
 
@@ -55,13 +54,13 @@ class Agent(BaseAgent):
             llm: The language model that will run the agent.
             function_calling_llm: The language model that will handle the tool calling for this agent, it overrides the crew function_calling_llm.
             max_iter: Maximum number of iterations for an agent to execute a task.
-            memory: Whether the agent should have memory or not.
             max_rpm: Maximum number of requests per minute for the agent execution to be respected.
             verbose: Whether the agent execution should be in verbose mode.
             allow_delegation: Whether the agent is allowed to delegate tasks to other agents.
             tools: Tools at agents disposal
             step_callback: Callback to be executed after each step of the agent execution.
             knowledge_sources: Knowledge sources for the agent.
+            embedder: Embedder configuration for the agent.
     """
 
     _times_executed: int = PrivateAttr(default=0)
@@ -71,9 +70,6 @@ class Agent(BaseAgent):
     )
     agent_ops_agent_name: str = None  # type: ignore # Incompatible types in assignment (expression has type "None", variable has type "str")
     agent_ops_agent_id: str = None  # type: ignore # Incompatible types in assignment (expression has type "None", variable has type "str")
-    cache_handler: InstanceOf[CacheHandler] = Field(
-        default=None, description="An instance of the CacheHandler class."
-    )
     step_callback: Optional[Any] = Field(
         default=None,
         description="Callback to be executed after each step of the agent execution.",
@@ -85,7 +81,7 @@ class Agent(BaseAgent):
     llm: Union[str, InstanceOf[LLM], Any] = Field(
         description="Language model that will run the agent.", default=None
     )
-    function_calling_llm: Optional[Any] = Field(
+    function_calling_llm: Optional[Union[str, InstanceOf[LLM], Any]] = Field(
         description="Language model that will run the agent.", default=None
     )
     system_template: Optional[str] = Field(
@@ -107,10 +103,6 @@ class Agent(BaseAgent):
         default=True,
         description="Keep messages under the context window size by summarizing content.",
     )
-    max_iter: int = Field(
-        default=20,
-        description="Maximum number of iterations for an agent to execute a task before giving it's best answer",
-    )
     max_retry_limit: int = Field(
         default=2,
         description="Maximum number of retries for an agent to execute a task when an error occurs.",
@@ -123,105 +115,19 @@ class Agent(BaseAgent):
         default="safe",
         description="Mode for code execution: 'safe' (using Docker) or 'unsafe' (direct execution).",
     )
-    embedder_config: Optional[Dict[str, Any]] = Field(
+    embedder: Optional[Dict[str, Any]] = Field(
         default=None,
         description="Embedder configuration for the agent.",
     )
-    knowledge_sources: Optional[List[BaseKnowledgeSource]] = Field(
-        default=None,
-        description="Knowledge sources for the agent.",
-    )
-    _knowledge: Optional[Knowledge] = PrivateAttr(
-        default=None,
-    )
 
     @model_validator(mode="after")
     def post_init_setup(self):
         self._set_knowledge()
         self.agent_ops_agent_name = self.role
-        unaccepted_attributes = [
-            "AWS_ACCESS_KEY_ID",
-            "AWS_SECRET_ACCESS_KEY",
-            "AWS_REGION_NAME",
-        ]
 
-        # 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)
-        elif isinstance(self.llm, LLM):
-            # If it's already an LLM instance, keep it as is
-            pass
-        elif self.llm is None:
-            # Determine the model name from environment variables or use default
-            model_name = (
-                os.environ.get("OPENAI_MODEL_NAME")
-                or os.environ.get("MODEL")
-                or "gpt-4o-mini"
-            )
-            llm_params = {"model": model_name}
-
-            api_base = os.environ.get("OPENAI_API_BASE") or os.environ.get(
-                "OPENAI_BASE_URL"
-            )
-            if api_base:
-                llm_params["base_url"] = api_base
-
-            set_provider = model_name.split("/")[0] if "/" in model_name else "openai"
-
-            # Iterate over all environment variables to find matching API keys or use defaults
-            for provider, env_vars in ENV_VARS.items():
-                if provider == set_provider:
-                    for env_var in env_vars:
-                        # Check if the environment variable is set
-                        key_name = env_var.get("key_name")
-                        if key_name and key_name not in unaccepted_attributes:
-                            env_value = os.environ.get(key_name)
-                            if env_value:
-                                key_name = key_name.lower()
-                                for pattern in LITELLM_PARAMS:
-                                    if pattern in key_name:
-                                        key_name = pattern
-                                        break
-                                llm_params[key_name] = env_value
-                        # Check for default values if the environment variable is not set
-                        elif env_var.get("default", False):
-                            for key, value in env_var.items():
-                                if key not in ["prompt", "key_name", "default"]:
-                                    # Only add default if the key is already set in os.environ
-                                    if key in os.environ:
-                                        llm_params[key] = value
-
-            self.llm = LLM(**llm_params)
-        else:
-            # For any other type, attempt to extract relevant attributes
-            llm_params = {
-                "model": getattr(self.llm, "model_name", None)
-                or getattr(self.llm, "deployment_name", None)
-                or str(self.llm),
-                "temperature": getattr(self.llm, "temperature", None),
-                "max_tokens": getattr(self.llm, "max_tokens", None),
-                "logprobs": getattr(self.llm, "logprobs", None),
-                "timeout": getattr(self.llm, "timeout", None),
-                "max_retries": getattr(self.llm, "max_retries", None),
-                "api_key": getattr(self.llm, "api_key", None),
-                "base_url": getattr(self.llm, "base_url", None),
-                "organization": getattr(self.llm, "organization", None),
-            }
-            # Remove None values to avoid passing unnecessary parameters
-            llm_params = {k: v for k, v in llm_params.items() if v is not None}
-            self.llm = LLM(**llm_params)
-
-        # Similar handling for function_calling_llm
-        if self.function_calling_llm:
-            if isinstance(self.function_calling_llm, str):
-                self.function_calling_llm = LLM(model=self.function_calling_llm)
-            elif not isinstance(self.function_calling_llm, LLM):
-                self.function_calling_llm = LLM(
-                    model=getattr(self.function_calling_llm, "model_name", None)
-                    or getattr(self.function_calling_llm, "deployment_name", None)
-                    or str(self.function_calling_llm)
-                )
+        self.llm = create_llm(self.llm)
+        if self.function_calling_llm and not isinstance(self.function_calling_llm, LLM):
+            self.function_calling_llm = create_llm(self.function_calling_llm)
 
         if not self.agent_executor:
             self._setup_agent_executor()
@@ -239,23 +145,16 @@ class Agent(BaseAgent):
     def _set_knowledge(self):
         try:
             if self.knowledge_sources:
-                knowledge_agent_name = f"{self.role.replace(' ', '_')}"
+                full_pattern = re.compile(r"[^a-zA-Z0-9\-_\r\n]|(\.\.)")
+                knowledge_agent_name = f"{re.sub(full_pattern, '_', self.role)}"
                 if isinstance(self.knowledge_sources, list) and all(
                     isinstance(k, BaseKnowledgeSource) for k in self.knowledge_sources
                 ):
-                    # Validate embedding configuration based on provider
-                    from crewai.utilities.constants import DEFAULT_EMBEDDING_PROVIDER
-                    provider = os.getenv("CREWAI_EMBEDDING_PROVIDER", DEFAULT_EMBEDDING_PROVIDER)
-                    
-                    if provider == "openai" and not os.getenv("OPENAI_API_KEY"):
-                        raise ValueError("Please provide an OpenAI API key via OPENAI_API_KEY environment variable")
-                    elif provider == "ollama" and not os.getenv("CREWAI_OLLAMA_URL", "http://localhost:11434/api/embeddings"):
-                        raise ValueError("Please provide Ollama URL via CREWAI_OLLAMA_URL environment variable")
-                    
-                    self._knowledge = Knowledge(
+                    self.knowledge = Knowledge(
                         sources=self.knowledge_sources,
-                        embedder_config=self.embedder_config,
+                        embedder=self.embedder,
                         collection_name=knowledge_agent_name,
+                        storage=self.knowledge_storage or None,
                     )
         except (TypeError, ValueError) as e:
             raise ValueError(f"Invalid Knowledge Configuration: {str(e)}")
@@ -289,13 +188,15 @@ class Agent(BaseAgent):
             if task.output_json:
                 # schema = json.dumps(task.output_json, indent=2)
                 schema = generate_model_description(task.output_json)
+                task_prompt += "\n" + self.i18n.slice(
+                    "formatted_task_instructions"
+                ).format(output_format=schema)
 
             elif task.output_pydantic:
                 schema = generate_model_description(task.output_pydantic)
-
-            task_prompt += "\n" + self.i18n.slice("formatted_task_instructions").format(
-                output_format=schema
-            )
+                task_prompt += "\n" + self.i18n.slice(
+                    "formatted_task_instructions"
+                ).format(output_format=schema)
 
         if context:
             task_prompt = self.i18n.slice("task_with_context").format(
@@ -314,8 +215,8 @@ class Agent(BaseAgent):
             if memory.strip() != "":
                 task_prompt += self.i18n.slice("memory").format(memory=memory)
 
-        if self._knowledge:
-            agent_knowledge_snippets = self._knowledge.query([task.prompt()])
+        if self.knowledge:
+            agent_knowledge_snippets = self.knowledge.query([task.prompt()])
             if agent_knowledge_snippets:
                 agent_knowledge_context = extract_knowledge_context(
                     agent_knowledge_snippets
@@ -348,6 +249,9 @@ class Agent(BaseAgent):
                 }
             )["output"]
         except Exception as e:
+            if e.__class__.__module__.startswith("litellm"):
+                # Do not retry on litellm errors
+                raise e
             self._times_executed += 1
             if self._times_executed > self.max_retry_limit:
                 raise e
@@ -420,13 +324,14 @@ class Agent(BaseAgent):
         tools = agent_tools.tools()
         return tools
 
-    def get_multimodal_tools(self) -> List[Tool]:
+    def get_multimodal_tools(self) -> Sequence[BaseTool]:
         from crewai.tools.agent_tools.add_image_tool import AddImageTool
+
         return [AddImageTool()]
 
     def get_code_execution_tools(self):
         try:
-            from crewai_tools import CodeInterpreterTool
+            from crewai_tools import CodeInterpreterTool  # type: ignore
 
             # Set the unsafe_mode based on the code_execution_mode attribute
             unsafe_mode = self.code_execution_mode == "unsafe"

src/crewai/agents/agent_builder/base_agent.py

@@ -113,7 +113,7 @@ class BaseAgent(ABC, BaseModel):
         description="Enable agent to delegate and ask questions among each other.",
     )
     tools: Optional[List[Any]] = Field(
-        default_factory=lambda: [], description="Tools at agents' disposal"
+        default_factory=list, description="Tools at agents' disposal"
     )
     max_iter: int = Field(
         default=25, description="Maximum iterations for an agent to execute a task"

src/crewai/cli/reset_memories_command.py

@@ -1,64 +1,114 @@
+"""CLI command for resetting memory storage."""
+import logging
 import subprocess
+import sys
+from typing import Optional
 
 import click
 
 from crewai.cli.utils import get_crew
+from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
+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
+from crewai.utilities.task_output_storage_handler import TaskOutputStorageHandler
+
+_logger = logging.getLogger(__name__)
 
 
+def _log_error(message: str) -> None:
+    """Log an error message."""
+    _logger.error(message)
+    click.echo(message, err=True)
+
+
+def _reset_all_memories() -> None:
+    """Reset all memory types."""
+    ShortTermMemory().reset()
+    EntityMemory().reset()
+    LongTermMemory().reset()
+    TaskOutputStorageHandler().reset()
+    KnowledgeStorage().reset()
+
+
+@click.command()
+@click.option("-l", "--long", is_flag=True, help="Reset long-term memory")
+@click.option("-s", "--short", is_flag=True, help="Reset short-term memory")
+@click.option("-e", "--entity", is_flag=True, help="Reset entity memory")
+@click.option("--knowledge", is_flag=True, help="Reset knowledge")
+@click.option("-k", "--kickoff-outputs", is_flag=True, help="Reset kickoff outputs")
+@click.option("-a", "--all", is_flag=True, help="Reset all memories")
 def reset_memories_command(
-    long,
-    short,
-    entity,
-    knowledge,
-    kickoff_outputs,
-    all,
-) -> None:
+    long: bool,
+    short: bool,
+    entity: bool,
+    knowledge: bool,
+    kickoff_outputs: bool,
+    all: bool,
+) -> int:
     """
     Reset the crew memories.
 
