Merge branch 'main' into bugfix/memory-reset-not-working

* Better support async

* Drop coroutine

.gitignore

@@ -21,4 +21,5 @@ crew_tasks_output.json
 .mypy_cache
 .ruff_cache
 .venv
-agentops.log
+agentops.log
+test_flow.html

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 +1,100 @@
+---
+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.
+
+[![Langfuse Overview Video](https://github.com/user-attachments/assets/3926b288-ff61-4b95-8aa1-45d041c70866)](https://langfuse.com/watch-demo)
+
+## 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
@@ -17,28 +16,20 @@ 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.events.agent_events import (
+    AgentExecutionCompletedEvent,
+    AgentExecutionErrorEvent,
+    AgentExecutionStartedEvent,
+)
+from crewai.utilities.events.crewai_event_bus import crewai_event_bus
+from crewai.utilities.llm_utils import create_llm
 from crewai.utilities.token_counter_callback import TokenCalcHandler
 from crewai.utilities.training_handler import CrewTrainingHandler
 
-agentops = None
 
-try:
-    import agentops  # type: ignore # Name "agentops" is already defined
-    from agentops import track_agent  # type: ignore
-except ImportError:
-
-    def track_agent():
-        def noop(f):
-            return f
-
-        return noop
-
-
-@track_agent()
 class Agent(BaseAgent):
     """Represents an agent in a system.
 
@@ -55,13 +46,13 @@ 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 +62,6 @@ 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 +73,7 @@ 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 +95,6 @@ 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 +107,19 @@ 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 +137,16 @@ 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 +180,15 @@ 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 +207,8 @@ 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
@@ -339,6 +232,15 @@ class Agent(BaseAgent):
             task_prompt = self._use_trained_data(task_prompt=task_prompt)
 
         try:
+            crewai_event_bus.emit(
+                self,
+                event=AgentExecutionStartedEvent(
+                    agent=self,
+                    tools=self.tools,
+                    task_prompt=task_prompt,
+                    task=task,
+                ),
+            )
             result = self.agent_executor.invoke(
                 {
                     "input": task_prompt,
@@ -348,8 +250,27 @@ class Agent(BaseAgent):
                 }
             )["output"]
         except Exception as e:
+            if e.__class__.__module__.startswith("litellm"):
+                # Do not retry on litellm errors
+                crewai_event_bus.emit(
+                    self,
+                    event=AgentExecutionErrorEvent(
+                        agent=self,
+                        task=task,
+                        error=str(e),
+                    ),
+                )
+                raise e
             self._times_executed += 1
             if self._times_executed > self.max_retry_limit:
+                crewai_event_bus.emit(
+                    self,
+                    event=AgentExecutionErrorEvent(
+                        agent=self,
+                        task=task,
+                        error=str(e),
+                    ),
+                )
                 raise e
             result = self.execute_task(task, context, tools)
 
@@ -362,7 +283,10 @@ class Agent(BaseAgent):
         for tool_result in self.tools_results:  # type: ignore # Item "None" of "list[Any] | None" has no attribute "__iter__" (not iterable)
             if tool_result.get("result_as_answer", False):
                 result = tool_result["result"]
-
+        crewai_event_bus.emit(
+            self,
+            event=AgentExecutionCompletedEvent(agent=self, task=task, output=result),
+        )
         return result
 
     def create_agent_executor(
@@ -420,13 +344,14 @@ 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

@@ -20,8 +20,7 @@ from crewai.agents.cache.cache_handler import CacheHandler
 from crewai.agents.tools_handler import ToolsHandler
 from crewai.knowledge.knowledge import Knowledge
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
-from crewai.tools import BaseTool
-from crewai.tools.base_tool import Tool
+from crewai.tools.base_tool import BaseTool, Tool
 from crewai.utilities import I18N, Logger, RPMController
 from crewai.utilities.config import process_config
 from crewai.utilities.converter import Converter
@@ -112,8 +111,8 @@ class BaseAgent(ABC, BaseModel):
         default=False,
         description="Enable agent to delegate and ask questions among each other.",
     )
-    tools: Optional[List[Any]] = Field(
-        default_factory=lambda: [], description="Tools at agents' disposal"
+    tools: Optional[List[BaseTool]] = Field(
+        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/agents/agent_builder/base_agent_executor_mixin.py

@@ -114,10 +114,15 @@ class CrewAgentExecutorMixin:
             prompt = (
                 "\n\n=====\n"
                 "## HUMAN FEEDBACK: Provide feedback on the Final Result and Agent's actions.\n"
-                "Respond with 'looks good' to accept or provide specific improvement requests.\n"
-                "You can provide multiple rounds of feedback until satisfied.\n"
+                "Please follow these guidelines:\n"
+                " - If you are happy with the result, simply hit Enter without typing anything.\n"
+                " - Otherwise, provide specific improvement requests.\n"
+                " - You can provide multiple rounds of feedback until satisfied.\n"
                 "=====\n"
             )
 
         self._printer.print(content=prompt, color="bold_yellow")
-        return input()
+        response = input()
+        if response.strip() != "":
+            self._printer.print(content="\nProcessing your feedback...", color="cyan")
+        return response

src/crewai/agents/agent_builder/utilities/base_output_converter.py

@@ -31,11 +31,11 @@ class OutputConverter(BaseModel, ABC):
     )
 
     @abstractmethod
-    def to_pydantic(self, current_attempt=1):
+    def to_pydantic(self, current_attempt=1) -> BaseModel:
         """Convert text to pydantic."""
         pass
 
     @abstractmethod
-    def to_json(self, current_attempt=1):
+    def to_json(self, current_attempt=1) -> dict:
         """Convert text to json."""
         pass

src/crewai/agents/crew_agent_executor.py

@@ -18,6 +18,12 @@ from crewai.tools.base_tool import BaseTool
 from crewai.tools.tool_usage import ToolUsage, ToolUsageErrorException
 from crewai.utilities import I18N, Printer
 from crewai.utilities.constants import MAX_LLM_RETRY, TRAINING_DATA_FILE
+from crewai.utilities.events import (
+    ToolUsageErrorEvent,
+    ToolUsageStartedEvent,
+    crewai_event_bus,
+)
+from crewai.utilities.events.tool_usage_events import ToolUsageStartedEvent
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededException,
 )
@@ -107,11 +113,11 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             )
             raise
         except Exception as e:
+            self._handle_unknown_error(e)
             if e.__class__.__module__.startswith("litellm"):
                 # Do not retry on litellm errors
                 raise e
             else:
-                self._handle_unknown_error(e)
                 raise e
 
         if self.ask_for_human_input:
@@ -349,40 +355,68 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                 )
 
     def _execute_tool_and_check_finality(self, agent_action: AgentAction) -> ToolResult:
-        tool_usage = ToolUsage(
-            tools_handler=self.tools_handler,
-            tools=self.tools,
-            original_tools=self.original_tools,
-            tools_description=self.tools_description,
-            tools_names=self.tools_names,
-            function_calling_llm=self.function_calling_llm,
-            task=self.task,  # type: ignore[arg-type]
-            agent=self.agent,
-            action=agent_action,
-        )
-        tool_calling = tool_usage.parse_tool_calling(agent_action.text)
-
-        if isinstance(tool_calling, ToolUsageErrorException):
-            tool_result = tool_calling.message
-            return ToolResult(result=tool_result, result_as_answer=False)
-        else:
-            if tool_calling.tool_name.casefold().strip() in [
-                name.casefold().strip() for name in self.tool_name_to_tool_map
-            ] or tool_calling.tool_name.casefold().replace("_", " ") in [
-                name.casefold().strip() for name in self.tool_name_to_tool_map
-            ]:
-                tool_result = tool_usage.use(tool_calling, agent_action.text)
-                tool = self.tool_name_to_tool_map.get(tool_calling.tool_name)
-                if tool:
-                    return ToolResult(
-                        result=tool_result, result_as_answer=tool.result_as_answer
-                    )
-            else:
-                tool_result = self._i18n.errors("wrong_tool_name").format(
-                    tool=tool_calling.tool_name,
-                    tools=", ".join([tool.name.casefold() for tool in self.tools]),
+        try:
+            if self.agent:
+                crewai_event_bus.emit(
+                    self,
+                    event=ToolUsageStartedEvent(
+                        agent_key=self.agent.key,
+                        agent_role=self.agent.role,
+                        tool_name=agent_action.tool,
+                        tool_args=agent_action.tool_input,
+                        tool_class=agent_action.tool,
+                    ),
                 )
-        return ToolResult(result=tool_result, result_as_answer=False)
+            tool_usage = ToolUsage(
+                tools_handler=self.tools_handler,
+                tools=self.tools,
+                original_tools=self.original_tools,
+                tools_description=self.tools_description,
+                tools_names=self.tools_names,
+                function_calling_llm=self.function_calling_llm,
+                task=self.task,  # type: ignore[arg-type]
+                agent=self.agent,
+                action=agent_action,
+            )
+            tool_calling = tool_usage.parse_tool_calling(agent_action.text)
+
+            if isinstance(tool_calling, ToolUsageErrorException):
+                tool_result = tool_calling.message
+                return ToolResult(result=tool_result, result_as_answer=False)
+            else:
+                if tool_calling.tool_name.casefold().strip() in [
+                    name.casefold().strip() for name in self.tool_name_to_tool_map
+                ] or tool_calling.tool_name.casefold().replace("_", " ") in [
+                    name.casefold().strip() for name in self.tool_name_to_tool_map
+                ]:
+                    tool_result = tool_usage.use(tool_calling, agent_action.text)
+                    tool = self.tool_name_to_tool_map.get(tool_calling.tool_name)
+                    if tool:
+                        return ToolResult(
+                            result=tool_result, result_as_answer=tool.result_as_answer
+                        )
+                else:
+                    tool_result = self._i18n.errors("wrong_tool_name").format(
+                        tool=tool_calling.tool_name,
+                        tools=", ".join([tool.name.casefold() for tool in self.tools]),
+                    )
+                return ToolResult(result=tool_result, result_as_answer=False)
+
+        except Exception as e:
+            # TODO: drop
+            if self.agent:
+                crewai_event_bus.emit(
+                    self,
+                    event=ToolUsageErrorEvent(  # validation error
+                        agent_key=self.agent.key,
+                        agent_role=self.agent.role,
+                        tool_name=agent_action.tool,
+                        tool_args=agent_action.tool_input,
+                        tool_class=agent_action.tool,
+                        error=str(e),
+                    ),
+                )
+            raise e
 
     def _summarize_messages(self) -> None:
         messages_groups = []
@@ -514,10 +548,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self, initial_answer: AgentFinish, feedback: str
     ) -> AgentFinish:
         """Process feedback for training scenarios with single iteration."""
-        self._printer.print(
-            content="\nProcessing training feedback.\n",
-            color="yellow",
-        )
         self._handle_crew_training_output(initial_answer, feedback)
         self.messages.append(
             self._format_msg(
@@ -537,9 +567,8 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         answer = current_answer
 
         while self.ask_for_human_input:
-            response = self._get_llm_feedback_response(feedback)
-
-            if not self._feedback_requires_changes(response):
+            # If the user provides a blank response, assume they are happy with the result
+            if feedback.strip() == "":
                 self.ask_for_human_input = False
             else:
                 answer = self._process_feedback_iteration(feedback)
@@ -547,27 +576,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
 
         return answer
 
-    def _get_llm_feedback_response(self, feedback: str) -> Optional[str]:
-        """Get LLM classification of whether feedback requires changes."""
-        prompt = self._i18n.slice("human_feedback_classification").format(
-            feedback=feedback
-        )
-        message = self._format_msg(prompt, role="system")
-
-        for retry in range(MAX_LLM_RETRY):
-            try:
-                response = self.llm.call([message], callbacks=self.callbacks)
-                return response.strip().lower() if response else None
-            except Exception as error:
-                self._log_feedback_error(retry, error)
-
-        self._log_max_retries_exceeded()
-        return None
-
-    def _feedback_requires_changes(self, response: Optional[str]) -> bool:
-        """Determine if feedback response indicates need for changes."""
-        return response == "true" if response else False
-
     def _process_feedback_iteration(self, feedback: str) -> AgentFinish:
         """Process a single feedback iteration."""
         self.messages.append(

src/crewai/agents/parser.py

@@ -94,6 +94,13 @@ class CrewAgentParser:
 
         elif includes_answer:
             final_answer = text.split(FINAL_ANSWER_ACTION)[-1].strip()
+            # Check whether the final answer ends with triple backticks.
+            if final_answer.endswith("```"):
+                # Count occurrences of triple backticks in the final answer.
+                count = final_answer.count("```")
+                # If count is odd then it's an unmatched trailing set; remove it.
+                if count % 2 != 0:
+                    final_answer = final_answer[:-3].rstrip()
             return AgentFinish(thought, final_answer, text)
 
         if not re.search(r"Action\s*\d*\s*:[\s]*(.*?)", text, re.DOTALL):
@@ -120,7 +127,10 @@ class CrewAgentParser:
         regex = r"(.*?)(?:\n\nAction|\n\nFinal Answer)"
         thought_match = re.search(regex, text, re.DOTALL)
         if thought_match:
-            return thought_match.group(1).strip()
+            thought = thought_match.group(1).strip()
+            # Remove any triple backticks from the thought string
+            thought = thought.replace("```", "").strip()
+            return thought
         return ""
 
     def _clean_action(self, text: str) -> str:

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
@@ -35,11 +38,24 @@ from crewai.tasks.task_output import TaskOutput
 from crewai.telemetry import Telemetry
 from crewai.tools.agent_tools.agent_tools import AgentTools
 from crewai.tools.base_tool import Tool
+from crewai.traces.unified_trace_controller import init_crew_main_trace
 from crewai.types.usage_metrics import UsageMetrics
 from crewai.utilities import I18N, FileHandler, Logger, RPMController
 from crewai.utilities.constants import TRAINING_DATA_FILE
 from crewai.utilities.evaluators.crew_evaluator_handler import CrewEvaluator
 from crewai.utilities.evaluators.task_evaluator import TaskEvaluator
+from crewai.utilities.events.crew_events import (
+    CrewKickoffCompletedEvent,
+    CrewKickoffFailedEvent,
+    CrewKickoffStartedEvent,
+    CrewTestCompletedEvent,
+    CrewTestFailedEvent,
+    CrewTestStartedEvent,
+    CrewTrainCompletedEvent,
+    CrewTrainFailedEvent,
+    CrewTrainStartedEvent,
+)
+from crewai.utilities.events.crewai_event_bus import crewai_event_bus
 from crewai.utilities.formatter import (
     aggregate_raw_outputs_from_task_outputs,
     aggregate_raw_outputs_from_tasks,
@@ -49,12 +65,6 @@ from crewai.utilities.planning_handler import CrewPlanner
 from crewai.utilities.task_output_storage_handler import TaskOutputStorageHandler
 from crewai.utilities.training_handler import CrewTrainingHandler
 
-try:
-    import agentops  # type: ignore
-except ImportError:
-    agentops = None
-
-
 warnings.filterwarnings("ignore", category=SyntaxWarning, module="pysbd")
 
 
@@ -272,12 +282,26 @@ class Crew(BaseModel):
                 if self.entity_memory
                 else EntityMemory(crew=self, embedder_config=self.embedder)
             )
-            if hasattr(self, "memory_config") and self.memory_config is not None:
-                self._user_memory = (
-                    self.user_memory if self.user_memory else UserMemory(crew=self)
-                )
+            if (
+                self.memory_config and "user_memory" in self.memory_config
+            ):  # Check for user_memory in config
+                user_memory_config = self.memory_config["user_memory"]
+                if isinstance(
+                    user_memory_config, UserMemory
+                ):  # Check if it is already an instance
+                    self._user_memory = user_memory_config
+                elif isinstance(
+                    user_memory_config, dict
+                ):  # Check if it's a configuration dict
+                    self._user_memory = UserMemory(
+                        crew=self, **user_memory_config
+                    )  # Initialize with config
+                else:
+                    raise TypeError(
+                        "user_memory must be a UserMemory instance or a configuration dictionary"
+                    )
             else:
-                self._user_memory = None
+                self._user_memory = None  # No user memory if not in config
         return self
 
     @model_validator(mode="after")
@@ -452,8 +476,6 @@ class Crew(BaseModel):
                         )
         return self
 
-
-
     @property
     def key(self) -> str:
         source = [agent.key for agent in self.agents] + [
@@ -506,10 +528,19 @@ class Crew(BaseModel):
         self, n_iterations: int, filename: str, inputs: Optional[Dict[str, Any]] = {}
     ) -> None:
         """Trains the crew for a given number of iterations."""
-        train_crew = self.copy()
-        train_crew._setup_for_training(filename)
-
         try:
+            crewai_event_bus.emit(
+                self,
+                CrewTrainStartedEvent(
+                    crew_name=self.name or "crew",
+                    n_iterations=n_iterations,
+                    filename=filename,
+                    inputs=inputs,
+                ),
+            )
+            train_crew = self.copy()
+            train_crew._setup_for_training(filename)
+
             for n_iteration in range(n_iterations):
                 train_crew._train_iteration = n_iteration
                 train_crew.kickoff(inputs=inputs)
@@ -524,70 +555,94 @@ class Crew(BaseModel):
                     CrewTrainingHandler(filename).save_trained_data(
                         agent_id=str(agent.role), trained_data=result.model_dump()
                     )
+
+            crewai_event_bus.emit(
+                self,
+                CrewTrainCompletedEvent(
+                    crew_name=self.name or "crew",
+                    n_iterations=n_iterations,
+                    filename=filename,
+                ),
+            )
         except Exception as e:
+            crewai_event_bus.emit(
+                self,
+                CrewTrainFailedEvent(error=str(e), crew_name=self.name or "crew"),
+            )
             self._logger.log("error", f"Training failed: {e}", color="red")
             CrewTrainingHandler(TRAINING_DATA_FILE).clear()
             CrewTrainingHandler(filename).clear()
             raise
 
+    @init_crew_main_trace
     def kickoff(
         self,
         inputs: Optional[Dict[str, Any]] = None,
     ) -> CrewOutput:
-        for before_callback in self.before_kickoff_callbacks:
-            if inputs is None:
-                inputs = {}
-            inputs = before_callback(inputs)
+        try:
+            for before_callback in self.before_kickoff_callbacks:
+                if inputs is None:
+                    inputs = {}
+                inputs = before_callback(inputs)
 
-        """Starts the crew to work on its assigned tasks."""
-        self._execution_span = self._telemetry.crew_execution_span(self, inputs)
-        self._task_output_handler.reset()
-        self._logging_color = "bold_purple"
-
-        if inputs is not None:
-            self._inputs = inputs
-            self._interpolate_inputs(inputs)
-        self._set_tasks_callbacks()
-
-        i18n = I18N(prompt_file=self.prompt_file)
-
-        for agent in self.agents:
-            agent.i18n = i18n
-            # type: ignore[attr-defined] # Argument 1 to "_interpolate_inputs" of "Crew" has incompatible type "dict[str, Any] | None"; expected "dict[str, Any]"
-            agent.crew = self  # type: ignore[attr-defined]
-            # TODO: Create an AgentFunctionCalling protocol for future refactoring
-            if not agent.function_calling_llm:  # type: ignore # "BaseAgent" has no attribute "function_calling_llm"
-                agent.function_calling_llm = self.function_calling_llm  # type: ignore # "BaseAgent" has no attribute "function_calling_llm"
-
-            if not agent.step_callback:  # type: ignore # "BaseAgent" has no attribute "step_callback"
-                agent.step_callback = self.step_callback  # type: ignore # "BaseAgent" has no attribute "step_callback"
-
-            agent.create_agent_executor()
-
-        if self.planning:
-            self._handle_crew_planning()
-
-        metrics: List[UsageMetrics] = []
-
-        if self.process == Process.sequential:
-            result = self._run_sequential_process()
-        elif self.process == Process.hierarchical:
-            result = self._run_hierarchical_process()
-        else:
-            raise NotImplementedError(
-                f"The process '{self.process}' is not implemented yet."
+            crewai_event_bus.emit(
+                self,
+                CrewKickoffStartedEvent(crew_name=self.name or "crew", inputs=inputs),
             )
 
-        for after_callback in self.after_kickoff_callbacks:
-            result = after_callback(result)
+            # Starts the crew to work on its assigned tasks.
+            self._task_output_handler.reset()
+            self._logging_color = "bold_purple"
 
-        metrics += [agent._token_process.get_summary() for agent in self.agents]
+            if inputs is not None:
+                self._inputs = inputs
+                self._interpolate_inputs(inputs)
+            self._set_tasks_callbacks()
 
-        self.usage_metrics = UsageMetrics()
-        for metric in metrics:
-            self.usage_metrics.add_usage_metrics(metric)
+            i18n = I18N(prompt_file=self.prompt_file)
 
-        return result
+            for agent in self.agents:
+                agent.i18n = i18n
+                # type: ignore[attr-defined] # Argument 1 to "_interpolate_inputs" of "Crew" has incompatible type "dict[str, Any] | None"; expected "dict[str, Any]"
+                agent.crew = self  # type: ignore[attr-defined]
+                # TODO: Create an AgentFunctionCalling protocol for future refactoring
+                if not agent.function_calling_llm:  # type: ignore # "BaseAgent" has no attribute "function_calling_llm"
+                    agent.function_calling_llm = self.function_calling_llm  # type: ignore # "BaseAgent" has no attribute "function_calling_llm"
+
+                if not agent.step_callback:  # type: ignore # "BaseAgent" has no attribute "step_callback"
+                    agent.step_callback = self.step_callback  # type: ignore # "BaseAgent" has no attribute "step_callback"
+
+                agent.create_agent_executor()
+
+            if self.planning:
+                self._handle_crew_planning()
+
+            metrics: List[UsageMetrics] = []
+
+            if self.process == Process.sequential:
+                result = self._run_sequential_process()
+            elif self.process == Process.hierarchical:
+                result = self._run_hierarchical_process()
+            else:
+                raise NotImplementedError(
+                    f"The process '{self.process}' is not implemented yet."
+                )
+
+            for after_callback in self.after_kickoff_callbacks:
+                result = after_callback(result)
+
+            metrics += [agent._token_process.get_summary() for agent in self.agents]
+
+            self.usage_metrics = UsageMetrics()
+            for metric in metrics:
+                self.usage_metrics.add_usage_metrics(metric)
+            return result
+        except Exception as e:
+            crewai_event_bus.emit(
+                self,
+                CrewKickoffFailedEvent(error=str(e), crew_name=self.name or "crew"),
+            )
+            raise
 
     def kickoff_for_each(self, inputs: List[Dict[str, Any]]) -> List[CrewOutput]:
         """Executes the Crew's workflow for each input in the list and aggregates results."""
@@ -925,17 +980,22 @@ class Crew(BaseModel):
     def _create_crew_output(self, task_outputs: List[TaskOutput]) -> CrewOutput:
         if not task_outputs:
             raise ValueError("No task outputs available to create crew output.")
-            
+
         # Filter out empty outputs and get the last valid one as the main output
         valid_outputs = [t for t in task_outputs if t.raw]
         if not valid_outputs:
             raise ValueError("No valid task outputs available to create crew output.")
         final_task_output = valid_outputs[-1]
-            
+
         final_string_output = final_task_output.raw
         self._finish_execution(final_string_output)
         token_usage = self.calculate_usage_metrics()
-
+        crewai_event_bus.emit(
+            self,
+            CrewKickoffCompletedEvent(
+                crew_name=self.name or "crew", output=final_task_output
+            ),
+        )
         return CrewOutput(
             raw=final_task_output.raw,
             pydantic=final_task_output.pydantic,
@@ -1121,13 +1181,6 @@ class Crew(BaseModel):
     def _finish_execution(self, final_string_output: str) -> None:
         if self.max_rpm:
             self._rpm_controller.stop_rpm_counter()
-        if agentops:
-            agentops.end_session(
-                end_state="Success",
-                end_state_reason="Finished Execution",
-                is_auto_end=True,
-            )
-        self._telemetry.end_crew(self, final_string_output)
 
     def calculate_usage_metrics(self) -> UsageMetrics:
         """Calculates and returns the usage metrics."""
@@ -1145,25 +1198,45 @@ class Crew(BaseModel):
     def test(
         self,
         n_iterations: int,
-        openai_model_name: Optional[str] = None,
+        eval_llm: Union[str, InstanceOf[LLM]],
         inputs: Optional[Dict[str, Any]] = None,
     ) -> None:
         """Test and evaluate the Crew with the given inputs for n iterations concurrently using concurrent.futures."""
-        test_crew = self.copy()
+        try:
+            eval_llm = create_llm(eval_llm)
+            if not eval_llm:
+                raise ValueError("Failed to create LLM instance.")
 
-        self._test_execution_span = test_crew._telemetry.test_execution_span(
-            test_crew,
-            n_iterations,
-            inputs,
-            openai_model_name,  # type: ignore[arg-type]
-        )  # type: ignore[arg-type]
-        evaluator = CrewEvaluator(test_crew, openai_model_name)  # type: ignore[arg-type]
+            crewai_event_bus.emit(
+                self,
+                CrewTestStartedEvent(
+                    crew_name=self.name or "crew",
+                    n_iterations=n_iterations,
+                    eval_llm=eval_llm,
+                    inputs=inputs,
+                ),
+            )
+            test_crew = self.copy()
+            evaluator = CrewEvaluator(test_crew, eval_llm)  # type: ignore[arg-type]
 
-        for i in range(1, n_iterations + 1):
-            evaluator.set_iteration(i)
-            test_crew.kickoff(inputs=inputs)
+            for i in range(1, n_iterations + 1):
+                evaluator.set_iteration(i)
+                test_crew.kickoff(inputs=inputs)
 
-        evaluator.print_crew_evaluation_result()
+            evaluator.print_crew_evaluation_result()
+
+            crewai_event_bus.emit(
+                self,
+                CrewTestCompletedEvent(
+                    crew_name=self.name or "crew",
+                ),
+            )
+        except Exception as e:
+            crewai_event_bus.emit(
+                self,
+                CrewTestFailedEvent(error=str(e), crew_name=self.name or "crew"),
+            )
+            raise
 
     def __repr__(self):
         return f"Crew(id={self.id}, process={self.process}, number_of_agents={len(self.agents)}, number_of_tasks={len(self.tasks)})"
@@ -1205,11 +1278,11 @@ class Crew(BaseModel):
     def _reset_all_memories(self) -> None:
         """Reset all available memory systems."""
         memory_systems = [
-            ("short term", self._short_term_memory),
-            ("entity", self._entity_memory),
-            ("long term", self._long_term_memory),
-            ("task output", self._task_output_handler),
-            ("knowledge", self.knowledge),
+            ("short term", getattr(self, "_short_term_memory", None)),
+            ("entity", getattr(self, "_entity_memory", None)),
+            ("long term", getattr(self, "_long_term_memory", None)),
+            ("task output", getattr(self, "_task_output_handler", None)),
+            ("knowledge", getattr(self, "knowledge", None)),
         ]
 
         for name, system in memory_systems:

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 (
@@ -16,19 +17,25 @@ from typing import (
 )
 from uuid import uuid4
 
-from blinker import Signal
 from pydantic import BaseModel, Field, ValidationError
 
-from crewai.flow.flow_events import (
-    FlowFinishedEvent,
-    FlowStartedEvent,
-    MethodExecutionFinishedEvent,
-    MethodExecutionStartedEvent,
-)
 from crewai.flow.flow_visualizer import plot_flow
 from crewai.flow.persistence.base import FlowPersistence
 from crewai.flow.utils import get_possible_return_constants
-from crewai.telemetry import Telemetry
+from crewai.traces.unified_trace_controller import (
+    init_flow_main_trace,
+    trace_flow_step,
+)
+from crewai.utilities.events.crewai_event_bus import crewai_event_bus
+from crewai.utilities.events.flow_events import (
+    FlowCreatedEvent,
+    FlowFinishedEvent,
+    FlowPlotEvent,
+    FlowStartedEvent,
+    MethodExecutionFailedEvent,
+    MethodExecutionFinishedEvent,
+    MethodExecutionStartedEvent,
+)
 from crewai.utilities.printer import Printer
 
 logger = logging.getLogger(__name__)
@@ -394,7 +401,6 @@ 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)
@@ -427,7 +433,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
 
     Type parameter T must be either Dict[str, Any] or a subclass of BaseModel."""
 
-    _telemetry = Telemetry()
     _printer = Printer()
 
     _start_methods: List[str] = []
@@ -435,7 +440,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
     _routers: Set[str] = set()
     _router_paths: Dict[str, List[str]] = {}
     initial_state: Union[Type[T], T, None] = None
-    event_emitter = Signal("event_emitter")
 
     def __class_getitem__(cls: Type["Flow"], item: Type[T]) -> Type["Flow"]:
         class _FlowGeneric(cls):  # type: ignore
@@ -469,7 +473,13 @@ class Flow(Generic[T], metaclass=FlowMeta):
         if kwargs:
             self._initialize_state(kwargs)
 
-        self._telemetry.flow_creation_span(self.__class__.__name__)
+        crewai_event_bus.emit(
+            self,
+            FlowCreatedEvent(
+                type="flow_created",
+                flow_name=self.__class__.__name__,
+            ),
+        )
 
         # Register all flow-related methods
         for method_name in dir(self):
@@ -569,6 +579,9 @@ 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
@@ -700,16 +713,35 @@ class Flow(Generic[T], metaclass=FlowMeta):
             raise TypeError(f"State must be dict or BaseModel, got {type(self._state)}")
 
     def kickoff(self, inputs: Optional[Dict[str, Any]] = None) -> Any:
-        """Start the flow execution.
+        """
+        Start the flow execution in a synchronous context.
+
+        This method wraps kickoff_async so that all state initialization and event
+        emission is handled in the asynchronous method.
+        """
+
+        async def run_flow():
+            return await self.kickoff_async(inputs)
+
+        return asyncio.run(run_flow())
+
+    @init_flow_main_trace
+    async def kickoff_async(self, inputs: Optional[Dict[str, Any]] = None) -> Any:
+        """
+        Start the flow execution asynchronously.
+
+        This method performs state restoration (if an 'id' is provided and persistence is available)
+        and updates the flow state with any additional inputs. It then emits the FlowStartedEvent,
+        logs the flow startup, and executes all start methods. Once completed, it emits the
+        FlowFinishedEvent and returns the final output.
 
         Args:
-            inputs: Optional dictionary containing input values and potentially a state ID to restore
-        """
-        # Handle state restoration if ID is provided in inputs
-        if inputs and "id" in inputs and self._persistence is not None:
-            restore_uuid = inputs["id"]
-            stored_state = self._persistence.load_state(restore_uuid)
+            inputs: Optional dictionary containing input values and/or a state ID for restoration.
 
+        Returns:
+            The final output from the flow, which is the result of the last executed method.
+        """
+        if inputs:
             # Override the id in the state if it exists in inputs
             if "id" in inputs:
                 if isinstance(self._state, dict):
@@ -717,59 +749,54 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 elif isinstance(self._state, BaseModel):
                     setattr(self._state, "id", inputs["id"])
 
-            if stored_state:
-                self._log_flow_event(
-                    f"Loading flow state from memory for UUID: {restore_uuid}",
-                    color="yellow",
-                )
-                # Restore the state
-                self._restore_state(stored_state)
-            else:
-                self._log_flow_event(
-                    f"No flow state found for UUID: {restore_uuid}", color="red"
-                )
+            # If persistence is enabled, attempt to restore the stored state using the provided id.
+            if "id" in inputs and self._persistence is not None:
+                restore_uuid = inputs["id"]
+                stored_state = self._persistence.load_state(restore_uuid)
+                if stored_state:
+                    self._log_flow_event(
+                        f"Loading flow state from memory for UUID: {restore_uuid}",
+                        color="yellow",
+                    )
+                    self._restore_state(stored_state)
+                else:
+                    self._log_flow_event(
+                        f"No flow state found for UUID: {restore_uuid}", color="red"
+                    )
 
-            # Apply any additional inputs after restoration
+            # Update state with any additional inputs (ignoring the 'id' key)
             filtered_inputs = {k: v for k, v in inputs.items() if k != "id"}
             if filtered_inputs:
                 self._initialize_state(filtered_inputs)
 
-        # Start flow execution
-        self.event_emitter.send(
+        # Emit FlowStartedEvent and log the start of the flow.
+        crewai_event_bus.emit(
             self,
-            event=FlowStartedEvent(
+            FlowStartedEvent(
                 type="flow_started",
                 flow_name=self.__class__.__name__,
+                inputs=inputs,
             ),
         )
         self._log_flow_event(
             f"Flow started with ID: {self.flow_id}", color="bold_magenta"
         )
 
-        if inputs is not None and "id" not in inputs:
-            self._initialize_state(inputs)
-
-        return asyncio.run(self.kickoff_async())
-
-    async def kickoff_async(self, inputs: Optional[Dict[str, Any]] = None) -> Any:
         if not self._start_methods:
             raise ValueError("No start method defined")
 
-        self._telemetry.flow_execution_span(
-            self.__class__.__name__, list(self._methods.keys())
-        )
-
+        # Execute all start methods concurrently.
         tasks = [
             self._execute_start_method(start_method)
             for start_method in self._start_methods
         ]
         await asyncio.gather(*tasks)
-
         final_output = self._method_outputs[-1] if self._method_outputs else None
 
-        self.event_emitter.send(
+        # Emit FlowFinishedEvent after all processing is complete.
+        crewai_event_bus.emit(
             self,
-            event=FlowFinishedEvent(
+            FlowFinishedEvent(
                 type="flow_finished",
                 flow_name=self.__class__.__name__,
                 result=final_output,
@@ -800,19 +827,59 @@ class Flow(Generic[T], metaclass=FlowMeta):
         )
         await self._execute_listeners(start_method_name, result)
 
+    @trace_flow_step
     async def _execute_method(
         self, method_name: str, method: Callable, *args: Any, **kwargs: Any
     ) -> Any:
-        result = (
-            await method(*args, **kwargs)
-            if asyncio.iscoroutinefunction(method)
-            else method(*args, **kwargs)
-        )
-        self._method_outputs.append(result)
-        self._method_execution_counts[method_name] = (
-            self._method_execution_counts.get(method_name, 0) + 1
-        )
-        return result
+        try:
+            dumped_params = {f"_{i}": arg for i, arg in enumerate(args)} | (
+                kwargs or {}
+            )
+            crewai_event_bus.emit(
+                self,
+                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)
+                else method(*args, **kwargs)
+            )
+
+            self._method_outputs.append(result)
+            self._method_execution_counts[method_name] = (
+                self._method_execution_counts.get(method_name, 0) + 1
+            )
+
+            crewai_event_bus.emit(
+                self,
+                MethodExecutionFinishedEvent(
+                    type="method_execution_finished",
+                    method_name=method_name,
+                    flow_name=self.__class__.__name__,
+                    state=self._copy_state(),
+                    result=result,
+                ),
+            )
+
+            return result
+        except Exception as e:
+            crewai_event_bus.emit(
+                self,
+                MethodExecutionFailedEvent(
+                    type="method_execution_failed",
+                    method_name=method_name,
+                    flow_name=self.__class__.__name__,
+                    error=e,
+                ),
+            )
+            raise e
 
     async def _execute_listeners(self, trigger_method: str, result: Any) -> None:
         """
@@ -951,15 +1018,6 @@ 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 +1029,6 @@ 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)
 
@@ -1018,7 +1067,11 @@ class Flow(Generic[T], metaclass=FlowMeta):
             logger.warning(message)
 
     def plot(self, filename: str = "crewai_flow") -> None:
-        self._telemetry.flow_plotting_span(
-            self.__class__.__name__, list(self._methods.keys())
+        crewai_event_bus.emit(
+            self,
+            FlowPlotEvent(
+                type="flow_plot",
+                flow_name=self.__class__.__name__,
+            ),
         )
         plot_flow(self, filename)

src/crewai/flow/flow_events.py

@@ -1,33 +0,0 @@
-from dataclasses import dataclass, field
-from datetime import datetime
-from typing import Any, Optional
-
-
-@dataclass
-class Event:
-    type: str
-    flow_name: str
-    timestamp: datetime = field(init=False)
-
-    def __post_init__(self):
-        self.timestamp = datetime.now()
-
-
-@dataclass
-class FlowStartedEvent(Event):
-    pass
-
-
-@dataclass
-class MethodExecutionStartedEvent(Event):
-    method_name: str
-
-
-@dataclass
-class MethodExecutionFinishedEvent(Event):
-    method_name: str
-
-
-@dataclass
-class FlowFinishedEvent(Event):
-    result: Optional[Any] = None

src/crewai/flow/persistence/decorators.py

@@ -58,7 +58,7 @@ class PersistenceDecorator:
     _printer = Printer()  # Class-level printer instance
 
     @classmethod
-    def persist_state(cls, flow_instance: Any, method_name: str, persistence_instance: FlowPersistence) -> None:
+    def persist_state(cls, flow_instance: Any, method_name: str, persistence_instance: FlowPersistence, verbose: bool = False) -> None:
         """Persist flow state with proper error handling and logging.
 