     Args:
-      long (bool): Whether to reset the long-term memory.
-      short (bool): Whether to reset the short-term memory.
-      entity (bool): Whether to reset the entity memory.
-      kickoff_outputs (bool): Whether to reset the latest kickoff task outputs.
-      all (bool): Whether to reset all memories.
-      knowledge (bool): Whether to reset the knowledge.
+        long: Reset long-term memory
+        short: Reset short-term memory
+        entity: Reset entity memory
+        knowledge: Reset knowledge
+        kickoff_outputs: Reset kickoff outputs
+        all: Reset all memories
     """
-
     try:
         crew = get_crew()
-        if not crew:
-            raise ValueError("No crew found.")
         if all:
-            crew.reset_memories(command_type="all")
+            if crew:
+                crew.reset_memories(command_type="all")
+            else:
+                # When no crew exists, use default storage paths
+                _reset_all_memories()
             click.echo("All memories have been reset.")
-            return
+            return 0
 
         if not any([long, short, entity, kickoff_outputs, knowledge]):
             click.echo(
-                "No memory type specified. Please specify at least one type to reset."
+                "Please specify at least one memory type to reset using the appropriate flags."
             )
-            return
+            return 0
 
-        if long:
-            crew.reset_memories(command_type="long")
-            click.echo("Long term memory has been reset.")
-        if short:
-            crew.reset_memories(command_type="short")
-            click.echo("Short term memory has been reset.")
-        if entity:
-            crew.reset_memories(command_type="entity")
-            click.echo("Entity memory has been reset.")
-        if kickoff_outputs:
-            crew.reset_memories(command_type="kickoff_outputs")
-            click.echo("Latest Kickoff outputs stored has been reset.")
-        if knowledge:
-            crew.reset_memories(command_type="knowledge")
-            click.echo("Knowledge has been reset.")
+        if not crew:
+            click.echo("No crew found. Use --all to reset all memories.")
+            return 0
+
+        try:
+            if long:
+                crew.reset_memories(command_type="long")
+                click.echo("Long term memory has been reset.")
+
+            if short:
+                crew.reset_memories(command_type="short")
+                click.echo("Short term memory has been reset.")
+
+            if entity:
+                crew.reset_memories(command_type="entity")
+                click.echo("Entity memory has been reset.")
+
+            if kickoff_outputs:
+                crew.reset_memories(command_type="kickoff_outputs")
+                click.echo("Latest Kickoff outputs stored has been reset.")
+
+            if knowledge:
+                crew.reset_memories(command_type="knowledge")
+                click.echo("Knowledge has been reset.")
+
+            return 0
+        except Exception as e:
+            _log_error(f"An unexpected error occurred: {e}")
+            raise click.exceptions.Exit(code=1)
 
     except subprocess.CalledProcessError as e:
-        click.echo(f"An error occurred while resetting the memories: {e}", err=True)
+        _log_error(f"An error occurred while resetting the memories: {e}")
         click.echo(e.output, err=True)
+        raise click.exceptions.Exit(code=1)
 
     except Exception as e:
-        click.echo(f"An unexpected error occurred: {e}", err=True)
+        _log_error(f"An unexpected error occurred: {e}")
+        raise click.exceptions.Exit(code=1)

src/crewai/cli/templates/crew/main.py

@@ -56,7 +56,8 @@ def test():
     Test the crew execution and returns the results.
     """
     inputs = {
-        "topic": "AI LLMs"
+        "topic": "AI LLMs",
+        "current_year": str(datetime.now().year)
     }
     try:
         {{crew_name}}().crew().test(n_iterations=int(sys.argv[1]), openai_model_name=sys.argv[2], inputs=inputs)

src/crewai/cli/templates/crew/pyproject.toml

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.100.1,<1.0.0"
+    "crewai[tools]>=0.102.0,<1.0.0"
 ]
 
 [project.scripts]

src/crewai/cli/templates/flow/pyproject.toml

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.100.1,<1.0.0",
+    "crewai[tools]>=0.102.0,<1.0.0",
 ]
 
 [project.scripts]

src/crewai/cli/templates/tool/pyproject.toml

@@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.100.1"
+    "crewai[tools]>=0.102.0"
 ]
 
 [tool.crewai]

src/crewai/crew.py

@@ -1,10 +1,12 @@
 import asyncio
 import json
+import re
 import uuid
 import warnings
 from concurrent.futures import Future
+from copy import copy as shallow_copy
 from hashlib import md5
-from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
+from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union
 
 from pydantic import (
     UUID4,
@@ -16,6 +18,7 @@ from pydantic import (
     field_validator,
     model_validator,
 )
+from pydantic_core import PydanticCustomError
 
 from crewai.agent import Agent
 from crewai.agents.agent_builder.base_agent import BaseAgent

src/crewai/crews/crew_output.py

@@ -1,9 +1,7 @@
 import json
-from typing import Any, Callable, Dict, Optional
+from typing import Any, Dict, Optional
 
 from pydantic import BaseModel, Field
-from pydantic.main import IncEx
-from typing_extensions import Literal
 
 from crewai.tasks.output_format import OutputFormat
 from crewai.tasks.task_output import TaskOutput
@@ -23,45 +21,16 @@ class CrewOutput(BaseModel):
     tasks_output: list[TaskOutput] = Field(
         description="Output of each task", default=[]
     )
-    token_usage: UsageMetrics = Field(description="Processed token summary", default_factory=UsageMetrics)
+    token_usage: UsageMetrics = Field(description="Processed token summary", default={})
 
-    def model_json(self) -> str:
-        """Get the JSON representation of the output."""
-        if self.tasks_output and self.tasks_output[-1].output_format != OutputFormat.JSON:
+    @property
+    def json(self) -> Optional[str]:
+        if self.tasks_output[-1].output_format != OutputFormat.JSON:
             raise ValueError(
                 "No JSON output found in the final task. Please make sure to set the output_json property in the final task in your crew."
             )
-        return json.dumps(self.json_dict) if self.json_dict else "{}"
 
-    def model_dump_json(
-        self,
-        *,
-        indent: Optional[int] = None,
-        include: Optional[IncEx] = None,
-        exclude: Optional[IncEx] = None,
-        context: Optional[Any] = None,
-        by_alias: bool = False,
-        exclude_unset: bool = False,
-        exclude_defaults: bool = False,
-        exclude_none: bool = False,
-        round_trip: bool = False,
-        warnings: bool | Literal["none", "warn", "error"] = False,
-        serialize_as_any: bool = False,
-    ) -> str:
-        """Override model_dump_json to handle custom JSON output."""
-        return super().model_dump_json(
-            indent=indent,
-            include=include,
-            exclude=exclude,
-            context=context,
-            by_alias=by_alias,
-            exclude_unset=exclude_unset,
-            exclude_defaults=exclude_defaults,
-            exclude_none=exclude_none,
-            round_trip=round_trip,
-            warnings=warnings,
-            serialize_as_any=serialize_as_any,
-        )
+        return json.dumps(self.json_dict)
 
     def to_dict(self) -> Dict[str, Any]:
         """Convert json_output and pydantic_output to a dictionary."""

src/crewai/flow/flow.py

@@ -1,4 +1,5 @@
 import asyncio
+import copy
 import inspect
 import logging
 from typing import (
@@ -394,7 +395,6 @@ class FlowMeta(type):
                 or hasattr(attr_value, "__trigger_methods__")
                 or hasattr(attr_value, "__is_router__")
             ):
-
                 # Register start methods
                 if hasattr(attr_value, "__is_start_method__"):
                     start_methods.append(attr_name)
@@ -569,6 +569,9 @@ class Flow(Generic[T], metaclass=FlowMeta):
             f"Initial state must be dict or BaseModel, got {type(self.initial_state)}"
         )
 
+    def _copy_state(self) -> T:
+        return copy.deepcopy(self._state)
+
     @property
     def state(self) -> T:
         return self._state
@@ -740,6 +743,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
             event=FlowStartedEvent(
                 type="flow_started",
                 flow_name=self.__class__.__name__,
+                inputs=inputs,
             ),
         )
         self._log_flow_event(
@@ -803,6 +807,18 @@ class Flow(Generic[T], metaclass=FlowMeta):
     async def _execute_method(
         self, method_name: str, method: Callable, *args: Any, **kwargs: Any
     ) -> Any:
+        dumped_params = {f"_{i}": arg for i, arg in enumerate(args)} | (kwargs or {})
+        self.event_emitter.send(
+            self,
+            event=MethodExecutionStartedEvent(
+                type="method_execution_started",
+                method_name=method_name,
+                flow_name=self.__class__.__name__,
+                params=dumped_params,
+                state=self._copy_state(),
+            ),
+        )
+
         result = (
             await method(*args, **kwargs)
             if asyncio.iscoroutinefunction(method)
@@ -812,6 +828,18 @@ class Flow(Generic[T], metaclass=FlowMeta):
         self._method_execution_counts[method_name] = (
             self._method_execution_counts.get(method_name, 0) + 1
         )
+
+        self.event_emitter.send(
+            self,
+            event=MethodExecutionFinishedEvent(
+                type="method_execution_finished",
+                method_name=method_name,
+                flow_name=self.__class__.__name__,
+                state=self._copy_state(),
+                result=result,
+            ),
+        )
+
         return result
 
     async def _execute_listeners(self, trigger_method: str, result: Any) -> None:
@@ -950,16 +978,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
         """
         try:
             method = self._methods[listener_name]
-
-            self.event_emitter.send(
-                self,
-                event=MethodExecutionStartedEvent(
-                    type="method_execution_started",
-                    method_name=listener_name,
-                    flow_name=self.__class__.__name__,
-                ),
-            )
-
             sig = inspect.signature(method)
             params = list(sig.parameters.values())
             method_params = [p for p in params if p.name != "self"]
@@ -971,15 +989,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
             else:
                 listener_result = await self._execute_method(listener_name, method)
 
-            self.event_emitter.send(
-                self,
-                event=MethodExecutionFinishedEvent(
-                    type="method_execution_finished",
-                    method_name=listener_name,
-                    flow_name=self.__class__.__name__,
-                ),
-            )
-
             # Execute listeners (and possibly routers) of this listener
             await self._execute_listeners(listener_name, listener_result)
 

src/crewai/flow/flow_events.py

@@ -1,6 +1,8 @@
 from dataclasses import dataclass, field
 from datetime import datetime
-from typing import Any, Optional
+from typing import Any, Dict, Optional, Union
+
+from pydantic import BaseModel
 
 
 @dataclass
@@ -15,17 +17,21 @@ class Event:
 
 @dataclass
 class FlowStartedEvent(Event):
-    pass
+    inputs: Optional[Dict[str, Any]] = None
 
 
 @dataclass
 class MethodExecutionStartedEvent(Event):
     method_name: str
+    state: Union[Dict[str, Any], BaseModel]
+    params: Optional[Dict[str, Any]] = None
 
 
 @dataclass
 class MethodExecutionFinishedEvent(Event):
     method_name: str
+    state: Union[Dict[str, Any], BaseModel]
+    result: Any = None
 
 
 @dataclass

src/crewai/knowledge/embedder/fastembed.py

@@ -47,7 +47,7 @@ class FastEmbed(BaseEmbedder):
             cache_dir=str(cache_dir) if cache_dir else None,
         )
 
-    def embed_chunks(self, chunks: List[str]) -> np.ndarray:
+    def embed_chunks(self, chunks: List[str]) -> List[np.ndarray]:
         """
         Generate embeddings for a list of text chunks
 
@@ -55,12 +55,12 @@ class FastEmbed(BaseEmbedder):
             chunks: List of text chunks to embed
 
         Returns:
-            Array of embeddings
+            List of embeddings
         """
         embeddings = list(self.model.embed(chunks))
-        return np.stack(embeddings)
+        return embeddings
 
-    def embed_texts(self, texts: List[str]) -> np.ndarray:
+    def embed_texts(self, texts: List[str]) -> List[np.ndarray]:
         """
         Generate embeddings for a list of texts
 
@@ -68,10 +68,10 @@ class FastEmbed(BaseEmbedder):
             texts: List of texts to embed
 
         Returns:
-            Array of embeddings
+            List of embeddings
         """
         embeddings = list(self.model.embed(texts))
-        return np.stack(embeddings)
+        return embeddings
 
     def embed_text(self, text: str) -> np.ndarray:
         """

src/crewai/knowledge/source/excel_knowledge_source.py

@@ -1,28 +1,138 @@
 from pathlib import Path
-from typing import Dict, List
+from typing import Dict, Iterator, List, Optional, Union
+from urllib.parse import urlparse
 
-from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource
+from pydantic import Field, field_validator
+
+from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
+from crewai.utilities.constants import KNOWLEDGE_DIRECTORY
+from crewai.utilities.logger import Logger
 
 
-class ExcelKnowledgeSource(BaseFileKnowledgeSource):
+class ExcelKnowledgeSource(BaseKnowledgeSource):
     """A knowledge source that stores and queries Excel file content using embeddings."""
 