         This method handles the persistence of flow state data, including proper
@@ -68,6 +68,7 @@ class PersistenceDecorator:
             flow_instance: The flow instance whose state to persist
             method_name: Name of the method that triggered persistence
             persistence_instance: The persistence backend to use
+            verbose: Whether to log persistence operations
 
         Raises:
             ValueError: If flow has no state or state lacks an ID
@@ -88,9 +89,10 @@ class PersistenceDecorator:
             if not flow_uuid:
                 raise ValueError("Flow state must have an 'id' field for persistence")
 
-            # Log state saving with consistent message
-            cls._printer.print(LOG_MESSAGES["save_state"].format(flow_uuid), color="cyan")
-            logger.info(LOG_MESSAGES["save_state"].format(flow_uuid))
+            # Log state saving only if verbose is True
+            if verbose:
+                cls._printer.print(LOG_MESSAGES["save_state"].format(flow_uuid), color="cyan")
+                logger.info(LOG_MESSAGES["save_state"].format(flow_uuid))
 
             try:
                 persistence_instance.save_state(
@@ -115,7 +117,7 @@ class PersistenceDecorator:
             raise ValueError(error_msg) from e
 
 
-def persist(persistence: Optional[FlowPersistence] = None):
+def persist(persistence: Optional[FlowPersistence] = None, verbose: bool = False):
     """Decorator to persist flow state.
 
     This decorator can be applied at either the class level or method level.
@@ -126,6 +128,7 @@ def persist(persistence: Optional[FlowPersistence] = None):
     Args:
         persistence: Optional FlowPersistence implementation to use.
                     If not provided, uses SQLiteFlowPersistence.
+        verbose: Whether to log persistence operations. Defaults to False.
 
     Returns:
         A decorator that can be applied to either a class or method
@@ -135,13 +138,12 @@ def persist(persistence: Optional[FlowPersistence] = None):
         RuntimeError: If state persistence fails
 
     Example:
-        @persist  # Class-level persistence with default SQLite
+        @persist(verbose=True)  # Class-level persistence with logging
         class MyFlow(Flow[MyState]):
             @start()
             def begin(self):
                 pass
     """
-
     def decorator(target: Union[Type, Callable[..., T]]) -> Union[Type, Callable[..., T]]:
         """Decorator that handles both class and method decoration."""
         actual_persistence = persistence or SQLiteFlowPersistence()
@@ -179,7 +181,7 @@ def persist(persistence: Optional[FlowPersistence] = None):
                         @functools.wraps(original_method)
                         async def method_wrapper(self: Any, *args: Any, **kwargs: Any) -> Any:
                             result = await original_method(self, *args, **kwargs)
-                            PersistenceDecorator.persist_state(self, method_name, actual_persistence)
+                            PersistenceDecorator.persist_state(self, method_name, actual_persistence, verbose)
                             return result
                         return method_wrapper
 
@@ -199,7 +201,7 @@ def persist(persistence: Optional[FlowPersistence] = None):
                         @functools.wraps(original_method)
                         def method_wrapper(self: Any, *args: Any, **kwargs: Any) -> Any:
                             result = original_method(self, *args, **kwargs)
-                            PersistenceDecorator.persist_state(self, method_name, actual_persistence)
+                            PersistenceDecorator.persist_state(self, method_name, actual_persistence, verbose)
                             return result
                         return method_wrapper
 
@@ -228,7 +230,7 @@ def persist(persistence: Optional[FlowPersistence] = None):
                         result = await method_coro
                     else:
                         result = method_coro
-                    PersistenceDecorator.persist_state(flow_instance, method.__name__, actual_persistence)
+                    PersistenceDecorator.persist_state(flow_instance, method.__name__, actual_persistence, verbose)
                     return result
 
                 for attr in ["__is_start_method__", "__trigger_methods__", "__condition_type__", "__is_router__"]:
@@ -240,7 +242,7 @@ def persist(persistence: Optional[FlowPersistence] = None):
                 @functools.wraps(method)
                 def method_sync_wrapper(flow_instance: Any, *args: Any, **kwargs: Any) -> T:
                     result = method(flow_instance, *args, **kwargs)
-                    PersistenceDecorator.persist_state(flow_instance, method.__name__, actual_persistence)
+                    PersistenceDecorator.persist_state(flow_instance, method.__name__, actual_persistence, verbose)
                     return result
 
                 for attr in ["__is_start_method__", "__trigger_methods__", "__condition_type__", "__is_router__"]:

src/crewai/flow/state_utils.py

@@ -0,0 +1,91 @@
+import json
+from datetime import date, datetime
+from typing import Any, Dict, List, Union
+
+from pydantic import BaseModel
+
+from crewai.flow import Flow
+
+SerializablePrimitive = Union[str, int, float, bool, None]
+Serializable = Union[
+    SerializablePrimitive, List["Serializable"], Dict[str, "Serializable"]
+]
+
+
+def export_state(flow: Flow) -> dict[str, Serializable]:
+    """Exports the Flow's internal state as JSON-compatible data structures.
+
+    Performs a one-way transformation of a Flow's state into basic Python types
+    that can be safely serialized to JSON. To prevent infinite recursion with
+    circular references, the conversion is limited to a depth of 5 levels.
+
+    Args:
+        flow: The Flow object whose state needs to be exported
+
+    Returns:
+        dict[str, Any]: The transformed state using JSON-compatible Python
+            types.
+    """
+    result = to_serializable(flow._state)
+    assert isinstance(result, dict)
+    return result
+
+
+def to_serializable(
+    obj: Any, max_depth: int = 5, _current_depth: int = 0
+) -> Serializable:
+    """Converts a Python object into a JSON-compatible representation.
+
+    Supports primitives, datetime objects, collections, dictionaries, and
+    Pydantic models. Recursion depth is limited to prevent infinite nesting.
+    Non-convertible objects default to their string representations.
+
+    Args:
+        obj (Any): Object to transform.
+        max_depth (int, optional): Maximum recursion depth. Defaults to 5.
+
+    Returns:
+        Serializable: A JSON-compatible structure.
+    """
+    if _current_depth >= max_depth:
+        return repr(obj)
+
+    if isinstance(obj, (str, int, float, bool, type(None))):
+        return obj
+    elif isinstance(obj, (date, datetime)):
+        return obj.isoformat()
+    elif isinstance(obj, (list, tuple, set)):
+        return [to_serializable(item, max_depth, _current_depth + 1) for item in obj]
+    elif isinstance(obj, dict):
+        return {
+            _to_serializable_key(key): to_serializable(
+                value, max_depth, _current_depth + 1
+            )
+            for key, value in obj.items()
+        }
+    elif isinstance(obj, BaseModel):
+        return to_serializable(obj.model_dump(), max_depth, _current_depth + 1)
+    else:
+        return repr(obj)
+
+
+def _to_serializable_key(key: Any) -> str:
+    if isinstance(key, (str, int)):
+        return str(key)
+    return f"key_{id(key)}_{repr(key)}"
+
+
+def to_string(obj: Any) -> str | None:
+    """Serializes an object into a JSON string.
+
+    Args:
+        obj (Any): Object to serialize.
+
+    Returns:
+        str | None: A JSON-formatted string or `None` if empty.
+    """
+    serializable = to_serializable(obj)
+    if serializable is None:
+        return None
+    else:
+        return json.dumps(serializable)

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

@@ -76,7 +76,7 @@ class KnowledgeStorage(BaseKnowledgeStorage):
                         "context": fetched["documents"][0][i],  # type: ignore
                         "score": fetched["distances"][0][i],  # type: ignore
                     }
-                    if result["score"] >= score_threshold:  # type: ignore
+                    if result["score"] >= score_threshold:
                         results.append(result)
                 return results
             else:
@@ -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/llm.py

@@ -1,3 +1,4 @@
+import inspect
 import json
 import logging
 import os
@@ -5,22 +6,37 @@ import sys
 import threading
 import warnings
 from contextlib import contextmanager
-from typing import Any, Dict, List, Literal, Optional, Type, Union, cast
+from typing import (
+    Any,
+    Dict,
+    List,
+    Literal,
+    Optional,
+    Tuple,
+    Type,
+    Union,
+    cast,
+)
 
 from dotenv import load_dotenv
 from pydantic import BaseModel
 
+from crewai.utilities.events.tool_usage_events import ToolExecutionErrorEvent
+
 with warnings.catch_warnings():
     warnings.simplefilter("ignore", UserWarning)
     import litellm
-    from litellm import Choices, get_supported_openai_params
+    from litellm import Choices
     from litellm.types.utils import ModelResponse
-    from litellm.utils import supports_response_schema
+    from litellm.utils import get_supported_openai_params, supports_response_schema
 
 
+from crewai.traces.unified_trace_controller import trace_llm_call
+from crewai.utilities.events import crewai_event_bus
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededException,
 )
+from crewai.utilities.protocols import AgentExecutorProtocol
 
 load_dotenv()
 
@@ -164,6 +180,7 @@ class LLM:
         self.context_window_size = 0
         self.reasoning_effort = reasoning_effort
         self.additional_params = kwargs
+        self._message_history: List[Dict[str, str]] = []
         self.is_anthropic = self._is_anthropic_model(model)
 
         litellm.drop_params = True
@@ -179,16 +196,22 @@ class LLM:
         self.set_callbacks(callbacks)
         self.set_env_callbacks()
 
+    @trace_llm_call
+    def _call_llm(self, params: Dict[str, Any]) -> Any:
+        with suppress_warnings():
+            response = litellm.completion(**params)
+            return response
+
     def _is_anthropic_model(self, model: str) -> bool:
         """Determine if the model is from Anthropic provider.
-        
+
         Args:
             model: The model identifier string.
-            
+
         Returns:
             bool: True if the model is from Anthropic, False otherwise.
         """
-        ANTHROPIC_PREFIXES = ('anthropic/', 'claude-', 'claude/')
+        ANTHROPIC_PREFIXES = ("anthropic/", "claude-", "claude/")
         return any(prefix in model.lower() for prefix in ANTHROPIC_PREFIXES)
 
     def call(
@@ -199,7 +222,7 @@ class LLM:
         available_functions: Optional[Dict[str, Any]] = None,
     ) -> Union[str, Any]:
         """High-level LLM call method.
-        
+
         Args:
             messages: Input messages for the LLM.
                      Can be a string or list of message dictionaries.
@@ -211,22 +234,22 @@ class LLM:
                       during and after the LLM call.
             available_functions: Optional dict mapping function names to callables
                                that can be invoked by the LLM.
-        
+
         Returns:
             Union[str, Any]: Either a text response from the LLM (str) or
                            the result of a tool function call (Any).
-        
+
         Raises:
             TypeError: If messages format is invalid
             ValueError: If response format is not supported
             LLMContextLengthExceededException: If input exceeds model's context limit
-        
+
         Examples:
             # Example 1: Simple string input
             >>> response = llm.call("Return the name of a random city.")
             >>> print(response)
             "Paris"
-            
+
             # Example 2: Message list with system and user messages
             >>> messages = [
             ...     {"role": "system", "content": "You are a geography expert"},
@@ -288,7 +311,7 @@ class LLM:
                 params = {k: v for k, v in params.items() if v is not None}
 
                 # --- 2) Make the completion call
-                response = litellm.completion(**params)
+                response = self._call_llm(params)
                 response_message = cast(Choices, cast(ModelResponse, response).choices)[
                     0
                 ].message
@@ -315,7 +338,7 @@ class LLM:
                 # --- 5) Handle the tool call
                 tool_call = tool_calls[0]
                 function_name = tool_call.function.name
-
+                print("function_name", function_name)
                 if function_name in available_functions:
                     try:
                         function_args = json.loads(tool_call.function.arguments)
@@ -333,6 +356,15 @@ class LLM:
                         logging.error(
                             f"Error executing function '{function_name}': {e}"
                         )
+                        crewai_event_bus.emit(
+                            self,
+                            event=ToolExecutionErrorEvent(
+                                tool_name=function_name,
+                                tool_args=function_args,
+                                tool_class=fn,
+                                error=str(e),
+                            ),
+                        )
                         return text_response
 
                 else:
@@ -348,36 +380,40 @@ class LLM:
                     logging.error(f"LiteLLM call failed: {str(e)}")
                 raise
 
-    def _format_messages_for_provider(self, messages: List[Dict[str, str]]) -> List[Dict[str, str]]:
+    def _format_messages_for_provider(
+        self, messages: List[Dict[str, str]]
+    ) -> List[Dict[str, str]]:
         """Format messages according to provider requirements.
-        
+
         Args:
             messages: List of message dictionaries with 'role' and 'content' keys.
                      Can be empty or None.
-        
+
         Returns:
             List of formatted messages according to provider requirements.
             For Anthropic models, ensures first message has 'user' role.
-        
+
         Raises:
             TypeError: If messages is None or contains invalid message format.
         """
         if messages is None:
             raise TypeError("Messages cannot be None")
-            
+
         # Validate message format first
         for msg in messages:
             if not isinstance(msg, dict) or "role" not in msg or "content" not in msg:
-                raise TypeError("Invalid message format. Each message must be a dict with 'role' and 'content' keys")
-            
+                raise TypeError(
+                    "Invalid message format. Each message must be a dict with 'role' and 'content' keys"
+                )
+
         if not self.is_anthropic:
             return messages
-            
+
         # Anthropic requires messages to start with 'user' role
         if not messages or messages[0]["role"] == "system":
             # If first message is system or empty, add a placeholder user message
             return [{"role": "user", "content": "."}, *messages]
-                
+
         return messages
 
     def _get_custom_llm_provider(self) -> str:
@@ -413,7 +449,7 @@ class LLM:
     def supports_function_calling(self) -> bool:
         try:
             params = get_supported_openai_params(model=self.model)
-            return "response_format" in params
+            return params is not None and "tools" in params
         except Exception as e:
             logging.error(f"Failed to get supported params: {str(e)}")
             return False
@@ -421,7 +457,7 @@ class LLM:
     def supports_stop_words(self) -> bool:
         try:
             params = get_supported_openai_params(model=self.model)
-            return "stop" in params
+            return params is not None and "stop" in params
         except Exception as e:
             logging.error(f"Failed to get supported params: {str(e)}")
             return False
@@ -495,3 +531,95 @@ class LLM:
 
                 litellm.success_callback = success_callbacks
                 litellm.failure_callback = failure_callbacks
+
+    def _get_execution_context(self) -> Tuple[Optional[Any], Optional[Any]]:
+        """Get the agent and task from the execution context.
+
+        Returns:
+            tuple: (agent, task) from any AgentExecutor context, or (None, None) if not found
+        """
+        frame = inspect.currentframe()
+        caller_frame = frame.f_back if frame else None
+        agent = None
+        task = None
+
+        # Add a maximum depth to prevent infinite loops
+        max_depth = 100  # Reasonable limit for call stack depth
+        current_depth = 0
+
+        while caller_frame and current_depth < max_depth:
+            if "self" in caller_frame.f_locals:
+                caller_self = caller_frame.f_locals["self"]
+                if isinstance(caller_self, AgentExecutorProtocol):
+                    agent = caller_self.agent
+                    task = caller_self.task
+                    break
+            caller_frame = caller_frame.f_back
+            current_depth += 1
+
+        return agent, task
+
+    def _get_new_messages(self, messages: List[Dict[str, str]]) -> List[Dict[str, str]]:
+        """Get only the new messages that haven't been processed before."""
+        if not hasattr(self, "_message_history"):
+            self._message_history = []
+
+        new_messages = []
+        for message in messages:
+            message_key = (message["role"], message["content"])
+            if message_key not in [
+                (m["role"], m["content"]) for m in self._message_history
+            ]:
+                new_messages.append(message)
+                self._message_history.append(message)
+        return new_messages
+
+    def _get_new_tool_results(self, agent) -> List[Dict]:
+        """Get only the new tool results that haven't been processed before."""
+        if not agent or not agent.tools_results:
+            return []
+
+        if not hasattr(self, "_tool_results_history"):
+            self._tool_results_history: List[Dict] = []
+
+        new_tool_results = []
+
+        for result in agent.tools_results:
+            # Process tool arguments to extract actual values
+            processed_args = {}
+            if isinstance(result["tool_args"], dict):
+                for key, value in result["tool_args"].items():
+                    if isinstance(value, dict) and "type" in value:
+                        # Skip metadata and just store the actual value
+                        continue
+                    processed_args[key] = value
+
+            # Create a clean result with processed arguments
+            clean_result = {
+                "tool_name": result["tool_name"],
+                "tool_args": processed_args,
+                "result": result["result"],
+                "content": result.get("content", ""),
+                "start_time": result.get("start_time", ""),
+            }
+
+            # Check if this exact tool execution exists in history
+            is_duplicate = False
+            for history_result in self._tool_results_history:
+                if (
+                    clean_result["tool_name"] == history_result["tool_name"]
+                    and str(clean_result["tool_args"])
+                    == str(history_result["tool_args"])
+                    and str(clean_result["result"]) == str(history_result["result"])
+                    and clean_result["content"] == history_result.get("content", "")
+                    and clean_result["start_time"]
+                    == history_result.get("start_time", "")
+                ):
+                    is_duplicate = True
+                    break
+
+            if not is_duplicate:
+                new_tool_results.append(clean_result)
+                self._tool_results_history.append(clean_result)
+
+        return new_tool_results

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
@@ -37,24 +32,15 @@ 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
 
     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, allow_reset=True, embedder_config=None, crew=None, path=None
     ):
         super().__init__(type, allow_reset, embedder_config, crew)
         agents = crew.agents if crew else []
@@ -64,6 +50,7 @@ class RAGStorage(BaseRAGStorage):
         self.storage_file_name = self._build_storage_file_name(type, agents)
 
         self.type = type
+
         self.allow_reset = allow_reset
         self.path = path
         self._initialize_app()
@@ -72,36 +59,26 @@ 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 +101,12 @@ 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 +114,7 @@ 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 +138,37 @@ 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,
@@ -23,7 +21,6 @@ from typing import (
     Union,
 )
 
-from opentelemetry.trace import Span
 from pydantic import (
     UUID4,
     BaseModel,
@@ -38,10 +35,15 @@ from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.tasks.guardrail_result import GuardrailResult
 from crewai.tasks.output_format import OutputFormat
 from crewai.tasks.task_output import TaskOutput
-from crewai.telemetry.telemetry import Telemetry
 from crewai.tools.base_tool import BaseTool
 from crewai.utilities.config import process_config
 from crewai.utilities.converter import Converter, convert_to_model
+from crewai.utilities.events import (
+    TaskCompletedEvent,
+    TaskFailedEvent,
+    TaskStartedEvent,
+)
+from crewai.utilities.events.crewai_event_bus import crewai_event_bus
 from crewai.utilities.i18n import I18N
 from crewai.utilities.printer import Printer
 
@@ -111,7 +113,7 @@ 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 +129,7 @@ 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",
@@ -185,8 +187,6 @@ class Task(BaseModel):
                     )
         return v
 
-    _telemetry: Telemetry = PrivateAttr(default_factory=Telemetry)
-    _execution_span: Optional[Span] = PrivateAttr(default=None)
     _original_description: Optional[str] = PrivateAttr(default=None)
     _original_expected_output: Optional[str] = PrivateAttr(default=None)
     _original_output_file: Optional[str] = PrivateAttr(default=None)
@@ -350,100 +350,102 @@ class Task(BaseModel):
         tools: Optional[List[Any]],
     ) -> TaskOutput:
         """Run the core execution logic of the task."""
-        agent = agent or self.agent
-        self.agent = agent
-        if not agent:
-            raise Exception(
-                f"The task '{self.description}' has no agent assigned, therefore it can't be executed directly and should be executed in a Crew using a specific process that support that, like hierarchical."
+        try:
+            agent = agent or self.agent
+            self.agent = agent
+            if not agent:
+                raise Exception(
+                    f"The task '{self.description}' has no agent assigned, therefore it can't be executed directly and should be executed in a Crew using a specific process that support that, like hierarchical."
+                )
+
+            self.start_time = datetime.datetime.now()
+
+            self.prompt_context = context
+            tools = tools or self.tools or []
+
+            self.processed_by_agents.add(agent.role)
+            crewai_event_bus.emit(self, TaskStartedEvent(context=context))
+            result = agent.execute_task(
+                task=self,
+                context=context,
+                tools=tools,
             )
 
-        self.start_time = datetime.datetime.now()
-        self._execution_span = self._telemetry.task_started(crew=agent.crew, task=self)
+            pydantic_output, json_output = self._export_output(result)
+            task_output = TaskOutput(
+                name=self.name,
+                description=self.description,
+                expected_output=self.expected_output,
+                raw=result,
+                pydantic=pydantic_output,
+                json_dict=json_output,
+                agent=agent.role,
+                output_format=self._get_output_format(),
+            )
 
-        self.prompt_context = context
-        tools = tools or self.tools or []
+            if self.guardrail:
+                guardrail_result = GuardrailResult.from_tuple(
+                    self.guardrail(task_output)
+                )
+                if not guardrail_result.success:
+                    if self.retry_count >= self.max_retries:
+                        raise Exception(
+                            f"Task failed guardrail validation after {self.max_retries} retries. "
+                            f"Last error: {guardrail_result.error}"
+                        )
 
-        self.processed_by_agents.add(agent.role)
+                    self.retry_count += 1
+                    context = self.i18n.errors("validation_error").format(
+                        guardrail_result_error=guardrail_result.error,
+                        task_output=task_output.raw,
+                    )
+                    printer = Printer()
+                    printer.print(
+                        content=f"Guardrail blocked, retrying, due to: {guardrail_result.error}\n",
+                        color="yellow",
+                    )
+                    return self._execute_core(agent, context, tools)
 
-        result = agent.execute_task(
-            task=self,
-            context=context,
-            tools=tools,
-        )
-
-        pydantic_output, json_output = self._export_output(result)
-        task_output = TaskOutput(
-            name=self.name,
-            description=self.description,
-            expected_output=self.expected_output,
-            raw=result,
-            pydantic=pydantic_output,
-            json_dict=json_output,
-            agent=agent.role,
-            output_format=self._get_output_format(),
-        )
-
-        if self.guardrail:
-            guardrail_result = GuardrailResult.from_tuple(self.guardrail(task_output))
-            if not guardrail_result.success:
-                if self.retry_count >= self.max_retries:
+                if guardrail_result.result is None:
                     raise Exception(
-                        f"Task failed guardrail validation after {self.max_retries} retries. "
-                        f"Last error: {guardrail_result.error}"
+                        "Task guardrail returned None as result. This is not allowed."
                     )
 
-                self.retry_count += 1
-                context = self.i18n.errors("validation_error").format(
-                    guardrail_result_error=guardrail_result.error,
-                    task_output=task_output.raw,
+                if isinstance(guardrail_result.result, str):
+                    task_output.raw = guardrail_result.result
+                    pydantic_output, json_output = self._export_output(
+                        guardrail_result.result
+                    )
+                    task_output.pydantic = pydantic_output
+                    task_output.json_dict = json_output
+                elif isinstance(guardrail_result.result, TaskOutput):
+                    task_output = guardrail_result.result
+
+            self.output = task_output
+            self.end_time = datetime.datetime.now()
+
+            if self.callback:
+                self.callback(self.output)
+
+            crew = self.agent.crew  # type: ignore[union-attr]
+            if crew and crew.task_callback and crew.task_callback != self.callback:
+                crew.task_callback(self.output)
+
+            if self.output_file:
+                content = (
+                    json_output
+                    if json_output
+                    else pydantic_output.model_dump_json()
+                    if pydantic_output
+                    else result
                 )
-                printer = Printer()
-                printer.print(
-                    content=f"Guardrail blocked, retrying, due to: {guardrail_result.error}\n",
-                    color="yellow",
-                )
-                return self._execute_core(agent, context, tools)
-
-            if guardrail_result.result is None:
-                raise Exception(
-                    "Task guardrail returned None as result. This is not allowed."
-                )
-
-            if isinstance(guardrail_result.result, str):
-                task_output.raw = guardrail_result.result
-                pydantic_output, json_output = self._export_output(
-                    guardrail_result.result
-                )
-                task_output.pydantic = pydantic_output
-                task_output.json_dict = json_output
-            elif isinstance(guardrail_result.result, TaskOutput):
-                task_output = guardrail_result.result
-
-        self.output = task_output
-        self.end_time = datetime.datetime.now()
-
-        if self.callback:
-            self.callback(self.output)
-
-        crew = self.agent.crew  # type: ignore[union-attr]
-        if crew and crew.task_callback and crew.task_callback != self.callback:
-            crew.task_callback(self.output)
-
-        if self._execution_span:
-            self._telemetry.task_ended(self._execution_span, self, agent.crew)
-            self._execution_span = None
-
-        if self.output_file:
-            content = (
-                json_output
-                if json_output
-                else pydantic_output.model_dump_json()
-                if pydantic_output
-                else result
-            )
-            self._save_file(content)
-
-        return task_output
+                self._save_file(content)
+            crewai_event_bus.emit(self, TaskCompletedEvent(output=task_output))
+            return task_output
+        except Exception as e:
+            self.end_time = datetime.datetime.now()
+            crewai_event_bus.emit(self, TaskFailedEvent(error=str(e)))
+            raise e  # Re-raise the exception after emitting the event
 
     def prompt(self) -> str:
         """Prompt the task.
@@ -608,56 +610,37 @@ 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
 
@@ -737,10 +720,9 @@ class Task(BaseModel):
                     file.write(str(result))
         except (OSError, IOError) as e:
             raise RuntimeError(
-                "\n".join([
-                    f"Failed to save output file: {e}",
-                    FILEWRITER_RECOMMENDATION
-                ])
+                "\n".join(
+                    [f"Failed to save output file: {e}", FILEWRITER_RECOMMENDATION]
+                )
             )
         return None
 

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/tools/tool_usage.py

@@ -2,6 +2,7 @@ import ast
 import datetime
 import json
 import time
+from datetime import UTC
 from difflib import SequenceMatcher
 from json import JSONDecodeError
 from textwrap import dedent
@@ -10,20 +11,21 @@ from typing import Any, Dict, List, Optional, Union
 import json5
 from json_repair import repair_json
 
-import crewai.utilities.events as events
 from crewai.agents.tools_handler import ToolsHandler
 from crewai.task import Task
 from crewai.telemetry import Telemetry
 from crewai.tools import BaseTool
 from crewai.tools.structured_tool import CrewStructuredTool
 from crewai.tools.tool_calling import InstructorToolCalling, ToolCalling
-from crewai.tools.tool_usage_events import ToolUsageError, ToolUsageFinished
 from crewai.utilities import I18N, Converter, ConverterError, Printer
+from crewai.utilities.events.crewai_event_bus import crewai_event_bus
+from crewai.utilities.events.tool_usage_events import (
+    ToolSelectionErrorEvent,
+    ToolUsageErrorEvent,
+    ToolUsageFinishedEvent,
+    ToolValidateInputErrorEvent,
+)
 
-try:
-    import agentops  # type: ignore
-except ImportError:
-    agentops = None
 OPENAI_BIGGER_MODELS = [
     "gpt-4",
     "gpt-4o",
@@ -116,7 +118,10 @@ class ToolUsage:
                 self._printer.print(content=f"\n\n{error}\n", color="red")
             return error
 
-        if isinstance(tool, CrewStructuredTool) and tool.name == self._i18n.tools("add_image")["name"]:  # type: ignore
+        if (
+            isinstance(tool, CrewStructuredTool)
+            and tool.name == self._i18n.tools("add_image")["name"]  # type: ignore
+        ):
             try:
                 result = self._use(tool_string=tool_string, tool=tool, calling=calling)
                 return result
@@ -136,7 +141,6 @@ class ToolUsage:
         tool: Any,
         calling: Union[ToolCalling, InstructorToolCalling],
     ) -> str:  # TODO: Fix this return type
-        tool_event = agentops.ToolEvent(name=calling.tool_name) if agentops else None  # type: ignore
         if self._check_tool_repeated_usage(calling=calling):  # type: ignore # _check_tool_repeated_usage of "ToolUsage" does not return a value (it only ever returns None)
             try:
                 result = self._i18n.errors("task_repeated_usage").format(
@@ -154,6 +158,7 @@ class ToolUsage:
                 self.task.increment_tools_errors()
 
         started_at = time.time()
+        started_at_trace = datetime.datetime.now(UTC)
         from_cache = False
 
         result = None  # type: ignore # Incompatible types in assignment (expression has type "None", variable has type "str")
@@ -181,7 +186,9 @@ class ToolUsage:
 
                 if calling.arguments:
                     try:
-                        acceptable_args = tool.args_schema.model_json_schema()["properties"].keys()  # type: ignore
+                        acceptable_args = tool.args_schema.model_json_schema()[
+                            "properties"
+                        ].keys()  # type: ignore
                         arguments = {
                             k: v
                             for k, v in calling.arguments.items()
@@ -202,7 +209,7 @@ class ToolUsage:
                         error=e, tool=tool.name, tool_inputs=tool.description
                     )
                     error = ToolUsageErrorException(
-                        f'\n{error_message}.\nMoving on then. {self._i18n.slice("format").format(tool_names=self.tools_names)}'
+                        f"\n{error_message}.\nMoving on then. {self._i18n.slice('format').format(tool_names=self.tools_names)}"
                     ).message
                     self.task.increment_tools_errors()
                     if self.agent.verbose:
@@ -212,10 +219,6 @@ class ToolUsage:
                     return error  # type: ignore # No return value expected
 
                 self.task.increment_tools_errors()
-                if agentops:
-                    agentops.record(
-                        agentops.ErrorEvent(exception=e, trigger_event=tool_event)
-                    )
                 return self.use(calling=calling, tool_string=tool_string)  # type: ignore # No return value expected
 
             if self.tools_handler:
@@ -231,9 +234,6 @@ class ToolUsage:
                 self.tools_handler.on_tool_use(
                     calling=calling, output=result, should_cache=should_cache
                 )
-
-        if agentops:
-            agentops.record(tool_event)
         self._telemetry.tool_usage(
             llm=self.function_calling_llm,
             tool_name=tool.name,
@@ -244,6 +244,7 @@ class ToolUsage:
             "result": result,
             "tool_name": tool.name,
             "tool_args": calling.arguments,
+            "start_time": started_at_trace,
         }
 
         self.on_tool_use_finished(
@@ -308,14 +309,33 @@ class ToolUsage:
             ):
                 return tool
         self.task.increment_tools_errors()
+        tool_selection_data = {
+            "agent_key": self.agent.key,
+            "agent_role": self.agent.role,
+            "tool_name": tool_name,
+            "tool_args": {},
+            "tool_class": self.tools_description,
+        }
         if tool_name and tool_name != "":
-            raise Exception(
-                f"Action '{tool_name}' don't exist, these are the only available Actions:\n{self.tools_description}"
+            error = f"Action '{tool_name}' don't exist, these are the only available Actions:\n{self.tools_description}"
+            crewai_event_bus.emit(
+                self,
+                ToolSelectionErrorEvent(
+                    **tool_selection_data,
+                    error=error,
+                ),
             )
+            raise Exception(error)
         else:
-            raise Exception(
-                f"I forgot the Action name, these are the only available Actions: {self.tools_description}"
+            error = f"I forgot the Action name, these are the only available Actions: {self.tools_description}"
+            crewai_event_bus.emit(
+                self,
+                ToolSelectionErrorEvent(
+                    **tool_selection_data,
+                    error=error,
+                ),
             )
+            raise Exception(error)
 
     def _render(self) -> str:
         """Render the tool name and description in plain text."""
@@ -368,7 +388,7 @@ class ToolUsage:
                 raise
             else:
                 return ToolUsageErrorException(
-                    f'{self._i18n.errors("tool_arguments_error")}'
+                    f"{self._i18n.errors('tool_arguments_error')}"
                 )
 
         if not isinstance(arguments, dict):
@@ -376,7 +396,7 @@ class ToolUsage:
                 raise
             else:
                 return ToolUsageErrorException(
-                    f'{self._i18n.errors("tool_arguments_error")}'
+                    f"{self._i18n.errors('tool_arguments_error')}"
                 )
 
         return ToolCalling(
@@ -404,7 +424,7 @@ class ToolUsage:
                 if self.agent.verbose:
                     self._printer.print(content=f"\n\n{e}\n", color="red")
                 return ToolUsageErrorException(  # type: ignore # Incompatible return value type (got "ToolUsageErrorException", expected "ToolCalling | InstructorToolCalling")
-                    f'{self._i18n.errors("tool_usage_error").format(error=e)}\nMoving on then. {self._i18n.slice("format").format(tool_names=self.tools_names)}'
+                    f"{self._i18n.errors('tool_usage_error').format(error=e)}\nMoving on then. {self._i18n.slice('format').format(tool_names=self.tools_names)}"
                 )
             return self._tool_calling(tool_string)
 
@@ -451,18 +471,33 @@ class ToolUsage:
             if isinstance(arguments, dict):
                 return arguments
         except Exception as e:
-            self._printer.print(content=f"Failed to repair JSON: {e}", color="red")
+            error = f"Failed to repair JSON: {e}"
+            self._printer.print(content=error, color="red")
 
-        # If all parsing attempts fail, raise an error
-        raise Exception(
+        error_message = (
             "Tool input must be a valid dictionary in JSON or Python literal format"
         )
+        self._emit_validate_input_error(error_message)
+        # If all parsing attempts fail, raise an error
+        raise Exception(error_message)
+
+    def _emit_validate_input_error(self, final_error: str):
+        tool_selection_data = {
+            "agent_key": self.agent.key,
+            "agent_role": self.agent.role,
+            "tool_name": self.action.tool,
+            "tool_args": str(self.action.tool_input),
+            "tool_class": self.__class__.__name__,
+        }
+
+        crewai_event_bus.emit(
+            self,
+            ToolValidateInputErrorEvent(**tool_selection_data, error=final_error),
+        )
 
     def on_tool_error(self, tool: Any, tool_calling: ToolCalling, e: Exception) -> None:
         event_data = self._prepare_event_data(tool, tool_calling)
-        events.emit(
-            source=self, event=ToolUsageError(**{**event_data, "error": str(e)})
-        )
+        crewai_event_bus.emit(self, ToolUsageErrorEvent(**{**event_data, "error": e}))
 
     def on_tool_use_finished(
         self, tool: Any, tool_calling: ToolCalling, from_cache: bool, started_at: float
@@ -476,7 +511,7 @@ class ToolUsage:
                 "from_cache": from_cache,
             }
         )
-        events.emit(source=self, event=ToolUsageFinished(**event_data))
+        crewai_event_bus.emit(self, ToolUsageFinishedEvent(**event_data))
 
     def _prepare_event_data(self, tool: Any, tool_calling: ToolCalling) -> dict:
         return {

src/crewai/tools/tool_usage_events.py

@@ -1,24 +0,0 @@
-from datetime import datetime
-from typing import Any, Dict
-
-from pydantic import BaseModel
-
-
-class ToolUsageEvent(BaseModel):
-    agent_key: str
-    agent_role: str
-    tool_name: str
-    tool_args: Dict[str, Any]
-    tool_class: str
-    run_attempts: int | None = None
-    delegations: int | None = None
-
-
-class ToolUsageFinished(ToolUsageEvent):
-    started_at: datetime
-    finished_at: datetime
-    from_cache: bool = False
-
-
-class ToolUsageError(ToolUsageEvent):
-    error: str

src/crewai/traces/context.py

@@ -0,0 +1,39 @@
+from contextlib import contextmanager
+from contextvars import ContextVar
+from typing import Generator
+
+
+class TraceContext:
+    """Maintains the current trace context throughout the execution stack.
+
+    This class provides a context manager for tracking trace execution across
+    async and sync code paths using ContextVars.
+    """
+
+    _context: ContextVar = ContextVar("trace_context", default=None)
+
+    @classmethod
+    def get_current(cls):
+        """Get the current trace context.
+
+        Returns:
+            Optional[UnifiedTraceController]: The current trace controller or None if not set.
+        """
+        return cls._context.get()
+
+    @classmethod
+    @contextmanager
+    def set_current(cls, trace):
+        """Set the current trace context within a context manager.
+
+        Args:
+            trace: The trace controller to set as current.
+
+        Yields:
+            UnifiedTraceController: The current trace controller.
+        """
+        token = cls._context.set(trace)
+        try:
+            yield trace
+        finally:
+            cls._context.reset(token)

src/crewai/traces/enums.py

@@ -0,0 +1,19 @@
+from enum import Enum
+
+
+class TraceType(Enum):
+    LLM_CALL = "llm_call"
+    TOOL_CALL = "tool_call"
+    FLOW_STEP = "flow_step"
+    START_CALL = "start_call"
+
+
+class RunType(Enum):
+    KICKOFF = "kickoff"
+    TRAIN = "train"
+    TEST = "test"
+
+
+class CrewType(Enum):
+    CREW = "crew"
+    FLOW = "flow"

src/crewai/traces/models.py

@@ -0,0 +1,89 @@
+from datetime import datetime
+from typing import Any, Dict, List, Optional
+
+from pydantic import BaseModel, Field
+
+
+class ToolCall(BaseModel):
+    """Model representing a tool call during execution"""
+
+    name: str
+    arguments: Dict[str, Any]
+    output: str
+    start_time: datetime
+    end_time: Optional[datetime] = None
+    latency_ms: Optional[int] = None
+    error: Optional[str] = None
+
+
+class LLMRequest(BaseModel):
+    """Model representing the LLM request details"""
+
+    model: str
+    messages: List[Dict[str, str]]
+    temperature: Optional[float] = None
+    max_tokens: Optional[int] = None
+    stop_sequences: Optional[List[str]] = None
+    additional_params: Dict[str, Any] = Field(default_factory=dict)
+
+
+class LLMResponse(BaseModel):
+    """Model representing the LLM response details"""
+
+    content: str
+    finish_reason: Optional[str] = None
+
+
+class FlowStepIO(BaseModel):
+    """Model representing flow step input/output details"""
+
+    function_name: str
+    inputs: Dict[str, Any] = Field(default_factory=dict)
+    outputs: Any
+    metadata: Dict[str, Any] = Field(default_factory=dict)
+
+
+class CrewTrace(BaseModel):
+    """Model for tracking detailed information about LLM interactions and Flow steps"""
+
+    deployment_instance_id: Optional[str] = Field(
+        description="ID of the deployment instance"
+    )
+    trace_id: str = Field(description="Unique identifier for this trace")
+    run_id: str = Field(description="Identifier for the execution run")
+    agent_role: Optional[str] = Field(description="Role of the agent")
+    task_id: Optional[str] = Field(description="ID of the current task being executed")
+    task_name: Optional[str] = Field(description="Name of the current task")
+    task_description: Optional[str] = Field(
+        description="Description of the current task"
+    )
+    trace_type: str = Field(description="Type of the trace")
+    crew_type: str = Field(description="Type of the crew")
+    run_type: str = Field(description="Type of the run")
+
+    # Timing information
+    start_time: Optional[datetime] = None
+    end_time: Optional[datetime] = None
+    latency_ms: Optional[int] = None
+
+    # Request/Response for LLM calls
+    request: Optional[LLMRequest] = None
+    response: Optional[LLMResponse] = None
+
+    # Input/Output for Flow steps
+    flow_step: Optional[FlowStepIO] = None
+
+    # Tool usage
+    tool_calls: List[ToolCall] = Field(default_factory=list)
+
+    # Metrics
+    tokens_used: Optional[int] = None
+    prompt_tokens: Optional[int] = None
+    completion_tokens: Optional[int] = None
+    cost: Optional[float] = None
+
+    # Additional metadata
+    status: str = "running"  # running, completed, error
+    error: Optional[str] = None
+    metadata: Dict[str, Any] = Field(default_factory=dict)
+    tags: List[str] = Field(default_factory=list)

src/crewai/traces/unified_trace_controller.py

@@ -0,0 +1,543 @@
+import inspect
+import os
+from datetime import UTC, datetime
+from functools import wraps
+from typing import Any, Awaitable, Callable, Dict, List, Optional
+from uuid import uuid4
+
+from crewai.traces.context import TraceContext
+from crewai.traces.enums import CrewType, RunType, TraceType
+from crewai.traces.models import (
+    CrewTrace,
+    FlowStepIO,
+    LLMRequest,
+    LLMResponse,
+    ToolCall,
+)
+
+
+class UnifiedTraceController:
+    """Controls and manages trace execution and recording.
+
+    This class handles the lifecycle of traces including creation, execution tracking,
+    and recording of results for various types of operations (LLM calls, tool calls, flow steps).
+    """
+
+    _task_traces: Dict[str, List["UnifiedTraceController"]] = {}
+
+    def __init__(
+        self,
+        trace_type: TraceType,
+        run_type: RunType,
+        crew_type: CrewType,
+        run_id: str,
+        deployment_instance_id: str = os.environ.get(
+            "CREWAI_DEPLOYMENT_INSTANCE_ID", ""
+        ),
+        parent_trace_id: Optional[str] = None,
+        agent_role: Optional[str] = "unknown",
+        task_name: Optional[str] = None,
+        task_description: Optional[str] = None,
+        task_id: Optional[str] = None,
+        flow_step: Dict[str, Any] = {},
+        tool_calls: List[ToolCall] = [],
+        **context: Any,
+    ) -> None:
+        """Initialize a new trace controller.
+
+        Args:
+            trace_type: Type of trace being recorded.
+            run_type: Type of run being executed.
+            crew_type: Type of crew executing the trace.
+            run_id: Unique identifier for the run.
+            deployment_instance_id: Optional deployment instance identifier.
+            parent_trace_id: Optional parent trace identifier for nested traces.
+            agent_role: Role of the agent executing the trace.
+            task_name: Optional name of the task being executed.
+            task_description: Optional description of the task.
+            task_id: Optional unique identifier for the task.
+            flow_step: Optional flow step information.
+            tool_calls: Optional list of tool calls made during execution.
+            **context: Additional context parameters.
+        """
+        self.trace_id = str(uuid4())
+        self.run_id = run_id
+        self.parent_trace_id = parent_trace_id
+        self.trace_type = trace_type
+        self.run_type = run_type
+        self.crew_type = crew_type
+        self.context = context
+        self.agent_role = agent_role
+        self.task_name = task_name
+        self.task_description = task_description
+        self.task_id = task_id
+        self.deployment_instance_id = deployment_instance_id
+        self.children: List[Dict[str, Any]] = []
+        self.start_time: Optional[datetime] = None
+        self.end_time: Optional[datetime] = None
+        self.error: Optional[str] = None
+        self.tool_calls = tool_calls
+        self.flow_step = flow_step
+        self.status: str = "running"
+
+        # Add trace to task's trace collection if task_id is present
+        if task_id:
+            self._add_to_task_traces()
+
+    def _add_to_task_traces(self) -> None:
+        """Add this trace to the task's trace collection."""
+        if not hasattr(UnifiedTraceController, "_task_traces"):
+            UnifiedTraceController._task_traces = {}
+
+        if self.task_id is None:
+            return
+
+        if self.task_id not in UnifiedTraceController._task_traces:
+            UnifiedTraceController._task_traces[self.task_id] = []
+
+        UnifiedTraceController._task_traces[self.task_id].append(self)
+
+    @classmethod
+    def get_task_traces(cls, task_id: str) -> List["UnifiedTraceController"]:
+        """Get all traces for a specific task.
+
+        Args:
+            task_id: The ID of the task to get traces for
+
+        Returns:
+            List of traces associated with the task
+        """
+        return cls._task_traces.get(task_id, [])
+
+    @classmethod
+    def clear_task_traces(cls, task_id: str) -> None:
+        """Clear traces for a specific task.
+
+        Args:
+            task_id: The ID of the task to clear traces for
+        """
+        if hasattr(cls, "_task_traces") and task_id in cls._task_traces:
+            del cls._task_traces[task_id]
+
+    def _get_current_trace(self) -> "UnifiedTraceController":
+        return TraceContext.get_current()
+
+    def start_trace(self) -> "UnifiedTraceController":
+        """Start the trace execution.
+
+        Returns:
+            UnifiedTraceController: Self for method chaining.
+        """
+        self.start_time = datetime.now(UTC)
+        return self
+
+    def end_trace(self, result: Any = None, error: Optional[str] = None) -> None:
+        """End the trace execution and record results.
+
+        Args:
+            result: Optional result from the trace execution.
+            error: Optional error message if the trace failed.
+        """
+        self.end_time = datetime.now(UTC)
+        self.status = "error" if error else "completed"
+        self.error = error
+        self._record_trace(result)
+
+    def add_child_trace(self, child_trace: Dict[str, Any]) -> None:
+        """Add a child trace to this trace's execution history.
+
+        Args:
+            child_trace: The child trace information to add.
+        """
+        self.children.append(child_trace)
+
+    def to_crew_trace(self) -> CrewTrace:
+        """Convert to CrewTrace format for storage.
+
+        Returns:
+            CrewTrace: The trace data in CrewTrace format.
+        """
+        latency_ms = None
+
+        if self.tool_calls and hasattr(self.tool_calls[0], "start_time"):
+            self.start_time = self.tool_calls[0].start_time
+
+        if self.start_time and self.end_time:
+            latency_ms = int((self.end_time - self.start_time).total_seconds() * 1000)
+
+        request = None
+        response = None
+        flow_step_obj = None
+
+        if self.trace_type in [TraceType.LLM_CALL, TraceType.TOOL_CALL]:
+            request = LLMRequest(
+                model=self.context.get("model", "unknown"),
+                messages=self.context.get("messages", []),
+                temperature=self.context.get("temperature"),
+                max_tokens=self.context.get("max_tokens"),
+                stop_sequences=self.context.get("stop_sequences"),
+            )
+            if "response" in self.context:
+                response = LLMResponse(
+                    content=self.context["response"].get("content", ""),
+                    finish_reason=self.context["response"].get("finish_reason"),
+                )
+
+        elif self.trace_type == TraceType.FLOW_STEP:
+            flow_step_obj = FlowStepIO(
+                function_name=self.flow_step.get("function_name", "unknown"),
+                inputs=self.flow_step.get("inputs", {}),
+                outputs={"result": self.context.get("response")},
+                metadata=self.flow_step.get("metadata", {}),
+            )
+
+        return CrewTrace(
+            deployment_instance_id=self.deployment_instance_id,
+            trace_id=self.trace_id,
+            task_id=self.task_id,
+            run_id=self.run_id,
+            agent_role=self.agent_role,
+            task_name=self.task_name,
+            task_description=self.task_description,
+            trace_type=self.trace_type.value,
+            crew_type=self.crew_type.value,
+            run_type=self.run_type.value,
+            start_time=self.start_time,
+            end_time=self.end_time,
+            latency_ms=latency_ms,
+            request=request,
+            response=response,
+            flow_step=flow_step_obj,
+            tool_calls=self.tool_calls,
+            tokens_used=self.context.get("tokens_used"),
+            prompt_tokens=self.context.get("prompt_tokens"),
+            completion_tokens=self.context.get("completion_tokens"),
+            status=self.status,
+            error=self.error,
+        )
+
+    def _record_trace(self, result: Any = None) -> None:
+        """Record the trace.
+
+        This method is called when a trace is completed. It ensures the trace
+        is properly recorded and associated with its task if applicable.
+
+        Args:
+            result: Optional result to include in the trace
+        """
+        if result:
+            self.context["response"] = result
+
+        # Add to task traces if this trace belongs to a task
+        if self.task_id:
+            self._add_to_task_traces()
+
+
+def should_trace() -> bool:
+    """Check if tracing is enabled via environment variable."""
+    return os.getenv("CREWAI_ENABLE_TRACING", "false").lower() == "true"
+
+
+# Crew main trace
+def init_crew_main_trace(func: Callable[..., Any]) -> Callable[..., Any]:
+    """Decorator to initialize and track the main crew execution trace.
+
+    This decorator sets up the trace context for the main crew execution,
+    handling both synchronous and asynchronous crew operations.
+
+    Args:
+        func: The crew function to be traced.
+
+    Returns:
+        Wrapped function that creates and manages the main crew trace context.
+    """
+
+    @wraps(func)
+    def wrapper(self: Any, *args: Any, **kwargs: Any) -> Any:
+        if not should_trace():
+            return func(self, *args, **kwargs)
+
+        trace = build_crew_main_trace(self)
+        with TraceContext.set_current(trace):
+            try:
+                return func(self, *args, **kwargs)
+            except Exception as e:
+                trace.end_trace(error=str(e))
+                raise
+
+    return wrapper
+
+
+def build_crew_main_trace(self: Any) -> "UnifiedTraceController":
+    """Build the main trace controller for a crew execution.
+
+    This function creates a trace controller configured for the main crew execution,
+    handling different run types (kickoff, test, train) and maintaining context.
+
+    Args:
+        self: The crew instance.
+
+    Returns:
+        UnifiedTraceController: The configured trace controller for the crew.
+    """
+    run_type = RunType.KICKOFF
+    if hasattr(self, "_test") and self._test:
+        run_type = RunType.TEST
+    elif hasattr(self, "_train") and self._train:
+        run_type = RunType.TRAIN
+
+    current_trace = TraceContext.get_current()
+
+    trace = UnifiedTraceController(
+        trace_type=TraceType.LLM_CALL,
+        run_type=run_type,
+        crew_type=current_trace.crew_type if current_trace else CrewType.CREW,
+        run_id=current_trace.run_id if current_trace else str(self.id),
+        parent_trace_id=current_trace.trace_id if current_trace else None,
+    )
+    return trace
+
+
+# Flow main trace
+def init_flow_main_trace(
+    func: Callable[..., Awaitable[Any]],
+) -> Callable[..., Awaitable[Any]]:
+    """Decorator to initialize and track the main flow execution trace.
+
+    Args:
+        func: The async flow function to be traced.
+
+    Returns:
+        Wrapped async function that creates and manages the main flow trace context.
+    """
+
+    @wraps(func)
+    async def wrapper(self: Any, *args: Any, **kwargs: Any) -> Any:
+        if not should_trace():
+            return await func(self, *args, **kwargs)
+
+        trace = build_flow_main_trace(self, *args, **kwargs)
+        with TraceContext.set_current(trace):
+            try:
+                return await func(self, *args, **kwargs)
+            except Exception:
+                raise
+
+    return wrapper
+
+
+def build_flow_main_trace(
+    self: Any, *args: Any, **kwargs: Any
+) -> "UnifiedTraceController":
+    """Build the main trace controller for a flow execution.
+
+    Args:
+        self: The flow instance.
+        *args: Variable positional arguments.
+        **kwargs: Variable keyword arguments.
+
+    Returns:
+        UnifiedTraceController: The configured trace controller for the flow.
+    """
+    current_trace = TraceContext.get_current()
+    trace = UnifiedTraceController(
+        trace_type=TraceType.FLOW_STEP,
+        run_id=current_trace.run_id if current_trace else str(self.flow_id),
+        parent_trace_id=current_trace.trace_id if current_trace else None,
+        crew_type=CrewType.FLOW,
+        run_type=RunType.KICKOFF,
+        context={
+            "crew_name": self.__class__.__name__,
+            "inputs": kwargs.get("inputs", {}),
+            "agents": [],
+            "tasks": [],
+        },
+    )
+    return trace
+
+
+# Flow step trace
+def trace_flow_step(
+    func: Callable[..., Awaitable[Any]],
+) -> Callable[..., Awaitable[Any]]:
+    """Decorator to trace individual flow step executions.
+
+    Args:
+        func: The async flow step function to be traced.
+
+    Returns:
+        Wrapped async function that creates and manages the flow step trace context.
+    """
+
+    @wraps(func)
+    async def wrapper(
+        self: Any,
+        method_name: str,
+        method: Callable[..., Any],
+        *args: Any,
+        **kwargs: Any,
+    ) -> Any:
+        if not should_trace():
+            return await func(self, method_name, method, *args, **kwargs)
+
+        trace = build_flow_step_trace(self, method_name, method, *args, **kwargs)
+        with TraceContext.set_current(trace):
+            trace.start_trace()
+            try:
+                result = await func(self, method_name, method, *args, **kwargs)
+                trace.end_trace(result=result)
+                return result
+            except Exception as e:
+                trace.end_trace(error=str(e))
+                raise
+
+    return wrapper
+
+
+def build_flow_step_trace(
+    self: Any, method_name: str, method: Callable[..., Any], *args: Any, **kwargs: Any
+) -> "UnifiedTraceController":
+    """Build a trace controller for an individual flow step.
+
+    Args:
+        self: The flow instance.
+        method_name: Name of the method being executed.
+        method: The actual method being executed.
+        *args: Variable positional arguments.
+        **kwargs: Variable keyword arguments.
+
+    Returns:
+        UnifiedTraceController: The configured trace controller for the flow step.
+    """
+    current_trace = TraceContext.get_current()
+
+    # Get method signature
+    sig = inspect.signature(method)
+    params = list(sig.parameters.values())
+
+    # Create inputs dictionary mapping parameter names to values
+    method_params = [p for p in params if p.name != "self"]
+    inputs: Dict[str, Any] = {}
+
+    # Map positional args to their parameter names
+    for i, param in enumerate(method_params):
+        if i < len(args):
+            inputs[param.name] = args[i]
+
+    # Add keyword arguments
+    inputs.update(kwargs)
+
+    trace = UnifiedTraceController(
+        trace_type=TraceType.FLOW_STEP,
+        run_type=current_trace.run_type if current_trace else RunType.KICKOFF,
+        crew_type=current_trace.crew_type if current_trace else CrewType.FLOW,
+        run_id=current_trace.run_id if current_trace else str(self.flow_id),
+        parent_trace_id=current_trace.trace_id if current_trace else None,
+        flow_step={
+            "function_name": method_name,
+            "inputs": inputs,
+            "metadata": {
+                "crew_name": self.__class__.__name__,
+            },
+        },
+    )
+    return trace
+
+
+# LLM trace
+def trace_llm_call(func: Callable[..., Any]) -> Callable[..., Any]:
+    """Decorator to trace LLM calls.
+
+    Args:
+        func: The function to trace.
+
+    Returns:
+        Wrapped function that creates and manages the LLM call trace context.
+    """
+
+    @wraps(func)
+    def wrapper(self: Any, *args: Any, **kwargs: Any) -> Any:
+        if not should_trace():
+            return func(self, *args, **kwargs)
+
+        trace = build_llm_trace(self, *args, **kwargs)
+        with TraceContext.set_current(trace):
+            trace.start_trace()
+            try:
+                response = func(self, *args, **kwargs)
+                # Extract relevant data from response
+                trace_response = {
+                    "content": response["choices"][0]["message"]["content"],
+                    "finish_reason": response["choices"][0].get("finish_reason"),
+                }
+
+                # Add usage metrics to context
+                if "usage" in response:
+                    trace.context["tokens_used"] = response["usage"].get(
+                        "total_tokens", 0
+                    )
+                    trace.context["prompt_tokens"] = response["usage"].get(
+                        "prompt_tokens", 0
+                    )
+                    trace.context["completion_tokens"] = response["usage"].get(
+                        "completion_tokens", 0
+                    )
+
+                trace.end_trace(trace_response)
+                return response
+            except Exception as e:
+                trace.end_trace(error=str(e))
+                raise
+
+    return wrapper
+
+
+def build_llm_trace(
+    self: Any, params: Dict[str, Any], *args: Any, **kwargs: Any
+) -> Any:
+    """Build a trace controller for an LLM call.
+
+    Args:
+        self: The LLM instance.
+        params: The parameters for the LLM call.
+        *args: Variable positional arguments.
+        **kwargs: Variable keyword arguments.
+
+    Returns:
+        UnifiedTraceController: The configured trace controller for the LLM call.
+    """
+    current_trace = TraceContext.get_current()
+    agent, task = self._get_execution_context()
+
+    # Get new messages and tool results
+    new_messages = self._get_new_messages(params.get("messages", []))
+    new_tool_results = self._get_new_tool_results(agent)
+
+    # Create trace context
+    trace = UnifiedTraceController(
+        trace_type=TraceType.TOOL_CALL if new_tool_results else TraceType.LLM_CALL,
+        crew_type=current_trace.crew_type if current_trace else CrewType.CREW,
+        run_type=current_trace.run_type if current_trace else RunType.KICKOFF,
+        run_id=current_trace.run_id if current_trace else str(uuid4()),
+        parent_trace_id=current_trace.trace_id if current_trace else None,
+        agent_role=agent.role if agent else "unknown",
+        task_id=str(task.id) if task else None,
+        task_name=task.name if task else None,
+        task_description=task.description if task else None,
+        model=self.model,
+        messages=new_messages,
+        temperature=self.temperature,
+        max_tokens=self.max_tokens,
+        stop_sequences=self.stop,
+        tool_calls=[
+            ToolCall(
+                name=result["tool_name"],
+                arguments=result["tool_args"],
+                output=str(result["result"]),
+                start_time=result.get("start_time", ""),
+                end_time=datetime.now(UTC),
+            )
+            for result in new_tool_results
+        ],
+    )
+    return trace

src/crewai/translations/en.json

@@ -23,7 +23,6 @@
     "summary": "This is a summary of our conversation so far:\n{merged_summary}",
     "manager_request": "Your best answer to your coworker asking you this, accounting for the context shared.",
     "formatted_task_instructions": "Ensure your final answer contains only the content in the following format: {output_format}\n\nEnsure the final output does not include any code block markers like ```json or ```python.",
-    "human_feedback_classification": "Determine if the following feedback indicates that the user is satisfied or if further changes are needed. Respond with 'True' if further changes are needed, or 'False' if the user is satisfied. **Important** Do not include any additional commentary outside of your 'True' or 'False' response.\n\nFeedback: \"{feedback}\"",
     "conversation_history_instruction": "You are a member of a crew collaborating to achieve a common goal. Your task is a specific action that contributes to this larger objective. For additional context, please review the conversation history between you and the user that led to the initiation of this crew. Use any relevant information or feedback from the conversation to inform your task execution and ensure your response aligns with both the immediate task and the crew's overall goals.",
     "feedback_instructions": "User feedback: {feedback}\nInstructions: Use this feedback to enhance the next output iteration.\nNote: Do not respond or add commentary."
   },

src/crewai/utilities/constants.py

@@ -4,7 +4,4 @@ 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"
+EMITTER_COLOR = "bold_blue"

src/crewai/utilities/converter.py

@@ -20,11 +20,11 @@ class ConverterError(Exception):
 class Converter(OutputConverter):
     """Class that converts text into either pydantic or json."""
 
-    def to_pydantic(self, current_attempt=1):
+    def to_pydantic(self, current_attempt=1) -> BaseModel:
         """Convert text to pydantic."""
         try:
             if self.llm.supports_function_calling():
-                return self._create_instructor().to_pydantic()
+                result = self._create_instructor().to_pydantic()
             else:
                 response = self.llm.call(
                     [
@@ -32,18 +32,40 @@ class Converter(OutputConverter):
                         {"role": "user", "content": self.text},
                     ]
                 )
-                return self.model.model_validate_json(response)
+                try:
+                    # Try to directly validate the response JSON
+                    result = self.model.model_validate_json(response)
+                except ValidationError:
+                    # If direct validation fails, attempt to extract valid JSON
+                    result = handle_partial_json(response, self.model, False, None)
+                    # Ensure result is a BaseModel instance
+                    if not isinstance(result, BaseModel):
+                        if isinstance(result, dict):
+                            result = self.model.parse_obj(result)
+                        elif isinstance(result, str):
+                            try:
+                                parsed = json.loads(result)
+                                result = self.model.parse_obj(parsed)
+                            except Exception as parse_err:
+                                raise ConverterError(
+                                    f"Failed to convert partial JSON result into Pydantic: {parse_err}"
+                                )
+                        else:
+                            raise ConverterError(
+                                "handle_partial_json returned an unexpected type."
+                            )
+            return result
         except ValidationError as e:
             if current_attempt < self.max_attempts:
                 return self.to_pydantic(current_attempt + 1)
             raise ConverterError(
-                f"Failed to convert text into a Pydantic model due to the following validation error: {e}"
+                f"Failed to convert text into a Pydantic model due to validation error: {e}"
             )
         except Exception as e:
             if current_attempt < self.max_attempts:
                 return self.to_pydantic(current_attempt + 1)
             raise ConverterError(
-                f"Failed to convert text into a Pydantic model due to the following error: {e}"
+                f"Failed to convert text into a Pydantic model due to error: {e}"
             )
 
     def to_json(self, current_attempt=1):
@@ -197,11 +219,15 @@ def get_conversion_instructions(model: Type[BaseModel], llm: Any) -> str:
     if llm.supports_function_calling():
         model_schema = PydanticSchemaParser(model=model).get_schema()
         instructions += (
-            f"\n\nThe JSON should follow this schema:\n```json\n{model_schema}\n```"
+            f"\n\nOutput ONLY the valid JSON and nothing else.\n\n"
+            f"The JSON must follow this schema exactly:\n```json\n{model_schema}\n```"
         )
     else:
         model_description = generate_model_description(model)
-        instructions += f"\n\nThe JSON should follow this format:\n{model_description}"
+        instructions += (
+            f"\n\nOutput ONLY the valid JSON and nothing else.\n\n"
+            f"The JSON must follow this format exactly:\n{model_description}"
+        )
     return instructions
 
 

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/evaluators/crew_evaluator_handler.py

@@ -1,11 +1,12 @@
 from collections import defaultdict
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, InstanceOf
 from rich.box import HEAVY_EDGE
 from rich.console import Console
 from rich.table import Table
 
 from crewai.agent import Agent
+from crewai.llm import LLM
 from crewai.task import Task
 from crewai.tasks.task_output import TaskOutput
 from crewai.telemetry import Telemetry
@@ -23,7 +24,7 @@ class CrewEvaluator:
 
     Attributes:
         crew (Crew): The crew of agents to evaluate.
-        openai_model_name (str): The model to use for evaluating the performance of the agents (for now ONLY OpenAI accepted).
+        eval_llm (LLM): Language model instance to use for evaluations
         tasks_scores (defaultdict): A dictionary to store the scores of the agents for each task.
         iteration (int): The current iteration of the evaluation.
     """
@@ -32,9 +33,9 @@ class CrewEvaluator:
     run_execution_times: defaultdict = defaultdict(list)
     iteration: int = 0
 
-    def __init__(self, crew, openai_model_name: str):
+    def __init__(self, crew, eval_llm: InstanceOf[LLM]):
         self.crew = crew
-        self.openai_model_name = openai_model_name
+        self.llm = eval_llm
         self._telemetry = Telemetry()
         self._setup_for_evaluating()
 
@@ -51,7 +52,7 @@ class CrewEvaluator:
             ),
             backstory="Evaluator agent for crew evaluation with precise capabilities to evaluate the performance of the agents in the crew based on the tasks they have performed",
             verbose=False,
-            llm=self.openai_model_name,
+            llm=self.llm,
         )
 
     def _evaluation_task(
@@ -181,7 +182,7 @@ class CrewEvaluator:
                 self.crew,
                 evaluation_result.pydantic.quality,
                 current_task.execution_duration,
-                self.openai_model_name,
+                self.llm.model,
             )
             self.tasks_scores[self.iteration].append(evaluation_result.pydantic.quality)
             self.run_execution_times[self.iteration].append(

src/crewai/utilities/evaluators/task_evaluator.py

@@ -3,19 +3,9 @@ from typing import List
 from pydantic import BaseModel, Field
 
 from crewai.utilities import Converter
+from crewai.utilities.events import TaskEvaluationEvent, crewai_event_bus
 from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
 
-agentops = None
-try:
-    from agentops import track_agent  # type: ignore
-except ImportError:
-
-    def track_agent(name):
-        def noop(f):
-            return f
-
-        return noop
-
 
 class Entity(BaseModel):
     name: str = Field(description="The name of the entity.")
@@ -48,12 +38,15 @@ class TrainingTaskEvaluation(BaseModel):
     )
 
 
-@track_agent(name="Task Evaluator")
 class TaskEvaluator:
     def __init__(self, original_agent):
         self.llm = original_agent.llm
+        self.original_agent = original_agent
 
     def evaluate(self, task, output) -> TaskEvaluation:
+        crewai_event_bus.emit(
+            self, TaskEvaluationEvent(evaluation_type="task_evaluation")
+        )
         evaluation_query = (
             f"Assess the quality of the task completed based on the description, expected output, and actual results.\n\n"
             f"Task Description:\n{task.description}\n\n"
@@ -90,6 +83,9 @@ class TaskEvaluator:
             - training_data (dict): The training data to be evaluated.
             - agent_id (str): The ID of the agent.
         """
+        crewai_event_bus.emit(
+            self, TaskEvaluationEvent(evaluation_type="training_data_evaluation")
+        )
 
         output_training_data = training_data[agent_id]
         final_aggregated_data = ""

src/crewai/utilities/events.py

@@ -1,44 +0,0 @@
-from functools import wraps
-from typing import Any, Callable, Dict, Generic, List, Type, TypeVar
-
-from pydantic import BaseModel
-
-T = TypeVar("T")
-EVT = TypeVar("EVT", bound=BaseModel)
-
-
-class Emitter(Generic[T, EVT]):
-    _listeners: Dict[Type[EVT], List[Callable]] = {}
-
-    def on(self, event_type: Type[EVT]):
-        def decorator(func: Callable):
-            @wraps(func)
-            def wrapper(*args, **kwargs):
-                return func(*args, **kwargs)
-
-            self._listeners.setdefault(event_type, []).append(wrapper)
-            return wrapper
-
-        return decorator
-
-    def emit(self, source: T, event: EVT) -> None:
-        event_type = type(event)
-        for func in self._listeners.get(event_type, []):
-            func(source, event)
-
-
-default_emitter = Emitter[Any, BaseModel]()
-
-
-def emit(source: Any, event: BaseModel, raise_on_error: bool = False) -> None:
-    try:
-        default_emitter.emit(source, event)
-    except Exception as e:
-        if raise_on_error:
-            raise e
-        else:
-            print(f"Error emitting event: {e}")
-
-
-def on(event_type: Type[BaseModel]) -> Callable:
-    return default_emitter.on(event_type)

src/crewai/utilities/events/__init__.py

@@ -0,0 +1,40 @@
+from .crew_events import (
+    CrewKickoffStartedEvent,
+    CrewKickoffCompletedEvent,
+    CrewKickoffFailedEvent,
+    CrewTrainStartedEvent,
+    CrewTrainCompletedEvent,
+    CrewTrainFailedEvent,
+    CrewTestStartedEvent,
+    CrewTestCompletedEvent,
+    CrewTestFailedEvent,
+)
+from .agent_events import (
+    AgentExecutionStartedEvent,
+    AgentExecutionCompletedEvent,
+    AgentExecutionErrorEvent,
+)
+from .task_events import TaskStartedEvent, TaskCompletedEvent, TaskFailedEvent, TaskEvaluationEvent
+from .flow_events import (
+    FlowCreatedEvent,
+    FlowStartedEvent,
+    FlowFinishedEvent,
+    FlowPlotEvent,
+    MethodExecutionStartedEvent,
+    MethodExecutionFinishedEvent,
+    MethodExecutionFailedEvent,
+)
+from .crewai_event_bus import CrewAIEventsBus, crewai_event_bus
+from .tool_usage_events import (
+    ToolUsageFinishedEvent,
+    ToolUsageErrorEvent,
+    ToolUsageStartedEvent,
+    ToolExecutionErrorEvent,
+    ToolSelectionErrorEvent,
+    ToolUsageEvent,
+    ToolValidateInputErrorEvent,
+)
+
+# events
+from .event_listener import EventListener
+from .third_party.agentops_listener import agentops_listener

src/crewai/utilities/events/agent_events.py

@@ -0,0 +1,40 @@
+from typing import TYPE_CHECKING, Any, Dict, Optional, Sequence, Union
+
+from crewai.agents.agent_builder.base_agent import BaseAgent
+from crewai.tools.base_tool import BaseTool
+from crewai.tools.structured_tool import CrewStructuredTool
+
+from .base_events import CrewEvent
+
+if TYPE_CHECKING:
+    from crewai.agents.agent_builder.base_agent import BaseAgent
+
+
+class AgentExecutionStartedEvent(CrewEvent):
+    """Event emitted when an agent starts executing a task"""
+
+    agent: BaseAgent
+    task: Any
+    tools: Optional[Sequence[Union[BaseTool, CrewStructuredTool]]]
+    task_prompt: str
+    type: str = "agent_execution_started"
+
+    model_config = {"arbitrary_types_allowed": True}
+
+
+class AgentExecutionCompletedEvent(CrewEvent):
+    """Event emitted when an agent completes executing a task"""
+
+    agent: BaseAgent
+    task: Any
+    output: str
+    type: str = "agent_execution_completed"
+
+
+class AgentExecutionErrorEvent(CrewEvent):
+    """Event emitted when an agent encounters an error during execution"""
+
+    agent: BaseAgent
+    task: Any
+    error: str
+    type: str = "agent_execution_error"

src/crewai/utilities/events/base_event_listener.py

@@ -0,0 +1,14 @@
+from abc import ABC, abstractmethod
+from logging import Logger
+
+from crewai.utilities.events.crewai_event_bus import CrewAIEventsBus, crewai_event_bus
+
+
+class BaseEventListener(ABC):
+    def __init__(self):
+        super().__init__()
+        self.setup_listeners(crewai_event_bus)
+
+    @abstractmethod
+    def setup_listeners(self, crewai_event_bus: CrewAIEventsBus):
+        pass

src/crewai/utilities/events/base_events.py

@@ -0,0 +1,10 @@
+from datetime import datetime
+
+from pydantic import BaseModel, Field
+
+
+class CrewEvent(BaseModel):
+    """Base class for all crew events"""
+
+    timestamp: datetime = Field(default_factory=datetime.now)
+    type: str