-    def load_content(self) -> Dict[Path, str]:
-        """Load and preprocess Excel file content."""
-        pd = self._import_dependencies()
+    # override content to be a dict of file paths to sheet names to csv content
 
+    _logger: Logger = Logger(verbose=True)
+
+    file_path: Optional[Union[Path, List[Path], str, List[str]]] = Field(
+        default=None,
+        description="[Deprecated] The path to the file. Use file_paths instead.",
+    )
+    file_paths: Optional[Union[Path, List[Path], str, List[str]]] = Field(
+        default_factory=list, description="The path to the file"
+    )
+    chunks: List[str] = Field(default_factory=list)
+    content: Dict[Path, Dict[str, str]] = Field(default_factory=dict)
+    safe_file_paths: List[Path] = Field(default_factory=list)
+
+    @field_validator("file_path", "file_paths", mode="before")
+    def validate_file_path(cls, v, info):
+        """Validate that at least one of file_path or file_paths is provided."""
+        # Single check if both are None, O(1) instead of nested conditions
+        if (
+            v is None
+            and info.data.get(
+                "file_path" if info.field_name == "file_paths" else "file_paths"
+            )
+            is None
+        ):
+            raise ValueError("Either file_path or file_paths must be provided")
+        return v
+
+    def _process_file_paths(self) -> List[Path]:
+        """Convert file_path to a list of Path objects."""
+
+        if hasattr(self, "file_path") and self.file_path is not None:
+            self._logger.log(
+                "warning",
+                "The 'file_path' attribute is deprecated and will be removed in a future version. Please use 'file_paths' instead.",
+                color="yellow",
+            )
+            self.file_paths = self.file_path
+
+        if self.file_paths is None:
+            raise ValueError("Your source must be provided with a file_paths: []")
+
+        # Convert single path to list
+        path_list: List[Union[Path, str]] = (
+            [self.file_paths]
+            if isinstance(self.file_paths, (str, Path))
+            else list(self.file_paths)
+            if isinstance(self.file_paths, list)
+            else []
+        )
+
+        if not path_list:
+            raise ValueError(
+                "file_path/file_paths must be a Path, str, or a list of these types"
+            )
+
+        return [self.convert_to_path(path) for path in path_list]
+
+    def validate_content(self):
+        """Validate the paths."""
+        for path in self.safe_file_paths:
+            if not path.exists():
+                self._logger.log(
+                    "error",
+                    f"File not found: {path}. Try adding sources to the knowledge directory. If it's inside the knowledge directory, use the relative path.",
+                    color="red",
+                )
+                raise FileNotFoundError(f"File not found: {path}")
+            if not path.is_file():
+                self._logger.log(
+                    "error",
+                    f"Path is not a file: {path}",
+                    color="red",
+                )
+
+    def model_post_init(self, _) -> None:
+        if self.file_path:
+            self._logger.log(
+                "warning",
+                "The 'file_path' attribute is deprecated and will be removed in a future version. Please use 'file_paths' instead.",
+                color="yellow",
+            )
+            self.file_paths = self.file_path
+        self.safe_file_paths = self._process_file_paths()
+        self.validate_content()
+        self.content = self._load_content()
+
+    def _load_content(self) -> Dict[Path, Dict[str, str]]:
+        """Load and preprocess Excel file content from multiple sheets.
+
+        Each sheet's content is converted to CSV format and stored.
+
+        Returns:
+            Dict[Path, Dict[str, str]]: A mapping of file paths to their respective sheet contents.
+
+        Raises:
+            ImportError: If required dependencies are missing.
+            FileNotFoundError: If the specified Excel file cannot be opened.
+        """
+        pd = self._import_dependencies()
         content_dict = {}
         for file_path in self.safe_file_paths:
             file_path = self.convert_to_path(file_path)
-            df = pd.read_excel(file_path)
-            content = df.to_csv(index=False)
-            content_dict[file_path] = content
+            with pd.ExcelFile(file_path) as xl:
+                sheet_dict = {
+                    str(sheet_name): str(
+                        pd.read_excel(xl, sheet_name).to_csv(index=False)
+                    )
+                    for sheet_name in xl.sheet_names
+                }
+            content_dict[file_path] = sheet_dict
         return content_dict
 
+    def convert_to_path(self, path: Union[Path, str]) -> Path:
+        """Convert a path to a Path object."""
+        return Path(KNOWLEDGE_DIRECTORY + "/" + path) if isinstance(path, str) else path
+
     def _import_dependencies(self):
         """Dynamically import dependencies."""
         try:
-            import openpyxl  # noqa
             import pandas as pd
 
             return pd
@@ -38,10 +148,14 @@ class ExcelKnowledgeSource(BaseFileKnowledgeSource):
         and save the embeddings.
         """
         # Convert dictionary values to a single string if content is a dictionary
-        if isinstance(self.content, dict):
-            content_str = "\n".join(str(value) for value in self.content.values())
-        else:
-            content_str = str(self.content)
+        # Updated to account for .xlsx workbooks with multiple tabs/sheets
+        content_str = ""
+        for value in self.content.values():
+            if isinstance(value, dict):
+                for sheet_value in value.values():
+                    content_str += str(sheet_value) + "\n"
+            else:
+                content_str += str(value) + "\n"
 
         new_chunks = self._chunk_text(content_str)
         self.chunks.extend(new_chunks)

src/crewai/knowledge/storage/knowledge_storage.py

@@ -154,15 +154,9 @@ class KnowledgeStorage(BaseKnowledgeStorage):
                 filtered_ids.append(doc_id)
 
             # If we have no metadata at all, set it to None
-            final_metadata: Optional[List[Dict[str, Union[str, int, float, bool]]]] = None
-            if not all(m is None for m in filtered_metadata):
-                final_metadata = []
-                for m in filtered_metadata:
-                    if m is not None:
-                        filtered_m = {k: v for k, v in m.items() if isinstance(v, (str, int, float, bool))}
-                        final_metadata.append(filtered_m)
-                    else:
-                        final_metadata.append({"empty": True})
+            final_metadata: Optional[OneOrMany[chromadb.Metadata]] = (
+                None if all(m is None for m in filtered_metadata) else filtered_metadata
+            )
 
             self.collection.upsert(
                 documents=filtered_docs,

src/crewai/memory/storage/rag_storage.py

@@ -6,17 +6,12 @@ import shutil
 import uuid
 from typing import Any, Dict, List, Optional
 
-from chromadb.api import ClientAPI, Collection
-from chromadb.api.types import Documents, Embeddings, Metadatas
+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
-from crewai.utilities.exceptions.embedding_exceptions import (
-    EmbeddingConfigurationError,
-    EmbeddingInitializationError
-)
 
 
 @contextlib.contextmanager
@@ -37,32 +32,52 @@ def suppress_logging(
 
 
 class RAGStorage(BaseRAGStorage):
-    """RAG-based Storage implementation using ChromaDB for vector storage and retrieval.
-
-    This class extends BaseRAGStorage to handle embeddings for memory entries,
-    improving search efficiency through vector similarity.
-
-    Attributes:
-        app: ChromaDB client instance
-        collection: ChromaDB collection for storing embeddings
-        type: Type of memory storage
-        allow_reset: Whether memory reset is allowed
-        path: Custom storage path for the database
+    """
+    Extends Storage to handle embeddings for memory entries, improving
+    search efficiency.
     """
 
     app: ClientAPI | None = None
-    collection: Any = None
+
+    """
+    RAG Storage implementation that handles both crew and no-crew scenarios.
+    
+    Args:
+        type: Type of storage
+        allow_reset: Whether storage can be reset
+        embedder_config: Configuration for embeddings
+        crew: Crew instance or None for no-crew scenario
+        path: Custom storage path
+    """
+
+    def _get_agents_string(self, crew) -> str:
+        """
+        Get a string representation of agents for storage path.
+        
+        Args:
+            crew: Optional crew instance. If None, returns "no_crew".
+        
+        Returns:
+            str: String representation of agents or "no_crew" if no crew exists.
+        """
+        return "no_crew" if not crew else "_".join([self._sanitize_role(agent.role) for agent in crew.agents])
 
     def __init__(
-        self, type: str, allow_reset: bool = True, embedder_config: Dict[str, Any] | None = None, crew: Any = None, path: str | None = None
+        self, type: str, allow_reset: bool = True, embedder_config=None, crew=None, path=None
     ):
+        """
+        Initialize RAG Storage implementation that handles both crew and no-crew scenarios.
+        
+        Args:
+            type: Type of storage
+            allow_reset: Whether storage can be reset
+            embedder_config: Configuration for embeddings
+            crew: Crew instance or None for no-crew scenario
+            path: Custom storage path
+        """
         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)
-        self.agents = agents
-        self.storage_file_name = self._build_storage_file_name(type, agents)
-
+        self.agents = self._get_agents_string(crew)
+        self.storage_file_name = self._build_storage_file_name(type, self.agents)
         self.type = type
         self.allow_reset = allow_reset
         self.path = path
@@ -72,36 +87,26 @@ class RAGStorage(BaseRAGStorage):
         configurator = EmbeddingConfigurator()
         self.embedder_config = configurator.configure_embedder(self.embedder_config)
 
-    def _initialize_app(self) -> None:
-        """Initialize the ChromaDB client and collection.
-
-        Raises:
-            RuntimeError: If ChromaDB client initialization fails
-            EmbeddingConfigurationError: If embedding configuration is invalid
-            EmbeddingInitializationError: If embedding function fails to initialize
-        """
+    def _initialize_app(self):
         import chromadb
         from chromadb.config import Settings
 
         self._set_embedder_config()
-        try:
-            self.app = chromadb.PersistentClient(
-                path=self.path if self.path else self.storage_file_name,
-                settings=Settings(allow_reset=self.allow_reset),
-            )
-            if not self.app:
-                raise RuntimeError("Failed to initialize ChromaDB client")
+        chroma_client = chromadb.PersistentClient(
+            path=self.path if self.path else self.storage_file_name,
+            settings=Settings(allow_reset=self.allow_reset),
+        )
 
-            try:
-                self.collection = self.app.get_collection(
-                    name=self.type, embedding_function=self.embedder_config
-                )
-            except Exception:
-                self.collection = self.app.create_collection(
-                    name=self.type, embedding_function=self.embedder_config
-                )
-        except Exception as e:
-            raise RuntimeError(f"Failed to initialize ChromaDB: {str(e)}")
+        self.app = chroma_client
+
+        try:
+            self.collection = self.app.get_collection(
+                name=self.type, embedding_function=self.embedder_config
+            )
+        except Exception:
+            self.collection = self.app.create_collection(
+                name=self.type, embedding_function=self.embedder_config
+            )
 
     def _sanitize_role(self, role: str) -> str:
         """
@@ -124,21 +129,12 @@ class RAGStorage(BaseRAGStorage):
         return f"{base_path}/{file_name}"
 
     def save(self, value: Any, metadata: Dict[str, Any]) -> None:
-        """Save a value with metadata to the memory storage.
-
-        Args:
-            value: The text content to store
-            metadata: Additional metadata for the stored content
-
-        Raises:
-            EmbeddingInitializationError: If embedding generation fails
-        """
         if not hasattr(self, "app") or not hasattr(self, "collection"):
             self._initialize_app()
         try:
             self._generate_embedding(value, metadata)
         except Exception as e:
-            raise EmbeddingInitializationError(self.type, str(e))
+            logging.error(f"Error during {self.type} save: {str(e)}")
 
     def search(
         self,
@@ -146,18 +142,7 @@ class RAGStorage(BaseRAGStorage):
         limit: int = 3,
         filter: Optional[dict] = None,
         score_threshold: float = 0.35,
-    ) -> List[Dict[str, Any]]:
-        """Search for similar content in memory.
-
-        Args:
-            query: The search query text
-            limit: Maximum number of results to return
-            filter: Optional filter criteria
-            score_threshold: Minimum similarity score threshold
-
-        Returns:
-            List of matching results with metadata and scores
-        """
+    ) -> List[Any]:
         if not hasattr(self, "app"):
             self._initialize_app()
 
@@ -181,50 +166,37 @@ class RAGStorage(BaseRAGStorage):
             logging.error(f"Error during {self.type} search: {str(e)}")
             return []
 
-    def _generate_embedding(self, text: str, metadata: Optional[Dict[str, Any]] = None) -> Any:
-        """Generate and store embeddings for the given text.
-
-        Args:
-            text: The text to generate embeddings for
-            metadata: Optional additional metadata to store with the embeddings
-
-        Returns:
-            Any: The generated embedding or None if only storing
-        """
+    def _generate_embedding(self, text: str, metadata: Dict[str, Any]) -> None:  # type: ignore
         if not hasattr(self, "app") or not hasattr(self, "collection"):
             self._initialize_app()
 