src/crewai/utilities/events/crew_events.py

@@ -0,0 +1,81 @@
+from typing import Any, Dict, Optional, Union
+
+from pydantic import InstanceOf
+
+from crewai.utilities.events.base_events import CrewEvent
+
+
+class CrewKickoffStartedEvent(CrewEvent):
+    """Event emitted when a crew starts execution"""
+
+    crew_name: Optional[str]
+    inputs: Optional[Dict[str, Any]]
+    type: str = "crew_kickoff_started"
+
+
+class CrewKickoffCompletedEvent(CrewEvent):
+    """Event emitted when a crew completes execution"""
+
+    crew_name: Optional[str]
+    output: Any
+    type: str = "crew_kickoff_completed"
+
+
+class CrewKickoffFailedEvent(CrewEvent):
+    """Event emitted when a crew fails to complete execution"""
+
+    error: str
+    crew_name: Optional[str]
+    type: str = "crew_kickoff_failed"
+
+
+class CrewTrainStartedEvent(CrewEvent):
+    """Event emitted when a crew starts training"""
+
+    crew_name: Optional[str]
+    n_iterations: int
+    filename: str
+    inputs: Optional[Dict[str, Any]]
+    type: str = "crew_train_started"
+
+
+class CrewTrainCompletedEvent(CrewEvent):
+    """Event emitted when a crew completes training"""
+
+    crew_name: Optional[str]
+    n_iterations: int
+    filename: str
+    type: str = "crew_train_completed"
+
+
+class CrewTrainFailedEvent(CrewEvent):
+    """Event emitted when a crew fails to complete training"""
+
+    error: str
+    crew_name: Optional[str]
+    type: str = "crew_train_failed"
+
+
+class CrewTestStartedEvent(CrewEvent):
+    """Event emitted when a crew starts testing"""
+
+    crew_name: Optional[str]
+    n_iterations: int
+    eval_llm: Optional[Union[str, Any]]
+    inputs: Optional[Dict[str, Any]]
+    type: str = "crew_test_started"
+
+
+class CrewTestCompletedEvent(CrewEvent):
+    """Event emitted when a crew completes testing"""
+
+    crew_name: Optional[str]
+    type: str = "crew_test_completed"
+
+
+class CrewTestFailedEvent(CrewEvent):
+    """Event emitted when a crew fails to complete testing"""
+
+    error: str
+    crew_name: Optional[str]
+    type: str = "crew_test_failed"

src/crewai/utilities/events/crewai_event_bus.py

@@ -0,0 +1,113 @@
+import threading
+from contextlib import contextmanager
+from typing import Any, Callable, Dict, List, Type, TypeVar, cast
+
+from blinker import Signal
+
+from crewai.utilities.events.base_events import CrewEvent
+from crewai.utilities.events.event_types import EventTypes
+
+EventT = TypeVar("EventT", bound=CrewEvent)
+
+
+class CrewAIEventsBus:
+    """
+    A singleton event bus that uses blinker signals for event handling.
+    Allows both internal (Flow/Crew) and external event handling.
+    """
+
+    _instance = None
+    _lock = threading.Lock()
+
+    def __new__(cls):
+        if cls._instance is None:
+            with cls._lock:
+                if cls._instance is None:  # prevent race condition
+                    cls._instance = super(CrewAIEventsBus, cls).__new__(cls)
+                    cls._instance._initialize()
+        return cls._instance
+
+    def _initialize(self) -> None:
+        """Initialize the event bus internal state"""
+        self._signal = Signal("crewai_event_bus")
+        self._handlers: Dict[Type[CrewEvent], List[Callable]] = {}
+
+    def on(
+        self, event_type: Type[EventT]
+    ) -> Callable[[Callable[[Any, EventT], None]], Callable[[Any, EventT], None]]:
+        """
+        Decorator to register an event handler for a specific event type.
+
+        Usage:
+            @crewai_event_bus.on(AgentExecutionCompletedEvent)
+            def on_agent_execution_completed(
+                source: Any, event: AgentExecutionCompletedEvent
+            ):
+                print(f"👍 Agent '{event.agent}' completed task")
+                print(f"   Output: {event.output}")
+        """
+
+        def decorator(
+            handler: Callable[[Any, EventT], None],
+        ) -> Callable[[Any, EventT], None]:
+            if event_type not in self._handlers:
+                self._handlers[event_type] = []
+            self._handlers[event_type].append(
+                cast(Callable[[Any, EventT], None], handler)
+            )
+            return handler
+
+        return decorator
+
+    def emit(self, source: Any, event: CrewEvent) -> None:
+        """
+        Emit an event to all registered handlers
+
+        Args:
+            source: The object emitting the event
+            event: The event instance to emit
+        """
+        event_type = type(event)
+        if event_type in self._handlers:
+            for handler in self._handlers[event_type]:
+                handler(source, event)
+        self._signal.send(source, event=event)
+
+    def clear_handlers(self) -> None:
+        """Clear all registered event handlers - useful for testing"""
+        self._handlers.clear()
+
+    def register_handler(
+        self, event_type: Type[EventTypes], handler: Callable[[Any, EventTypes], None]
+    ) -> None:
+        """Register an event handler for a specific event type"""
+        if event_type not in self._handlers:
+            self._handlers[event_type] = []
+        self._handlers[event_type].append(
+            cast(Callable[[Any, EventTypes], None], handler)
+        )
+
+    @contextmanager
+    def scoped_handlers(self):
+        """
+        Context manager for temporary event handling scope.
+        Useful for testing or temporary event handling.
+
+        Usage:
+            with crewai_event_bus.scoped_handlers():
+                @crewai_event_bus.on(CrewKickoffStarted)
+                def temp_handler(source, event):
+                    print("Temporary handler")
+                # Do stuff...
+            # Handlers are cleared after the context
+        """
+        previous_handlers = self._handlers.copy()
+        self._handlers.clear()
+        try:
+            yield
+        finally:
+            self._handlers = previous_handlers
+
+
+# Global instance
+crewai_event_bus = CrewAIEventsBus()

src/crewai/utilities/events/event_listener.py

@@ -0,0 +1,257 @@
+from pydantic import PrivateAttr
+
+from crewai.telemetry.telemetry import Telemetry
+from crewai.utilities import Logger
+from crewai.utilities.constants import EMITTER_COLOR
+from crewai.utilities.events.base_event_listener import BaseEventListener
+
+from .agent_events import AgentExecutionCompletedEvent, AgentExecutionStartedEvent
+from .crew_events import (
+    CrewKickoffCompletedEvent,
+    CrewKickoffFailedEvent,
+    CrewKickoffStartedEvent,
+    CrewTestCompletedEvent,
+    CrewTestFailedEvent,
+    CrewTestStartedEvent,
+    CrewTrainCompletedEvent,
+    CrewTrainFailedEvent,
+    CrewTrainStartedEvent,
+)
+from .flow_events import (
+    FlowCreatedEvent,
+    FlowFinishedEvent,
+    FlowStartedEvent,
+    MethodExecutionFailedEvent,
+    MethodExecutionFinishedEvent,
+    MethodExecutionStartedEvent,
+)
+from .task_events import TaskCompletedEvent, TaskFailedEvent, TaskStartedEvent
+from .tool_usage_events import (
+    ToolUsageErrorEvent,
+    ToolUsageFinishedEvent,
+    ToolUsageStartedEvent,
+)
+
+
+class EventListener(BaseEventListener):
+    _instance = None
+    _telemetry: Telemetry = PrivateAttr(default_factory=lambda: Telemetry())
+    logger = Logger(verbose=True, default_color=EMITTER_COLOR)
+
+    def __new__(cls):
+        if cls._instance is None:
+            cls._instance = super().__new__(cls)
+            cls._instance._initialized = False
+        return cls._instance
+
+    def __init__(self):
+        if not hasattr(self, "_initialized") or not self._initialized:
+            super().__init__()
+            self._telemetry = Telemetry()
+            self._telemetry.set_tracer()
+            self._initialized = True
+
+    # ----------- CREW EVENTS -----------
+
+    def setup_listeners(self, crewai_event_bus):
+        @crewai_event_bus.on(CrewKickoffStartedEvent)
+        def on_crew_started(source, event: CrewKickoffStartedEvent):
+            self.logger.log(
+                f"🚀 Crew '{event.crew_name}' started",
+                event.timestamp,
+            )
+            self._telemetry.crew_execution_span(source, event.inputs)
+
+        @crewai_event_bus.on(CrewKickoffCompletedEvent)
+        def on_crew_completed(source, event: CrewKickoffCompletedEvent):
+            final_string_output = event.output.raw
+            self._telemetry.end_crew(source, final_string_output)
+            self.logger.log(
+                f"✅ Crew '{event.crew_name}' completed",
+                event.timestamp,
+            )
+
+        @crewai_event_bus.on(CrewKickoffFailedEvent)
+        def on_crew_failed(source, event: CrewKickoffFailedEvent):
+            self.logger.log(
+                f"❌ Crew '{event.crew_name}' failed",
+                event.timestamp,
+            )
+
+        @crewai_event_bus.on(CrewTestStartedEvent)
+        def on_crew_test_started(source, event: CrewTestStartedEvent):
+            cloned_crew = source.copy()
+            cloned_crew._telemetry.test_execution_span(
+                cloned_crew,
+                event.n_iterations,
+                event.inputs,
+                event.eval_llm,
+            )
+            self.logger.log(
+                f"🚀 Crew '{event.crew_name}' started test",
+                event.timestamp,
+            )
+
+        @crewai_event_bus.on(CrewTestCompletedEvent)
+        def on_crew_test_completed(source, event: CrewTestCompletedEvent):
+            self.logger.log(
+                f"✅ Crew '{event.crew_name}' completed test",
+                event.timestamp,
+            )
+
+        @crewai_event_bus.on(CrewTestFailedEvent)
+        def on_crew_test_failed(source, event: CrewTestFailedEvent):
+            self.logger.log(
+                f"❌ Crew '{event.crew_name}' failed test",
+                event.timestamp,
+            )
+
+        @crewai_event_bus.on(CrewTrainStartedEvent)
+        def on_crew_train_started(source, event: CrewTrainStartedEvent):
+            self.logger.log(
+                f"📋 Crew '{event.crew_name}' started train",
+                event.timestamp,
+            )
+
+        @crewai_event_bus.on(CrewTrainCompletedEvent)
+        def on_crew_train_completed(source, event: CrewTrainCompletedEvent):
+            self.logger.log(
+                f"✅ Crew '{event.crew_name}' completed train",
+                event.timestamp,
+            )
+
+        @crewai_event_bus.on(CrewTrainFailedEvent)
+        def on_crew_train_failed(source, event: CrewTrainFailedEvent):
+            self.logger.log(
+                f"❌ Crew '{event.crew_name}' failed train",
+                event.timestamp,
+            )
+
+        # ----------- TASK EVENTS -----------
+
+        @crewai_event_bus.on(TaskStartedEvent)
+        def on_task_started(source, event: TaskStartedEvent):
+            source._execution_span = self._telemetry.task_started(
+                crew=source.agent.crew, task=source
+            )
+            self.logger.log(
+                f"📋 Task started: {source.description}",
+                event.timestamp,
+            )
+
+        @crewai_event_bus.on(TaskCompletedEvent)
+        def on_task_completed(source, event: TaskCompletedEvent):
+            if source._execution_span:
+                self._telemetry.task_ended(
+                    source._execution_span, source, source.agent.crew
+                )
+            self.logger.log(
+                f"✅ Task completed: {source.description}",
+                event.timestamp,
+            )
+            source._execution_span = None
+
+        @crewai_event_bus.on(TaskFailedEvent)
+        def on_task_failed(source, event: TaskFailedEvent):
+            if source._execution_span:
+                if source.agent and source.agent.crew:
+                    self._telemetry.task_ended(
+                        source._execution_span, source, source.agent.crew
+                    )
+                source._execution_span = None
+            self.logger.log(
+                f"❌ Task failed: {source.description}",
+                event.timestamp,
+            )
+
+        # ----------- AGENT EVENTS -----------
+
+        @crewai_event_bus.on(AgentExecutionStartedEvent)
+        def on_agent_execution_started(source, event: AgentExecutionStartedEvent):
+            self.logger.log(
+                f"🤖 Agent '{event.agent.role}' started task",
+                event.timestamp,
+            )
+
+        @crewai_event_bus.on(AgentExecutionCompletedEvent)
+        def on_agent_execution_completed(source, event: AgentExecutionCompletedEvent):
+            self.logger.log(
+                f"✅ Agent '{event.agent.role}' completed task",
+                event.timestamp,
+            )
+
+        # ----------- FLOW EVENTS -----------
+
+        @crewai_event_bus.on(FlowCreatedEvent)
+        def on_flow_created(source, event: FlowCreatedEvent):
+            self._telemetry.flow_creation_span(self.__class__.__name__)
+            self.logger.log(
+                f"🌊 Flow Created: '{event.flow_name}'",
+                event.timestamp,
+            )
+
+        @crewai_event_bus.on(FlowStartedEvent)
+        def on_flow_started(source, event: FlowStartedEvent):
+            self._telemetry.flow_execution_span(
+                source.__class__.__name__, list(source._methods.keys())
+            )
+            self.logger.log(
+                f"🤖 Flow Started: '{event.flow_name}'",
+                event.timestamp,
+            )
+
+        @crewai_event_bus.on(FlowFinishedEvent)
+        def on_flow_finished(source, event: FlowFinishedEvent):
+            self.logger.log(
+                f"👍 Flow Finished: '{event.flow_name}'",
+                event.timestamp,
+            )
+
+        @crewai_event_bus.on(MethodExecutionStartedEvent)
+        def on_method_execution_started(source, event: MethodExecutionStartedEvent):
+            self.logger.log(
+                f"🤖 Flow Method Started: '{event.method_name}'",
+                event.timestamp,
+            )
+
+        @crewai_event_bus.on(MethodExecutionFailedEvent)
+        def on_method_execution_failed(source, event: MethodExecutionFailedEvent):
+            self.logger.log(
+                f"❌ Flow Method Failed: '{event.method_name}'",
+                event.timestamp,
+            )
+
+        @crewai_event_bus.on(MethodExecutionFinishedEvent)
+        def on_method_execution_finished(source, event: MethodExecutionFinishedEvent):
+            self.logger.log(
+                f"👍 Flow Method Finished: '{event.method_name}'",
+                event.timestamp,
+            )
+
+        # ----------- TOOL USAGE EVENTS -----------
+
+        @crewai_event_bus.on(ToolUsageStartedEvent)
+        def on_tool_usage_started(source, event: ToolUsageStartedEvent):
+            self.logger.log(
+                f"🤖 Tool Usage Started: '{event.tool_name}'",
+                event.timestamp,
+            )
+
+        @crewai_event_bus.on(ToolUsageFinishedEvent)
+        def on_tool_usage_finished(source, event: ToolUsageFinishedEvent):
+            self.logger.log(
+                f"✅ Tool Usage Finished: '{event.tool_name}'",
+                event.timestamp,
+                #
+            )
+
+        @crewai_event_bus.on(ToolUsageErrorEvent)
+        def on_tool_usage_error(source, event: ToolUsageErrorEvent):
+            self.logger.log(
+                f"❌ Tool Usage Error: '{event.tool_name}'",
+                event.timestamp,
+                #
+            )
+
+
+event_listener = EventListener()

src/crewai/utilities/events/event_types.py

@@ -0,0 +1,61 @@
+from typing import Union
+
+from .agent_events import (
+    AgentExecutionCompletedEvent,
+    AgentExecutionErrorEvent,
+    AgentExecutionStartedEvent,
+)
+from .crew_events import (
+    CrewKickoffCompletedEvent,
+    CrewKickoffFailedEvent,
+    CrewKickoffStartedEvent,
+    CrewTestCompletedEvent,
+    CrewTestFailedEvent,
+    CrewTestStartedEvent,
+    CrewTrainCompletedEvent,
+    CrewTrainFailedEvent,
+    CrewTrainStartedEvent,
+)
+from .flow_events import (
+    FlowFinishedEvent,
+    FlowStartedEvent,
+    MethodExecutionFailedEvent,
+    MethodExecutionFinishedEvent,
+    MethodExecutionStartedEvent,
+)
+from .task_events import (
+    TaskCompletedEvent,
+    TaskFailedEvent,
+    TaskStartedEvent,
+)
+from .tool_usage_events import (
+    ToolUsageErrorEvent,
+    ToolUsageFinishedEvent,
+    ToolUsageStartedEvent,
+)
+
+EventTypes = Union[
+    CrewKickoffStartedEvent,
+    CrewKickoffCompletedEvent,
+    CrewKickoffFailedEvent,
+    CrewTestStartedEvent,
+    CrewTestCompletedEvent,
+    CrewTestFailedEvent,
+    CrewTrainStartedEvent,
+    CrewTrainCompletedEvent,
+    CrewTrainFailedEvent,
+    AgentExecutionStartedEvent,
+    AgentExecutionCompletedEvent,
+    TaskStartedEvent,
+    TaskCompletedEvent,
+    TaskFailedEvent,
+    FlowStartedEvent,
+    FlowFinishedEvent,
+    MethodExecutionStartedEvent,
+    MethodExecutionFinishedEvent,
+    MethodExecutionFailedEvent,
+    AgentExecutionErrorEvent,
+    ToolUsageFinishedEvent,
+    ToolUsageErrorEvent,
+    ToolUsageStartedEvent,
+]

src/crewai/utilities/events/flow_events.py

@@ -0,0 +1,71 @@
+from typing import Any, Dict, Optional, Union
+
+from pydantic import BaseModel
+
+from .base_events import CrewEvent
+
+
+class FlowEvent(CrewEvent):
+    """Base class for all flow events"""
+
+    type: str
+    flow_name: str
+
+
+class FlowStartedEvent(FlowEvent):
+    """Event emitted when a flow starts execution"""
+
+    flow_name: str
+    inputs: Optional[Dict[str, Any]] = None
+    type: str = "flow_started"
+
+
+class FlowCreatedEvent(FlowEvent):
+    """Event emitted when a flow is created"""
+
+    flow_name: str
+    type: str = "flow_created"
+
+
+class MethodExecutionStartedEvent(FlowEvent):
+    """Event emitted when a flow method starts execution"""
+
+    flow_name: str
+    method_name: str
+    state: Union[Dict[str, Any], BaseModel]
+    params: Optional[Dict[str, Any]] = None
+    type: str = "method_execution_started"
+
+
+class MethodExecutionFinishedEvent(FlowEvent):
+    """Event emitted when a flow method completes execution"""
+
+    flow_name: str
+    method_name: str
+    result: Any = None
+    state: Union[Dict[str, Any], BaseModel]
+    type: str = "method_execution_finished"
+
+
+class MethodExecutionFailedEvent(FlowEvent):
+    """Event emitted when a flow method fails execution"""
+
+    flow_name: str
+    method_name: str
+    error: Any
+    type: str = "method_execution_failed"
+
+
+class FlowFinishedEvent(FlowEvent):
+    """Event emitted when a flow completes execution"""
+
+    flow_name: str
+    result: Optional[Any] = None
+    type: str = "flow_finished"
+
+
+class FlowPlotEvent(FlowEvent):
+    """Event emitted when a flow plot is created"""
+
+    flow_name: str
+    type: str = "flow_plot"

src/crewai/utilities/events/task_events.py

@@ -0,0 +1,32 @@
+from typing import Any, Optional
+
+from crewai.tasks.task_output import TaskOutput
+from crewai.utilities.events.base_events import CrewEvent
+
+
+class TaskStartedEvent(CrewEvent):
+    """Event emitted when a task starts"""
+
+    type: str = "task_started"
+    context: Optional[str]
+
+
+class TaskCompletedEvent(CrewEvent):
+    """Event emitted when a task completes"""
+
+    output: TaskOutput
+    type: str = "task_completed"
+
+
+class TaskFailedEvent(CrewEvent):
+    """Event emitted when a task fails"""
+
+    error: str
+    type: str = "task_failed"
+
+
+class TaskEvaluationEvent(CrewEvent):
+    """Event emitted when a task evaluation is completed"""
+
+    type: str = "task_evaluation"
+    evaluation_type: str

src/crewai/utilities/events/third_party/__init__.py

@@ -0,0 +1 @@
+from .agentops_listener import agentops_listener

src/crewai/utilities/events/third_party/agentops_listener.py

@@ -0,0 +1,67 @@
+from typing import Optional
+
+from crewai.utilities.events import (
+    CrewKickoffCompletedEvent,
+    ToolUsageErrorEvent,
+    ToolUsageStartedEvent,
+)
+from crewai.utilities.events.base_event_listener import BaseEventListener
+from crewai.utilities.events.crew_events import CrewKickoffStartedEvent
+from crewai.utilities.events.task_events import TaskEvaluationEvent
+
+try:
+    import agentops
+
+    AGENTOPS_INSTALLED = True
+except ImportError:
+    AGENTOPS_INSTALLED = False
+
+
+class AgentOpsListener(BaseEventListener):
+    tool_event: Optional["agentops.ToolEvent"] = None
+    session: Optional["agentops.Session"] = None
+
+    def __init__(self):
+        super().__init__()
+
+    def setup_listeners(self, crewai_event_bus):
+        if not AGENTOPS_INSTALLED:
+            return
+
+        @crewai_event_bus.on(CrewKickoffStartedEvent)
+        def on_crew_kickoff_started(source, event: CrewKickoffStartedEvent):
+            self.session = agentops.init()
+            for agent in source.agents:
+                if self.session:
+                    self.session.create_agent(
+                        name=agent.role,
+                        agent_id=str(agent.id),
+                    )
+
+        @crewai_event_bus.on(CrewKickoffCompletedEvent)
+        def on_crew_kickoff_completed(source, event: CrewKickoffCompletedEvent):
+            if self.session:
+                self.session.end_session(
+                    end_state="Success",
+                    end_state_reason="Finished Execution",
+                )
+
+        @crewai_event_bus.on(ToolUsageStartedEvent)
+        def on_tool_usage_started(source, event: ToolUsageStartedEvent):
+            self.tool_event = agentops.ToolEvent(name=event.tool_name)
+            if self.session:
+                self.session.record(self.tool_event)
+
+        @crewai_event_bus.on(ToolUsageErrorEvent)
+        def on_tool_usage_error(source, event: ToolUsageErrorEvent):
+            agentops.ErrorEvent(exception=event.error, trigger_event=self.tool_event)
+
+        @crewai_event_bus.on(TaskEvaluationEvent)
+        def on_task_evaluation(source, event: TaskEvaluationEvent):
+            if self.session:
+                self.session.create_agent(
+                    name="Task Evaluator", agent_id=str(source.original_agent.id)
+                )
+
+
+agentops_listener = AgentOpsListener()

src/crewai/utilities/events/tool_usage_events.py

@@ -0,0 +1,64 @@
+from datetime import datetime
+from typing import Any, Callable, Dict
+
+from .base_events import CrewEvent
+
+
+class ToolUsageEvent(CrewEvent):
+    """Base event for tool usage tracking"""
+
+    agent_key: str
+    agent_role: str
+    tool_name: str
+    tool_args: Dict[str, Any] | str
+    tool_class: str
+    run_attempts: int | None = None
+    delegations: int | None = None
+
+    model_config = {"arbitrary_types_allowed": True}
+
+
+class ToolUsageStartedEvent(ToolUsageEvent):
+    """Event emitted when a tool execution is started"""
+
+    type: str = "tool_usage_started"
+
+
+class ToolUsageFinishedEvent(ToolUsageEvent):
+    """Event emitted when a tool execution is completed"""
+
+    started_at: datetime
+    finished_at: datetime
+    from_cache: bool = False
+    type: str = "tool_usage_finished"
+
+
+class ToolUsageErrorEvent(ToolUsageEvent):
+    """Event emitted when a tool execution encounters an error"""
+
+    error: Any
+    type: str = "tool_usage_error"
+
+
+class ToolValidateInputErrorEvent(ToolUsageEvent):
+    """Event emitted when a tool input validation encounters an error"""
+
+    error: Any
+    type: str = "tool_validate_input_error"
+
+
+class ToolSelectionErrorEvent(ToolUsageEvent):
+    """Event emitted when a tool selection encounters an error"""
+
+    error: Any
+    type: str = "tool_selection_error"
+
+
+class ToolExecutionErrorEvent(CrewEvent):
+    """Event emitted when a tool execution encounters an error"""
+
+    error: Any
+    type: str = "tool_execution_error"
+    tool_name: str
+    tool_args: Dict[str, Any]
+    tool_class: Callable

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)

src/crewai/utilities/logger.py

@@ -8,8 +8,11 @@ from crewai.utilities.printer import Printer
 class Logger(BaseModel):
     verbose: bool = Field(default=False)
     _printer: Printer = PrivateAttr(default_factory=Printer)
+    default_color: str = Field(default="bold_yellow")
 
-    def log(self, level, message, color="bold_yellow"):
+    def log(self, level, message, color=None):
+        if color is None:
+            color = self.default_color
         if self.verbose:
             timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
             self._printer.print(

src/crewai/utilities/protocols.py

@@ -0,0 +1,12 @@
+from typing import Any, Protocol, runtime_checkable
+
+
+@runtime_checkable
+class AgentExecutorProtocol(Protocol):
+    """Protocol defining the expected interface for an agent executor."""
+
+    @property
+    def agent(self) -> Any: ...
+
+    @property
+    def task(self) -> Any: ...

tests/agent_test.py

@@ -8,7 +8,7 @@ import pytest
 
 from crewai import Agent, Crew, Task
 from crewai.agents.cache import CacheHandler
-from crewai.agents.crew_agent_executor import CrewAgentExecutor
+from crewai.agents.crew_agent_executor import AgentFinish, CrewAgentExecutor
 from crewai.agents.parser import AgentAction, CrewAgentParser, OutputParserException
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
@@ -16,9 +16,9 @@ from crewai.llm import LLM
 from crewai.tools import tool
 from crewai.tools.tool_calling import InstructorToolCalling
 from crewai.tools.tool_usage import ToolUsage
-from crewai.tools.tool_usage_events import ToolUsageFinished
 from crewai.utilities import RPMController
-from crewai.utilities.events import Emitter
+from crewai.utilities.events import crewai_event_bus
+from crewai.utilities.events.tool_usage_events import ToolUsageFinishedEvent
 
 
 def test_agent_llm_creation_with_env_vars():
@@ -154,15 +154,19 @@ def test_agent_execution_with_tools():
         agent=agent,
         expected_output="The result of the multiplication.",
     )
-    with patch.object(Emitter, "emit") as emit:
-        output = agent.execute_task(task)
-        assert output == "The result of the multiplication is 12."
-        assert emit.call_count == 1
-        args, _ = emit.call_args
-        assert isinstance(args[1], ToolUsageFinished)
-        assert not args[1].from_cache
-        assert args[1].tool_name == "multiplier"
-        assert args[1].tool_args == {"first_number": 3, "second_number": 4}
+    received_events = []
+
+    @crewai_event_bus.on(ToolUsageFinishedEvent)
+    def handle_tool_end(source, event):
+        received_events.append(event)
+
+    output = agent.execute_task(task)
+    assert output == "The result of the multiplication is 12."
+
+    assert len(received_events) == 1
+    assert isinstance(received_events[0], ToolUsageFinishedEvent)
+    assert received_events[0].tool_name == "multiplier"
+    assert received_events[0].tool_args == {"first_number": 3, "second_number": 4}
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -249,10 +253,14 @@ def test_cache_hitting():
         "multiplier-{'first_number': 3, 'second_number': 3}": 9,
         "multiplier-{'first_number': 12, 'second_number': 3}": 36,
     }
+    received_events = []
+
+    @crewai_event_bus.on(ToolUsageFinishedEvent)
+    def handle_tool_end(source, event):
+        received_events.append(event)
 
     with (
         patch.object(CacheHandler, "read") as read,
-        patch.object(Emitter, "emit") as emit,
     ):
         read.return_value = "0"
         task = Task(
@@ -265,10 +273,9 @@ def test_cache_hitting():
         read.assert_called_with(
             tool="multiplier", input={"first_number": 2, "second_number": 6}
         )
-        assert emit.call_count == 1
-        args, _ = emit.call_args
-        assert isinstance(args[1], ToolUsageFinished)
-        assert args[1].from_cache
+        assert len(received_events) == 1
+        assert isinstance(received_events[0], ToolUsageFinishedEvent)
+        assert received_events[0].from_cache
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -908,6 +915,8 @@ def test_tool_result_as_answer_is_the_final_answer_for_the_agent():
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_tool_usage_information_is_appended_to_agent():
+    from datetime import UTC, datetime
+
     from crewai.tools import BaseTool
 
     class MyCustomTool(BaseTool):
@@ -917,30 +926,36 @@ def test_tool_usage_information_is_appended_to_agent():
         def _run(self) -> str:
             return "Howdy!"
 
-    agent1 = Agent(
-        role="Friendly Neighbor",
-        goal="Make everyone feel welcome",
-        backstory="You are the friendly neighbor",
-        tools=[MyCustomTool(result_as_answer=True)],
-    )
+    fixed_datetime = datetime(2025, 2, 10, 12, 0, 0, tzinfo=UTC)
+    with patch("datetime.datetime") as mock_datetime:
+        mock_datetime.now.return_value = fixed_datetime
+        mock_datetime.side_effect = lambda *args, **kw: datetime(*args, **kw)
 
-    greeting = Task(
-        description="Say an appropriate greeting.",
-        expected_output="The greeting.",
-        agent=agent1,
-    )
-    tasks = [greeting]
-    crew = Crew(agents=[agent1], tasks=tasks)
+        agent1 = Agent(
+            role="Friendly Neighbor",
+            goal="Make everyone feel welcome",
+            backstory="You are the friendly neighbor",
+            tools=[MyCustomTool(result_as_answer=True)],
+        )
 
-    crew.kickoff()
-    assert agent1.tools_results == [
-        {
-            "result": "Howdy!",
-            "tool_name": "Decide Greetings",
-            "tool_args": {},
-            "result_as_answer": True,
-        }
-    ]
+        greeting = Task(
+            description="Say an appropriate greeting.",
+            expected_output="The greeting.",
+            agent=agent1,
+        )
+        tasks = [greeting]
+        crew = Crew(agents=[agent1], tasks=tasks)
+
+        crew.kickoff()
+        assert agent1.tools_results == [
+            {
+                "result": "Howdy!",
+                "tool_name": "Decide Greetings",
+                "tool_args": {},
+                "result_as_answer": True,
+                "start_time": fixed_datetime,
+            }
+        ]
 
 
 def test_agent_definition_based_on_dict():
@@ -983,23 +998,35 @@ def test_agent_human_input():
     # Side effect function for _ask_human_input to simulate multiple feedback iterations
     feedback_responses = iter(
         [
-            "Don't say hi, say Hello instead!",  # First feedback
-            "looks good",  # Second feedback to exit loop
+            "Don't say hi, say Hello instead!",  # First feedback: instruct change
+            "",  # Second feedback: empty string signals acceptance
         ]
     )
 
     def ask_human_input_side_effect(*args, **kwargs):
         return next(feedback_responses)
 
-    with patch.object(
-        CrewAgentExecutor, "_ask_human_input", side_effect=ask_human_input_side_effect
-    ) as mock_human_input:
+    # Patch both _ask_human_input and _invoke_loop to avoid real API/network calls.
+    with (
+        patch.object(
+            CrewAgentExecutor,
+            "_ask_human_input",
+            side_effect=ask_human_input_side_effect,
+        ) as mock_human_input,
+        patch.object(
+            CrewAgentExecutor,
+            "_invoke_loop",
+            return_value=AgentFinish(output="Hello", thought="", text=""),
+        ) as mock_invoke_loop,
+    ):
         # Execute the task
         output = agent.execute_task(task)
 