-        try:
-            self.collection.add(
-                documents=[text],
-                metadatas=[metadata or {}],
-                ids=[str(uuid.uuid4())],
-            )
-            return None
-        except Exception as e:
-            raise EmbeddingInitializationError(self.type, f"Failed to generate embedding: {str(e)}")
+        self.collection.add(
+            documents=[text],
+            metadatas=[metadata or {}],
+            ids=[str(uuid.uuid4())],
+        )
 
     def reset(self) -> None:
-        """Reset the memory storage by clearing the database and removing files.
-
-        Raises:
-            RuntimeError: If memory reset fails and allow_reset is False
-            EmbeddingConfigurationError: If embedding configuration is invalid during reinitialization
-        """
         try:
             if self.app:
                 self.app.reset()
-                storage_path = self.path if self.path else db_storage_path()
-                db_dir = os.path.join(storage_path, self.type)
-                if os.path.exists(db_dir):
-                    shutil.rmtree(db_dir)
+                shutil.rmtree(f"{db_storage_path()}/{self.type}")
                 self.app = None
                 self.collection = None
         except Exception as e:
             if "attempt to write a readonly database" in str(e):
-                # Ignore this specific error as it's expected in some environments
+                # Ignore this specific error
                 pass
             else:
-                if not self.allow_reset:
-                    raise RuntimeError(f"Failed to reset {self.type} memory: {str(e)}")
-                logging.error(f"Error during {self.type} memory reset: {str(e)}")
+                raise Exception(
+                    f"An error occurred while resetting the {self.type} memory: {e}"
+                )
+
+    def _create_default_embedding_function(self):
+        from chromadb.utils.embedding_functions.openai_embedding_function import (
+            OpenAIEmbeddingFunction,
+        )
+
+        return OpenAIEmbeddingFunction(
+            api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"
+        )

src/crewai/task.py

@@ -9,13 +9,11 @@ from copy import copy
 from hashlib import md5
 from pathlib import Path
 from typing import (
-    AbstractSet,
     Any,
     Callable,
     ClassVar,
     Dict,
     List,
-    Mapping,
     Optional,
     Set,
     Tuple,
@@ -111,7 +109,7 @@ class Task(BaseModel):
         description="Task output, it's final result after being executed", default=None
     )
     tools: Optional[List[BaseTool]] = Field(
-        default_factory=lambda: [],
+        default_factory=list,
         description="Tools the agent is limited to use for this task.",
     )
     id: UUID4 = Field(
@@ -127,7 +125,7 @@ class Task(BaseModel):
         description="A converter class used to export structured output",
         default=None,
     )
-    processed_by_agents: Set[str] = Field(default_factory=lambda: set())
+    processed_by_agents: Set[str] = Field(default_factory=set)
     guardrail: Optional[Callable[[TaskOutput], Tuple[bool, Any]]] = Field(
         default=None,
         description="Function to validate task output before proceeding to next task",
@@ -608,56 +606,37 @@ class Task(BaseModel):
         self.delegations += 1
 
     def copy(
-        self,
-        *,
-        include: Optional[AbstractSet[int] | AbstractSet[str] | Mapping[int, Any] | Mapping[str, Any]] = None,
-        exclude: Optional[AbstractSet[int] | AbstractSet[str] | Mapping[int, Any] | Mapping[str, Any]] = None,
-        update: Optional[Dict[str, Any]] = None,
-        deep: bool = False,
-    ) -> "Task":
-        """Create a copy of the Task."""
-        exclude_set = {"id", "agent", "context", "tools"}
-        if exclude:
-            if isinstance(exclude, (AbstractSet, set)):
-                exclude_set.update(str(x) for x in exclude)
-            elif isinstance(exclude, Mapping):
-                exclude_set.update(str(x) for x in exclude.keys())
-        
-        copied_task = super().copy(
-            include=include,
-            exclude=exclude_set,
-            update=update,
-            deep=deep,
-        )
-        
-        copied_task.id = uuid.uuid4()
-        copied_task.agent = None
-        copied_task.context = None
-        copied_task.tools = []
-        
-        return copied_task
-
-    def copy_with_agents(
         self, agents: List["BaseAgent"], task_mapping: Dict[str, "Task"]
     ) -> "Task":
-        """Create a copy of the Task with agent references."""
-        copied_task = self.copy()
+        """Create a deep copy of the Task."""
+        exclude = {
+            "id",
+            "agent",
+            "context",
+            "tools",
+        }
+
+        copied_data = self.model_dump(exclude=exclude)
+        copied_data = {k: v for k, v in copied_data.items() if v is not None}
+
+        cloned_context = (
+            [task_mapping[context_task.key] for context_task in self.context]
+            if self.context
+            else None
+        )
 
         def get_agent_by_role(role: str) -> Union["BaseAgent", None]:
             return next((agent for agent in agents if agent.role == role), None)
 
-        if self.agent:
-            copied_task.agent = get_agent_by_role(self.agent.role)
+        cloned_agent = get_agent_by_role(self.agent.role) if self.agent else None
+        cloned_tools = copy(self.tools) if self.tools else []
 
-        if self.context:
-            copied_task.context = [
-                task_mapping[context_task.key]
-                for context_task in self.context
-                if context_task.key in task_mapping
-            ]
-
-        if self.tools:
-            copied_task.tools = copy(self.tools)
+        copied_task = Task(
+            **copied_data,
+            context=cloned_context,
+            agent=cloned_agent,
+            tools=cloned_tools,
+        )
 
         return copied_task
 

src/crewai/tasks/task_output.py

@@ -1,9 +1,7 @@
 import json
-from typing import Any, Callable, Dict, Optional
+from typing import Any, Dict, Optional
 
 from pydantic import BaseModel, Field, model_validator
-from pydantic.main import IncEx
-from typing_extensions import Literal
 
 from crewai.tasks.output_format import OutputFormat
 
@@ -36,8 +34,8 @@ class TaskOutput(BaseModel):
         self.summary = f"{excerpt}..."
         return self
 
-    def model_json(self) -> str:
-        """Get the JSON representation of the output."""
+    @property
+    def json(self) -> Optional[str]:
         if self.output_format != OutputFormat.JSON:
             raise ValueError(
                 """
@@ -46,37 +44,8 @@ class TaskOutput(BaseModel):
                 please make sure to set the output_json property for the task
                 """
             )
-        return json.dumps(self.json_dict) if self.json_dict else "{}"
 
-    def model_dump_json(
-        self,
-        *,
-        indent: Optional[int] = None,
-        include: Optional[IncEx] = None,
-        exclude: Optional[IncEx] = None,
-        context: Optional[Any] = None,
-        by_alias: bool = False,
-        exclude_unset: bool = False,
-        exclude_defaults: bool = False,
-        exclude_none: bool = False,
-        round_trip: bool = False,
-        warnings: bool | Literal["none", "warn", "error"] = False,
-        serialize_as_any: bool = False,
-    ) -> str:
-        """Override model_dump_json to handle custom JSON output."""
-        return super().model_dump_json(
-            indent=indent,
-            include=include,
-            exclude=exclude,
-            context=context,
-            by_alias=by_alias,
-            exclude_unset=exclude_unset,
-            exclude_defaults=exclude_defaults,
-            exclude_none=exclude_none,
-            round_trip=round_trip,
-            warnings=warnings,
-            serialize_as_any=serialize_as_any,
-        )
+        return json.dumps(self.json_dict)
 
     def to_dict(self) -> Dict[str, Any]:
         """Convert json_output and pydantic_output to a dictionary."""

src/crewai/tools/agent_tools/base_agent_tools.py

@@ -82,12 +82,12 @@ class BaseAgentTool(BaseTool):
             available_agents = [agent.role for agent in self.agents]
             logger.debug(f"Available agents: {available_agents}")
 
-            matching_agents = [
+            agent = [  # type: ignore # Incompatible types in assignment (expression has type "list[BaseAgent]", variable has type "str | None")
                 available_agent
                 for available_agent in self.agents
                 if self.sanitize_agent_name(available_agent.role) == sanitized_name
             ]
-            logger.debug(f"Found {len(matching_agents)} matching agents for role '{sanitized_name}'")
+            logger.debug(f"Found {len(agent)} matching agents for role '{sanitized_name}'")
         except (AttributeError, ValueError) as e:
             # Handle specific exceptions that might occur during role name processing
             return self.i18n.errors("agent_tool_unexisting_coworker").format(
@@ -97,7 +97,7 @@ class BaseAgentTool(BaseTool):
                 error=str(e)
             )
 
-        if not matching_agents:
+        if not agent:
             # No matching agent found after sanitization
             return self.i18n.errors("agent_tool_unexisting_coworker").format(
                 coworkers="\n".join(
@@ -106,19 +106,19 @@ class BaseAgentTool(BaseTool):
                 error=f"No agent found with role '{sanitized_name}'"
             )
 
-        selected_agent = matching_agents[0]
+        agent = agent[0]
         try:
             task_with_assigned_agent = Task(
                 description=task,
-                agent=selected_agent,
-                expected_output=selected_agent.i18n.slice("manager_request"),
-                i18n=selected_agent.i18n,
+                agent=agent,
+                expected_output=agent.i18n.slice("manager_request"),
+                i18n=agent.i18n,
             )
-            logger.debug(f"Created task for agent '{self.sanitize_agent_name(selected_agent.role)}': {task}")
-            return selected_agent.execute_task(task_with_assigned_agent, context)
+            logger.debug(f"Created task for agent '{self.sanitize_agent_name(agent.role)}': {task}")
+            return agent.execute_task(task_with_assigned_agent, context)
         except Exception as e:
             # Handle task creation or execution errors
             return self.i18n.errors("agent_tool_execution_error").format(
-                agent_role=self.sanitize_agent_name(selected_agent.role),
+                agent_role=self.sanitize_agent_name(agent.role),
                 error=str(e)
             )

src/crewai/tools/base_tool.py

@@ -1,36 +1,40 @@
+import warnings
 from abc import ABC, abstractmethod
 from inspect import signature
-from typing import Any, Callable, Dict, Optional, Type, Tuple, get_args, get_origin
+from typing import Any, Callable, Type, get_args, get_origin
 
-from pydantic import BaseModel, ConfigDict, Field, create_model, validator
-from pydantic.fields import FieldInfo
+from pydantic import (
+    BaseModel,
+    ConfigDict,
+    Field,
+    PydanticDeprecatedSince20,
+    create_model,
+    validator,
+)
 from pydantic import BaseModel as PydanticBaseModel
 
 from crewai.tools.structured_tool import CrewStructuredTool
 
-def _create_model_fields(fields: Dict[str, Tuple[Any, FieldInfo]]) -> Dict[str, Any]:
-    """Helper function to create model fields with proper type hints."""
-    return {name: (annotation, field) for name, (annotation, field) in fields.items()}
+# Ignore all "PydanticDeprecatedSince20" warnings globally
+warnings.filterwarnings("ignore", category=PydanticDeprecatedSince20)
+
 
 class BaseTool(BaseModel, ABC):
-    """Base class for all tools."""
-
     class _ArgsSchemaPlaceholder(PydanticBaseModel):
         pass
 
-    model_config = ConfigDict(arbitrary_types_allowed=True)
-    func: Optional[Callable] = None
+    model_config = ConfigDict()
 
     name: str
     """The unique name of the tool that clearly communicates its purpose."""
     description: str
     """Used to tell the model how/when/why to use the tool."""
-    args_schema: Type[PydanticBaseModel] = Field(default=_ArgsSchemaPlaceholder)
+    args_schema: Type[PydanticBaseModel] = Field(default_factory=_ArgsSchemaPlaceholder)
     """The schema for the arguments that the tool accepts."""
     description_updated: bool = False
     """Flag to check if the description has been updated."""
     cache_function: Callable = lambda _args=None, _result=None: True
-    """Function that will be used to determine if the tool should be cached."""
+    """Function that will be used to determine if the tool should be cached, should return a boolean. If None, the tool will be cached."""
     result_as_answer: bool = False
     """Flag to check if the tool should be the final agent answer."""
 
@@ -53,6 +57,7 @@ class BaseTool(BaseModel, ABC):
 
     def model_post_init(self, __context: Any) -> None:
         self._generate_description()
+
         super().model_post_init(__context)
 
     def run(
@@ -82,7 +87,50 @@ class BaseTool(BaseModel, ABC):
             result_as_answer=self.result_as_answer,
         )
 
-    def _set_args_schema(self) -> None:
+    @classmethod
+    def from_langchain(cls, tool: Any) -> "BaseTool":
+        """Create a Tool instance from a CrewStructuredTool.
+
+        This method takes a CrewStructuredTool object and converts it into a
+        Tool instance. It ensures that the provided tool has a callable 'func'
+        attribute and infers the argument schema if not explicitly provided.
+        """
+        if not hasattr(tool, "func") or not callable(tool.func):
+            raise ValueError("The provided tool must have a callable 'func' attribute.")
+
+        args_schema = getattr(tool, "args_schema", None)
+
+        if args_schema is None:
+            # Infer args_schema from the function signature if not provided
+            func_signature = signature(tool.func)
+            annotations = func_signature.parameters
+            args_fields = {}
+            for name, param in annotations.items():
+                if name != "self":
+                    param_annotation = (
+                        param.annotation if param.annotation != param.empty else Any
+                    )
+                    field_info = Field(
+                        default=...,
+                        description="",
+                    )
+                    args_fields[name] = (param_annotation, field_info)
+            if args_fields:
+                args_schema = create_model(f"{tool.name}Input", **args_fields)
+            else:
+                # Create a default schema with no fields if no parameters are found
+                args_schema = create_model(
+                    f"{tool.name}Input", __base__=PydanticBaseModel
+                )
+
+        return cls(
+            name=getattr(tool, "name", "Unnamed Tool"),
+            description=getattr(tool, "description", ""),
+            func=tool.func,
+            args_schema=args_schema,
+        )
+
+    def _set_args_schema(self):
         if self.args_schema is None:
             class_name = f"{self.__class__.__name__}Schema"
             self.args_schema = type(
@@ -97,7 +145,7 @@ class BaseTool(BaseModel, ABC):
                 },
             )
 
-    def _generate_description(self) -> None:
+    def _generate_description(self):
         args_schema = {
             name: {
                 "description": field.description,
@@ -131,25 +179,79 @@ class BaseTool(BaseModel, ABC):
 
 
 class Tool(BaseTool):
-    """Tool class that wraps a function."""
+    """The function that will be executed when the tool is called."""
 
     func: Callable
-    model_config = ConfigDict(arbitrary_types_allowed=True)
-
-    def __init__(self, **kwargs):
-        if "func" not in kwargs:
-            raise ValueError("Tool requires a 'func' argument")
-        super().__init__(**kwargs)
 
     def _run(self, *args: Any, **kwargs: Any) -> Any:
         return self.func(*args, **kwargs)
 
+    @classmethod
+    def from_langchain(cls, tool: Any) -> "Tool":
+        """Create a Tool instance from a CrewStructuredTool.
 
-def tool(*args: Any) -> Any:
-    """Decorator to create a tool from a function."""
+        This method takes a CrewStructuredTool object and converts it into a
+        Tool instance. It ensures that the provided tool has a callable 'func'
+        attribute and infers the argument schema if not explicitly provided.
+
+        Args:
+            tool (Any): The CrewStructuredTool object to be converted.
+
+        Returns:
+            Tool: A new Tool instance created from the provided CrewStructuredTool.
+
+        Raises:
+            ValueError: If the provided tool does not have a callable 'func' attribute.
+        """
+        if not hasattr(tool, "func") or not callable(tool.func):
+            raise ValueError("The provided tool must have a callable 'func' attribute.")
+
+        args_schema = getattr(tool, "args_schema", None)
+
+        if args_schema is None:
+            # Infer args_schema from the function signature if not provided
+            func_signature = signature(tool.func)
+            annotations = func_signature.parameters
+            args_fields = {}
+            for name, param in annotations.items():
+                if name != "self":
+                    param_annotation = (
+                        param.annotation if param.annotation != param.empty else Any
+                    )
+                    field_info = Field(
+                        default=...,
+                        description="",
+                    )
+                    args_fields[name] = (param_annotation, field_info)
+            if args_fields:
+                args_schema = create_model(f"{tool.name}Input", **args_fields)
+            else:
+                # Create a default schema with no fields if no parameters are found
+                args_schema = create_model(
+                    f"{tool.name}Input", __base__=PydanticBaseModel
+                )
+
+        return cls(
+            name=getattr(tool, "name", "Unnamed Tool"),
+            description=getattr(tool, "description", ""),
+            func=tool.func,
+            args_schema=args_schema,
+        )
+
+
+def to_langchain(
+    tools: list[BaseTool | CrewStructuredTool],
+) -> list[CrewStructuredTool]:
+    return [t.to_structured_tool() if isinstance(t, BaseTool) else t for t in tools]
+
+
+def tool(*args):
+    """
+    Decorator to create a tool from a function.
+    """
 
     def _make_with_name(tool_name: str) -> Callable:
-        def _make_tool(f: Callable) -> Tool:
+        def _make_tool(f: Callable) -> BaseTool:
             if f.__doc__ is None:
                 raise ValueError("Function must have a docstring")
             if f.__annotations__ is None:

src/crewai/tools/structured_tool.py

@@ -2,14 +2,9 @@ from __future__ import annotations
 
 import inspect
 import textwrap
-from typing import Any, Callable, Dict, Optional, Tuple, Union, get_type_hints
+from typing import Any, Callable, Optional, Union, get_type_hints
 
-from pydantic import BaseModel, ConfigDict, Field, create_model
-from pydantic.fields import FieldInfo
-
-def _create_model_fields(fields: Dict[str, Tuple[Any, FieldInfo]]) -> Dict[str, Any]:
-    """Helper function to create model fields with proper type hints."""
-    return {name: (annotation, field) for name, (annotation, field) in fields.items()}
+from pydantic import BaseModel, Field, create_model
 
 from crewai.utilities.logger import Logger
 
@@ -147,8 +142,7 @@ class CrewStructuredTool:
 
         # Create model
         schema_name = f"{name.title()}Schema"
-        model_fields = _create_model_fields(fields)
-        return create_model(schema_name, __base__=BaseModel, **model_fields)
+        return create_model(schema_name, **fields)
 
     def _validate_function_signature(self) -> None:
         """Validate that the function signature matches the args schema."""

src/crewai/utilities/constants.py

@@ -4,7 +4,3 @@ DEFAULT_SCORE_THRESHOLD = 0.35
 KNOWLEDGE_DIRECTORY = "knowledge"
 MAX_LLM_RETRY = 3
 MAX_FILE_NAME_LENGTH = 255
-
-# Default embedding configuration
-DEFAULT_EMBEDDING_PROVIDER = "openai"
-DEFAULT_EMBEDDING_MODEL = "text-embedding-3-small"

src/crewai/utilities/embedding_configurator.py

@@ -1,15 +1,9 @@
 import os
-from typing import Any, Dict, List, Optional, cast
+from typing import Any, Dict, Optional, cast
 
 from chromadb import Documents, EmbeddingFunction, Embeddings
 from chromadb.api.types import validate_embedding_function
 
-from crewai.utilities.exceptions.embedding_exceptions import (
-    EmbeddingConfigurationError,
-    EmbeddingProviderError,
-    EmbeddingInitializationError
-)
-
 
 class EmbeddingConfigurator:
     def __init__(self):
@@ -20,9 +14,11 @@ class EmbeddingConfigurator:
             "vertexai": self._configure_vertexai,
             "google": self._configure_google,
             "cohere": self._configure_cohere,
+            "voyageai": self._configure_voyageai,
             "bedrock": self._configure_bedrock,
             "huggingface": self._configure_huggingface,
             "watson": self._configure_watson,
+            "custom": self._configure_custom,
         }
 
     def configure_embedder(
@@ -35,119 +31,156 @@ class EmbeddingConfigurator:
 
         provider = embedder_config.get("provider")
         config = embedder_config.get("config", {})
-        model_name = config.get("model")
+        model_name = config.get("model") if provider != "custom" else None
 
-        if isinstance(provider, EmbeddingFunction):
-            try:
-                validate_embedding_function(provider)
-                return provider
-            except Exception as e:
-                raise EmbeddingConfigurationError(f"Invalid custom embedding function: {str(e)}")
+        if provider not in self.embedding_functions:
+            raise Exception(
+                f"Unsupported embedding provider: {provider}, supported providers: {list(self.embedding_functions.keys())}"
+            )
 
-        if not provider or provider not in self.embedding_functions:
-            raise EmbeddingProviderError(str(provider), list(self.embedding_functions.keys()))
-
-        try:
-            return self.embedding_functions[str(provider)](config, model_name)
-        except Exception as e:
-            raise EmbeddingInitializationError(str(provider), str(e))
-
-    @staticmethod
-    def _create_default_embedding_function() -> EmbeddingFunction:
-        from crewai.utilities.constants import DEFAULT_EMBEDDING_PROVIDER, DEFAULT_EMBEDDING_MODEL
-        
-        provider = os.getenv("CREWAI_EMBEDDING_PROVIDER", DEFAULT_EMBEDDING_PROVIDER)
-        model = os.getenv("CREWAI_EMBEDDING_MODEL", DEFAULT_EMBEDDING_MODEL)
-        
-        if provider == "openai":
-            api_key = os.getenv("OPENAI_API_KEY")
-            if not api_key:
-                raise EmbeddingConfigurationError("OpenAI API key is required but not provided")
-            from chromadb.utils.embedding_functions.openai_embedding_function import OpenAIEmbeddingFunction
-            return OpenAIEmbeddingFunction(api_key=api_key, model_name=model)
-        elif provider == "ollama":
-            from chromadb.utils.embedding_functions.ollama_embedding_function import OllamaEmbeddingFunction
-            url = os.getenv("CREWAI_OLLAMA_URL", "http://localhost:11434/api/embeddings")
-            return OllamaEmbeddingFunction(url=url, model_name=model)
-        else:
-            raise EmbeddingProviderError(provider, ["openai", "ollama"])
-
-    @staticmethod
-    def _configure_openai(config: Dict[str, Any], model_name: str) -> EmbeddingFunction:
-        from chromadb.utils.embedding_functions.openai_embedding_function import OpenAIEmbeddingFunction
-        return OpenAIEmbeddingFunction(
-            api_key=config.get("api_key") or os.getenv("OPENAI_API_KEY"),
-            model_name=model_name,
+        embedding_function = self.embedding_functions[provider]
+        return (
+            embedding_function(config)
+            if provider == "custom"
+            else embedding_function(config, model_name)
         )
 
     @staticmethod
-    def _configure_azure(config: Dict[str, Any], model_name: str) -> EmbeddingFunction:
-        from chromadb.utils.embedding_functions.openai_embedding_function import OpenAIEmbeddingFunction
+    def _create_default_embedding_function():
+        from chromadb.utils.embedding_functions.openai_embedding_function import (
+            OpenAIEmbeddingFunction,
+        )
+
+        return OpenAIEmbeddingFunction(
+            api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"
+        )
+
+    @staticmethod
+    def _configure_openai(config, model_name):
+        from chromadb.utils.embedding_functions.openai_embedding_function import (
+            OpenAIEmbeddingFunction,
+        )
+
+        return OpenAIEmbeddingFunction(
+            api_key=config.get("api_key") or os.getenv("OPENAI_API_KEY"),
+            model_name=model_name,
+            api_base=config.get("api_base", None),
+            api_type=config.get("api_type", None),
+            api_version=config.get("api_version", None),
+            default_headers=config.get("default_headers", None),
+            dimensions=config.get("dimensions", None),
+            deployment_id=config.get("deployment_id", None),
+            organization_id=config.get("organization_id", None),
+        )
+
+    @staticmethod
+    def _configure_azure(config, model_name):
+        from chromadb.utils.embedding_functions.openai_embedding_function import (
+            OpenAIEmbeddingFunction,
+        )
+
         return OpenAIEmbeddingFunction(
             api_key=config.get("api_key"),
             api_base=config.get("api_base"),
             api_type=config.get("api_type", "azure"),
             api_version=config.get("api_version"),
             model_name=model_name,
+            default_headers=config.get("default_headers"),
+            dimensions=config.get("dimensions"),
+            deployment_id=config.get("deployment_id"),
+            organization_id=config.get("organization_id"),
         )
 
     @staticmethod
-    def _configure_ollama(config: Dict[str, Any], model_name: str) -> EmbeddingFunction:
-        from chromadb.utils.embedding_functions.ollama_embedding_function import OllamaEmbeddingFunction
+    def _configure_ollama(config, model_name):
+        from chromadb.utils.embedding_functions.ollama_embedding_function import (
+            OllamaEmbeddingFunction,
+        )
+
         return OllamaEmbeddingFunction(
             url=config.get("url", "http://localhost:11434/api/embeddings"),
             model_name=model_name,
         )
 
     @staticmethod
-    def _configure_vertexai(config: Dict[str, Any], model_name: str) -> EmbeddingFunction:
-        from chromadb.utils.embedding_functions.google_embedding_function import GoogleVertexEmbeddingFunction
+    def _configure_vertexai(config, model_name):
+        from chromadb.utils.embedding_functions.google_embedding_function import (
+            GoogleVertexEmbeddingFunction,
+        )
+
         return GoogleVertexEmbeddingFunction(
             model_name=model_name,
             api_key=config.get("api_key"),
+            project_id=config.get("project_id"),
+            region=config.get("region"),
         )
 
     @staticmethod
-    def _configure_google(config: Dict[str, Any], model_name: str) -> EmbeddingFunction:
-        from chromadb.utils.embedding_functions.google_embedding_function import GoogleGenerativeAiEmbeddingFunction
+    def _configure_google(config, model_name):
+        from chromadb.utils.embedding_functions.google_embedding_function import (
+            GoogleGenerativeAiEmbeddingFunction,
+        )
+
         return GoogleGenerativeAiEmbeddingFunction(
             model_name=model_name,
             api_key=config.get("api_key"),
+            task_type=config.get("task_type"),
         )
 
     @staticmethod
-    def _configure_cohere(config: Dict[str, Any], model_name: str) -> EmbeddingFunction:
-        from chromadb.utils.embedding_functions.cohere_embedding_function import CohereEmbeddingFunction
+    def _configure_cohere(config, model_name):
+        from chromadb.utils.embedding_functions.cohere_embedding_function import (
+            CohereEmbeddingFunction,
+        )
+
         return CohereEmbeddingFunction(
             model_name=model_name,
             api_key=config.get("api_key"),
         )
 
     @staticmethod
-    def _configure_bedrock(config: Dict[str, Any], model_name: str) -> EmbeddingFunction:
-        from chromadb.utils.embedding_functions.amazon_bedrock_embedding_function import AmazonBedrockEmbeddingFunction
-        return AmazonBedrockEmbeddingFunction(
-            session=config.get("session"),
+    def _configure_voyageai(config, model_name):
+        from chromadb.utils.embedding_functions.voyageai_embedding_function import (
+            VoyageAIEmbeddingFunction,
+        )
+
+        return VoyageAIEmbeddingFunction(
+            model_name=model_name,
+            api_key=config.get("api_key"),
         )
 
     @staticmethod
-    def _configure_huggingface(config: Dict[str, Any], model_name: str) -> EmbeddingFunction:
-        from chromadb.utils.embedding_functions.huggingface_embedding_function import HuggingFaceEmbeddingServer
+    def _configure_bedrock(config, model_name):
+        from chromadb.utils.embedding_functions.amazon_bedrock_embedding_function import (
+            AmazonBedrockEmbeddingFunction,
+        )
+
+        # Allow custom model_name override with backwards compatibility
+        kwargs = {"session": config.get("session")}
+        if model_name is not None:
+            kwargs["model_name"] = model_name
+        return AmazonBedrockEmbeddingFunction(**kwargs)
+
+    @staticmethod
+    def _configure_huggingface(config, model_name):
+        from chromadb.utils.embedding_functions.huggingface_embedding_function import (
+            HuggingFaceEmbeddingServer,
+        )
+
         return HuggingFaceEmbeddingServer(
             url=config.get("api_url"),
         )
 