-        # Assertions to ensure the agent behaves correctly
-        assert mock_human_input.call_count == 2  # Should have asked for feedback twice
-        assert output.strip().lower() == "hello"  # Final output should be 'Hello'
+        # Assertions to ensure the agent behaves correctly.
+        # It should have requested feedback twice.
+        assert mock_human_input.call_count == 2
+        # The final result should be processed to "Hello"
+        assert output.strip().lower() == "hello"
 
 
 def test_interpolate_inputs():

tests/cassettes/test_agent_human_input.yaml

@@ -1,520 +0,0 @@
-interactions:
-- request:
-    body: !!binary |
-      CqcXCiQKIgoMc2VydmljZS5uYW1lEhIKEGNyZXdBSS10ZWxlbWV0cnkS/hYKEgoQY3Jld2FpLnRl
-      bGVtZXRyeRJ5ChBuJJtOdNaB05mOW/p3915eEgj2tkAd3rZcASoQVG9vbCBVc2FnZSBFcnJvcjAB
-      OYa7/URvKBUYQUpcFEVvKBUYShoKDmNyZXdhaV92ZXJzaW9uEggKBjAuODYuMEoPCgNsbG0SCAoG
-      Z3B0LTRvegIYAYUBAAEAABLJBwoQifhX01E5i+5laGdALAlZBBIIBuGM1aN+OPgqDENyZXcgQ3Jl
-      YXRlZDABORVGruBvKBUYQaipwOBvKBUYShoKDmNyZXdhaV92ZXJzaW9uEggKBjAuODYuMEoaCg5w
-      eXRob25fdmVyc2lvbhIICgYzLjEyLjdKLgoIY3Jld19rZXkSIgogN2U2NjA4OTg5ODU5YTY3ZWVj
-      ODhlZWY3ZmNlODUyMjVKMQoHY3Jld19pZBImCiRiOThiNWEwMC01YTI1LTQxMDctYjQwNS1hYmYz
-      MjBhOGYzYThKHAoMY3Jld19wcm9jZXNzEgwKCnNlcXVlbnRpYWxKEQoLY3Jld19tZW1vcnkSAhAA
-      ShoKFGNyZXdfbnVtYmVyX29mX3Rhc2tzEgIYAUobChVjcmV3X251bWJlcl9vZl9hZ2VudHMSAhgB
-      SuQCCgtjcmV3X2FnZW50cxLUAgrRAlt7ImtleSI6ICIyMmFjZDYxMWU0NGVmNWZhYzA1YjUzM2Q3
-      NWU4ODkzYiIsICJpZCI6ICJkNWIyMzM1YS0yMmIyLTQyZWEtYmYwNS03OTc3NmU3MmYzOTIiLCAi
-      cm9sZSI6ICJEYXRhIFNjaWVudGlzdCIsICJ2ZXJib3NlPyI6IGZhbHNlLCAibWF4X2l0ZXIiOiAy
-      MCwgIm1heF9ycG0iOiBudWxsLCAiZnVuY3Rpb25fY2FsbGluZ19sbG0iOiAiIiwgImxsbSI6ICJn
-      cHQtNG8tbWluaSIsICJkZWxlZ2F0aW9uX2VuYWJsZWQ/IjogZmFsc2UsICJhbGxvd19jb2RlX2V4
-      ZWN1dGlvbj8iOiBmYWxzZSwgIm1heF9yZXRyeV9saW1pdCI6IDIsICJ0b29sc19uYW1lcyI6IFsi
-      Z2V0IGdyZWV0aW5ncyJdfV1KkgIKCmNyZXdfdGFza3MSgwIKgAJbeyJrZXkiOiAiYTI3N2IzNGIy
-      YzE0NmYwYzU2YzVlMTM1NmU4ZjhhNTciLCAiaWQiOiAiMjJiZWMyMzEtY2QyMS00YzU4LTgyN2Ut
-      MDU4MWE4ZjBjMTExIiwgImFzeW5jX2V4ZWN1dGlvbj8iOiBmYWxzZSwgImh1bWFuX2lucHV0PyI6
-      IGZhbHNlLCAiYWdlbnRfcm9sZSI6ICJEYXRhIFNjaWVudGlzdCIsICJhZ2VudF9rZXkiOiAiMjJh
-      Y2Q2MTFlNDRlZjVmYWMwNWI1MzNkNzVlODg5M2IiLCAidG9vbHNfbmFtZXMiOiBbImdldCBncmVl
-      dGluZ3MiXX1degIYAYUBAAEAABKOAgoQ5WYoxRtTyPjge4BduhL0rRIIv2U6rvWALfwqDFRhc2sg
-      Q3JlYXRlZDABOX068uBvKBUYQZkv8+BvKBUYSi4KCGNyZXdfa2V5EiIKIDdlNjYwODk4OTg1OWE2
-      N2VlYzg4ZWVmN2ZjZTg1MjI1SjEKB2NyZXdfaWQSJgokYjk4YjVhMDAtNWEyNS00MTA3LWI0MDUt
-      YWJmMzIwYThmM2E4Si4KCHRhc2tfa2V5EiIKIGEyNzdiMzRiMmMxNDZmMGM1NmM1ZTEzNTZlOGY4
-      YTU3SjEKB3Rhc2tfaWQSJgokMjJiZWMyMzEtY2QyMS00YzU4LTgyN2UtMDU4MWE4ZjBjMTExegIY
-      AYUBAAEAABKQAQoQXyeDtJDFnyp2Fjk9YEGTpxIIaNE7gbhPNYcqClRvb2wgVXNhZ2UwATkaXTvj
-      bygVGEGvx0rjbygVGEoaCg5jcmV3YWlfdmVyc2lvbhIICgYwLjg2LjBKHAoJdG9vbF9uYW1lEg8K
-      DUdldCBHcmVldGluZ3NKDgoIYXR0ZW1wdHMSAhgBegIYAYUBAAEAABLVBwoQMWfznt0qwauEzl7T
-      UOQxRBII9q+pUS5EdLAqDENyZXcgQ3JlYXRlZDABORONPORvKBUYQSAoS+RvKBUYShoKDmNyZXdh
-      aV92ZXJzaW9uEggKBjAuODYuMEoaCg5weXRob25fdmVyc2lvbhIICgYzLjEyLjdKLgoIY3Jld19r
-      ZXkSIgogYzMwNzYwMDkzMjY3NjE0NDRkNTdjNzFkMWRhM2YyN2NKMQoHY3Jld19pZBImCiQ3OTQw
-      MTkyNS1iOGU5LTQ3MDgtODUzMC00NDhhZmEzYmY4YjBKHAoMY3Jld19wcm9jZXNzEgwKCnNlcXVl
-      bnRpYWxKEQoLY3Jld19tZW1vcnkSAhAAShoKFGNyZXdfbnVtYmVyX29mX3Rhc2tzEgIYAUobChVj
-      cmV3X251bWJlcl9vZl9hZ2VudHMSAhgBSuoCCgtjcmV3X2FnZW50cxLaAgrXAlt7ImtleSI6ICI5
-      OGYzYjFkNDdjZTk2OWNmMDU3NzI3Yjc4NDE0MjVjZCIsICJpZCI6ICI5OTJkZjYyZi1kY2FiLTQy
-      OTUtOTIwNi05MDBkNDExNGIxZTkiLCAicm9sZSI6ICJGcmllbmRseSBOZWlnaGJvciIsICJ2ZXJi
-      b3NlPyI6IGZhbHNlLCAibWF4X2l0ZXIiOiAyMCwgIm1heF9ycG0iOiBudWxsLCAiZnVuY3Rpb25f
-      Y2FsbGluZ19sbG0iOiAiIiwgImxsbSI6ICJncHQtNG8tbWluaSIsICJkZWxlZ2F0aW9uX2VuYWJs
-      ZWQ/IjogZmFsc2UsICJhbGxvd19jb2RlX2V4ZWN1dGlvbj8iOiBmYWxzZSwgIm1heF9yZXRyeV9s
-      aW1pdCI6IDIsICJ0b29sc19uYW1lcyI6IFsiZGVjaWRlIGdyZWV0aW5ncyJdfV1KmAIKCmNyZXdf
-      dGFza3MSiQIKhgJbeyJrZXkiOiAiODBkN2JjZDQ5MDk5MjkwMDgzODMyZjBlOTgzMzgwZGYiLCAi
-      aWQiOiAiMmZmNjE5N2UtYmEyNy00YjczLWI0YTctNGZhMDQ4ZTYyYjQ3IiwgImFzeW5jX2V4ZWN1
-      dGlvbj8iOiBmYWxzZSwgImh1bWFuX2lucHV0PyI6IGZhbHNlLCAiYWdlbnRfcm9sZSI6ICJGcmll
-      bmRseSBOZWlnaGJvciIsICJhZ2VudF9rZXkiOiAiOThmM2IxZDQ3Y2U5NjljZjA1NzcyN2I3ODQx
-      NDI1Y2QiLCAidG9vbHNfbmFtZXMiOiBbImRlY2lkZSBncmVldGluZ3MiXX1degIYAYUBAAEAABKO
-      AgoQnjTp5boK7/+DQxztYIpqihIIgGnMUkBtzHEqDFRhc2sgQ3JlYXRlZDABOcpYcuRvKBUYQalE
-      c+RvKBUYSi4KCGNyZXdfa2V5EiIKIGMzMDc2MDA5MzI2NzYxNDQ0ZDU3YzcxZDFkYTNmMjdjSjEK
-      B2NyZXdfaWQSJgokNzk0MDE5MjUtYjhlOS00NzA4LTg1MzAtNDQ4YWZhM2JmOGIwSi4KCHRhc2tf
-      a2V5EiIKIDgwZDdiY2Q0OTA5OTI5MDA4MzgzMmYwZTk4MzM4MGRmSjEKB3Rhc2tfaWQSJgokMmZm
-      NjE5N2UtYmEyNy00YjczLWI0YTctNGZhMDQ4ZTYyYjQ3egIYAYUBAAEAABKTAQoQ26H9pLUgswDN
-      p9XhJwwL6BIIx3bw7mAvPYwqClRvb2wgVXNhZ2UwATmy7NPlbygVGEEvb+HlbygVGEoaCg5jcmV3
-      YWlfdmVyc2lvbhIICgYwLjg2LjBKHwoJdG9vbF9uYW1lEhIKEERlY2lkZSBHcmVldGluZ3NKDgoI
-      YXR0ZW1wdHMSAhgBegIYAYUBAAEAAA==
-    headers:
-      Accept:
-      - '*/*'
-      Accept-Encoding:
-      - gzip, deflate
-      Connection:
-      - keep-alive
-      Content-Length:
-      - '2986'
-      Content-Type:
-      - application/x-protobuf
-      User-Agent:
-      - OTel-OTLP-Exporter-Python/1.27.0
-    method: POST
-    uri: https://telemetry.crewai.com:4319/v1/traces
-  response:
-    body:
-      string: "\n\0"
-    headers:
-      Content-Length:
-      - '2'
-      Content-Type:
-      - application/x-protobuf
-      Date:
-      - Fri, 27 Dec 2024 22:14:53 GMT
-    status:
-      code: 200
-      message: OK
-- request:
-    body: '{"messages": [{"role": "system", "content": "You are test role. test backstory\nYour
-      personal goal is: test goal\nTo give my best complete final answer to the task
-      use the exact following format:\n\nThought: I now can give a great answer\nFinal
-      Answer: Your final answer must be the great and the most complete as possible,
-      it must be outcome described.\n\nI MUST use these formats, my job depends on
-      it!"}, {"role": "user", "content": "\nCurrent Task: Say the word: Hi\n\nThis
-      is the expect criteria for your final answer: The word: Hi\nyou MUST return
-      the actual complete content as the final answer, not a summary.\n\nBegin! This
-      is VERY important to you, use the tools available and give your best Final Answer,
-      your job depends on it!\n\nThought:"}], "model": "gpt-4o-mini", "stop": ["\nObservation:"],
-      "stream": false}'
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate
-      connection:
-      - keep-alive
-      content-length:
-      - '824'
-      content-type:
-      - application/json
-      cookie:
-      - _cfuvid=ePJSDFdHag2D8lj21_ijAMWjoA6xfnPNxN4uekvC728-1727226247743-0.0.1.1-604800000
-      host:
-      - api.openai.com
-      user-agent:
-      - OpenAI/Python 1.52.1
-      x-stainless-arch:
-      - x64
-      x-stainless-async:
-      - 'false'
-      x-stainless-lang:
-      - python
-      x-stainless-os:
-      - Linux
-      x-stainless-package-version:
-      - 1.52.1
-      x-stainless-raw-response:
-      - 'true'
-      x-stainless-retry-count:
-      - '0'
-      x-stainless-runtime:
-      - CPython
-      x-stainless-runtime-version:
-      - 3.12.7
-    method: POST
-    uri: https://api.openai.com/v1/chat/completions
-  response:
-    content: "{\n  \"id\": \"chatcmpl-AjCtZLLrWi8ZASpP9bz6HaCV7xBIn\",\n  \"object\":
-      \"chat.completion\",\n  \"created\": 1735337693,\n  \"model\": \"gpt-4o-mini-2024-07-18\",\n
-      \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
-      \"assistant\",\n        \"content\": \"I now can give a great answer  \\nFinal
-      Answer: Hi\",\n        \"refusal\": null\n      },\n      \"logprobs\": null,\n
-      \     \"finish_reason\": \"stop\"\n    }\n  ],\n  \"usage\": {\n    \"prompt_tokens\":
-      158,\n    \"completion_tokens\": 12,\n    \"total_tokens\": 170,\n    \"prompt_tokens_details\":
-      {\n      \"cached_tokens\": 0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\":
-      {\n      \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\":
-      0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"system_fingerprint\":
-      \"fp_0aa8d3e20b\"\n}\n"
-    headers:
-      CF-Cache-Status:
-      - DYNAMIC
-      CF-RAY:
-      - 8f8caa83deca756b-SEA
-      Connection:
-      - keep-alive
-      Content-Encoding:
-      - gzip
-      Content-Type:
-      - application/json
-      Date:
-      - Fri, 27 Dec 2024 22:14:53 GMT
-      Server:
-      - cloudflare
-      Set-Cookie:
-      - __cf_bm=wJkq_yLkzE3OdxE0aMJz.G0kce969.9JxRmZ0ratl4c-1735337693-1.0.1.1-OKpUoRrSPFGvWv5Hp5ET1PNZ7iZNHPKEAuakpcQUxxPSeisUIIR3qIOZ31MGmYugqB5.wkvidgbxOAagqJvmnw;
-        path=/; expires=Fri, 27-Dec-24 22:44:53 GMT; domain=.api.openai.com; HttpOnly;
-        Secure; SameSite=None
-      - _cfuvid=A_ASCLNAVfQoyucWOAIhecWtEpNotYoZr0bAFihgNxs-1735337693273-0.0.1.1-604800000;
-        path=/; domain=.api.openai.com; HttpOnly; Secure; SameSite=None
-      Transfer-Encoding:
-      - chunked
-      X-Content-Type-Options:
-      - nosniff
-      access-control-expose-headers:
-      - X-Request-ID
-      alt-svc:
-      - h3=":443"; ma=86400
-      openai-organization:
-      - crewai-iuxna1
-      openai-processing-ms:
-      - '404'
-      openai-version:
-      - '2020-10-01'
-      strict-transport-security:
-      - max-age=31536000; includeSubDomains; preload
-      x-ratelimit-limit-requests:
-      - '30000'
-      x-ratelimit-limit-tokens:
-      - '150000000'
-      x-ratelimit-remaining-requests:
-      - '29999'
-      x-ratelimit-remaining-tokens:
-      - '149999816'
-      x-ratelimit-reset-requests:
-      - 2ms
-      x-ratelimit-reset-tokens:
-      - 0s
-      x-request-id:
-      - req_6ac84634bff9193743c4b0911c09b4a6
-    http_version: HTTP/1.1
-    status_code: 200
-- request:
-    body: '{"messages": [{"role": "system", "content": "Determine if the following
-      feedback indicates that the user is satisfied or if further changes are needed.
-      Respond with ''True'' if further changes are needed, or ''False'' if the user
-      is satisfied. **Important** Do not include any additional commentary outside
-      of your ''True'' or ''False'' response.\n\nFeedback: \"Don''t say hi, say Hello
-      instead!\""}], "model": "gpt-4o-mini", "stop": ["\nObservation:"], "stream":
-      false}'
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate
-      connection:
-      - keep-alive
-      content-length:
-      - '461'
-      content-type:
-      - application/json
-      cookie:
-      - _cfuvid=A_ASCLNAVfQoyucWOAIhecWtEpNotYoZr0bAFihgNxs-1735337693273-0.0.1.1-604800000;
-        __cf_bm=wJkq_yLkzE3OdxE0aMJz.G0kce969.9JxRmZ0ratl4c-1735337693-1.0.1.1-OKpUoRrSPFGvWv5Hp5ET1PNZ7iZNHPKEAuakpcQUxxPSeisUIIR3qIOZ31MGmYugqB5.wkvidgbxOAagqJvmnw
-      host:
-      - api.openai.com
-      user-agent:
-      - OpenAI/Python 1.52.1
-      x-stainless-arch:
-      - x64
-      x-stainless-async:
-      - 'false'
-      x-stainless-lang:
-      - python
-      x-stainless-os:
-      - Linux
-      x-stainless-package-version:
-      - 1.52.1
-      x-stainless-raw-response:
-      - 'true'
-      x-stainless-retry-count:
-      - '0'
-      x-stainless-runtime:
-      - CPython
-      x-stainless-runtime-version:
-      - 3.12.7
-    method: POST
-    uri: https://api.openai.com/v1/chat/completions
-  response:
-    content: "{\n  \"id\": \"chatcmpl-AjCtZNlWdrrPZhq0MJDqd16sMuQEJ\",\n  \"object\":
-      \"chat.completion\",\n  \"created\": 1735337693,\n  \"model\": \"gpt-4o-mini-2024-07-18\",\n
-      \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
-      \"assistant\",\n        \"content\": \"True\",\n        \"refusal\": null\n
-      \     },\n      \"logprobs\": null,\n      \"finish_reason\": \"stop\"\n    }\n
-      \ ],\n  \"usage\": {\n    \"prompt_tokens\": 78,\n    \"completion_tokens\":
-      1,\n    \"total_tokens\": 79,\n    \"prompt_tokens_details\": {\n      \"cached_tokens\":
-      0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\": {\n
-      \     \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\":
-      0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"system_fingerprint\":
-      \"fp_0aa8d3e20b\"\n}\n"
-    headers:
-      CF-Cache-Status:
-      - DYNAMIC
-      CF-RAY:
-      - 8f8caa87094f756b-SEA
-      Connection:
-      - keep-alive
-      Content-Encoding:
-      - gzip
-      Content-Type:
-      - application/json
-      Date:
-      - Fri, 27 Dec 2024 22:14:53 GMT
-      Server:
-      - cloudflare
-      Transfer-Encoding:
-      - chunked
-      X-Content-Type-Options:
-      - nosniff
-      access-control-expose-headers:
-      - X-Request-ID
-      alt-svc:
-      - h3=":443"; ma=86400
-      openai-organization:
-      - crewai-iuxna1
-      openai-processing-ms:
-      - '156'
-      openai-version:
-      - '2020-10-01'
-      strict-transport-security:
-      - max-age=31536000; includeSubDomains; preload
-      x-ratelimit-limit-requests:
-      - '30000'
-      x-ratelimit-limit-tokens:
-      - '150000000'
-      x-ratelimit-remaining-requests:
-      - '29999'
-      x-ratelimit-remaining-tokens:
-      - '149999898'
-      x-ratelimit-reset-requests:
-      - 2ms
-      x-ratelimit-reset-tokens:
-      - 0s
-      x-request-id:
-      - req_ec74bef2a9ef7b2144c03fd7f7bbeab0
-    http_version: HTTP/1.1
-    status_code: 200
-- request:
-    body: '{"messages": [{"role": "system", "content": "You are test role. test backstory\nYour
-      personal goal is: test goal\nTo give my best complete final answer to the task
-      use the exact following format:\n\nThought: I now can give a great answer\nFinal
-      Answer: Your final answer must be the great and the most complete as possible,
-      it must be outcome described.\n\nI MUST use these formats, my job depends on
-      it!"}, {"role": "user", "content": "\nCurrent Task: Say the word: Hi\n\nThis
-      is the expect criteria for your final answer: The word: Hi\nyou MUST return
-      the actual complete content as the final answer, not a summary.\n\nBegin! This
-      is VERY important to you, use the tools available and give your best Final Answer,
-      your job depends on it!\n\nThought:"}, {"role": "assistant", "content": "I now
-      can give a great answer  \nFinal Answer: Hi"}, {"role": "user", "content": "Feedback:
-      Don''t say hi, say Hello instead!"}], "model": "gpt-4o-mini", "stop": ["\nObservation:"],
-      "stream": false}'
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate
-      connection:
-      - keep-alive
-      content-length:
-      - '986'
-      content-type:
-      - application/json
-      cookie:
-      - _cfuvid=A_ASCLNAVfQoyucWOAIhecWtEpNotYoZr0bAFihgNxs-1735337693273-0.0.1.1-604800000;
-        __cf_bm=wJkq_yLkzE3OdxE0aMJz.G0kce969.9JxRmZ0ratl4c-1735337693-1.0.1.1-OKpUoRrSPFGvWv5Hp5ET1PNZ7iZNHPKEAuakpcQUxxPSeisUIIR3qIOZ31MGmYugqB5.wkvidgbxOAagqJvmnw
-      host:
-      - api.openai.com
-      user-agent:
-      - OpenAI/Python 1.52.1
-      x-stainless-arch:
-      - x64
-      x-stainless-async:
-      - 'false'
-      x-stainless-lang:
-      - python
-      x-stainless-os:
-      - Linux
-      x-stainless-package-version:
-      - 1.52.1
-      x-stainless-raw-response:
-      - 'true'
-      x-stainless-retry-count:
-      - '0'
-      x-stainless-runtime:
-      - CPython
-      x-stainless-runtime-version:
-      - 3.12.7
-    method: POST
-    uri: https://api.openai.com/v1/chat/completions
-  response:
-    content: "{\n  \"id\": \"chatcmpl-AjCtZGv4f3h7GDdhyOy9G0sB1lRgC\",\n  \"object\":
-      \"chat.completion\",\n  \"created\": 1735337693,\n  \"model\": \"gpt-4o-mini-2024-07-18\",\n
-      \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
-      \"assistant\",\n        \"content\": \"Thought: I understand the feedback and
-      will adjust my response accordingly.  \\nFinal Answer: Hello\",\n        \"refusal\":
-      null\n      },\n      \"logprobs\": null,\n      \"finish_reason\": \"stop\"\n
-      \   }\n  ],\n  \"usage\": {\n    \"prompt_tokens\": 188,\n    \"completion_tokens\":
-      18,\n    \"total_tokens\": 206,\n    \"prompt_tokens_details\": {\n      \"cached_tokens\":
-      0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\": {\n
-      \     \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\":
-      0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"system_fingerprint\":
-      \"fp_0aa8d3e20b\"\n}\n"
-    headers:
-      CF-Cache-Status:
-      - DYNAMIC
-      CF-RAY:
-      - 8f8caa88cac4756b-SEA
-      Connection:
-      - keep-alive
-      Content-Encoding:
-      - gzip
-      Content-Type:
-      - application/json
-      Date:
-      - Fri, 27 Dec 2024 22:14:54 GMT
-      Server:
-      - cloudflare
-      Transfer-Encoding:
-      - chunked
-      X-Content-Type-Options:
-      - nosniff
-      access-control-expose-headers:
-      - X-Request-ID
-      alt-svc:
-      - h3=":443"; ma=86400
-      openai-organization:
-      - crewai-iuxna1
-      openai-processing-ms:
-      - '358'
-      openai-version:
-      - '2020-10-01'
-      strict-transport-security:
-      - max-age=31536000; includeSubDomains; preload
-      x-ratelimit-limit-requests:
-      - '30000'
-      x-ratelimit-limit-tokens:
-      - '150000000'
-      x-ratelimit-remaining-requests:
-      - '29999'
-      x-ratelimit-remaining-tokens:
-      - '149999793'
-      x-ratelimit-reset-requests:
-      - 2ms
-      x-ratelimit-reset-tokens:
-      - 0s
-      x-request-id:
-      - req_ae1ab6b206d28ded6fee3c83ed0c2ab7
-    http_version: HTTP/1.1
-    status_code: 200
-- request:
-    body: '{"messages": [{"role": "system", "content": "Determine if the following
-      feedback indicates that the user is satisfied or if further changes are needed.
-      Respond with ''True'' if further changes are needed, or ''False'' if the user
-      is satisfied. **Important** Do not include any additional commentary outside
-      of your ''True'' or ''False'' response.\n\nFeedback: \"looks good\""}], "model":
-      "gpt-4o-mini", "stop": ["\nObservation:"], "stream": false}'
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate
-      connection:
-      - keep-alive
-      content-length:
-      - '439'
-      content-type:
-      - application/json
-      cookie:
-      - _cfuvid=A_ASCLNAVfQoyucWOAIhecWtEpNotYoZr0bAFihgNxs-1735337693273-0.0.1.1-604800000;
-        __cf_bm=wJkq_yLkzE3OdxE0aMJz.G0kce969.9JxRmZ0ratl4c-1735337693-1.0.1.1-OKpUoRrSPFGvWv5Hp5ET1PNZ7iZNHPKEAuakpcQUxxPSeisUIIR3qIOZ31MGmYugqB5.wkvidgbxOAagqJvmnw
-      host:
-      - api.openai.com
-      user-agent:
-      - OpenAI/Python 1.52.1
-      x-stainless-arch:
-      - x64
-      x-stainless-async:
-      - 'false'
-      x-stainless-lang:
-      - python
-      x-stainless-os:
-      - Linux
-      x-stainless-package-version:
-      - 1.52.1
-      x-stainless-raw-response:
-      - 'true'
-      x-stainless-retry-count:
-      - '0'
-      x-stainless-runtime:
-      - CPython
-      x-stainless-runtime-version:
-      - 3.12.7
-    method: POST
-    uri: https://api.openai.com/v1/chat/completions
-  response:
-    content: "{\n  \"id\": \"chatcmpl-AjCtaiHL4TY8Dssk0j2miqmjrzquy\",\n  \"object\":
-      \"chat.completion\",\n  \"created\": 1735337694,\n  \"model\": \"gpt-4o-mini-2024-07-18\",\n
-      \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
-      \"assistant\",\n        \"content\": \"False\",\n        \"refusal\": null\n
-      \     },\n      \"logprobs\": null,\n      \"finish_reason\": \"stop\"\n    }\n
-      \ ],\n  \"usage\": {\n    \"prompt_tokens\": 73,\n    \"completion_tokens\":
-      1,\n    \"total_tokens\": 74,\n    \"prompt_tokens_details\": {\n      \"cached_tokens\":
-      0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\": {\n
-      \     \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\":
-      0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"system_fingerprint\":
-      \"fp_0aa8d3e20b\"\n}\n"
-    headers:
-      CF-Cache-Status:
-      - DYNAMIC
-      CF-RAY:
-      - 8f8caa8bdd26756b-SEA
-      Connection:
-      - keep-alive
-      Content-Encoding:
-      - gzip
-      Content-Type:
-      - application/json
-      Date:
-      - Fri, 27 Dec 2024 22:14:54 GMT
-      Server:
-      - cloudflare
-      Transfer-Encoding:
-      - chunked
-      X-Content-Type-Options:
-      - nosniff
-      access-control-expose-headers:
-      - X-Request-ID
-      alt-svc:
-      - h3=":443"; ma=86400
-      openai-organization:
-      - crewai-iuxna1
-      openai-processing-ms:
-      - '184'
-      openai-version:
-      - '2020-10-01'
-      strict-transport-security:
-      - max-age=31536000; includeSubDomains; preload
-      x-ratelimit-limit-requests:
-      - '30000'
-      x-ratelimit-limit-tokens:
-      - '150000000'
-      x-ratelimit-remaining-requests:
-      - '29999'
-      x-ratelimit-remaining-tokens:
-      - '149999902'
-      x-ratelimit-reset-requests:
-      - 2ms
-      x-ratelimit-reset-tokens:
-      - 0s
-      x-request-id:
-      - req_652891f79c1104a7a8436275d78a69f1
-    http_version: HTTP/1.1
-    status_code: 200
-version: 1

tests/cassettes/test_tool_execution_error_event.yaml

@@ -0,0 +1,112 @@
+interactions:
+- request:
+    body: '{"messages": [{"role": "user", "content": "Use the failing tool"}], "model":
+      "gpt-4o-mini", "stop": [], "tools": [{"type": "function", "function": {"name":
+      "failing_tool", "description": "This tool always fails.", "parameters": {"type":
+      "object", "properties": {"param": {"type": "string", "description": "A test
+      parameter"}}, "required": ["param"]}}}]}'
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate
+      connection:
+      - keep-alive
+      content-length:
+      - '353'
+      content-type:
+      - application/json
+      host:
+      - api.openai.com
+      user-agent:
+      - OpenAI/Python 1.61.0
+      x-stainless-arch:
+      - arm64
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - MacOS
+      x-stainless-package-version:
+      - 1.61.0
+      x-stainless-raw-response:
+      - 'true'
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.12.8
+    method: POST
+    uri: https://api.openai.com/v1/chat/completions
+  response:
+    content: "{\n  \"id\": \"chatcmpl-B2P4zoJZuES7Aom8ugEq1modz5Vsl\",\n  \"object\":
+      \"chat.completion\",\n  \"created\": 1739912761,\n  \"model\": \"gpt-4o-mini-2024-07-18\",\n
+      \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
+      \"assistant\",\n        \"content\": null,\n        \"tool_calls\": [\n          {\n
+      \           \"id\": \"call_F6fJxISpMKUBIGV6dd2vjRNG\",\n            \"type\":
+      \"function\",\n            \"function\": {\n              \"name\": \"failing_tool\",\n
+      \             \"arguments\": \"{\\\"param\\\":\\\"test\\\"}\"\n            }\n
+      \         }\n        ],\n        \"refusal\": null\n      },\n      \"logprobs\":
+      null,\n      \"finish_reason\": \"tool_calls\"\n    }\n  ],\n  \"usage\": {\n
+      \   \"prompt_tokens\": 51,\n    \"completion_tokens\": 15,\n    \"total_tokens\":
+      66,\n    \"prompt_tokens_details\": {\n      \"cached_tokens\": 0,\n      \"audio_tokens\":
+      0\n    },\n    \"completion_tokens_details\": {\n      \"reasoning_tokens\":
+      0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\": 0,\n      \"rejected_prediction_tokens\":
+      0\n    }\n  },\n  \"service_tier\": \"default\",\n  \"system_fingerprint\":
+      \"fp_00428b782a\"\n}\n"
+    headers:
+      CF-RAY:
+      - 9140fa827f38eb1e-SJC
+      Connection:
+      - keep-alive
+      Content-Encoding:
+      - gzip
+      Content-Type:
+      - application/json
+      Date:
+      - Tue, 18 Feb 2025 21:06:02 GMT
+      Server:
+      - cloudflare
+      Set-Cookie:
+      - __cf_bm=xbuu3IQpCMh.43ZrqL1TRMECOc6QldgHV0hzOX1GrWI-1739912762-1.0.1.1-t7iyq5xMioPrwfeaHLvPT9rwRPp7Q9A9uIm69icH9dPxRD4xMA3cWqb1aXj1_e2IyAEQQWFe1UWjlmJ22aHh3Q;
+        path=/; expires=Tue, 18-Feb-25 21:36:02 GMT; domain=.api.openai.com; HttpOnly;
+        Secure; SameSite=None
+      - _cfuvid=x9l.Rhja8_wXDN.j8qcEU1PvvEqAwZp4Fd3s_aj4qwM-1739912762161-0.0.1.1-604800000;
+        path=/; domain=.api.openai.com; HttpOnly; Secure; SameSite=None
+      Transfer-Encoding:
+      - chunked
+      X-Content-Type-Options:
+      - nosniff
+      access-control-expose-headers:
+      - X-Request-ID
+      alt-svc:
+      - h3=":443"; ma=86400
+      cf-cache-status:
+      - DYNAMIC
+      openai-organization:
+      - crewai-iuxna1
+      openai-processing-ms:
+      - '861'
+      openai-version:
+      - '2020-10-01'
+      strict-transport-security:
+      - max-age=31536000; includeSubDomains; preload
+      x-ratelimit-limit-requests:
+      - '30000'
+      x-ratelimit-limit-tokens:
+      - '150000000'
+      x-ratelimit-remaining-requests:
+      - '29999'
+      x-ratelimit-remaining-tokens:
+      - '149999978'
+      x-ratelimit-reset-requests:
+      - 2ms
+      x-ratelimit-reset-tokens:
+      - 0s
+      x-request-id:
+      - req_8666ec3aa6677cb346ba00993556051d
+    http_version: HTTP/1.1
+    status_code: 200
+version: 1

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

@@ -6,7 +6,6 @@ from concurrent.futures import Future
 from unittest import mock
 from unittest.mock import MagicMock, patch
 
-import instructor
 import pydantic_core
 import pytest
 
@@ -15,15 +14,24 @@ from crewai.agents.cache import CacheHandler
 from crewai.crew import Crew
 from crewai.crews.crew_output import CrewOutput
 from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
+from crewai.llm import LLM
 from crewai.memory.contextual.contextual_memory import ContextualMemory
 from crewai.process import Process
-from crewai.project import crew
 from crewai.task import Task
 from crewai.tasks.conditional_task import ConditionalTask
 from crewai.tasks.output_format import OutputFormat
 from crewai.tasks.task_output import TaskOutput
 from crewai.types.usage_metrics import UsageMetrics
 from crewai.utilities import Logger
+from crewai.utilities.events import (
+    CrewTrainCompletedEvent,
+    CrewTrainStartedEvent,
+    crewai_event_bus,
+)
+from crewai.utilities.events.crew_events import (
+    CrewTestCompletedEvent,
+    CrewTestStartedEvent,
+)
 from crewai.utilities.rpm_controller import RPMController
 from crewai.utilities.task_output_storage_handler import TaskOutputStorageHandler
 
@@ -51,6 +59,7 @@ 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 +91,7 @@ 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 +599,12 @@ 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 +663,12 @@ 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 +792,17 @@ 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
@@ -841,8 +851,21 @@ def test_crew_verbose_output(capsys):
     crew.verbose = False
     crew._logger = Logger(verbose=False)
     crew.kickoff()
+    expected_listener_logs = [
+        "[🚀 CREW 'CREW' STARTED]",
+        "[📋 TASK STARTED: RESEARCH AI ADVANCEMENTS.]",
+        "[🤖 AGENT 'RESEARCHER' STARTED TASK]",
+        "[✅ AGENT 'RESEARCHER' COMPLETED TASK]",
+        "[✅ TASK COMPLETED: RESEARCH AI ADVANCEMENTS.]",
+        "[📋 TASK STARTED: WRITE ABOUT AI IN HEALTHCARE.]",
+        "[🤖 AGENT 'SENIOR WRITER' STARTED TASK]",
+        "[✅ AGENT 'SENIOR WRITER' COMPLETED TASK]",
+        "[✅ TASK COMPLETED: WRITE ABOUT AI IN HEALTHCARE.]",
+        "[✅ CREW 'CREW' COMPLETED]",
+    ]
     captured = capsys.readouterr()
-    assert captured.out == ""
+    for log in expected_listener_logs:
+        assert log in captured.out
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -1280,9 +1303,9 @@ def test_kickoff_for_each_invalid_input():
 
     crew = Crew(agents=[agent], tasks=[task])
 
-    with pytest.raises(TypeError):
+    with pytest.raises(pydantic_core._pydantic_core.ValidationError):
         # Pass a string instead of a list
-        crew.kickoff_for_each("invalid input")
+        crew.kickoff_for_each(["invalid input"])
 
 
 def test_kickoff_for_each_error_handling():
@@ -1593,9 +1616,9 @@ 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 +1975,7 @@ 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 +2125,22 @@ 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 +2159,37 @@ 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 +2199,20 @@ 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 +2231,46 @@ 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 +2280,20 @@ 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 +2312,7 @@ 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 +2320,7 @@ 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
@@ -2550,6 +2589,16 @@ def test_crew_train_success(
     # Create a mock for the copied crew
     copy_mock.return_value = crew
 
+    received_events = []
+
+    @crewai_event_bus.on(CrewTrainStartedEvent)
+    def on_crew_train_started(source, event: CrewTrainStartedEvent):
+        received_events.append(event)
+
+    @crewai_event_bus.on(CrewTrainCompletedEvent)
+    def on_crew_train_completed(source, event: CrewTrainCompletedEvent):
+        received_events.append(event)
+
     crew.train(
         n_iterations=2, inputs={"topic": "AI"}, filename="trained_agents_data.pkl"
     )
@@ -2595,6 +2644,10 @@ def test_crew_train_success(
         ]
     )
 
+    assert len(received_events) == 2
+    assert isinstance(received_events[0], CrewTrainStartedEvent)
+    assert isinstance(received_events[1], CrewTrainCompletedEvent)
+
 
 def test_crew_train_error():
     task = Task(
@@ -3323,7 +3376,19 @@ def test_crew_testing_function(kickoff_mock, copy_mock, crew_evaluator):
     copy_mock.return_value = crew
 
     n_iterations = 2
-    crew.test(n_iterations, openai_model_name="gpt-4o-mini", inputs={"topic": "AI"})
+    llm_instance = LLM("gpt-4o-mini")
+
+    received_events = []
+
+    @crewai_event_bus.on(CrewTestStartedEvent)
+    def on_crew_test_started(source, event: CrewTestStartedEvent):
+        received_events.append(event)
+
+    @crewai_event_bus.on(CrewTestCompletedEvent)
+    def on_crew_test_completed(source, event: CrewTestCompletedEvent):
+        received_events.append(event)
+
+    crew.test(n_iterations, llm_instance, inputs={"topic": "AI"})
 
     # Ensure kickoff is called on the copied crew
     kickoff_mock.assert_has_calls(
@@ -3332,13 +3397,17 @@ def test_crew_testing_function(kickoff_mock, copy_mock, crew_evaluator):
 
     crew_evaluator.assert_has_calls(
         [
-            mock.call(crew, "gpt-4o-mini"),
+            mock.call(crew, llm_instance),
             mock.call().set_iteration(1),
             mock.call().set_iteration(2),
             mock.call().print_crew_evaluation_result(),
         ]
     )
 
+    assert len(received_events) == 2
+    assert isinstance(received_events[0], CrewTestStartedEvent)
+    assert isinstance(received_events[1], CrewTestCompletedEvent)
+
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_hierarchical_verbose_manager_agent():
@@ -3400,9 +3469,9 @@ 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 +3544,20 @@ 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 +3601,9 @@ 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 +3829,9 @@ 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_state_utils.py