     @staticmethod
-    def _configure_watson(config: Dict[str, Any], model_name: str) -> EmbeddingFunction:
+    def _configure_watson(config, model_name):
         try:
             import ibm_watsonx_ai.foundation_models as watson_models
             from ibm_watsonx_ai import Credentials
             from ibm_watsonx_ai.metanames import EmbedTextParamsMetaNames as EmbedParams
         except ImportError as e:
-            raise EmbeddingConfigurationError(
-                "IBM Watson dependencies are not installed. Please install them to use Watson embedding.",
-                provider="watson"
-            )
+            raise ImportError(
+                "IBM Watson dependencies are not installed. Please install them to use Watson embedding."
+            ) from e
 
         class WatsonEmbeddingFunction(EmbeddingFunction):
             def __call__(self, input: Documents) -> Embeddings:
@@ -172,6 +205,32 @@ class EmbeddingConfigurator:
                     embeddings = embedding.embed_documents(input)
                     return cast(Embeddings, embeddings)
                 except Exception as e:
-                    raise EmbeddingInitializationError("watson", str(e))
+                    print("Error during Watson embedding:", e)
+                    raise e
 
         return WatsonEmbeddingFunction()
+
+    @staticmethod
+    def _configure_custom(config):
+        custom_embedder = config.get("embedder")
+        if isinstance(custom_embedder, EmbeddingFunction):
+            try:
+                validate_embedding_function(custom_embedder)
+                return custom_embedder
+            except Exception as e:
+                raise ValueError(f"Invalid custom embedding function: {str(e)}")
+        elif callable(custom_embedder):
+            try:
+                instance = custom_embedder()
+                if isinstance(instance, EmbeddingFunction):
+                    validate_embedding_function(instance)
+                    return instance
+                raise ValueError(
+                    "Custom embedder does not create an EmbeddingFunction instance"
+                )
+            except Exception as e:
+                raise ValueError(f"Error instantiating custom embedder: {str(e)}")
+        else:
+            raise ValueError(
+                "Custom embedder must be an instance of `EmbeddingFunction` or a callable that creates one"
+            )

src/crewai/utilities/exceptions/embedding_exceptions.py

@@ -1,20 +0,0 @@
-from typing import List, Optional
-
-
-class EmbeddingConfigurationError(Exception):
-    def __init__(self, message: str, provider: Optional[str] = None):
-        self.message = message
-        self.provider = provider
-        super().__init__(self.message)
-
-
-class EmbeddingProviderError(EmbeddingConfigurationError):
-    def __init__(self, provider: str, supported_providers: List[str]):
-        message = f"Unsupported embedding provider: {provider}, supported providers: {supported_providers}"
-        super().__init__(message, provider)
-
-
-class EmbeddingInitializationError(EmbeddingConfigurationError):
-    def __init__(self, provider: str, error: str):
-        message = f"Failed to initialize embedding function for provider {provider}: {error}"
-        super().__init__(message, provider)

tests/cli/cli_test.py

@@ -4,6 +4,8 @@ from unittest import mock
 import pytest
 from click.testing import CliRunner
 
+from crewai.cli.reset_memories_command import reset_memories_command
+
 from crewai.cli.cli import (
     deploy_create,
     deploy_list,
@@ -12,12 +14,12 @@ from crewai.cli.cli import (
     deploy_remove,
     deply_status,
     flow_add_crew,
-    reset_memories,
     signup,
     test,
     train,
     version,
 )
+from crewai.cli.reset_memories_command import reset_memories_command
 
 
 @pytest.fixture
@@ -55,21 +57,74 @@ def test_train_invalid_string_iterations(train_crew, runner):
     )
 
 
-@mock.patch("crewai.cli.reset_memories_command.get_crew")
-def test_reset_all_memories(mock_get_crew, runner):
-    mock_crew = mock.Mock()
-    mock_get_crew.return_value = mock_crew
-    result = runner.invoke(reset_memories, ["-a"])
+class TestResetMemoriesCommand:
+    """Tests for the reset-memories command."""
 
-    mock_crew.reset_memories.assert_called_once_with(command_type="all")
-    assert result.output == "All memories have been reset.\n"
+    @mock.patch("crewai.cli.reset_memories_command.get_crew")
+    def test_reset_all_memories(self, mock_get_crew, runner):
+        """Test resetting all memories when crew exists."""
+        mock_crew = mock.Mock()
+        mock_get_crew.return_value = mock_crew
+        result = runner.invoke(reset_memories_command, ["-a"])
+
+        mock_crew.reset_memories.assert_called_once_with(command_type="all")
+        assert result.output == "All memories have been reset.\n"
+
+    @mock.patch("crewai.cli.reset_memories_command.get_crew")
+    @mock.patch("crewai.cli.reset_memories_command.ShortTermMemory")
+    @mock.patch("crewai.cli.reset_memories_command.EntityMemory")
+    @mock.patch("crewai.cli.reset_memories_command.LongTermMemory")
+    @mock.patch("crewai.cli.reset_memories_command.TaskOutputStorageHandler")
+    @mock.patch("crewai.cli.reset_memories_command.KnowledgeStorage")
+    def test_reset_all_memories_no_crew(
+        self,
+        MockKnowledgeStorage,
+        MockTaskOutputStorageHandler,
+        MockLongTermMemory,
+        MockEntityMemory,
+        MockShortTermMemory,
+        mock_get_crew,
+        runner,
+    ):
+        """
+        Test resetting all memories when no crew exists.
+        Should reset all memory types individually.
+        """
+        mock_get_crew.return_value = None
+        result = runner.invoke(reset_memories_command, ["-a"])
+
+        MockShortTermMemory().reset.assert_called_once()
+        MockEntityMemory().reset.assert_called_once()
+        MockLongTermMemory().reset.assert_called_once()
+        MockTaskOutputStorageHandler().reset.assert_called_once()
+        MockKnowledgeStorage().reset.assert_called_once()
+        assert result.output == "All memories have been reset.\n"
+        assert result.exit_code == 0
+
+    @mock.patch("crewai.cli.reset_memories_command.get_crew")
+    def test_reset_memories_handles_failure(
+        self,
+        mock_get_crew,
+        runner,
+    ):
+        """
+        Test handling of memory reset failures.
+        Should handle exceptions gracefully and return appropriate error code.
+        """
+        mock_crew = mock.Mock()
+        mock_get_crew.return_value = mock_crew
+        mock_crew.reset_memories.side_effect = Exception("Failed to reset")
+        result = runner.invoke(reset_memories_command, ["-s"], catch_exceptions=True)
+
+        assert result.exit_code == 1
+        assert "An unexpected error occurred: Failed to reset" in result.output
 
 
 @mock.patch("crewai.cli.reset_memories_command.get_crew")
 def test_reset_short_term_memories(mock_get_crew, runner):
     mock_crew = mock.Mock()
     mock_get_crew.return_value = mock_crew
-    result = runner.invoke(reset_memories, ["-s"])
+    result = runner.invoke(reset_memories_command, ["-s"])
 
     mock_crew.reset_memories.assert_called_once_with(command_type="short")
     assert result.output == "Short term memory has been reset.\n"
@@ -79,7 +134,7 @@ def test_reset_short_term_memories(mock_get_crew, runner):
 def test_reset_entity_memories(mock_get_crew, runner):
     mock_crew = mock.Mock()
     mock_get_crew.return_value = mock_crew
-    result = runner.invoke(reset_memories, ["-e"])
+    result = runner.invoke(reset_memories_command, ["-e"])
 
     mock_crew.reset_memories.assert_called_once_with(command_type="entity")
     assert result.output == "Entity memory has been reset.\n"
@@ -89,7 +144,7 @@ def test_reset_entity_memories(mock_get_crew, runner):
 def test_reset_long_term_memories(mock_get_crew, runner):
     mock_crew = mock.Mock()
     mock_get_crew.return_value = mock_crew
-    result = runner.invoke(reset_memories, ["-l"])
+    result = runner.invoke(reset_memories_command, ["-l"])
 
     mock_crew.reset_memories.assert_called_once_with(command_type="long")
     assert result.output == "Long term memory has been reset.\n"
@@ -99,7 +154,7 @@ def test_reset_long_term_memories(mock_get_crew, runner):
 def test_reset_kickoff_outputs(mock_get_crew, runner):
     mock_crew = mock.Mock()
     mock_get_crew.return_value = mock_crew
-    result = runner.invoke(reset_memories, ["-k"])
+    result = runner.invoke(reset_memories_command, ["-k"])
 
     mock_crew.reset_memories.assert_called_once_with(command_type="kickoff_outputs")
     assert result.output == "Latest Kickoff outputs stored has been reset.\n"
@@ -109,7 +164,7 @@ def test_reset_kickoff_outputs(mock_get_crew, runner):
 def test_reset_multiple_memory_flags(mock_get_crew, runner):
     mock_crew = mock.Mock()
     mock_get_crew.return_value = mock_crew
-    result = runner.invoke(reset_memories, ["-s", "-l"])
+    result = runner.invoke(reset_memories_command, ["-s", "-l"])
 
     # Check that reset_memories was called twice with the correct arguments
     assert mock_crew.reset_memories.call_count == 2
@@ -126,7 +181,7 @@ def test_reset_multiple_memory_flags(mock_get_crew, runner):
 def test_reset_knowledge(mock_get_crew, runner):
     mock_crew = mock.Mock()
     mock_get_crew.return_value = mock_crew
-    result = runner.invoke(reset_memories, ["--knowledge"])
+    result = runner.invoke(reset_memories_command, ["--knowledge"])
 
     mock_crew.reset_memories.assert_called_once_with(command_type="knowledge")
     assert result.output == "Knowledge has been reset.\n"
@@ -134,7 +189,7 @@ def test_reset_knowledge(mock_get_crew, runner):
 
 def test_reset_no_memory_flags(runner):
     result = runner.invoke(
-        reset_memories,
+        reset_memories_command,
     )
     assert (
         result.output

tests/conftest.py

@@ -1,30 +1,37 @@
 # conftest.py
 import os
+import tempfile
+from pathlib import Path
+
 import pytest
 from dotenv import load_dotenv
 
 load_result = load_dotenv(override=True)
 
 @pytest.fixture(autouse=True)
-def setup_test_env():
-    """Configure test environment to use Ollama as the default embedding provider."""
-    # Store original environment variables
-    original_env = {
-        "CREWAI_EMBEDDING_PROVIDER": os.environ.get("CREWAI_EMBEDDING_PROVIDER"),
-        "CREWAI_EMBEDDING_MODEL": os.environ.get("CREWAI_EMBEDDING_MODEL"),
-        "CREWAI_OLLAMA_URL": os.environ.get("CREWAI_OLLAMA_URL"),
-    }
-    
-    # Set test environment
-    os.environ["CREWAI_EMBEDDING_PROVIDER"] = "ollama"
-    os.environ["CREWAI_EMBEDDING_MODEL"] = "llama2"
-    os.environ["CREWAI_OLLAMA_URL"] = "http://localhost:11434/api/embeddings"
-    
-    yield
-    
-    # Restore original environment
-    for key, value in original_env.items():
-        if value is None:
-            os.environ.pop(key, None)
-        else:
-            os.environ[key] = value
+def setup_test_environment():
+    """Set up test environment with a temporary directory for SQLite storage."""
+    with tempfile.TemporaryDirectory() as temp_dir:
+        # Create the directory with proper permissions
+        storage_dir = Path(temp_dir) / "crewai_test_storage"
+        storage_dir.mkdir(parents=True, exist_ok=True)
+        
+        # Validate that the directory was created successfully
+        if not storage_dir.exists() or not storage_dir.is_dir():
+            raise RuntimeError(f"Failed to create test storage directory: {storage_dir}")
+        
+        # Verify directory permissions
+        try:
+            # Try to create a test file to verify write permissions
+            test_file = storage_dir / ".permissions_test"
+            test_file.touch()
+            test_file.unlink()
+        except (OSError, IOError) as e:
+            raise RuntimeError(f"Test storage directory {storage_dir} is not writable: {e}")
+        
+        # Set environment variable to point to the test storage directory
+        os.environ["CREWAI_STORAGE_DIR"] = str(storage_dir)
+        
+        yield
+        
+        # Cleanup is handled automatically when tempfile context exits

tests/crew_test.py

@@ -51,6 +51,7 @@ writer = Agent(
 
 def test_crew_with_only_conditional_tasks_raises_error():
     """Test that creating a crew with only conditional tasks raises an error."""
+
     def condition_func(task_output: TaskOutput) -> bool:
         return True
 
@@ -82,6 +83,7 @@ def test_crew_with_only_conditional_tasks_raises_error():
             tasks=[conditional1, conditional2, conditional3],
         )
 
+
 def test_crew_config_conditional_requirement():
     with pytest.raises(ValueError):
         Crew(process=Process.sequential)
@@ -589,12 +591,12 @@ def test_crew_with_delegating_agents_should_not_override_task_tools():
         _, kwargs = mock_execute_sync.call_args
         tools = kwargs["tools"]
 
-        assert any(isinstance(tool, TestTool) for tool in tools), (
-            "TestTool should be present"
-        )
-        assert any("delegate" in tool.name.lower() for tool in tools), (
-            "Delegation tool should be present"
-        )
+        assert any(
+            isinstance(tool, TestTool) for tool in tools
+        ), "TestTool should be present"
+        assert any(
+            "delegate" in tool.name.lower() for tool in tools
+        ), "Delegation tool should be present"
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -653,12 +655,12 @@ def test_crew_with_delegating_agents_should_not_override_agent_tools():
         _, kwargs = mock_execute_sync.call_args
         tools = kwargs["tools"]
 