@@ -0,0 +1,150 @@
+from datetime import date, datetime
+from typing import List
+from unittest.mock import Mock
+
+import pytest
+from pydantic import BaseModel
+
+from crewai.flow import Flow
+from crewai.flow.state_utils import export_state, to_string
+
+
+class Address(BaseModel):
+    street: str
+    city: str
+    country: str
+
+
+class Person(BaseModel):
+    name: str
+    age: int
+    address: Address
+    birthday: date
+    skills: List[str]
+
+
+@pytest.fixture
+def mock_flow():
+    def create_flow(state):
+        flow = Mock(spec=Flow)
+        flow._state = state
+        return flow
+
+    return create_flow
+
+
+@pytest.mark.parametrize(
+    "test_input,expected",
+    [
+        ({"text": "hello world"}, {"text": "hello world"}),
+        ({"number": 42}, {"number": 42}),
+        ({"decimal": 3.14}, {"decimal": 3.14}),
+        ({"flag": True}, {"flag": True}),
+        ({"empty": None}, {"empty": None}),
+        ({"list": [1, 2, 3]}, {"list": [1, 2, 3]}),
+        ({"tuple": (1, 2, 3)}, {"tuple": [1, 2, 3]}),
+        ({"set": {1, 2, 3}}, {"set": [1, 2, 3]}),
+        ({"nested": [1, [2, 3], {4, 5}]}, {"nested": [1, [2, 3], [4, 5]]}),
+    ],
+)
+def test_basic_serialization(mock_flow, test_input, expected):
+    flow = mock_flow(test_input)
+    result = export_state(flow)
+    assert result == expected
+
+
+@pytest.mark.parametrize(
+    "input_date,expected",
+    [
+        (date(2024, 1, 1), "2024-01-01"),
+        (datetime(2024, 1, 1, 12, 30), "2024-01-01T12:30:00"),
+    ],
+)
+def test_temporal_serialization(mock_flow, input_date, expected):
+    flow = mock_flow({"date": input_date})
+    result = export_state(flow)
+    assert result["date"] == expected
+
+
+@pytest.mark.parametrize(
+    "key,value,expected_key_type",
+    [
+        (("tuple", "key"), "value", str),
+        (None, "value", str),
+        (123, "value", str),
+        ("normal", "value", str),
+    ],
+)
+def test_dictionary_key_serialization(mock_flow, key, value, expected_key_type):
+    flow = mock_flow({key: value})
+    result = export_state(flow)
+    assert len(result) == 1
+    result_key = next(iter(result.keys()))
+    assert isinstance(result_key, expected_key_type)
+    assert result[result_key] == value
+
+
+@pytest.mark.parametrize(
+    "callable_obj,expected_in_result",
+    [
+        (lambda x: x * 2, "lambda"),
+        (str.upper, "upper"),
+    ],
+)
+def test_callable_serialization(mock_flow, callable_obj, expected_in_result):
+    flow = mock_flow({"func": callable_obj})
+    result = export_state(flow)
+    assert isinstance(result["func"], str)
+    assert expected_in_result in result["func"].lower()
+
+
+def test_pydantic_model_serialization(mock_flow):
+    address = Address(street="123 Main St", city="Tech City", country="Pythonia")
+
+    person = Person(
+        name="John Doe",
+        age=30,
+        address=address,
+        birthday=date(1994, 1, 1),
+        skills=["Python", "Testing"],
+    )
+
+    flow = mock_flow(
+        {
+            "single_model": address,
+            "nested_model": person,
+            "model_list": [address, address],
+            "model_dict": {"home": address},
+        }
+    )
+
+    result = export_state(flow)
+    assert (
+        to_string(result)
+        == '{"single_model": {"street": "123 Main St", "city": "Tech City", "country": "Pythonia"}, "nested_model": {"name": "John Doe", "age": 30, "address": {"street": "123 Main St", "city": "Tech City", "country": "Pythonia"}, "birthday": "1994-01-01", "skills": ["Python", "Testing"]}, "model_list": [{"street": "123 Main St", "city": "Tech City", "country": "Pythonia"}, {"street": "123 Main St", "city": "Tech City", "country": "Pythonia"}], "model_dict": {"home": {"street": "123 Main St", "city": "Tech City", "country": "Pythonia"}}}'
+    )
+
+
+def test_depth_limit(mock_flow):
+    """Test max depth handling with a deeply nested structure"""
+
+    def create_nested(depth):
+        if depth == 0:
+            return "value"
+        return {"next": create_nested(depth - 1)}
+
+    deep_structure = create_nested(10)
+    flow = mock_flow(deep_structure)
+    result = export_state(flow)
+
+    assert result == {
+        "next": {
+            "next": {
+                "next": {
+                    "next": {
+                        "next": "{'next': {'next': {'next': {'next': {'next': 'value'}}}}}"
+                    }
+                }
+            }
+        }
+    }

tests/flow_test.py

@@ -1,11 +1,20 @@
 """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.utilities.events import (
+    FlowFinishedEvent,
+    FlowStartedEvent,
+    MethodExecutionFinishedEvent,
+    MethodExecutionStartedEvent,
+    crewai_event_bus,
+)
+from crewai.utilities.events.flow_events import FlowPlotEvent
 
 
 def test_simple_sequential_flow():
@@ -398,3 +407,250 @@ 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
+            return "roses are red\nviolets are blue"
+
+    flow = PoemFlow()
+    received_events = []
+
+    @crewai_event_bus.on(FlowStartedEvent)
+    def handle_flow_start(source, event):
+        received_events.append(event)
+
+    @crewai_event_bus.on(MethodExecutionStartedEvent)
+    def handle_method_start(source, event):
+        received_events.append(event)
+
+    @crewai_event_bus.on(FlowFinishedEvent)
+    def handle_flow_end(source, event):
+        received_events.append(event)
+
+    flow.kickoff(inputs={"separator": ", "})
+    assert isinstance(received_events[0], FlowStartedEvent)
+    assert received_events[0].flow_name == "PoemFlow"
+    assert received_events[0].inputs == {"separator": ", "}
+    assert isinstance(received_events[0].timestamp, datetime)
+
+    # All subsequent events are MethodExecutionStartedEvent
+    for event in received_events[1:-1]:
+        assert isinstance(event, MethodExecutionStartedEvent)
+        assert event.flow_name == "PoemFlow"
+        assert isinstance(event.state, dict)
+        assert isinstance(event.state["id"], str)
+        assert event.state["separator"] == ", "
+
+    assert received_events[1].method_name == "prepare_flower"
+    assert received_events[1].params == {}
+    assert "flower" not in received_events[1].state
+
+    assert received_events[2].method_name == "prepare_color"
+    assert received_events[2].params == {}
+    print("received_events[2]", received_events[2])
+    assert "flower" in received_events[2].state
+
+    assert received_events[3].method_name == "write_first_sentence"
+    assert received_events[3].params == {}
+    assert received_events[3].state["flower"] == "roses"
+    assert received_events[3].state["color"] == "red"
+
+    assert received_events[4].method_name == "finish_poem"
+    assert received_events[4].params == {"_0": "roses are red"}
+    assert received_events[4].state["flower"] == "roses"
+    assert received_events[4].state["color"] == "red"
+
+    assert received_events[5].method_name == "save_poem_to_database"
+    assert received_events[5].params == {}
+    assert received_events[5].state["flower"] == "roses"
+    assert received_events[5].state["color"] == "red"
+
+    assert isinstance(received_events[6], FlowFinishedEvent)
+    assert received_events[6].flow_name == "PoemFlow"
+    assert received_events[6].result == "roses are red\nviolets are blue"
+    assert isinstance(received_events[6].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}!"
+
+    flow = OnboardingFlow()
+    flow.kickoff(inputs={"name": "Anakin"})
+
+    received_events = []
+
+    @crewai_event_bus.on(FlowStartedEvent)
+    def handle_flow_start(source, event):
+        received_events.append(event)
+
+    @crewai_event_bus.on(MethodExecutionStartedEvent)
+    def handle_method_start(source, event):
+        received_events.append(event)
+
+    @crewai_event_bus.on(MethodExecutionFinishedEvent)
+    def handle_method_end(source, event):
+        received_events.append(event)
+
+    @crewai_event_bus.on(FlowFinishedEvent)
+    def handle_flow_end(source, event):
+        received_events.append(event)
+
+    flow.kickoff(inputs={"name": "Anakin"})
+
+    assert isinstance(received_events[0], FlowStartedEvent)
+    assert received_events[0].flow_name == "OnboardingFlow"
+    assert received_events[0].inputs == {"name": "Anakin"}
+    assert isinstance(received_events[0].timestamp, datetime)
+
+    assert isinstance(received_events[1], MethodExecutionStartedEvent)
+    assert received_events[1].method_name == "user_signs_up"
+
+    assert isinstance(received_events[2], MethodExecutionFinishedEvent)
+    assert received_events[2].method_name == "user_signs_up"
+
+    assert isinstance(received_events[3], MethodExecutionStartedEvent)
+    assert received_events[3].method_name == "send_welcome_message"
+    assert received_events[3].params == {}
+    assert getattr(received_events[3].state, "sent") is False
+
+    assert isinstance(received_events[4], MethodExecutionFinishedEvent)
+    assert received_events[4].method_name == "send_welcome_message"
+    assert getattr(received_events[4].state, "sent") is True
+    assert received_events[4].result == "Welcome, Anakin!"
+
+    assert isinstance(received_events[5], FlowFinishedEvent)
+    assert received_events[5].flow_name == "OnboardingFlow"
+    assert received_events[5].result == "Welcome, Anakin!"
+    assert isinstance(received_events[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()
+    received_events = []
+
+    @crewai_event_bus.on(FlowStartedEvent)
+    def handle_flow_start(source, event):
+        received_events.append(event)
+
+    @crewai_event_bus.on(MethodExecutionStartedEvent)
+    def handle_method_start(source, event):
+        received_events.append(event)
+
+    @crewai_event_bus.on(MethodExecutionFinishedEvent)
+    def handle_method_end(source, event):
+        received_events.append(event)
+
+    @crewai_event_bus.on(FlowFinishedEvent)
+    def handle_flow_end(source, event):
+        received_events.append(event)
+
+    flow.kickoff()
+
+    assert isinstance(received_events[0], FlowStartedEvent)
+    assert received_events[0].flow_name == "StatelessFlow"
+    assert received_events[0].inputs is None
+    assert isinstance(received_events[0].timestamp, datetime)
+
+    assert isinstance(received_events[1], MethodExecutionStartedEvent)
+    assert received_events[1].method_name == "init"
+
+    assert isinstance(received_events[2], MethodExecutionFinishedEvent)
+    assert received_events[2].method_name == "init"
+
+    assert isinstance(received_events[3], MethodExecutionStartedEvent)
+    assert received_events[3].method_name == "process"
+
+    assert isinstance(received_events[4], MethodExecutionFinishedEvent)
+    assert received_events[4].method_name == "process"
+
+    assert isinstance(received_events[5], FlowFinishedEvent)
+    assert received_events[5].flow_name == "StatelessFlow"
+    assert (
+        received_events[5].result
+        == "Deeds will not be less valiant because they are unpraised."
+    )
+    assert isinstance(received_events[5].timestamp, datetime)
+
+
+def test_flow_plotting():
+    class StatelessFlow(Flow):
+        @start()
+        def init(self):
+            return "Initializing flow..."
+
+        @listen(init)
+        def process(self):
+            return "Deeds will not be less valiant because they are unpraised."
+
+    flow = StatelessFlow()
+    flow.kickoff()
+    received_events = []
+
+    @crewai_event_bus.on(FlowPlotEvent)
+    def handle_flow_plot(source, event):
+        received_events.append(event)
+
+    flow.plot("test_flow")
+
+    assert len(received_events) == 1
+    assert isinstance(received_events[0], FlowPlotEvent)
+    assert received_events[0].flow_name == "StatelessFlow"
+    assert isinstance(received_events[0].timestamp, datetime)

tests/llm_test.py

@@ -7,7 +7,8 @@ from pydantic import BaseModel
 
 from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
 from crewai.llm import LLM
-from crewai.tools import tool
+from crewai.utilities.events import crewai_event_bus
+from crewai.utilities.events.tool_usage_events import ToolExecutionErrorEvent
 from crewai.utilities.token_counter_callback import TokenCalcHandler
 
 
@@ -291,32 +292,36 @@ def anthropic_llm():
     """Fixture providing an Anthropic LLM instance."""
     return LLM(model="anthropic/claude-3-sonnet")
 
+
 @pytest.fixture
 def system_message():
     """Fixture providing a system message."""
     return {"role": "system", "content": "test"}
 
+
 @pytest.fixture
 def user_message():
     """Fixture providing a user message."""
     return {"role": "user", "content": "test"}
 
+
 def test_anthropic_message_formatting_edge_cases(anthropic_llm):
     """Test edge cases for Anthropic message formatting."""
     # Test None messages
     with pytest.raises(TypeError, match="Messages cannot be None"):
         anthropic_llm._format_messages_for_provider(None)
-        
+
     # Test empty message list
     formatted = anthropic_llm._format_messages_for_provider([])
     assert len(formatted) == 1
     assert formatted[0]["role"] == "user"
     assert formatted[0]["content"] == "."
-    
+
     # Test invalid message format
     with pytest.raises(TypeError, match="Invalid message format"):
         anthropic_llm._format_messages_for_provider([{"invalid": "message"}])
 
+
 def test_anthropic_model_detection():
     """Test Anthropic model detection with various formats."""
     models = [
@@ -327,11 +332,12 @@ def test_anthropic_model_detection():
         ("", False),
         ("anthropomorphic", False),  # Should not match partial words
     ]
-    
+
     for model, expected in models:
         llm = LLM(model=model)
         assert llm.is_anthropic == expected, f"Failed for model: {model}"
 
+
 def test_anthropic_message_formatting(anthropic_llm, system_message, user_message):
     """Test Anthropic message formatting with fixtures."""
     # Test when first message is system
@@ -371,3 +377,51 @@ def test_deepseek_r1_with_open_router():
     result = llm.call("What is the capital of France?")
     assert isinstance(result, str)
     assert "Paris" in result
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_tool_execution_error_event():
+    llm = LLM(model="gpt-4o-mini")
+
+    def failing_tool(param: str) -> str:
+        """This tool always fails."""
+        raise Exception("Tool execution failed!")
+
+    tool_schema = {
+        "type": "function",
+        "function": {
+            "name": "failing_tool",
+            "description": "This tool always fails.",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "param": {"type": "string", "description": "A test parameter"}
+                },
+                "required": ["param"],
+            },
+        },
+    }
+
+    received_events = []
+
+    @crewai_event_bus.on(ToolExecutionErrorEvent)
+    def event_handler(source, event):
+        received_events.append(event)
+
+    available_functions = {"failing_tool": failing_tool}
+
+    messages = [{"role": "user", "content": "Use the failing tool"}]
+
+    llm.call(
+        messages,
+        tools=[tool_schema],
+        available_functions=available_functions,
+    )
+
+    assert len(received_events) == 1
+    event = received_events[0]
+    assert isinstance(event, ToolExecutionErrorEvent)
+    assert event.tool_name == "failing_tool"
+    assert event.tool_args == {"param": "test"}
+    assert event.tool_class == failing_tool
+    assert "Tool execution failed!" in event.error

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"))

tests/test_flow_persistence.py

@@ -13,11 +13,12 @@ from crewai.flow.persistence.sqlite import SQLiteFlowPersistence
 
 class TestState(FlowState):
     """Test state model with required id field."""
+
     counter: int = 0
     message: str = ""
 
 
-def test_persist_decorator_saves_state(tmp_path):
+def test_persist_decorator_saves_state(tmp_path, caplog):
     """Test that @persist decorator saves state in SQLite."""
     db_path = os.path.join(tmp_path, "test_flows.db")
     persistence = SQLiteFlowPersistence(db_path)
@@ -73,7 +74,6 @@ def test_flow_state_restoration(tmp_path):
 
     # First flow execution to create initial state
     class RestorableFlow(Flow[TestState]):
-
         @start()
         @persist(persistence)
         def set_message(self):
@@ -89,10 +89,7 @@ def test_flow_state_restoration(tmp_path):
 
     # Test case 1: Restore using restore_uuid with field override
     flow2 = RestorableFlow(persistence=persistence)
-    flow2.kickoff(inputs={
-        "id": original_uuid,
-        "counter": 43
-    })
+    flow2.kickoff(inputs={"id": original_uuid, "counter": 43})
 
     # Verify state restoration and selective field override
     assert flow2.state.id == original_uuid
@@ -101,10 +98,7 @@ def test_flow_state_restoration(tmp_path):
 
     # Test case 2: Restore using kwargs['id']
     flow3 = RestorableFlow(persistence=persistence)
-    flow3.kickoff(inputs={
-        "id": original_uuid,
-        "message": "Updated message"
-    })
+    flow3.kickoff(inputs={"id": original_uuid, "message": "Updated message"})
 
     # Verify state restoration and selective field override
     assert flow3.state.id == original_uuid
@@ -174,3 +168,43 @@ def test_multiple_method_persistence(tmp_path):
     final_state = flow2.state
     assert final_state.counter == 99999
     assert final_state.message == "Step 99999"
+
+
+def test_persist_decorator_verbose_logging(tmp_path, caplog):
+    """Test that @persist decorator's verbose parameter controls logging."""
+    # Set logging level to ensure we capture all logs
+    caplog.set_level("INFO")
+
+    db_path = os.path.join(tmp_path, "test_flows.db")
+    persistence = SQLiteFlowPersistence(db_path)
+
+    # Test with verbose=False (default)
+    class QuietFlow(Flow[Dict[str, str]]):
+        initial_state = dict()
+
+        @start()
+        @persist(persistence)  # Default verbose=False
+        def init_step(self):
+            self.state["message"] = "Hello, World!"
+            self.state["id"] = "test-uuid-1"
+
+    flow = QuietFlow(persistence=persistence)
+    flow.kickoff()
+    assert "Saving flow state" not in caplog.text
+
+    # Clear the log
+    caplog.clear()
+
+    # Test with verbose=True
+    class VerboseFlow(Flow[Dict[str, str]]):
+        initial_state = dict()
+
+        @start()
+        @persist(persistence, verbose=True)
+        def init_step(self):
+            self.state["message"] = "Hello, World!"
+            self.state["id"] = "test-uuid-2"
+
+    flow = VerboseFlow(persistence=persistence)
+    flow.kickoff()
+    assert "Saving flow state" in caplog.text

tests/tools/test_tool_usage.py

@@ -1,6 +1,6 @@
 import json
 import random
-from unittest.mock import MagicMock
+from unittest.mock import MagicMock, patch
 
 import pytest
 from pydantic import BaseModel, Field
@@ -8,6 +8,11 @@ from pydantic import BaseModel, Field
 from crewai import Agent, Task
 from crewai.tools import BaseTool
 from crewai.tools.tool_usage import ToolUsage
+from crewai.utilities.events import crewai_event_bus
+from crewai.utilities.events.tool_usage_events import (
+    ToolSelectionErrorEvent,
+    ToolValidateInputErrorEvent,
+)
 
 
 class RandomNumberToolInput(BaseModel):
@@ -226,7 +231,7 @@ def test_validate_tool_input_with_special_characters():
     )
 
     # Input with special characters
-    tool_input = '{"message": "Hello, world! \u263A", "valid": True}'
+    tool_input = '{"message": "Hello, world! \u263a", "valid": True}'
     expected_arguments = {"message": "Hello, world! ☺", "valid": True}
 
     arguments = tool_usage._validate_tool_input(tool_input)
@@ -331,6 +336,19 @@ def test_validate_tool_input_with_trailing_commas():
 
 
 def test_validate_tool_input_invalid_input():
+    # Create mock agent with proper string values
+    mock_agent = MagicMock()
+    mock_agent.key = "test_agent_key"  # Must be a string
+    mock_agent.role = "test_agent_role"  # Must be a string
+    mock_agent._original_role = "test_agent_role"  # Must be a string
+    mock_agent.i18n = MagicMock()
+    mock_agent.verbose = False
+
+    # Create mock action with proper string value
+    mock_action = MagicMock()
+    mock_action.tool = "test_tool"  # Must be a string
+    mock_action.tool_input = "test_input"  # Must be a string
+
     tool_usage = ToolUsage(
         tools_handler=MagicMock(),
         tools=[],
@@ -339,8 +357,8 @@ def test_validate_tool_input_invalid_input():
         tools_names="",
         task=MagicMock(),
         function_calling_llm=None,
-        agent=MagicMock(),
-        action=MagicMock(),
+        agent=mock_agent,
+        action=mock_action,
     )
 
     invalid_inputs = [
@@ -360,7 +378,7 @@ def test_validate_tool_input_invalid_input():
 
     # Test for None input separately
     arguments = tool_usage._validate_tool_input(None)
-    assert arguments == {}  # Expecting an empty dictionary
+    assert arguments == {}
 
 
 def test_validate_tool_input_complex_structure():
@@ -468,18 +486,141 @@ def test_validate_tool_input_large_json_content():
     assert arguments == expected_arguments
 
 
-def test_validate_tool_input_none_input():
+def test_tool_selection_error_event_direct():
+    """Test tool selection error event emission directly from ToolUsage class."""
+    mock_agent = MagicMock()
+    mock_agent.key = "test_key"
+    mock_agent.role = "test_role"
+    mock_agent.i18n = MagicMock()
+    mock_agent.verbose = False
+
+    mock_task = MagicMock()
+    mock_tools_handler = MagicMock()
+
+    class TestTool(BaseTool):
+        name: str = "Test Tool"
+        description: str = "A test tool"
+
+        def _run(self, input: dict) -> str:
+            return "test result"
+
+    test_tool = TestTool()
+
     tool_usage = ToolUsage(
-        tools_handler=MagicMock(),
-        tools=[],
-        original_tools=[],
-        tools_description="",
-        tools_names="",
-        task=MagicMock(),
+        tools_handler=mock_tools_handler,
+        tools=[test_tool],
+        original_tools=[test_tool],
+        tools_description="Test Tool Description",
+        tools_names="Test Tool",
+        task=mock_task,
         function_calling_llm=None,
-        agent=MagicMock(),
+        agent=mock_agent,
         action=MagicMock(),
     )
 
-    arguments = tool_usage._validate_tool_input(None)
-    assert arguments == {}  # Expecting an empty dictionary
+    received_events = []
+
+    @crewai_event_bus.on(ToolSelectionErrorEvent)
+    def event_handler(source, event):
+        received_events.append(event)
+
+    with pytest.raises(Exception) as exc_info:
+        tool_usage._select_tool("Non Existent Tool")
+    assert len(received_events) == 1
+    event = received_events[0]
+    assert isinstance(event, ToolSelectionErrorEvent)
+    assert event.agent_key == "test_key"
+    assert event.agent_role == "test_role"
+    assert event.tool_name == "Non Existent Tool"
+    assert event.tool_args == {}
+    assert event.tool_class == "Test Tool Description"
+    assert "don't exist" in event.error
+
+    received_events.clear()
+    with pytest.raises(Exception) as exc_info:
+        tool_usage._select_tool("")
+
+    assert len(received_events) == 1
+    event = received_events[0]
+    assert isinstance(event, ToolSelectionErrorEvent)
+    assert event.agent_key == "test_key"
+    assert event.agent_role == "test_role"
+    assert event.tool_name == ""
+    assert event.tool_args == {}
+    assert event.tool_class == "Test Tool Description"
+    assert "forgot the Action name" in event.error
+
+
+def test_tool_validate_input_error_event():
+    """Test tool validation input error event emission from ToolUsage class."""
+    # Mock agent and required components
+    mock_agent = MagicMock()
+    mock_agent.key = "test_key"
+    mock_agent.role = "test_role"
+    mock_agent.verbose = False
+    mock_agent._original_role = "test_role"
+
+    # Mock i18n with error message
+    mock_i18n = MagicMock()
+    mock_i18n.errors.return_value = (
+        "Tool input must be a valid dictionary in JSON or Python literal format"
+    )
+    mock_agent.i18n = mock_i18n
+
+    # Mock task and tools handler
+    mock_task = MagicMock()
+    mock_tools_handler = MagicMock()
+
+    # Mock printer
+    mock_printer = MagicMock()
+
+    # Create test tool
+    class TestTool(BaseTool):
+        name: str = "Test Tool"
+        description: str = "A test tool"
+
+        def _run(self, input: dict) -> str:
+            return "test result"
+
+    test_tool = TestTool()
+
+    # Create ToolUsage instance
+    tool_usage = ToolUsage(
+        tools_handler=mock_tools_handler,
+        tools=[test_tool],
+        original_tools=[test_tool],
+        tools_description="Test Tool Description",
+        tools_names="Test Tool",
+        task=mock_task,
+        function_calling_llm=None,
+        agent=mock_agent,
+        action=MagicMock(tool="test_tool"),
+    )
+    tool_usage._printer = mock_printer
+
+    # Mock all parsing attempts to fail
+    with (
+        patch("json.loads", side_effect=json.JSONDecodeError("Test Error", "", 0)),
+        patch("ast.literal_eval", side_effect=ValueError),
+        patch("json5.loads", side_effect=json.JSONDecodeError("Test Error", "", 0)),
+        patch("json_repair.repair_json", side_effect=Exception("Failed to repair")),
+    ):
+        received_events = []
+
+        @crewai_event_bus.on(ToolValidateInputErrorEvent)
+        def event_handler(source, event):
+            received_events.append(event)
+
+        # Test invalid input
+        invalid_input = "invalid json {[}"
+        with pytest.raises(Exception) as exc_info:
+            tool_usage._validate_tool_input(invalid_input)
+
+        # Verify event was emitted
+        assert len(received_events) == 1, "Expected one event to be emitted"
+        event = received_events[0]
+        assert isinstance(event, ToolValidateInputErrorEvent)
+        assert event.agent_key == "test_key"
+        assert event.agent_role == "test_role"
+        assert event.tool_name == "test_tool"
+        assert "must be a valid dictionary" in event.error

tests/traces/test_unified_trace_controller.py

@@ -0,0 +1,360 @@
+import os
+from datetime import UTC, datetime
+from unittest.mock import MagicMock, patch
+from uuid import UUID
+
+import pytest
+
+from crewai.traces.context import TraceContext
+from crewai.traces.enums import CrewType, RunType, TraceType
+from crewai.traces.models import (
+    CrewTrace,
+    FlowStepIO,
+    LLMRequest,
+    LLMResponse,
+)
+from crewai.traces.unified_trace_controller import (
+    UnifiedTraceController,
+    init_crew_main_trace,
+    init_flow_main_trace,
+    should_trace,
+    trace_flow_step,
+    trace_llm_call,
+)
+
+
+class TestUnifiedTraceController:
+    @pytest.fixture
+    def basic_trace_controller(self):
+        return UnifiedTraceController(
+            trace_type=TraceType.LLM_CALL,
+            run_type=RunType.KICKOFF,
+            crew_type=CrewType.CREW,
+            run_id="test-run-id",
+            agent_role="test-agent",
+            task_name="test-task",
+            task_description="test description",
+            task_id="test-task-id",
+        )
+
+    def test_initialization(self, basic_trace_controller):
+        """Test basic initialization of UnifiedTraceController"""
+        assert basic_trace_controller.trace_type == TraceType.LLM_CALL
+        assert basic_trace_controller.run_type == RunType.KICKOFF
+        assert basic_trace_controller.crew_type == CrewType.CREW
+        assert basic_trace_controller.run_id == "test-run-id"
+        assert basic_trace_controller.agent_role == "test-agent"
+        assert basic_trace_controller.task_name == "test-task"
+        assert basic_trace_controller.task_description == "test description"
+        assert basic_trace_controller.task_id == "test-task-id"
+        assert basic_trace_controller.status == "running"
+        assert isinstance(UUID(basic_trace_controller.trace_id), UUID)
+
+    def test_start_trace(self, basic_trace_controller):
+        """Test starting a trace"""
+        result = basic_trace_controller.start_trace()
+        assert result == basic_trace_controller
+        assert basic_trace_controller.start_time is not None
+        assert isinstance(basic_trace_controller.start_time, datetime)
+
+    def test_end_trace_success(self, basic_trace_controller):
+        """Test ending a trace successfully"""
+        basic_trace_controller.start_trace()
+        basic_trace_controller.end_trace(result={"test": "result"})
+
+        assert basic_trace_controller.end_time is not None
+        assert basic_trace_controller.status == "completed"
+        assert basic_trace_controller.error is None
+        assert basic_trace_controller.context.get("response") == {"test": "result"}
+
+    def test_end_trace_with_error(self, basic_trace_controller):
+        """Test ending a trace with an error"""
+        basic_trace_controller.start_trace()
+        basic_trace_controller.end_trace(error="Test error occurred")
+
+        assert basic_trace_controller.end_time is not None
+        assert basic_trace_controller.status == "error"
+        assert basic_trace_controller.error == "Test error occurred"
+
+    def test_add_child_trace(self, basic_trace_controller):
+        """Test adding a child trace"""
+        child_trace = {"id": "child-1", "type": "test"}
+        basic_trace_controller.add_child_trace(child_trace)
+        assert len(basic_trace_controller.children) == 1
+        assert basic_trace_controller.children[0] == child_trace
+
+    def test_to_crew_trace_llm_call(self):
+        """Test converting to CrewTrace for LLM call"""
+        test_messages = [{"role": "user", "content": "test"}]
+        test_response = {
+            "content": "test response",
+            "finish_reason": "stop",
+        }
+
+        controller = UnifiedTraceController(
+            trace_type=TraceType.LLM_CALL,
+            run_type=RunType.KICKOFF,
+            crew_type=CrewType.CREW,
+            run_id="test-run-id",
+            context={
+                "messages": test_messages,
+                "temperature": 0.7,
+                "max_tokens": 100,
+            },
+        )
+
+        # Set model and messages in the context
+        controller.context["model"] = "gpt-4"
+        controller.context["messages"] = test_messages
+
+        controller.start_trace()
+        controller.end_trace(result=test_response)
+
+        crew_trace = controller.to_crew_trace()
+        assert isinstance(crew_trace, CrewTrace)
+        assert isinstance(crew_trace.request, LLMRequest)
+        assert isinstance(crew_trace.response, LLMResponse)
+        assert crew_trace.request.model == "gpt-4"
+        assert crew_trace.request.messages == test_messages
+        assert crew_trace.response.content == test_response["content"]
+        assert crew_trace.response.finish_reason == test_response["finish_reason"]
+
+    def test_to_crew_trace_flow_step(self):
+        """Test converting to CrewTrace for flow step"""
+        flow_step_data = {
+            "function_name": "test_function",
+            "inputs": {"param1": "value1"},
+            "metadata": {"meta": "data"},
+        }
+
+        controller = UnifiedTraceController(
+            trace_type=TraceType.FLOW_STEP,
+            run_type=RunType.KICKOFF,
+            crew_type=CrewType.FLOW,
+            run_id="test-run-id",
+            flow_step=flow_step_data,
+        )
+
+        controller.start_trace()
+        controller.end_trace(result="test result")
+
+        crew_trace = controller.to_crew_trace()
+        assert isinstance(crew_trace, CrewTrace)
+        assert isinstance(crew_trace.flow_step, FlowStepIO)
+        assert crew_trace.flow_step.function_name == "test_function"
+        assert crew_trace.flow_step.inputs == {"param1": "value1"}
+        assert crew_trace.flow_step.outputs == {"result": "test result"}
+
+    def test_should_trace(self):
+        """Test should_trace function"""
+        with patch.dict(os.environ, {"CREWAI_ENABLE_TRACING": "true"}):
+            assert should_trace() is True
+
+        with patch.dict(os.environ, {"CREWAI_ENABLE_TRACING": "false"}):
+            assert should_trace() is False
+
+        with patch.dict(os.environ, clear=True):
+            assert should_trace() is False
+
+    @pytest.mark.asyncio
+    async def test_trace_flow_step_decorator(self):
+        """Test trace_flow_step decorator"""
+
+        class TestFlow:
+            flow_id = "test-flow-id"
+
+            @trace_flow_step
+            async def test_method(self, method_name, method, *args, **kwargs):
+                return "test result"
+
+        with patch.dict(os.environ, {"CREWAI_ENABLE_TRACING": "true"}):
+            flow = TestFlow()
+            result = await flow.test_method("test_method", lambda x: x, arg1="value1")
+            assert result == "test result"
+
+    def test_trace_llm_call_decorator(self):
+        """Test trace_llm_call decorator"""
+
+        class TestLLM:
+            model = "gpt-4"
+            temperature = 0.7
+            max_tokens = 100
+            stop = None
+
+            def _get_execution_context(self):
+                return MagicMock(), MagicMock()
+
+            def _get_new_messages(self, messages):
+                return messages
+
+            def _get_new_tool_results(self, agent):
+                return []
+
+            @trace_llm_call
+            def test_method(self, params):
+                return {
+                    "choices": [
+                        {
+                            "message": {"content": "test response"},
+                            "finish_reason": "stop",
+                        }
+                    ],
+                    "usage": {
+                        "total_tokens": 50,
+                        "prompt_tokens": 20,
+                        "completion_tokens": 30,
+                    },
+                }
+
+        with patch.dict(os.environ, {"CREWAI_ENABLE_TRACING": "true"}):
+            llm = TestLLM()
+            result = llm.test_method({"messages": []})
+            assert result["choices"][0]["message"]["content"] == "test response"
+
+    def test_init_crew_main_trace_kickoff(self):
+        """Test init_crew_main_trace in kickoff mode"""
+        trace_context = None
+
+        class TestCrew:
+            id = "test-crew-id"
+            _test = False
+            _train = False
+
+        @init_crew_main_trace
+        def test_method(self):
+            nonlocal trace_context
+            trace_context = TraceContext.get_current()
+            return "test result"
+
+        with patch.dict(os.environ, {"CREWAI_ENABLE_TRACING": "true"}):
+            crew = TestCrew()
+            result = test_method(crew)
+            assert result == "test result"
+            assert trace_context is not None
+            assert trace_context.trace_type == TraceType.LLM_CALL
+            assert trace_context.run_type == RunType.KICKOFF
+            assert trace_context.crew_type == CrewType.CREW
+            assert trace_context.run_id == str(crew.id)
+
+    def test_init_crew_main_trace_test_mode(self):
+        """Test init_crew_main_trace in test mode"""
+        trace_context = None
+
+        class TestCrew:
+            id = "test-crew-id"
+            _test = True
+            _train = False
+
+        @init_crew_main_trace
+        def test_method(self):
+            nonlocal trace_context
+            trace_context = TraceContext.get_current()
+            return "test result"
+
+        with patch.dict(os.environ, {"CREWAI_ENABLE_TRACING": "true"}):
+            crew = TestCrew()
+            result = test_method(crew)
+            assert result == "test result"
+            assert trace_context is not None
+            assert trace_context.run_type == RunType.TEST
+
+    def test_init_crew_main_trace_train_mode(self):
+        """Test init_crew_main_trace in train mode"""
+        trace_context = None
+
+        class TestCrew:
+            id = "test-crew-id"
+            _test = False
+            _train = True
+
+        @init_crew_main_trace
+        def test_method(self):
+            nonlocal trace_context
+            trace_context = TraceContext.get_current()
+            return "test result"
+
+        with patch.dict(os.environ, {"CREWAI_ENABLE_TRACING": "true"}):
+            crew = TestCrew()
+            result = test_method(crew)
+            assert result == "test result"
+            assert trace_context is not None
+            assert trace_context.run_type == RunType.TRAIN
+
+    @pytest.mark.asyncio
+    async def test_init_flow_main_trace(self):
+        """Test init_flow_main_trace decorator"""
+        trace_context = None
+        test_inputs = {"test": "input"}
+
+        class TestFlow:
+            flow_id = "test-flow-id"
+
+            @init_flow_main_trace
+            async def test_method(self, **kwargs):
+                nonlocal trace_context
+                trace_context = TraceContext.get_current()
+                # Verify the context is set during execution
+                assert trace_context.context["context"]["inputs"] == test_inputs
+                return "test result"
+
+        with patch.dict(os.environ, {"CREWAI_ENABLE_TRACING": "true"}):
+            flow = TestFlow()
+            result = await flow.test_method(inputs=test_inputs)
+            assert result == "test result"
+            assert trace_context is not None
+            assert trace_context.trace_type == TraceType.FLOW_STEP
+            assert trace_context.crew_type == CrewType.FLOW
+            assert trace_context.run_type == RunType.KICKOFF
+            assert trace_context.run_id == str(flow.flow_id)
+            assert trace_context.context["context"]["inputs"] == test_inputs
+
+    def test_trace_context_management(self):
+        """Test TraceContext management"""
+        trace1 = UnifiedTraceController(
+            trace_type=TraceType.LLM_CALL,
+            run_type=RunType.KICKOFF,
+            crew_type=CrewType.CREW,
+            run_id="test-run-1",
+        )
+
+        trace2 = UnifiedTraceController(
+            trace_type=TraceType.FLOW_STEP,
+            run_type=RunType.TEST,
+            crew_type=CrewType.FLOW,
+            run_id="test-run-2",
+        )
+
+        # Test that context is initially empty
+        assert TraceContext.get_current() is None
+
+        # Test setting and getting context
+        with TraceContext.set_current(trace1):
+            assert TraceContext.get_current() == trace1
+
+            # Test nested context
+            with TraceContext.set_current(trace2):
+                assert TraceContext.get_current() == trace2
+
+            # Test context restoration after nested block
+            assert TraceContext.get_current() == trace1
+
+        # Test context cleanup after with block
+        assert TraceContext.get_current() is None
+
+    def test_trace_context_error_handling(self):
+        """Test TraceContext error handling"""
+        trace = UnifiedTraceController(
+            trace_type=TraceType.LLM_CALL,
+            run_type=RunType.KICKOFF,
+            crew_type=CrewType.CREW,
+            run_id="test-run",
+        )
+
+        # Test that context is properly cleaned up even if an error occurs
+        try:
+            with TraceContext.set_current(trace):
+                raise ValueError("Test error")
+        except ValueError:
+            pass
+
+        assert TraceContext.get_current() is None

tests/utilities/cassettes/test_agent_emits_execution_started_and_completed_events.yaml