-        assert any(isinstance(tool, TestTool) for tool in new_ceo.tools), (
-            "TestTool should be present"
-        )
-        assert any("delegate" in tool.name.lower() for tool in tools), (
-            "Delegation tool should be present"
-        )
+        assert any(
+            isinstance(tool, TestTool) for tool in new_ceo.tools
+        ), "TestTool should be present"
+        assert any(
+            "delegate" in tool.name.lower() for tool in tools
+        ), "Delegation tool should be present"
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -782,17 +784,17 @@ def test_task_tools_override_agent_tools_with_allow_delegation():
         used_tools = kwargs["tools"]
 
         # Confirm AnotherTestTool is present but TestTool is not
-        assert any(isinstance(tool, AnotherTestTool) for tool in used_tools), (
-            "AnotherTestTool should be present"
-        )
-        assert not any(isinstance(tool, TestTool) for tool in used_tools), (
-            "TestTool should not be present among used tools"
-        )
+        assert any(
+            isinstance(tool, AnotherTestTool) for tool in used_tools
+        ), "AnotherTestTool should be present"
+        assert not any(
+            isinstance(tool, TestTool) for tool in used_tools
+        ), "TestTool should not be present among used tools"
 
         # Confirm delegation tool(s) are present
-        assert any("delegate" in tool.name.lower() for tool in used_tools), (
-            "Delegation tool should be present"
-        )
+        assert any(
+            "delegate" in tool.name.lower() for tool in used_tools
+        ), "Delegation tool should be present"
 
     # Finally, make sure the agent's original tools remain unchanged
     assert len(researcher_with_delegation.tools) == 1
@@ -1593,9 +1595,9 @@ def test_code_execution_flag_adds_code_tool_upon_kickoff():
 
         # Verify that exactly one tool was used and it was a CodeInterpreterTool
         assert len(used_tools) == 1, "Should have exactly one tool"
-        assert isinstance(used_tools[0], CodeInterpreterTool), (
-            "Tool should be CodeInterpreterTool"
-        )
+        assert isinstance(
+            used_tools[0], CodeInterpreterTool
+        ), "Tool should be CodeInterpreterTool"
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -1952,6 +1954,7 @@ def test_task_callback_on_crew():
 
 def test_task_callback_both_on_task_and_crew():
     from unittest.mock import MagicMock, patch
+
     mock_callback_on_task = MagicMock()
     mock_callback_on_crew = MagicMock()
 
@@ -2101,21 +2104,22 @@ def test_conditional_task_uses_last_output():
         expected_output="First output",
         agent=researcher,
     )
+
     def condition_fails(task_output: TaskOutput) -> bool:
         # This condition will never be met
         return "never matches" in task_output.raw.lower()
-    
+
     def condition_succeeds(task_output: TaskOutput) -> bool:
         # This condition will match first task's output
         return "first success" in task_output.raw.lower()
-    
+
     conditional_task1 = ConditionalTask(
         description="Second task - conditional that fails condition",
         expected_output="Second output",
         agent=researcher,
         condition=condition_fails,
     )
-    
+
     conditional_task2 = ConditionalTask(
         description="Third task - conditional that succeeds using first task output",
         expected_output="Third output",
@@ -2134,35 +2138,37 @@ def test_conditional_task_uses_last_output():
         raw="First success output",  # Will be used by third task's condition
         agent=researcher.role,
     )
-    mock_skipped = TaskOutput(
-        description="Second task output",
-        raw="",  # Empty output since condition fails
-        agent=researcher.role,
-    )
     mock_third = TaskOutput(
         description="Third task output",
         raw="Third task executed",  # Output when condition succeeds using first task output
         agent=writer.role,
     )
-    
+
     # Set up mocks for task execution and conditional logic
     with patch.object(ConditionalTask, "should_execute") as mock_should_execute:
         # First conditional fails, second succeeds
         mock_should_execute.side_effect = [False, True]
-        
         with patch.object(Task, "execute_sync") as mock_execute:
             mock_execute.side_effect = [mock_first, mock_third]
             result = crew.kickoff()
-            
+
             # Verify execution behavior
             assert mock_execute.call_count == 2  # Only first and third tasks execute
             assert mock_should_execute.call_count == 2  # Both conditionals checked
-            
-            # Verify outputs collection
+
+            # Verify outputs collection:
+            # First executed task output, followed by an automatically generated (skipped) output, then the conditional execution
             assert len(result.tasks_output) == 3
-            assert result.tasks_output[0].raw == "First success output"  # First task succeeded
-            assert result.tasks_output[1].raw == ""  # Second task skipped (condition failed)
-            assert result.tasks_output[2].raw == "Third task executed"  # Third task used first task's output
+            assert (
+                result.tasks_output[0].raw == "First success output"
+            )  # First task succeeded
+            assert (
+                result.tasks_output[1].raw == ""
+            )  # Second task skipped (condition failed)
+            assert (
+                result.tasks_output[2].raw == "Third task executed"
+            )  # Third task used first task's output
+
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_conditional_tasks_result_collection():
@@ -2172,20 +2178,20 @@ def test_conditional_tasks_result_collection():
         expected_output="First output",
         agent=researcher,
     )
-    
+
     def condition_never_met(task_output: TaskOutput) -> bool:
         return "never matches" in task_output.raw.lower()
-    
+
     def condition_always_met(task_output: TaskOutput) -> bool:
         return "success" in task_output.raw.lower()
-    
+
     task2 = ConditionalTask(
         description="Conditional task that never executes",
         expected_output="Second output",
         agent=researcher,
         condition=condition_never_met,
     )
-    
+
     task3 = ConditionalTask(
         description="Conditional task that always executes",
         expected_output="Third output",
@@ -2204,35 +2210,46 @@ def test_conditional_tasks_result_collection():
         raw="Success output",  # Triggers third task's condition
         agent=researcher.role,
     )
-    mock_skipped = TaskOutput(
-        description="Skipped output",
-        raw="",  # Empty output for skipped task
-        agent=researcher.role,
-    )
     mock_conditional = TaskOutput(
         description="Conditional output",
         raw="Conditional task executed",
         agent=writer.role,
     )
-    
+
     # Set up mocks for task execution and conditional logic
     with patch.object(ConditionalTask, "should_execute") as mock_should_execute:
         # First conditional fails, second succeeds
         mock_should_execute.side_effect = [False, True]
-        
         with patch.object(Task, "execute_sync") as mock_execute:
             mock_execute.side_effect = [mock_success, mock_conditional]
             result = crew.kickoff()
-            
+
             # Verify execution behavior
             assert mock_execute.call_count == 2  # Only first and third tasks execute
             assert mock_should_execute.call_count == 2  # Both conditionals checked
-            
+
+            # Verify task output collection:
+            # There should be three outputs: normal task, skipped conditional task (empty output),
+            # and the conditional task that executed.
+            assert len(result.tasks_output) == 3
+            assert (
+                result.tasks_output[0].raw == "Success output"
+            )  # Normal task executed
+            assert result.tasks_output[1].raw == ""  # Second task skipped
+            assert (
+                result.tasks_output[2].raw == "Conditional task executed"
+            )  # Third task executed
+
             # Verify task output collection
             assert len(result.tasks_output) == 3
-            assert result.tasks_output[0].raw == "Success output"      # Normal task executed
-            assert result.tasks_output[1].raw == ""                    # Second task skipped
-            assert result.tasks_output[2].raw == "Conditional task executed"  # Third task executed
+            assert (
+                result.tasks_output[0].raw == "Success output"
+            )  # Normal task executed
+            assert result.tasks_output[1].raw == ""  # Second task skipped
+            assert (
+                result.tasks_output[2].raw == "Conditional task executed"
+            )  # Third task executed
+
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_multiple_conditional_tasks():
@@ -2242,20 +2259,20 @@ def test_multiple_conditional_tasks():
         expected_output="Research output",
         agent=researcher,
     )
-    
+
     def condition1(task_output: TaskOutput) -> bool:
         return "success" in task_output.raw.lower()
-    
+
     def condition2(task_output: TaskOutput) -> bool:
         return "proceed" in task_output.raw.lower()
-    
+
     task2 = ConditionalTask(
         description="First conditional task",
         expected_output="Conditional output 1",
         agent=writer,
         condition=condition1,
     )
-    
+
     task3 = ConditionalTask(
         description="Second conditional task",
         expected_output="Conditional output 2",
@@ -2274,7 +2291,7 @@ def test_multiple_conditional_tasks():
         raw="Success and proceed output",
         agent=researcher.role,
     )
-    
+
     # Set up mocks for task execution
     with patch.object(Task, "execute_sync", return_value=mock_success) as mock_execute:
         result = crew.kickoff()
@@ -2282,6 +2299,7 @@ def test_multiple_conditional_tasks():
         assert mock_execute.call_count == 3
         assert len(result.tasks_output) == 3
 
+
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_using_contextual_memory():
     from unittest.mock import patch
@@ -3400,9 +3418,9 @@ def test_fetch_inputs():
     expected_placeholders = {"role_detail", "topic", "field"}
     actual_placeholders = crew.fetch_inputs()
 
-    assert actual_placeholders == expected_placeholders, (
-        f"Expected {expected_placeholders}, but got {actual_placeholders}"
-    )
+    assert (
+        actual_placeholders == expected_placeholders
+    ), f"Expected {expected_placeholders}, but got {actual_placeholders}"
 
 
 def test_task_tools_preserve_code_execution_tools():
@@ -3475,20 +3493,20 @@ def test_task_tools_preserve_code_execution_tools():
         used_tools = kwargs["tools"]
 
         # Verify all expected tools are present
-        assert any(isinstance(tool, TestTool) for tool in used_tools), (
-            "Task's TestTool should be present"
-        )
-        assert any(isinstance(tool, CodeInterpreterTool) for tool in used_tools), (
-            "CodeInterpreterTool should be present"
-        )
-        assert any("delegate" in tool.name.lower() for tool in used_tools), (
-            "Delegation tool should be present"
-        )
+        assert any(
+            isinstance(tool, TestTool) for tool in used_tools
+        ), "Task's TestTool should be present"
+        assert any(
+            isinstance(tool, CodeInterpreterTool) for tool in used_tools
+        ), "CodeInterpreterTool should be present"
+        assert any(
+            "delegate" in tool.name.lower() for tool in used_tools
+        ), "Delegation tool should be present"
 
         # Verify the total number of tools (TestTool + CodeInterpreter + 2 delegation tools)
-        assert len(used_tools) == 4, (
-            "Should have TestTool, CodeInterpreter, and 2 delegation tools"
-        )
+        assert (
+            len(used_tools) == 4
+        ), "Should have TestTool, CodeInterpreter, and 2 delegation tools"
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -3532,9 +3550,9 @@ def test_multimodal_flag_adds_multimodal_tools():
         used_tools = kwargs["tools"]
 
         # Check that the multimodal tool was added
-        assert any(isinstance(tool, AddImageTool) for tool in used_tools), (
-            "AddImageTool should be present when agent is multimodal"
-        )
+        assert any(
+            isinstance(tool, AddImageTool) for tool in used_tools
+        ), "AddImageTool should be present when agent is multimodal"
 
         # Verify we have exactly one tool (just the AddImageTool)
         assert len(used_tools) == 1, "Should only have the AddImageTool"
@@ -3760,9 +3778,9 @@ def test_crew_guardrail_feedback_in_context():
     assert len(execution_contexts) > 1, "Task should have been executed multiple times"
 
     # Verify that the second execution included the guardrail feedback
-    assert "Output must contain the keyword 'IMPORTANT'" in execution_contexts[1], (
-        "Guardrail feedback should be included in retry context"
-    )
+    assert (
+        "Output must contain the keyword 'IMPORTANT'" in execution_contexts[1]
+    ), "Guardrail feedback should be included in retry context"
 
     # Verify final output meets guardrail requirements
     assert "IMPORTANT" in result.raw, "Final output should contain required keyword"

tests/flow_test.py

@@ -1,11 +1,18 @@
 """Test Flow creation and execution basic functionality."""
 
 import asyncio
+from datetime import datetime
 
 import pytest
 from pydantic import BaseModel
 
 from crewai.flow.flow import Flow, and_, listen, or_, router, start
+from crewai.flow.flow_events import (
+    FlowFinishedEvent,
+    FlowStartedEvent,
+    MethodExecutionFinishedEvent,
+    MethodExecutionStartedEvent,
+)
 
 
 def test_simple_sequential_flow():
@@ -398,3 +405,218 @@ def test_router_with_multiple_conditions():
 
     # final_step should run after router_and
     assert execution_order.index("log_final_step") > execution_order.index("router_and")
+
+
+def test_unstructured_flow_event_emission():
+    """Test that the correct events are emitted during unstructured flow
+    execution with all fields validated."""
+
+    class PoemFlow(Flow):
+        @start()
+        def prepare_flower(self):
+            self.state["flower"] = "roses"
+            return "foo"
+
+        @start()
+        def prepare_color(self):
+            self.state["color"] = "red"
+            return "bar"
+
+        @listen(prepare_color)
+        def write_first_sentence(self):
+            return f"{self.state['flower']} are {self.state['color']}"
+
+        @listen(write_first_sentence)
+        def finish_poem(self, first_sentence):
+            separator = self.state.get("separator", "\n")
+            return separator.join([first_sentence, "violets are blue"])
+
+        @listen(finish_poem)
+        def save_poem_to_database(self):
+            # A method without args/kwargs to ensure events are sent correctly
+            pass
+
+    event_log = []
+
+    def handle_event(_, event):
+        event_log.append(event)
+
+    flow = PoemFlow()
+    flow.event_emitter.connect(handle_event)
+    flow.kickoff(inputs={"separator": ", "})
+
+    assert isinstance(event_log[0], FlowStartedEvent)
+    assert event_log[0].flow_name == "PoemFlow"
+    assert event_log[0].inputs == {"separator": ", "}
+    assert isinstance(event_log[0].timestamp, datetime)
+
+    # Asserting for concurrent start method executions in a for loop as you
+    # can't guarantee ordering in asynchronous executions
+    for i in range(1, 5):
+        event = event_log[i]
+        assert isinstance(event.state, dict)
+        assert isinstance(event.state["id"], str)
+
+        if event.method_name == "prepare_flower":
+            if isinstance(event, MethodExecutionStartedEvent):
+                assert event.params == {}
+                assert event.state["separator"] == ", "
+            elif isinstance(event, MethodExecutionFinishedEvent):
+                assert event.result == "foo"
+                assert event.state["flower"] == "roses"
+                assert event.state["separator"] == ", "
+            else:
+                assert False, "Unexpected event type for prepare_flower"
+        elif event.method_name == "prepare_color":
+            if isinstance(event, MethodExecutionStartedEvent):
+                assert event.params == {}
+                assert event.state["separator"] == ", "
+            elif isinstance(event, MethodExecutionFinishedEvent):
+                assert event.result == "bar"
+                assert event.state["color"] == "red"
+                assert event.state["separator"] == ", "
+            else:
+                assert False, "Unexpected event type for prepare_color"
+        else:
+            assert False, f"Unexpected method {event.method_name} in prepare events"
+
+    assert isinstance(event_log[5], MethodExecutionStartedEvent)
+    assert event_log[5].method_name == "write_first_sentence"
+    assert event_log[5].params == {}
+    assert isinstance(event_log[5].state, dict)
+    assert event_log[5].state["flower"] == "roses"
+    assert event_log[5].state["color"] == "red"
+    assert event_log[5].state["separator"] == ", "
+
+    assert isinstance(event_log[6], MethodExecutionFinishedEvent)
+    assert event_log[6].method_name == "write_first_sentence"
+    assert event_log[6].result == "roses are red"
+
+    assert isinstance(event_log[7], MethodExecutionStartedEvent)
+    assert event_log[7].method_name == "finish_poem"
+    assert event_log[7].params == {"_0": "roses are red"}
+    assert isinstance(event_log[7].state, dict)
+    assert event_log[7].state["flower"] == "roses"
+    assert event_log[7].state["color"] == "red"
+
+    assert isinstance(event_log[8], MethodExecutionFinishedEvent)
+    assert event_log[8].method_name == "finish_poem"
+    assert event_log[8].result == "roses are red, violets are blue"
+
+    assert isinstance(event_log[9], MethodExecutionStartedEvent)
+    assert event_log[9].method_name == "save_poem_to_database"
+    assert event_log[9].params == {}
+    assert isinstance(event_log[9].state, dict)
+    assert event_log[9].state["flower"] == "roses"
+    assert event_log[9].state["color"] == "red"
+
+    assert isinstance(event_log[10], MethodExecutionFinishedEvent)
+    assert event_log[10].method_name == "save_poem_to_database"
+    assert event_log[10].result is None
+
+    assert isinstance(event_log[11], FlowFinishedEvent)
+    assert event_log[11].flow_name == "PoemFlow"
+    assert event_log[11].result is None
+    assert isinstance(event_log[11].timestamp, datetime)
+
+
+def test_structured_flow_event_emission():
+    """Test that the correct events are emitted during structured flow
+    execution with all fields validated."""
+
+    class OnboardingState(BaseModel):
+        name: str = ""
+        sent: bool = False
+
+    class OnboardingFlow(Flow[OnboardingState]):
+        @start()
+        def user_signs_up(self):
+            self.state.sent = False
+
+        @listen(user_signs_up)
+        def send_welcome_message(self):
+            self.state.sent = True
+            return f"Welcome, {self.state.name}!"
+
+    event_log = []
+
+    def handle_event(_, event):
+        event_log.append(event)
+
+    flow = OnboardingFlow()
+    flow.event_emitter.connect(handle_event)
+    flow.kickoff(inputs={"name": "Anakin"})
+
+    assert isinstance(event_log[0], FlowStartedEvent)
+    assert event_log[0].flow_name == "OnboardingFlow"
+    assert event_log[0].inputs == {"name": "Anakin"}
+    assert isinstance(event_log[0].timestamp, datetime)
+
+    assert isinstance(event_log[1], MethodExecutionStartedEvent)
+    assert event_log[1].method_name == "user_signs_up"
+
+    assert isinstance(event_log[2], MethodExecutionFinishedEvent)
+    assert event_log[2].method_name == "user_signs_up"
+
+    assert isinstance(event_log[3], MethodExecutionStartedEvent)
+    assert event_log[3].method_name == "send_welcome_message"
+    assert event_log[3].params == {}
+    assert getattr(event_log[3].state, "sent") is False
+
+    assert isinstance(event_log[4], MethodExecutionFinishedEvent)
+    assert event_log[4].method_name == "send_welcome_message"
+    assert getattr(event_log[4].state, "sent") is True
+    assert event_log[4].result == "Welcome, Anakin!"
+
+    assert isinstance(event_log[5], FlowFinishedEvent)
+    assert event_log[5].flow_name == "OnboardingFlow"
+    assert event_log[5].result == "Welcome, Anakin!"
+    assert isinstance(event_log[5].timestamp, datetime)
+
+
+def test_stateless_flow_event_emission():
+    """Test that the correct events are emitted stateless during flow execution
+    with all fields validated."""
+
+    class StatelessFlow(Flow):
+        @start()
+        def init(self):
+            pass
+
+        @listen(init)
+        def process(self):
+            return "Deeds will not be less valiant because they are unpraised."
+
+    event_log = []
+
+    def handle_event(_, event):
+        event_log.append(event)
+
+    flow = StatelessFlow()
+    flow.event_emitter.connect(handle_event)
+    flow.kickoff()
+
+    assert isinstance(event_log[0], FlowStartedEvent)
+    assert event_log[0].flow_name == "StatelessFlow"
+    assert event_log[0].inputs is None
+    assert isinstance(event_log[0].timestamp, datetime)
+
+    assert isinstance(event_log[1], MethodExecutionStartedEvent)
+    assert event_log[1].method_name == "init"
+
+    assert isinstance(event_log[2], MethodExecutionFinishedEvent)
+    assert event_log[2].method_name == "init"
+
+    assert isinstance(event_log[3], MethodExecutionStartedEvent)
+    assert event_log[3].method_name == "process"
+
+    assert isinstance(event_log[4], MethodExecutionFinishedEvent)
+    assert event_log[4].method_name == "process"
+
+    assert isinstance(event_log[5], FlowFinishedEvent)
+    assert event_log[5].flow_name == "StatelessFlow"
+    assert (
+        event_log[5].result
+        == "Deeds will not be less valiant because they are unpraised."
+    )
+    assert isinstance(event_log[5].timestamp, datetime)

tests/memory/test_memory_reset.py

@@ -1,91 +0,0 @@
-import os
-import tempfile
-import pytest
-from crewai.memory import ShortTermMemory, LongTermMemory, EntityMemory
-from crewai.utilities.exceptions.embedding_exceptions import (
-    EmbeddingConfigurationError,
-    EmbeddingProviderError
-)
-from crewai.utilities import EmbeddingConfigurator
-
-@pytest.fixture
-def temp_db_dir():
-    with tempfile.TemporaryDirectory() as tmpdir:
-        yield tmpdir
-
-def test_memory_reset_with_ollama(temp_db_dir):
-    os.environ["CREWAI_EMBEDDING_PROVIDER"] = "ollama"
-    os.environ["CREWAI_EMBEDDING_MODEL"] = "llama2"
-    
-    memories = [
-        ShortTermMemory(path=temp_db_dir),
-        LongTermMemory(path=temp_db_dir),
-        EntityMemory(path=temp_db_dir)
-    ]
-    for memory in memories:
-        memory.reset()
-
-def test_memory_reset_with_openai(temp_db_dir):
-    os.environ["CREWAI_EMBEDDING_PROVIDER"] = "openai"
-    os.environ["CREWAI_EMBEDDING_MODEL"] = "text-embedding-3-small"
-    
-    memories = [
-        ShortTermMemory(path=temp_db_dir),
-        LongTermMemory(path=temp_db_dir),
-        EntityMemory(path=temp_db_dir)
-    ]
-    for memory in memories:
-        memory.reset()
-
-def test_memory_reset_with_invalid_provider(temp_db_dir):
-    os.environ["CREWAI_EMBEDDING_PROVIDER"] = "invalid_provider"
-    with pytest.raises(EmbeddingProviderError):
-        memories = [
-            ShortTermMemory(path=temp_db_dir),
-            LongTermMemory(path=temp_db_dir),
-            EntityMemory(path=temp_db_dir)
-        ]
-        for memory in memories:
-            memory.reset()
-
-def test_memory_reset_with_invalid_configuration(temp_db_dir):
-    os.environ["CREWAI_EMBEDDING_PROVIDER"] = "openai"
-    os.environ.pop("OPENAI_API_KEY", None)
-    
-    with pytest.raises(EmbeddingConfigurationError):
-        memories = [
-            ShortTermMemory(path=temp_db_dir),
-            LongTermMemory(path=temp_db_dir),
-            EntityMemory(path=temp_db_dir)
-        ]
-        for memory in memories:
-            memory.reset()
-
-def test_memory_reset_with_missing_ollama_url(temp_db_dir):
-    os.environ["CREWAI_EMBEDDING_PROVIDER"] = "ollama"
-    os.environ.pop("CREWAI_OLLAMA_URL", None)
-    # Should use default URL when CREWAI_OLLAMA_URL is not set
-    memories = [
-        ShortTermMemory(path=temp_db_dir),
-        LongTermMemory(path=temp_db_dir),
-        EntityMemory(path=temp_db_dir)
-    ]
-    for memory in memories:
-        memory.reset()
-
-def test_memory_reset_with_custom_path(temp_db_dir):
-    os.environ["CREWAI_EMBEDDING_PROVIDER"] = "ollama"
-    custom_path = os.path.join(temp_db_dir, "custom")
-    os.makedirs(custom_path, exist_ok=True)
-    
-    memories = [
-        ShortTermMemory(path=custom_path),
-        LongTermMemory(path=custom_path),
-        EntityMemory(path=custom_path)
-    ]
-    for memory in memories:
-        memory.reset()
-        
-    assert not os.path.exists(os.path.join(custom_path, "short_term"))
-    assert not os.path.exists(os.path.join(custom_path, "long_term"))
-    assert not os.path.exists(os.path.join(custom_path, "entity"))

uv.lock

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-version = "1.3.1"
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-dependencies = [
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-]
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-]
-
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 name = "autoflake"
 version = "2.3.1"
@@ -595,14 +565,14 @@ wheels = [
 
 [[package]]
 name = "click"
-version = "8.1.7"
+version = "8.1.8"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "colorama", marker = "platform_system == 'Windows'" },
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+    { url = "https://files.pythonhosted.org/packages/7e/d4/7ebdbd03970677812aac39c869717059dbb71a4cfc033ca6e5221787892c/click-8.1.8-py3-none-any.whl", hash = "sha256:63c132bbbed01578a06712a2d1f497bb62d9c1c0d329b7903a866228027263b2", size = 98188 },
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 [[package]]
@@ -649,7 +619,7 @@ wheels = [
 
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 name = "crewai"
-version = "0.100.1"
+version = "0.102.0"
 source = { editable = "." }
 dependencies = [
     { name = "appdirs" },
@@ -733,7 +703,7 @@ requires-dist = [
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     { name = "chromadb", specifier = ">=0.5.23" },
     { name = "click", specifier = ">=8.1.7" },
-    { name = "crewai-tools", marker = "extra == 'tools'", specifier = ">=0.32.1" },
+    { name = "crewai-tools", marker = "extra == 'tools'", specifier = ">=0.36.0" },
     { name = "docling", marker = "extra == 'docling'", specifier = ">=2.12.0" },
     { name = "fastembed", marker = "extra == 'fastembed'", specifier = ">=0.4.1" },
     { name = "instructor", specifier = ">=1.3.3" },
@@ -782,33 +752,24 @@ dev = [
 
 [[package]]
 name = "crewai-tools"
-version = "0.32.1"
+version = "0.36.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "beautifulsoup4" },
     { name = "chromadb" },
+    { name = "click" },
     { name = "crewai" },
     { name = "docker" },
-    { name = "docx2txt" },
     { name = "embedchain" },
     { name = "lancedb" },
-    { name = "linkup-sdk" },
     { name = "openai" },
-    { name = "patronus" },
     { name = "pydantic" },
     { name = "pyright" },
     { name = "pytube" },
     { name = "requests" },
-    { name = "scrapegraph-py" },
-    { name = "selenium" },
-    { name = "serpapi" },
-    { name = "snowflake" },
-    { name = "spider-client" },
-    { name = "weaviate-client" },
 ]
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+sdist = { url = "https://files.pythonhosted.org/packages/4d/e1/d65778cf4aea106f3f60a4208521f04bc7f1d26be4e34eeb63cae6297d50/crewai_tools-0.36.0.tar.gz", hash = "sha256:761b396ee6a4019a988720dd6a14e1409f5de9d0cdc2a8662b487d87efb1a6bf", size = 900178 }
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-
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