@@ -0,0 +1,243 @@
+interactions:
+- request:
+    body: '{"messages": [{"role": "system", "content": "You are base_agent. You are
+      a helpful assistant that just says hi\nYour personal goal is: Just say hi\nTo
+      give my best complete final answer to the task respond using the exact following
+      format:\n\nThought: I now can give a great answer\nFinal Answer: Your final
+      answer must be the great and the most complete as possible, it must be outcome
+      described.\n\nI MUST use these formats, my job depends on it!"}, {"role": "user",
+      "content": "\nCurrent Task: Just say hi\n\nThis is the expect criteria for your
+      final answer: hi\nyou MUST return the actual complete content as the final answer,
+      not a summary.\n\nBegin! This is VERY important to you, use the tools available
+      and give your best Final Answer, your job depends on it!\n\nThought:"}], "model":
+      "gpt-4o-mini", "stop": ["\nObservation:"]}'
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate
+      connection:
+      - keep-alive
+      content-length:
+      - '836'
+      content-type:
+      - application/json
+      host:
+      - api.openai.com
+      user-agent:
+      - OpenAI/Python 1.61.0
+      x-stainless-arch:
+      - arm64
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - MacOS
+      x-stainless-package-version:
+      - 1.61.0
+      x-stainless-raw-response:
+      - 'true'
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.12.8
+    method: POST
+    uri: https://api.openai.com/v1/chat/completions
+  response:
+    content: "{\n  \"id\": \"chatcmpl-AzTXAk4GatJOmLO9sEOCCITIjf1Dx\",\n  \"object\":
+      \"chat.completion\",\n  \"created\": 1739214900,\n  \"model\": \"gpt-4o-mini-2024-07-18\",\n
+      \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
+      \"assistant\",\n        \"content\": \"I now can give a great answer  \\nFinal
+      Answer: hi\",\n        \"refusal\": null\n      },\n      \"logprobs\": null,\n
+      \     \"finish_reason\": \"stop\"\n    }\n  ],\n  \"usage\": {\n    \"prompt_tokens\":
+      161,\n    \"completion_tokens\": 12,\n    \"total_tokens\": 173,\n    \"prompt_tokens_details\":
+      {\n      \"cached_tokens\": 0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\":
+      {\n      \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\":
+      0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"service_tier\":
+      \"default\",\n  \"system_fingerprint\": \"fp_72ed7ab54c\"\n}\n"
+    headers:
+      CF-RAY:
+      - 90fe6ce92eba67b3-SJC
+      Connection:
+      - keep-alive
+      Content-Encoding:
+      - gzip
+      Content-Type:
+      - application/json
+      Date:
+      - Mon, 10 Feb 2025 19:15:01 GMT
+      Server:
+      - cloudflare
+      Set-Cookie:
+      - __cf_bm=pjX1I6y8RlqCjS.gvOqvXk4vM69UNwFwmslh1BhALNg-1739214901-1.0.1.1-nJcNlSdNcug82eDl7KSvteLbsg0xCiEh2yI1TZX2jMAblL7AMQ8LFhvXkJLlAMfk49RMzRzWy2aiQgeM7WRHPg;
+        path=/; expires=Mon, 10-Feb-25 19:45:01 GMT; domain=.api.openai.com; HttpOnly;
+        Secure; SameSite=None
+      - _cfuvid=efIHP1NUsh1dFewGJBu4YoBu6hhGa8vjOOKQglYQGno-1739214901306-0.0.1.1-604800000;
+        path=/; domain=.api.openai.com; HttpOnly; Secure; SameSite=None
+      Transfer-Encoding:
+      - chunked
+      X-Content-Type-Options:
+      - nosniff
+      access-control-expose-headers:
+      - X-Request-ID
+      alt-svc:
+      - h3=":443"; ma=86400
+      cf-cache-status:
+      - DYNAMIC
+      openai-organization:
+      - crewai-iuxna1
+      openai-processing-ms:
+      - '571'
+      openai-version:
+      - '2020-10-01'
+      strict-transport-security:
+      - max-age=31536000; includeSubDomains; preload
+      x-ratelimit-limit-requests:
+      - '30000'
+      x-ratelimit-limit-tokens:
+      - '150000000'
+      x-ratelimit-remaining-requests:
+      - '29999'
+      x-ratelimit-remaining-tokens:
+      - '149999810'
+      x-ratelimit-reset-requests:
+      - 2ms
+      x-ratelimit-reset-tokens:
+      - 0s
+      x-request-id:
+      - req_a95183a7a85e6bdfe381b2510bf70f34
+    http_version: HTTP/1.1
+    status_code: 200
+- request:
+    body: '{"messages": [{"role": "user", "content": "Assess the quality of the task
+      completed based on the description, expected output, and actual results.\n\nTask
+      Description:\nJust say hi\n\nExpected Output:\nhi\n\nActual Output:\nhi\n\nPlease
+      provide:\n- Bullet points suggestions to improve future similar tasks\n- A score
+      from 0 to 10 evaluating on completion, quality, and overall performance- Entities
+      extracted from the task output, if any, their type, description, and relationships"}],
+      "model": "gpt-4o-mini", "tool_choice": {"type": "function", "function": {"name":
+      "TaskEvaluation"}}, "tools": [{"type": "function", "function": {"name": "TaskEvaluation",
+      "description": "Correctly extracted `TaskEvaluation` with all the required parameters
+      with correct types", "parameters": {"$defs": {"Entity": {"properties": {"name":
+      {"description": "The name of the entity.", "title": "Name", "type": "string"},
+      "type": {"description": "The type of the entity.", "title": "Type", "type":
+      "string"}, "description": {"description": "Description of the entity.", "title":
+      "Description", "type": "string"}, "relationships": {"description": "Relationships
+      of the entity.", "items": {"type": "string"}, "title": "Relationships", "type":
+      "array"}}, "required": ["name", "type", "description", "relationships"], "title":
+      "Entity", "type": "object"}}, "properties": {"suggestions": {"description":
+      "Suggestions to improve future similar tasks.", "items": {"type": "string"},
+      "title": "Suggestions", "type": "array"}, "quality": {"description": "A score
+      from 0 to 10 evaluating on completion, quality, and overall performance, all
+      taking into account the task description, expected output, and the result of
+      the task.", "title": "Quality", "type": "number"}, "entities": {"description":
+      "Entities extracted from the task output.", "items": {"$ref": "#/$defs/Entity"},
+      "title": "Entities", "type": "array"}}, "required": ["entities", "quality",
+      "suggestions"], "type": "object"}}}]}'
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate
+      connection:
+      - keep-alive
+      content-length:
+      - '1962'
+      content-type:
+      - application/json
+      cookie:
+      - __cf_bm=pjX1I6y8RlqCjS.gvOqvXk4vM69UNwFwmslh1BhALNg-1739214901-1.0.1.1-nJcNlSdNcug82eDl7KSvteLbsg0xCiEh2yI1TZX2jMAblL7AMQ8LFhvXkJLlAMfk49RMzRzWy2aiQgeM7WRHPg;
+        _cfuvid=efIHP1NUsh1dFewGJBu4YoBu6hhGa8vjOOKQglYQGno-1739214901306-0.0.1.1-604800000
+      host:
+      - api.openai.com
+      user-agent:
+      - OpenAI/Python 1.61.0
+      x-stainless-arch:
+      - arm64
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - MacOS
+      x-stainless-package-version:
+      - 1.61.0
+      x-stainless-raw-response:
+      - 'true'
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.12.8
+    method: POST
+    uri: https://api.openai.com/v1/chat/completions
+  response:
+    content: "{\n  \"id\": \"chatcmpl-AzTXDcgKWq3yosIyBal8LcY8dDrn1\",\n  \"object\":
+      \"chat.completion\",\n  \"created\": 1739214903,\n  \"model\": \"gpt-4o-mini-2024-07-18\",\n
+      \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
+      \"assistant\",\n        \"content\": null,\n        \"tool_calls\": [\n          {\n
+      \           \"id\": \"call_c41SAnqyEKNXEAZd5XV3jKF3\",\n            \"type\":
+      \"function\",\n            \"function\": {\n              \"name\": \"TaskEvaluation\",\n
+      \             \"arguments\": \"{\\\"suggestions\\\":[\\\"Consider specifying
+      the tone or context of the greeting for more engaging interactions.\\\",\\\"Clarify
+      if additional greetings or responses are acceptable to enhance the task's scope.\\\"],\\\"quality\\\":10,\\\"entities\\\":[]
+      }\"\n            }\n          }\n        ],\n        \"refusal\": null\n      },\n
+      \     \"logprobs\": null,\n      \"finish_reason\": \"stop\"\n    }\n  ],\n
+      \ \"usage\": {\n    \"prompt_tokens\": 273,\n    \"completion_tokens\": 43,\n
+      \   \"total_tokens\": 316,\n    \"prompt_tokens_details\": {\n      \"cached_tokens\":
+      0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\": {\n
+      \     \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\":
+      0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"service_tier\":
+      \"default\",\n  \"system_fingerprint\": \"fp_72ed7ab54c\"\n}\n"
+    headers:
+      CF-Cache-Status:
+      - DYNAMIC
+      CF-RAY:
+      - 90fe6cf8c96e67b3-SJC
+      Connection:
+      - keep-alive
+      Content-Encoding:
+      - gzip
+      Content-Type:
+      - application/json
+      Date:
+      - Mon, 10 Feb 2025 19:15:04 GMT
+      Server:
+      - cloudflare
+      Transfer-Encoding:
+      - chunked
+      X-Content-Type-Options:
+      - nosniff
+      access-control-expose-headers:
+      - X-Request-ID
+      alt-svc:
+      - h3=":443"; ma=86400
+      openai-organization:
+      - crewai-iuxna1
+      openai-processing-ms:
+      - '1181'
+      openai-version:
+      - '2020-10-01'
+      strict-transport-security:
+      - max-age=31536000; includeSubDomains; preload
+      x-ratelimit-limit-requests:
+      - '30000'
+      x-ratelimit-limit-tokens:
+      - '150000000'
+      x-ratelimit-remaining-requests:
+      - '29999'
+      x-ratelimit-remaining-tokens:
+      - '149999876'
+      x-ratelimit-reset-requests:
+      - 2ms
+      x-ratelimit-reset-tokens:
+      - 0s
+      x-request-id:
+      - req_b2286c8ae6f9b2a42f46a3e2c52b4211
+    http_version: HTTP/1.1
+    status_code: 200
+version: 1

tests/utilities/cassettes/test_converter_with_llama3_2_model.yaml

@@ -1,14 +1,9 @@
 interactions:
 - request:
-    body: '{"model": "llama3.2:3b", "prompt": "### User:\nName: Alice Llama, Age:
-      30\n\n### System:\nProduce JSON OUTPUT ONLY! Adhere to this format {\"name\":
-      \"function_name\", \"arguments\":{\"argument_name\": \"argument_value\"}} The
-      following functions are available to you:\n{''type'': ''function'', ''function'':
-      {''name'': ''SimpleModel'', ''description'': ''Correctly extracted `SimpleModel`
-      with all the required parameters with correct types'', ''parameters'': {''properties'':
-      {''name'': {''title'': ''Name'', ''type'': ''string''}, ''age'': {''title'':
-      ''Age'', ''type'': ''integer''}}, ''required'': [''age'', ''name''], ''type'':
-      ''object''}}}\n\n\n", "options": {}, "stream": false, "format": "json"}'
+    body: '{"model": "llama3.2:3b", "prompt": "### System:\nPlease convert the following
+      text into valid JSON.\n\nOutput ONLY the valid JSON and nothing else.\n\nThe
+      JSON must follow this format exactly:\n{\n  \"name\": str,\n  \"age\": int\n}\n\n###
+      User:\nName: Alice Llama, Age: 30\n\n", "options": {"stop": []}, "stream": false}'
     headers:
       accept:
       - '*/*'
@@ -17,23 +12,23 @@ interactions:
       connection:
       - keep-alive
       content-length:
-      - '657'
+      - '321'
       host:
       - localhost:11434
       user-agent:
-      - litellm/1.57.4
+      - litellm/1.60.2
     method: POST
     uri: http://localhost:11434/api/generate
   response:
-    content: '{"model":"llama3.2:3b","created_at":"2025-01-15T20:47:11.926411Z","response":"{\"name\":
-      \"SimpleModel\", \"arguments\":{\"name\": \"Alice Llama\", \"age\": 30}}","done":true,"done_reason":"stop","context":[128006,9125,128007,271,38766,1303,33025,2696,25,6790,220,2366,18,271,128009,128006,882,128007,271,14711,2724,512,678,25,30505,445,81101,11,13381,25,220,966,271,14711,744,512,1360,13677,4823,32090,27785,0,2467,6881,311,420,3645,5324,609,794,330,1723,1292,498,330,16774,23118,14819,1292,794,330,14819,3220,32075,578,2768,5865,527,2561,311,499,512,13922,1337,1232,364,1723,518,364,1723,1232,5473,609,1232,364,16778,1747,518,364,4789,1232,364,34192,398,28532,1595,16778,1747,63,449,682,279,2631,5137,449,4495,4595,518,364,14105,1232,5473,13495,1232,5473,609,1232,5473,2150,1232,364,678,518,364,1337,1232,364,928,25762,364,425,1232,5473,2150,1232,364,17166,518,364,1337,1232,364,11924,8439,2186,364,6413,1232,2570,425,518,364,609,4181,364,1337,1232,364,1735,23742,3818,128009,128006,78191,128007,271,5018,609,794,330,16778,1747,498,330,16774,23118,609,794,330,62786,445,81101,498,330,425,794,220,966,3500],"total_duration":3374470708,"load_duration":1075750500,"prompt_eval_count":167,"prompt_eval_duration":1871000000,"eval_count":24,"eval_duration":426000000}'
+    content: '{"model":"llama3.2:3b","created_at":"2025-02-21T02:57:55.059392Z","response":"{\"name\":
+      \"Alice Llama\", \"age\": 30}","done":true,"done_reason":"stop","context":[128006,9125,128007,271,38766,1303,33025,2696,25,6790,220,2366,18,271,128009,128006,882,128007,271,14711,744,512,5618,5625,279,2768,1495,1139,2764,4823,382,5207,27785,279,2764,4823,323,4400,775,382,791,4823,2011,1833,420,3645,7041,512,517,220,330,609,794,610,345,220,330,425,794,528,198,633,14711,2724,512,678,25,30505,445,81101,11,13381,25,220,966,271,128009,128006,78191,128007,271,5018,609,794,330,62786,445,81101,498,330,425,794,220,966,92],"total_duration":4675906000,"load_duration":836091458,"prompt_eval_count":82,"prompt_eval_duration":3561000000,"eval_count":15,"eval_duration":275000000}'
     headers:
       Content-Length:
-      - '1263'
+      - '761'
       Content-Type:
       - application/json; charset=utf-8
       Date:
-      - Wed, 15 Jan 2025 20:47:12 GMT
+      - Fri, 21 Feb 2025 02:57:55 GMT
     http_version: HTTP/1.1
     status_code: 200
 - request:
@@ -52,7 +47,7 @@ interactions:
       host:
       - localhost:11434
       user-agent:
-      - litellm/1.57.4
+      - litellm/1.60.2
     method: POST
     uri: http://localhost:11434/api/show
   response:
@@ -228,7 +223,7 @@ interactions:
       Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama
       3.2: LlamaUseReport@meta.com\",\"modelfile\":\"# Modelfile generated by \\\"ollama
       show\\\"\\n# To build a new Modelfile based on this, replace FROM with:\\n#
-      FROM llama3.2:3b\\n\\nFROM /Users/brandonhancock/.ollama/models/blobs/sha256-dde5aa3fc5ffc17176b5e8bdc82f587b24b2678c6c66101bf7da77af9f7ccdff\\nTEMPLATE
+      FROM llama3.2:3b\\n\\nFROM /Users/joaomoura/.ollama/models/blobs/sha256-dde5aa3fc5ffc17176b5e8bdc82f587b24b2678c6c66101bf7da77af9f7ccdff\\nTEMPLATE
       \\\"\\\"\\\"\\u003c|start_header_id|\\u003esystem\\u003c|end_header_id|\\u003e\\n\\nCutting
       Knowledge Date: December 2023\\n\\n{{ if .System }}{{ .System }}\\n{{- end }}\\n{{-
       if .Tools }}When you receive a tool call response, use the output to format
@@ -441,12 +436,12 @@ interactions:
       .Content }}\\n{{- end }}{{ if not $last }}\\u003c|eot_id|\\u003e{{ end }}\\n{{-
       else if eq .Role \\\"tool\\\" }}\\u003c|start_header_id|\\u003eipython\\u003c|end_header_id|\\u003e\\n\\n{{
       .Content }}\\u003c|eot_id|\\u003e{{ if $last }}\\u003c|start_header_id|\\u003eassistant\\u003c|end_header_id|\\u003e\\n\\n{{
-      end }}\\n{{- end }}\\n{{- end }}\",\"details\":{\"parent_model\":\"\",\"format\":\"gguf\",\"family\":\"llama\",\"families\":[\"llama\"],\"parameter_size\":\"3.2B\",\"quantization_level\":\"Q4_K_M\"},\"model_info\":{\"general.architecture\":\"llama\",\"general.basename\":\"Llama-3.2\",\"general.file_type\":15,\"general.finetune\":\"Instruct\",\"general.languages\":[\"en\",\"de\",\"fr\",\"it\",\"pt\",\"hi\",\"es\",\"th\"],\"general.parameter_count\":3212749888,\"general.quantization_version\":2,\"general.size_label\":\"3B\",\"general.tags\":[\"facebook\",\"meta\",\"pytorch\",\"llama\",\"llama-3\",\"text-generation\"],\"general.type\":\"model\",\"llama.attention.head_count\":24,\"llama.attention.head_count_kv\":8,\"llama.attention.key_length\":128,\"llama.attention.layer_norm_rms_epsilon\":0.00001,\"llama.attention.value_length\":128,\"llama.block_count\":28,\"llama.context_length\":131072,\"llama.embedding_length\":3072,\"llama.feed_forward_length\":8192,\"llama.rope.dimension_count\":128,\"llama.rope.freq_base\":500000,\"llama.vocab_size\":128256,\"tokenizer.ggml.bos_token_id\":128000,\"tokenizer.ggml.eos_token_id\":128009,\"tokenizer.ggml.merges\":null,\"tokenizer.ggml.model\":\"gpt2\",\"tokenizer.ggml.pre\":\"llama-bpe\",\"tokenizer.ggml.token_type\":null,\"tokenizer.ggml.tokens\":null},\"modified_at\":\"2024-12-31T11:53:14.529771974-05:00\"}"
+      end }}\\n{{- end }}\\n{{- end }}\",\"details\":{\"parent_model\":\"\",\"format\":\"gguf\",\"family\":\"llama\",\"families\":[\"llama\"],\"parameter_size\":\"3.2B\",\"quantization_level\":\"Q4_K_M\"},\"model_info\":{\"general.architecture\":\"llama\",\"general.basename\":\"Llama-3.2\",\"general.file_type\":15,\"general.finetune\":\"Instruct\",\"general.languages\":[\"en\",\"de\",\"fr\",\"it\",\"pt\",\"hi\",\"es\",\"th\"],\"general.parameter_count\":3212749888,\"general.quantization_version\":2,\"general.size_label\":\"3B\",\"general.tags\":[\"facebook\",\"meta\",\"pytorch\",\"llama\",\"llama-3\",\"text-generation\"],\"general.type\":\"model\",\"llama.attention.head_count\":24,\"llama.attention.head_count_kv\":8,\"llama.attention.key_length\":128,\"llama.attention.layer_norm_rms_epsilon\":0.00001,\"llama.attention.value_length\":128,\"llama.block_count\":28,\"llama.context_length\":131072,\"llama.embedding_length\":3072,\"llama.feed_forward_length\":8192,\"llama.rope.dimension_count\":128,\"llama.rope.freq_base\":500000,\"llama.vocab_size\":128256,\"tokenizer.ggml.bos_token_id\":128000,\"tokenizer.ggml.eos_token_id\":128009,\"tokenizer.ggml.merges\":null,\"tokenizer.ggml.model\":\"gpt2\",\"tokenizer.ggml.pre\":\"llama-bpe\",\"tokenizer.ggml.token_type\":null,\"tokenizer.ggml.tokens\":null},\"modified_at\":\"2025-02-20T18:55:09.150577031-08:00\"}"
     headers:
       Content-Type:
       - application/json; charset=utf-8
       Date:
-      - Wed, 15 Jan 2025 20:47:12 GMT
+      - Fri, 21 Feb 2025 02:57:55 GMT
       Transfer-Encoding:
       - chunked
     http_version: HTTP/1.1
@@ -467,7 +462,7 @@ interactions:
       host:
       - localhost:11434
       user-agent:
-      - litellm/1.57.4
+      - litellm/1.60.2
     method: POST
     uri: http://localhost:11434/api/show
   response:
@@ -643,7 +638,7 @@ interactions:
       Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama
       3.2: LlamaUseReport@meta.com\",\"modelfile\":\"# Modelfile generated by \\\"ollama
       show\\\"\\n# To build a new Modelfile based on this, replace FROM with:\\n#
-      FROM llama3.2:3b\\n\\nFROM /Users/brandonhancock/.ollama/models/blobs/sha256-dde5aa3fc5ffc17176b5e8bdc82f587b24b2678c6c66101bf7da77af9f7ccdff\\nTEMPLATE
+      FROM llama3.2:3b\\n\\nFROM /Users/joaomoura/.ollama/models/blobs/sha256-dde5aa3fc5ffc17176b5e8bdc82f587b24b2678c6c66101bf7da77af9f7ccdff\\nTEMPLATE
       \\\"\\\"\\\"\\u003c|start_header_id|\\u003esystem\\u003c|end_header_id|\\u003e\\n\\nCutting
       Knowledge Date: December 2023\\n\\n{{ if .System }}{{ .System }}\\n{{- end }}\\n{{-
       if .Tools }}When you receive a tool call response, use the output to format
@@ -856,12 +851,12 @@ interactions:
       .Content }}\\n{{- end }}{{ if not $last }}\\u003c|eot_id|\\u003e{{ end }}\\n{{-
       else if eq .Role \\\"tool\\\" }}\\u003c|start_header_id|\\u003eipython\\u003c|end_header_id|\\u003e\\n\\n{{
       .Content }}\\u003c|eot_id|\\u003e{{ if $last }}\\u003c|start_header_id|\\u003eassistant\\u003c|end_header_id|\\u003e\\n\\n{{
-      end }}\\n{{- end }}\\n{{- end }}\",\"details\":{\"parent_model\":\"\",\"format\":\"gguf\",\"family\":\"llama\",\"families\":[\"llama\"],\"parameter_size\":\"3.2B\",\"quantization_level\":\"Q4_K_M\"},\"model_info\":{\"general.architecture\":\"llama\",\"general.basename\":\"Llama-3.2\",\"general.file_type\":15,\"general.finetune\":\"Instruct\",\"general.languages\":[\"en\",\"de\",\"fr\",\"it\",\"pt\",\"hi\",\"es\",\"th\"],\"general.parameter_count\":3212749888,\"general.quantization_version\":2,\"general.size_label\":\"3B\",\"general.tags\":[\"facebook\",\"meta\",\"pytorch\",\"llama\",\"llama-3\",\"text-generation\"],\"general.type\":\"model\",\"llama.attention.head_count\":24,\"llama.attention.head_count_kv\":8,\"llama.attention.key_length\":128,\"llama.attention.layer_norm_rms_epsilon\":0.00001,\"llama.attention.value_length\":128,\"llama.block_count\":28,\"llama.context_length\":131072,\"llama.embedding_length\":3072,\"llama.feed_forward_length\":8192,\"llama.rope.dimension_count\":128,\"llama.rope.freq_base\":500000,\"llama.vocab_size\":128256,\"tokenizer.ggml.bos_token_id\":128000,\"tokenizer.ggml.eos_token_id\":128009,\"tokenizer.ggml.merges\":null,\"tokenizer.ggml.model\":\"gpt2\",\"tokenizer.ggml.pre\":\"llama-bpe\",\"tokenizer.ggml.token_type\":null,\"tokenizer.ggml.tokens\":null},\"modified_at\":\"2024-12-31T11:53:14.529771974-05:00\"}"
+      end }}\\n{{- end }}\\n{{- end }}\",\"details\":{\"parent_model\":\"\",\"format\":\"gguf\",\"family\":\"llama\",\"families\":[\"llama\"],\"parameter_size\":\"3.2B\",\"quantization_level\":\"Q4_K_M\"},\"model_info\":{\"general.architecture\":\"llama\",\"general.basename\":\"Llama-3.2\",\"general.file_type\":15,\"general.finetune\":\"Instruct\",\"general.languages\":[\"en\",\"de\",\"fr\",\"it\",\"pt\",\"hi\",\"es\",\"th\"],\"general.parameter_count\":3212749888,\"general.quantization_version\":2,\"general.size_label\":\"3B\",\"general.tags\":[\"facebook\",\"meta\",\"pytorch\",\"llama\",\"llama-3\",\"text-generation\"],\"general.type\":\"model\",\"llama.attention.head_count\":24,\"llama.attention.head_count_kv\":8,\"llama.attention.key_length\":128,\"llama.attention.layer_norm_rms_epsilon\":0.00001,\"llama.attention.value_length\":128,\"llama.block_count\":28,\"llama.context_length\":131072,\"llama.embedding_length\":3072,\"llama.feed_forward_length\":8192,\"llama.rope.dimension_count\":128,\"llama.rope.freq_base\":500000,\"llama.vocab_size\":128256,\"tokenizer.ggml.bos_token_id\":128000,\"tokenizer.ggml.eos_token_id\":128009,\"tokenizer.ggml.merges\":null,\"tokenizer.ggml.model\":\"gpt2\",\"tokenizer.ggml.pre\":\"llama-bpe\",\"tokenizer.ggml.token_type\":null,\"tokenizer.ggml.tokens\":null},\"modified_at\":\"2025-02-20T18:55:09.150577031-08:00\"}"
     headers:
       Content-Type:
       - application/json; charset=utf-8
       Date:
-      - Wed, 15 Jan 2025 20:47:12 GMT
+      - Fri, 21 Feb 2025 02:57:55 GMT
       Transfer-Encoding:
       - chunked
     http_version: HTTP/1.1

tests/utilities/cassettes/test_crew_emits_end_kickoff_event.yaml

@@ -0,0 +1,315 @@
+interactions:
+- request:
+    body: '{"messages": [{"role": "system", "content": "You are base_agent. You are
+      a helpful assistant that just says hi\nYour personal goal is: Just say hi\nTo
+      give my best complete final answer to the task respond using the exact following
+      format:\n\nThought: I now can give a great answer\nFinal Answer: Your final
+      answer must be the great and the most complete as possible, it must be outcome
+      described.\n\nI MUST use these formats, my job depends on it!"}, {"role": "user",
+      "content": "\nCurrent Task: Just say hi\n\nThis is the expect criteria for your
+      final answer: hi\nyou MUST return the actual complete content as the final answer,
+      not a summary.\n\nBegin! This is VERY important to you, use the tools available
+      and give your best Final Answer, your job depends on it!\n\nThought:"}], "model":
+      "gpt-4o-mini", "stop": ["\nObservation:"]}'
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate
+      connection:
+      - keep-alive
+      content-length:
+      - '836'
+      content-type:
+      - application/json
+      cookie:
+      - __cf_bm=4s6sWmJ49B9F_wNc1STtdZF1nikfl6uN9_ov3Xzfa8U-1738698987-1.0.1.1-lmbRRS1MHrDbnU93Gh16CP3qNczxxIrQnyBU7vpHSwNf6PdmuWOHKd1mkl5SBx6rg7p1NLaNUMyqDDcE0Mvjzw;
+        _cfuvid=Cl48aI8.jSRja0Pqr6Jrh3mAnigd4rDn6lhGicyjMPY-1738698987673-0.0.1.1-604800000
+      host:
+      - api.openai.com
+      user-agent:
+      - OpenAI/Python 1.61.0
+      x-stainless-arch:
+      - arm64
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - MacOS
+      x-stainless-package-version:
+      - 1.61.0
+      x-stainless-raw-response:
+      - 'true'
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.12.8
+    method: POST
+    uri: https://api.openai.com/v1/chat/completions
+  response:
+    content: "{\n  \"id\": \"chatcmpl-AxJK2OCJSkUj1plgbj59b4dC39QV2\",\n  \"object\":
+      \"chat.completion\",\n  \"created\": 1738698990,\n  \"model\": \"gpt-4o-mini-2024-07-18\",\n
+      \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
+      \"assistant\",\n        \"content\": \"I now can give a great answer  \\nFinal
+      Answer: hi\",\n        \"refusal\": null\n      },\n      \"logprobs\": null,\n
+      \     \"finish_reason\": \"stop\"\n    }\n  ],\n  \"usage\": {\n    \"prompt_tokens\":
+      161,\n    \"completion_tokens\": 12,\n    \"total_tokens\": 173,\n    \"prompt_tokens_details\":
+      {\n      \"cached_tokens\": 0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\":
+      {\n      \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\":
+      0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"service_tier\":
+      \"default\",\n  \"system_fingerprint\": \"fp_72ed7ab54c\"\n}\n"
+    headers:
+      CF-RAY:
+      - 90cd396c0ab71698-SJC
+      Connection:
+      - keep-alive
+      Content-Encoding:
+      - gzip
+      Content-Type:
+      - application/json
+      Date:
+      - Tue, 04 Feb 2025 19:56:30 GMT
+      Server:
+      - cloudflare
+      Transfer-Encoding:
+      - chunked
+      X-Content-Type-Options:
+      - nosniff
+      access-control-expose-headers:
+      - X-Request-ID
+      alt-svc:
+      - h3=":443"; ma=86400
+      cf-cache-status:
+      - DYNAMIC
+      openai-organization:
+      - crewai-iuxna1
+      openai-processing-ms:
+      - '951'
+      openai-version:
+      - '2020-10-01'
+      strict-transport-security:
+      - max-age=31536000; includeSubDomains; preload
+      x-ratelimit-limit-requests:
+      - '30000'
+      x-ratelimit-limit-tokens:
+      - '150000000'
+      x-ratelimit-remaining-requests:
+      - '29999'
+      x-ratelimit-remaining-tokens:
+      - '149999810'
+      x-ratelimit-reset-requests:
+      - 2ms
+      x-ratelimit-reset-tokens:
+      - 0s
+      x-request-id:
+      - req_2c3cb5caed61ccd1e058ef3e6301c691
+    http_version: HTTP/1.1
+    status_code: 200
+- request:
+    body: !!binary |
+      Cq0TCiQKIgoMc2VydmljZS5uYW1lEhIKEGNyZXdBSS10ZWxlbWV0cnkShBMKEgoQY3Jld2FpLnRl
+      bGVtZXRyeRKkBwoQzBQBWCz+GLuI1awj3OPWrRIIGpT16t5bk6MqDENyZXcgQ3JlYXRlZDABOUBz
+      OyuEGSEYQYDBSCuEGSEYShsKDmNyZXdhaV92ZXJzaW9uEgkKBzAuMTAwLjBKGgoOcHl0aG9uX3Zl
+      cnNpb24SCAoGMy4xMi44Si4KCGNyZXdfa2V5EiIKIGU1ODA3MDFkNTJlYjY1YWZmMjRlZWZlNzhj
+      NzQ2MjhjSjEKB2NyZXdfaWQSJgokMDE3NjQ5ZWMtYTBlMS00MzYxLWFlNjgtYzA1N2E3ZGM5YzI5
+      ShwKDGNyZXdfcHJvY2VzcxIMCgpzZXF1ZW50aWFsShEKC2NyZXdfbWVtb3J5EgIQAEoaChRjcmV3
+      X251bWJlcl9vZl90YXNrcxICGAFKGwoVY3Jld19udW1iZXJfb2ZfYWdlbnRzEgIYAUrRAgoLY3Jl
+      d19hZ2VudHMSwQIKvgJbeyJrZXkiOiAiYWQxNTMxNjFjNWM1YTg1NmFhMGQwNmIyNDljNGM2NGEi
+      LCAiaWQiOiAiOGU3NzgyN2QtN2Y2OC00ZDA2LWI2YTctOWI4YjRkMGE0YzMzIiwgInJvbGUiOiAi
+      YmFzZV9hZ2VudCIsICJ2ZXJib3NlPyI6IGZhbHNlLCAibWF4X2l0ZXIiOiAyMCwgIm1heF9ycG0i
+      OiBudWxsLCAiZnVuY3Rpb25fY2FsbGluZ19sbG0iOiAiIiwgImxsbSI6ICJncHQtNG8tbWluaSIs
+      ICJkZWxlZ2F0aW9uX2VuYWJsZWQ/IjogZmFsc2UsICJhbGxvd19jb2RlX2V4ZWN1dGlvbj8iOiBm
+      YWxzZSwgIm1heF9yZXRyeV9saW1pdCI6IDIsICJ0b29sc19uYW1lcyI6IFtdfV1K/wEKCmNyZXdf
+      dGFza3MS8AEK7QFbeyJrZXkiOiAiMWIxNWVmMjM5MTViMjc1NWU4OWEwZWMzYjI2YTEzZDIiLCAi
+      aWQiOiAiOTJiZDIzMWYtYzAxMC00ZDI3LWIxNGYtZjE5NjEyZTBmZTkzIiwgImFzeW5jX2V4ZWN1
+      dGlvbj8iOiBmYWxzZSwgImh1bWFuX2lucHV0PyI6IGZhbHNlLCAiYWdlbnRfcm9sZSI6ICJiYXNl
+      X2FnZW50IiwgImFnZW50X2tleSI6ICJhZDE1MzE2MWM1YzVhODU2YWEwZDA2YjI0OWM0YzY0YSIs
+      ICJ0b29sc19uYW1lcyI6IFtdfV16AhgBhQEAAQAAEo4CChC22Au0eMkAAjV6cfU1NrNIEggxb1Bq
+      Xnll/ioMVGFzayBDcmVhdGVkMAE5IOJaK4QZIRhBwG5bK4QZIRhKLgoIY3Jld19rZXkSIgogZTU4
+      MDcwMWQ1MmViNjVhZmYyNGVlZmU3OGM3NDYyOGNKMQoHY3Jld19pZBImCiQwMTc2NDllYy1hMGUx
+      LTQzNjEtYWU2OC1jMDU3YTdkYzljMjlKLgoIdGFza19rZXkSIgogMWIxNWVmMjM5MTViMjc1NWU4
+      OWEwZWMzYjI2YTEzZDJKMQoHdGFza19pZBImCiQ5MmJkMjMxZi1jMDEwLTRkMjctYjE0Zi1mMTk2
+      MTJlMGZlOTN6AhgBhQEAAQAAEqQHChC63jCLGR8RP8RmYiHrdNVeEggZ39ffmGm5xyoMQ3JldyBD
+      cmVhdGVkMAE5GFEe04QZIRhBELEq04QZIRhKGwoOY3Jld2FpX3ZlcnNpb24SCQoHMC4xMDAuMEoa
+      Cg5weXRob25fdmVyc2lvbhIICgYzLjEyLjhKLgoIY3Jld19rZXkSIgogZTU4MDcwMWQ1MmViNjVh
+      ZmYyNGVlZmU3OGM3NDYyOGNKMQoHY3Jld19pZBImCiQ5MTY4YmQxNC0yN2Q2LTQ3NWMtODljOC01
+      NjJjOTAyMGIxOTBKHAoMY3Jld19wcm9jZXNzEgwKCnNlcXVlbnRpYWxKEQoLY3Jld19tZW1vcnkS
+      AhAAShoKFGNyZXdfbnVtYmVyX29mX3Rhc2tzEgIYAUobChVjcmV3X251bWJlcl9vZl9hZ2VudHMS
+      AhgBStECCgtjcmV3X2FnZW50cxLBAgq+Alt7ImtleSI6ICJhZDE1MzE2MWM1YzVhODU2YWEwZDA2
+      YjI0OWM0YzY0YSIsICJpZCI6ICI4ZTc3ODI3ZC03ZjY4LTRkMDYtYjZhNy05YjhiNGQwYTRjMzMi
+      LCAicm9sZSI6ICJiYXNlX2FnZW50IiwgInZlcmJvc2U/IjogZmFsc2UsICJtYXhfaXRlciI6IDIw
+      LCAibWF4X3JwbSI6IG51bGwsICJmdW5jdGlvbl9jYWxsaW5nX2xsbSI6ICIiLCAibGxtIjogImdw
+      dC00by1taW5pIiwgImRlbGVnYXRpb25fZW5hYmxlZD8iOiBmYWxzZSwgImFsbG93X2NvZGVfZXhl
+      Y3V0aW9uPyI6IGZhbHNlLCAibWF4X3JldHJ5X2xpbWl0IjogMiwgInRvb2xzX25hbWVzIjogW119
+      XUr/AQoKY3Jld190YXNrcxLwAQrtAVt7ImtleSI6ICIxYjE1ZWYyMzkxNWIyNzU1ZTg5YTBlYzNi
+      MjZhMTNkMiIsICJpZCI6ICI5MmJkMjMxZi1jMDEwLTRkMjctYjE0Zi1mMTk2MTJlMGZlOTMiLCAi
+      YXN5bmNfZXhlY3V0aW9uPyI6IGZhbHNlLCAiaHVtYW5faW5wdXQ/IjogZmFsc2UsICJhZ2VudF9y
+      b2xlIjogImJhc2VfYWdlbnQiLCAiYWdlbnRfa2V5IjogImFkMTUzMTYxYzVjNWE4NTZhYTBkMDZi
+      MjQ5YzRjNjRhIiwgInRvb2xzX25hbWVzIjogW119XXoCGAGFAQABAAASjgIKEOo6FGs7r9hHrN+f
+      qhMTUysSCJgbYV+vQMbCKgxUYXNrIENyZWF0ZWQwATlAxjrThBkhGEEYIDvThBkhGEouCghjcmV3
+      X2tleRIiCiBlNTgwNzAxZDUyZWI2NWFmZjI0ZWVmZTc4Yzc0NjI4Y0oxCgdjcmV3X2lkEiYKJDkx
+      NjhiZDE0LTI3ZDYtNDc1Yy04OWM4LTU2MmM5MDIwYjE5MEouCgh0YXNrX2tleRIiCiAxYjE1ZWYy
+      MzkxNWIyNzU1ZTg5YTBlYzNiMjZhMTNkMkoxCgd0YXNrX2lkEiYKJDkyYmQyMzFmLWMwMTAtNGQy
+      Ny1iMTRmLWYxOTYxMmUwZmU5M3oCGAGFAQABAAA=
+    headers:
+      Accept:
+      - '*/*'
+      Accept-Encoding:
+      - gzip, deflate
+      Connection:
+      - keep-alive
+      Content-Length:
+      - '2480'
+      Content-Type:
+      - application/x-protobuf
+      User-Agent:
+      - OTel-OTLP-Exporter-Python/1.27.0
+    method: POST
+    uri: https://telemetry.crewai.com:4319/v1/traces
+  response:
+    body:
+      string: "\n\0"
+    headers:
+      Content-Length:
+      - '2'
+      Content-Type:
+      - application/x-protobuf
+      Date:
+      - Tue, 04 Feb 2025 19:56:31 GMT
+    status:
+      code: 200
+      message: OK
+- request:
+    body: '{"messages": [{"role": "user", "content": "Assess the quality of the task
+      completed based on the description, expected output, and actual results.\n\nTask
+      Description:\nJust say hi\n\nExpected Output:\nhi\n\nActual Output:\nhi\n\nPlease
+      provide:\n- Bullet points suggestions to improve future similar tasks\n- A score
+      from 0 to 10 evaluating on completion, quality, and overall performance- Entities
+      extracted from the task output, if any, their type, description, and relationships"}],
+      "model": "gpt-4o-mini", "tool_choice": {"type": "function", "function": {"name":
+      "TaskEvaluation"}}, "tools": [{"type": "function", "function": {"name": "TaskEvaluation",
+      "description": "Correctly extracted `TaskEvaluation` with all the required parameters
+      with correct types", "parameters": {"$defs": {"Entity": {"properties": {"name":
+      {"description": "The name of the entity.", "title": "Name", "type": "string"},
+      "type": {"description": "The type of the entity.", "title": "Type", "type":
+      "string"}, "description": {"description": "Description of the entity.", "title":
+      "Description", "type": "string"}, "relationships": {"description": "Relationships
+      of the entity.", "items": {"type": "string"}, "title": "Relationships", "type":
+      "array"}}, "required": ["name", "type", "description", "relationships"], "title":
+      "Entity", "type": "object"}}, "properties": {"suggestions": {"description":
+      "Suggestions to improve future similar tasks.", "items": {"type": "string"},
+      "title": "Suggestions", "type": "array"}, "quality": {"description": "A score
+      from 0 to 10 evaluating on completion, quality, and overall performance, all
+      taking into account the task description, expected output, and the result of
+      the task.", "title": "Quality", "type": "number"}, "entities": {"description":
+      "Entities extracted from the task output.", "items": {"$ref": "#/$defs/Entity"},
+      "title": "Entities", "type": "array"}}, "required": ["entities", "quality",
+      "suggestions"], "type": "object"}}}]}'
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate
+      connection:
+      - keep-alive
+      content-length:
+      - '1962'
+      content-type:
+      - application/json
+      cookie:
+      - __cf_bm=4s6sWmJ49B9F_wNc1STtdZF1nikfl6uN9_ov3Xzfa8U-1738698987-1.0.1.1-lmbRRS1MHrDbnU93Gh16CP3qNczxxIrQnyBU7vpHSwNf6PdmuWOHKd1mkl5SBx6rg7p1NLaNUMyqDDcE0Mvjzw;
+        _cfuvid=Cl48aI8.jSRja0Pqr6Jrh3mAnigd4rDn6lhGicyjMPY-1738698987673-0.0.1.1-604800000
+      host:
+      - api.openai.com
+      user-agent:
+      - OpenAI/Python 1.61.0
+      x-stainless-arch:
+      - arm64
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - MacOS
+      x-stainless-package-version:
+      - 1.61.0
+      x-stainless-raw-response:
+      - 'true'
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.12.8
+    method: POST
+    uri: https://api.openai.com/v1/chat/completions
+  response:
+    content: "{\n  \"id\": \"chatcmpl-AxJK3bJiyqGhPeqdCcCjoeNavGHrR\",\n  \"object\":
+      \"chat.completion\",\n  \"created\": 1738698991,\n  \"model\": \"gpt-4o-mini-2024-07-18\",\n
+      \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
+      \"assistant\",\n        \"content\": null,\n        \"tool_calls\": [\n          {\n
+      \           \"id\": \"call_uAFkclWHIRqgrXFrQFcEoUIS\",\n            \"type\":
+      \"function\",\n            \"function\": {\n              \"name\": \"TaskEvaluation\",\n
+      \             \"arguments\": \"{\\\"suggestions\\\":[\\\"Include additional
+      context for the greeting to make it more meaningful.\\\",\\\"Specify if you
+      want a casual or formal tone for greetings.\\\",\\\"Provide examples of variations
+      of the greeting if necessary.\\\"],\\\"quality\\\":10,\\\"entities\\\":[],\\\"relationships\\\":[]}\"\n
+      \           }\n          }\n        ],\n        \"refusal\": null\n      },\n
+      \     \"logprobs\": null,\n      \"finish_reason\": \"stop\"\n    }\n  ],\n
+      \ \"usage\": {\n    \"prompt_tokens\": 273,\n    \"completion_tokens\": 50,\n
+      \   \"total_tokens\": 323,\n    \"prompt_tokens_details\": {\n      \"cached_tokens\":
+      0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\": {\n
+      \     \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\":
+      0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"service_tier\":
+      \"default\",\n  \"system_fingerprint\": \"fp_bd83329f63\"\n}\n"
+    headers:
+      CF-RAY:
+      - 90cd3973589f1698-SJC
+      Connection:
+      - keep-alive
+      Content-Encoding:
+      - gzip
+      Content-Type:
+      - application/json
+      Date:
+      - Tue, 04 Feb 2025 19:56:32 GMT
+      Server:
+      - cloudflare
+      Transfer-Encoding:
+      - chunked
+      X-Content-Type-Options:
+      - nosniff
+      access-control-expose-headers:
+      - X-Request-ID
+      alt-svc:
+      - h3=":443"; ma=86400
+      cf-cache-status:
+      - DYNAMIC
+      openai-organization:
+      - crewai-iuxna1
+      openai-processing-ms:
+      - '1408'
+      openai-version:
+      - '2020-10-01'
+      strict-transport-security:
+      - max-age=31536000; includeSubDomains; preload
+      x-ratelimit-limit-requests:
+      - '30000'
+      x-ratelimit-limit-tokens:
+      - '150000000'
+      x-ratelimit-remaining-requests:
+      - '29999'
+      x-ratelimit-remaining-tokens:
+      - '149999876'
+      x-ratelimit-reset-requests:
+      - 2ms
+      x-ratelimit-reset-tokens:
+      - 0s
+      x-request-id:
+      - req_519fd27ca3d5da4d541c4331654e0520
+    http_version: HTTP/1.1
+    status_code: 200
+version: 1

tests/utilities/cassettes/test_crew_emits_end_task_event.yaml

@@ -0,0 +1,357 @@
+interactions:
+- request:
+    body: '{"messages": [{"role": "system", "content": "You are base_agent. You are
+      a helpful assistant that just says hi\nYour personal goal is: Just say hi\nTo
+      give my best complete final answer to the task respond using the exact following
+      format:\n\nThought: I now can give a great answer\nFinal Answer: Your final
+      answer must be the great and the most complete as possible, it must be outcome
+      described.\n\nI MUST use these formats, my job depends on it!"}, {"role": "user",
+      "content": "\nCurrent Task: Just say hi\n\nThis is the expect criteria for your
+      final answer: hi\nyou MUST return the actual complete content as the final answer,
+      not a summary.\n\nBegin! This is VERY important to you, use the tools available
+      and give your best Final Answer, your job depends on it!\n\nThought:"}], "model":
+      "gpt-4o-mini", "stop": ["\nObservation:"]}'
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate
+      connection:
+      - keep-alive
+      content-length:
+      - '836'
+      content-type:
+      - application/json
+      cookie:
+      - __cf_bm=nedOdWE1YnKQYt1kSbrcA.zhwa3bZDzmZqTOjZYER0c-1738700521-1.0.1.1-xQk9iXOvqvyXNhkIOgc8Ws2WYcT1mJFkDCvCC8xA5joFD8QfNrBIAr_Qs6sIxt2EzXyeFwBA6gA8ZgWApCHx0Q;
+        _cfuvid=Cl48aI8.jSRja0Pqr6Jrh3mAnigd4rDn6lhGicyjMPY-1738698987673-0.0.1.1-604800000
+      host:
+      - api.openai.com
+      user-agent:
+      - OpenAI/Python 1.61.0
+      x-stainless-arch:
+      - arm64
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - MacOS
+      x-stainless-package-version:
+      - 1.61.0
+      x-stainless-raw-response:
+      - 'true'
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.12.8
+    method: POST
+    uri: https://api.openai.com/v1/chat/completions
+  response:
+    content: "{\n  \"id\": \"chatcmpl-AxJlK2np8dMxYgsDIuyz2TSKKELWh\",\n  \"object\":
+      \"chat.completion\",\n  \"created\": 1738700682,\n  \"model\": \"gpt-4o-mini-2024-07-18\",\n
+      \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
+      \"assistant\",\n        \"content\": \"I now can give a great answer  \\nFinal
+      Answer: hi\",\n        \"refusal\": null\n      },\n      \"logprobs\": null,\n
+      \     \"finish_reason\": \"stop\"\n    }\n  ],\n  \"usage\": {\n    \"prompt_tokens\":
+      161,\n    \"completion_tokens\": 12,\n    \"total_tokens\": 173,\n    \"prompt_tokens_details\":
+      {\n      \"cached_tokens\": 0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\":
+      {\n      \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\":
+      0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"service_tier\":
+      \"default\",\n  \"system_fingerprint\": \"fp_72ed7ab54c\"\n}\n"
+    headers:
+      CF-RAY:
+      - 90cd62c1fdb0fa6a-SJC
+      Connection:
+      - keep-alive
+      Content-Encoding:
+      - gzip
+      Content-Type:
+      - application/json
+      Date:
+      - Tue, 04 Feb 2025 20:24:42 GMT
+      Server:
+      - cloudflare
+      Transfer-Encoding:
+      - chunked
+      X-Content-Type-Options:
+      - nosniff
+      access-control-expose-headers:
+      - X-Request-ID
+      alt-svc:
+      - h3=":443"; ma=86400
+      cf-cache-status:
+      - DYNAMIC
+      openai-organization:
+      - crewai-iuxna1
+      openai-processing-ms:
+      - '326'
+      openai-version:
+      - '2020-10-01'
+      strict-transport-security:
+      - max-age=31536000; includeSubDomains; preload
+      x-ratelimit-limit-requests:
+      - '30000'
+      x-ratelimit-limit-tokens:
+      - '150000000'
+      x-ratelimit-remaining-requests:
+      - '29999'
+      x-ratelimit-remaining-tokens:
+      - '149999810'
+      x-ratelimit-reset-requests:
+      - 2ms
+      x-ratelimit-reset-tokens:
+      - 0s
+      x-request-id:
+      - req_22be86be6fd9d69ca8d310ef534e7bec
+    http_version: HTTP/1.1
+    status_code: 200
+- request:
+    body: !!binary |
+      Cp0mCiQKIgoMc2VydmljZS5uYW1lEhIKEGNyZXdBSS10ZWxlbWV0cnkS9CUKEgoQY3Jld2FpLnRl
+      bGVtZXRyeRKkBwoQfRmsiIy66Scw43CcUw5ZiBIIfYDEhWJTOTkqDENyZXcgQ3JlYXRlZDABOcD6
+      XvsOGyEYQWC2bPsOGyEYShsKDmNyZXdhaV92ZXJzaW9uEgkKBzAuMTAwLjBKGgoOcHl0aG9uX3Zl
+      cnNpb24SCAoGMy4xMi44Si4KCGNyZXdfa2V5EiIKIGU1ODA3MDFkNTJlYjY1YWZmMjRlZWZlNzhj
+      NzQ2MjhjSjEKB2NyZXdfaWQSJgokMDMzNTZiYmEtMzJmZC00OThmLTgxYTItYzc1ZDBkMzc2N2Qx
+      ShwKDGNyZXdfcHJvY2VzcxIMCgpzZXF1ZW50aWFsShEKC2NyZXdfbWVtb3J5EgIQAEoaChRjcmV3
+      X251bWJlcl9vZl90YXNrcxICGAFKGwoVY3Jld19udW1iZXJfb2ZfYWdlbnRzEgIYAUrRAgoLY3Jl
+      d19hZ2VudHMSwQIKvgJbeyJrZXkiOiAiYWQxNTMxNjFjNWM1YTg1NmFhMGQwNmIyNDljNGM2NGEi
+      LCAiaWQiOiAiZjY5YTRmYTMtMzQ4OC00MmFhLTlhMTQtMGEzZmEyOWJmYjZjIiwgInJvbGUiOiAi
+      YmFzZV9hZ2VudCIsICJ2ZXJib3NlPyI6IGZhbHNlLCAibWF4X2l0ZXIiOiAyMCwgIm1heF9ycG0i
+      OiBudWxsLCAiZnVuY3Rpb25fY2FsbGluZ19sbG0iOiAiIiwgImxsbSI6ICJncHQtNG8tbWluaSIs
+      ICJkZWxlZ2F0aW9uX2VuYWJsZWQ/IjogZmFsc2UsICJhbGxvd19jb2RlX2V4ZWN1dGlvbj8iOiBm
+      YWxzZSwgIm1heF9yZXRyeV9saW1pdCI6IDIsICJ0b29sc19uYW1lcyI6IFtdfV1K/wEKCmNyZXdf
+      dGFza3MS8AEK7QFbeyJrZXkiOiAiMWIxNWVmMjM5MTViMjc1NWU4OWEwZWMzYjI2YTEzZDIiLCAi
+      aWQiOiAiNDllNzk0MmMtZTJiMy00YmE1LTg5MTUtMTYwYjQxMDU2ZmVlIiwgImFzeW5jX2V4ZWN1
+      dGlvbj8iOiBmYWxzZSwgImh1bWFuX2lucHV0PyI6IGZhbHNlLCAiYWdlbnRfcm9sZSI6ICJiYXNl
+      X2FnZW50IiwgImFnZW50X2tleSI6ICJhZDE1MzE2MWM1YzVhODU2YWEwZDA2YjI0OWM0YzY0YSIs
+      ICJ0b29sc19uYW1lcyI6IFtdfV16AhgBhQEAAQAAEo4CChCLgjutPq/3tkNyPqVjfbZhEgjhB7lb
+      clxzdyoMVGFzayBDcmVhdGVkMAE5CC9++w4bIRhBeMN++w4bIRhKLgoIY3Jld19rZXkSIgogZTU4
+      MDcwMWQ1MmViNjVhZmYyNGVlZmU3OGM3NDYyOGNKMQoHY3Jld19pZBImCiQwMzM1NmJiYS0zMmZk
+      LTQ5OGYtODFhMi1jNzVkMGQzNzY3ZDFKLgoIdGFza19rZXkSIgogMWIxNWVmMjM5MTViMjc1NWU4
+      OWEwZWMzYjI2YTEzZDJKMQoHdGFza19pZBImCiQ0OWU3OTQyYy1lMmIzLTRiYTUtODkxNS0xNjBi
+      NDEwNTZmZWV6AhgBhQEAAQAAEqQHChC7SpRSs6eG9XFmYuMQgghQEghpZMlScOy2DyoMQ3JldyBD
+      cmVhdGVkMAE50O2cAA8bIRhBOFmrAA8bIRhKGwoOY3Jld2FpX3ZlcnNpb24SCQoHMC4xMDAuMEoa
+      Cg5weXRob25fdmVyc2lvbhIICgYzLjEyLjhKLgoIY3Jld19rZXkSIgogZTU4MDcwMWQ1MmViNjVh
+      ZmYyNGVlZmU3OGM3NDYyOGNKMQoHY3Jld19pZBImCiRlYTQ1MWY2OS00Zjk3LTQ4MjYtOWNlYi04
+      NTAzMDk2MTQ2MDlKHAoMY3Jld19wcm9jZXNzEgwKCnNlcXVlbnRpYWxKEQoLY3Jld19tZW1vcnkS
+      AhAAShoKFGNyZXdfbnVtYmVyX29mX3Rhc2tzEgIYAUobChVjcmV3X251bWJlcl9vZl9hZ2VudHMS
+      AhgBStECCgtjcmV3X2FnZW50cxLBAgq+Alt7ImtleSI6ICJhZDE1MzE2MWM1YzVhODU2YWEwZDA2
+      YjI0OWM0YzY0YSIsICJpZCI6ICJmNjlhNGZhMy0zNDg4LTQyYWEtOWExNC0wYTNmYTI5YmZiNmMi
+      LCAicm9sZSI6ICJiYXNlX2FnZW50IiwgInZlcmJvc2U/IjogZmFsc2UsICJtYXhfaXRlciI6IDIw
+      LCAibWF4X3JwbSI6IG51bGwsICJmdW5jdGlvbl9jYWxsaW5nX2xsbSI6ICIiLCAibGxtIjogImdw
+      dC00by1taW5pIiwgImRlbGVnYXRpb25fZW5hYmxlZD8iOiBmYWxzZSwgImFsbG93X2NvZGVfZXhl
+      Y3V0aW9uPyI6IGZhbHNlLCAibWF4X3JldHJ5X2xpbWl0IjogMiwgInRvb2xzX25hbWVzIjogW119
+      XUr/AQoKY3Jld190YXNrcxLwAQrtAVt7ImtleSI6ICIxYjE1ZWYyMzkxNWIyNzU1ZTg5YTBlYzNi
+      MjZhMTNkMiIsICJpZCI6ICI0OWU3OTQyYy1lMmIzLTRiYTUtODkxNS0xNjBiNDEwNTZmZWUiLCAi
+      YXN5bmNfZXhlY3V0aW9uPyI6IGZhbHNlLCAiaHVtYW5faW5wdXQ/IjogZmFsc2UsICJhZ2VudF9y
+      b2xlIjogImJhc2VfYWdlbnQiLCAiYWdlbnRfa2V5IjogImFkMTUzMTYxYzVjNWE4NTZhYTBkMDZi
+      MjQ5YzRjNjRhIiwgInRvb2xzX25hbWVzIjogW119XXoCGAGFAQABAAASjgIKEM1q5zTl6Q6WKHPQ
+      eIqEm7sSCCWi2wvaFqpfKgxUYXNrIENyZWF0ZWQwATmIObwADxshGEEAo7wADxshGEouCghjcmV3
+      X2tleRIiCiBlNTgwNzAxZDUyZWI2NWFmZjI0ZWVmZTc4Yzc0NjI4Y0oxCgdjcmV3X2lkEiYKJGVh
+      NDUxZjY5LTRmOTctNDgyNi05Y2ViLTg1MDMwOTYxNDYwOUouCgh0YXNrX2tleRIiCiAxYjE1ZWYy
+      MzkxNWIyNzU1ZTg5YTBlYzNiMjZhMTNkMkoxCgd0YXNrX2lkEiYKJDQ5ZTc5NDJjLWUyYjMtNGJh
+      NS04OTE1LTE2MGI0MTA1NmZlZXoCGAGFAQABAAASpAcKEEhIZzGdZRAdvfcluDR5qvESCFMGo60X
+      V/dYKgxDcmV3IENyZWF0ZWQwATnY8LgBDxshGEGQrsUBDxshGEobCg5jcmV3YWlfdmVyc2lvbhIJ
+      CgcwLjEwMC4wShoKDnB5dGhvbl92ZXJzaW9uEggKBjMuMTIuOEouCghjcmV3X2tleRIiCiBlNTgw
+      NzAxZDUyZWI2NWFmZjI0ZWVmZTc4Yzc0NjI4Y0oxCgdjcmV3X2lkEiYKJDA3MDJmOGU1LWRjMTYt
+      NDlhYi1hMWY1LThjOWQyY2IwMDYwYkocCgxjcmV3X3Byb2Nlc3MSDAoKc2VxdWVudGlhbEoRCgtj
+      cmV3X21lbW9yeRICEABKGgoUY3Jld19udW1iZXJfb2ZfdGFza3MSAhgBShsKFWNyZXdfbnVtYmVy
+      X29mX2FnZW50cxICGAFK0QIKC2NyZXdfYWdlbnRzEsECCr4CW3sia2V5IjogImFkMTUzMTYxYzVj
+      NWE4NTZhYTBkMDZiMjQ5YzRjNjRhIiwgImlkIjogImY2OWE0ZmEzLTM0ODgtNDJhYS05YTE0LTBh
+      M2ZhMjliZmI2YyIsICJyb2xlIjogImJhc2VfYWdlbnQiLCAidmVyYm9zZT8iOiBmYWxzZSwgIm1h
+      eF9pdGVyIjogMjAsICJtYXhfcnBtIjogbnVsbCwgImZ1bmN0aW9uX2NhbGxpbmdfbGxtIjogIiIs
+      ICJsbG0iOiAiZ3B0LTRvLW1pbmkiLCAiZGVsZWdhdGlvbl9lbmFibGVkPyI6IGZhbHNlLCAiYWxs
+      b3dfY29kZV9leGVjdXRpb24/IjogZmFsc2UsICJtYXhfcmV0cnlfbGltaXQiOiAyLCAidG9vbHNf
+      bmFtZXMiOiBbXX1dSv8BCgpjcmV3X3Rhc2tzEvABCu0BW3sia2V5IjogIjFiMTVlZjIzOTE1YjI3
+      NTVlODlhMGVjM2IyNmExM2QyIiwgImlkIjogIjQ5ZTc5NDJjLWUyYjMtNGJhNS04OTE1LTE2MGI0
+      MTA1NmZlZSIsICJhc3luY19leGVjdXRpb24/IjogZmFsc2UsICJodW1hbl9pbnB1dD8iOiBmYWxz
+      ZSwgImFnZW50X3JvbGUiOiAiYmFzZV9hZ2VudCIsICJhZ2VudF9rZXkiOiAiYWQxNTMxNjFjNWM1
+      YTg1NmFhMGQwNmIyNDljNGM2NGEiLCAidG9vbHNfbmFtZXMiOiBbXX1degIYAYUBAAEAABKOAgoQ
+      PkgvMtq3aT5YPXE8gCRxPBIIMx/zQOuC+8sqDFRhc2sgQ3JlYXRlZDABOYjk2AEPGyEYQZiI2QEP
+      GyEYSi4KCGNyZXdfa2V5EiIKIGU1ODA3MDFkNTJlYjY1YWZmMjRlZWZlNzhjNzQ2MjhjSjEKB2Ny
+      ZXdfaWQSJgokMDcwMmY4ZTUtZGMxNi00OWFiLWExZjUtOGM5ZDJjYjAwNjBiSi4KCHRhc2tfa2V5
+      EiIKIDFiMTVlZjIzOTE1YjI3NTVlODlhMGVjM2IyNmExM2QySjEKB3Rhc2tfaWQSJgokNDllNzk0
+      MmMtZTJiMy00YmE1LTg5MTUtMTYwYjQxMDU2ZmVlegIYAYUBAAEAABKkBwoQex7nA0gUUrZHbN6F
+      gWp/gBIIKPd4fiRi7DwqDENyZXcgQ3JlYXRlZDABOTiIrAIPGyEYQdCAtwIPGyEYShsKDmNyZXdh
+      aV92ZXJzaW9uEgkKBzAuMTAwLjBKGgoOcHl0aG9uX3ZlcnNpb24SCAoGMy4xMi44Si4KCGNyZXdf
+      a2V5EiIKIGU1ODA3MDFkNTJlYjY1YWZmMjRlZWZlNzhjNzQ2MjhjSjEKB2NyZXdfaWQSJgokMGNm
+      YjUzZWItMDA2Mi00YmVmLTk1ZTgtMDgwMjQ3NmNkMWRlShwKDGNyZXdfcHJvY2VzcxIMCgpzZXF1
+      ZW50aWFsShEKC2NyZXdfbWVtb3J5EgIQAEoaChRjcmV3X251bWJlcl9vZl90YXNrcxICGAFKGwoV
+      Y3Jld19udW1iZXJfb2ZfYWdlbnRzEgIYAUrRAgoLY3Jld19hZ2VudHMSwQIKvgJbeyJrZXkiOiAi
+      YWQxNTMxNjFjNWM1YTg1NmFhMGQwNmIyNDljNGM2NGEiLCAiaWQiOiAiZjY5YTRmYTMtMzQ4OC00
+      MmFhLTlhMTQtMGEzZmEyOWJmYjZjIiwgInJvbGUiOiAiYmFzZV9hZ2VudCIsICJ2ZXJib3NlPyI6
+      IGZhbHNlLCAibWF4X2l0ZXIiOiAyMCwgIm1heF9ycG0iOiBudWxsLCAiZnVuY3Rpb25fY2FsbGlu
+      Z19sbG0iOiAiIiwgImxsbSI6ICJncHQtNG8tbWluaSIsICJkZWxlZ2F0aW9uX2VuYWJsZWQ/Ijog
+      ZmFsc2UsICJhbGxvd19jb2RlX2V4ZWN1dGlvbj8iOiBmYWxzZSwgIm1heF9yZXRyeV9saW1pdCI6
+      IDIsICJ0b29sc19uYW1lcyI6IFtdfV1K/wEKCmNyZXdfdGFza3MS8AEK7QFbeyJrZXkiOiAiMWIx
+      NWVmMjM5MTViMjc1NWU4OWEwZWMzYjI2YTEzZDIiLCAiaWQiOiAiNDllNzk0MmMtZTJiMy00YmE1
+      LTg5MTUtMTYwYjQxMDU2ZmVlIiwgImFzeW5jX2V4ZWN1dGlvbj8iOiBmYWxzZSwgImh1bWFuX2lu
+      cHV0PyI6IGZhbHNlLCAiYWdlbnRfcm9sZSI6ICJiYXNlX2FnZW50IiwgImFnZW50X2tleSI6ICJh
+      ZDE1MzE2MWM1YzVhODU2YWEwZDA2YjI0OWM0YzY0YSIsICJ0b29sc19uYW1lcyI6IFtdfV16AhgB
+      hQEAAQAAEo4CChD+ymllejCksajGYDua8tgNEghnjlejrbuw2SoMVGFzayBDcmVhdGVkMAE5EDrI
+      Ag8bIRhBGIzIAg8bIRhKLgoIY3Jld19rZXkSIgogZTU4MDcwMWQ1MmViNjVhZmYyNGVlZmU3OGM3
+      NDYyOGNKMQoHY3Jld19pZBImCiQwY2ZiNTNlYi0wMDYyLTRiZWYtOTVlOC0wODAyNDc2Y2QxZGVK
+      LgoIdGFza19rZXkSIgogMWIxNWVmMjM5MTViMjc1NWU4OWEwZWMzYjI2YTEzZDJKMQoHdGFza19p
+      ZBImCiQ0OWU3OTQyYy1lMmIzLTRiYTUtODkxNS0xNjBiNDEwNTZmZWV6AhgBhQEAAQAA
+    headers:
+      Accept:
+      - '*/*'
+      Accept-Encoding:
+      - gzip, deflate
+      Connection:
+      - keep-alive
+      Content-Length:
+      - '4896'
+      Content-Type:
+      - application/x-protobuf
+      User-Agent:
+      - OTel-OTLP-Exporter-Python/1.27.0
+    method: POST
+    uri: https://telemetry.crewai.com:4319/v1/traces
+  response:
+    body:
+      string: "\n\0"
+    headers:
+      Content-Length:
+      - '2'
+      Content-Type:
+      - application/x-protobuf
+      Date:
+      - Tue, 04 Feb 2025 20:24:47 GMT
+    status:
+      code: 200
+      message: OK
+- request:
+    body: '{"messages": [{"role": "user", "content": "Assess the quality of the task
+      completed based on the description, expected output, and actual results.\n\nTask
+      Description:\nJust say hi\n\nExpected Output:\nhi\n\nActual Output:\nhi\n\nPlease
+      provide:\n- Bullet points suggestions to improve future similar tasks\n- A score
+      from 0 to 10 evaluating on completion, quality, and overall performance- Entities
+      extracted from the task output, if any, their type, description, and relationships"}],
+      "model": "gpt-4o-mini", "tool_choice": {"type": "function", "function": {"name":
+      "TaskEvaluation"}}, "tools": [{"type": "function", "function": {"name": "TaskEvaluation",
+      "description": "Correctly extracted `TaskEvaluation` with all the required parameters
+      with correct types", "parameters": {"$defs": {"Entity": {"properties": {"name":
+      {"description": "The name of the entity.", "title": "Name", "type": "string"},
+      "type": {"description": "The type of the entity.", "title": "Type", "type":
+      "string"}, "description": {"description": "Description of the entity.", "title":
+      "Description", "type": "string"}, "relationships": {"description": "Relationships
+      of the entity.", "items": {"type": "string"}, "title": "Relationships", "type":
+      "array"}}, "required": ["name", "type", "description", "relationships"], "title":
+      "Entity", "type": "object"}}, "properties": {"suggestions": {"description":
+      "Suggestions to improve future similar tasks.", "items": {"type": "string"},
+      "title": "Suggestions", "type": "array"}, "quality": {"description": "A score
+      from 0 to 10 evaluating on completion, quality, and overall performance, all
+      taking into account the task description, expected output, and the result of
+      the task.", "title": "Quality", "type": "number"}, "entities": {"description":
+      "Entities extracted from the task output.", "items": {"$ref": "#/$defs/Entity"},
+      "title": "Entities", "type": "array"}}, "required": ["entities", "quality",
+      "suggestions"], "type": "object"}}}]}'
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate
+      connection:
+      - keep-alive
+      content-length:
+      - '1962'
+      content-type:
+      - application/json
+      cookie:
+      - __cf_bm=nedOdWE1YnKQYt1kSbrcA.zhwa3bZDzmZqTOjZYER0c-1738700521-1.0.1.1-xQk9iXOvqvyXNhkIOgc8Ws2WYcT1mJFkDCvCC8xA5joFD8QfNrBIAr_Qs6sIxt2EzXyeFwBA6gA8ZgWApCHx0Q;
+        _cfuvid=Cl48aI8.jSRja0Pqr6Jrh3mAnigd4rDn6lhGicyjMPY-1738698987673-0.0.1.1-604800000
+      host:
+      - api.openai.com
+      user-agent:
+      - OpenAI/Python 1.61.0
+      x-stainless-arch:
+      - arm64
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - MacOS
+      x-stainless-package-version:
+      - 1.61.0
+      x-stainless-raw-response:
+      - 'true'
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.12.8
+    method: POST
+    uri: https://api.openai.com/v1/chat/completions
+  response:
+    content: "{\n  \"id\": \"chatcmpl-AxJlLVC3gCB9gRI0ZSkoPCZY7EwpQ\",\n  \"object\":
+      \"chat.completion\",\n  \"created\": 1738700683,\n  \"model\": \"gpt-4o-mini-2024-07-18\",\n
+      \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
+      \"assistant\",\n        \"content\": null,\n        \"tool_calls\": [\n          {\n
+      \           \"id\": \"call_mgwImOITW8lkjzAyf9Pp76cL\",\n            \"type\":
+      \"function\",\n            \"function\": {\n              \"name\": \"TaskEvaluation\",\n
+      \             \"arguments\": \"{\\\"suggestions\\\":[\\\"Provide context or
+      additional information to make tasks more engaging.\\\",\\\"Encourage variations
+      in responses to make the interaction more dynamic.\\\"],\\\"quality\\\":10,\\\"entities\\\":[{\\\"name\\\":\\\"hi\\\",\\\"type\\\":\\\"greeting\\\",\\\"description\\\":\\\"A
+      common word used to initiate a conversation or express friendliness.\\\",\\\"relationships\\\":[\\\"initiates
+      conversation\\\",\\\"expresses friendliness\\\"]}]}\"\n            }\n          }\n
+      \       ],\n        \"refusal\": null\n      },\n      \"logprobs\": null,\n
+      \     \"finish_reason\": \"stop\"\n    }\n  ],\n  \"usage\": {\n    \"prompt_tokens\":
+      273,\n    \"completion_tokens\": 71,\n    \"total_tokens\": 344,\n    \"prompt_tokens_details\":
+      {\n      \"cached_tokens\": 0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\":
+      {\n      \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\":
+      0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"service_tier\":
+      \"default\",\n  \"system_fingerprint\": \"fp_bd83329f63\"\n}\n"
+    headers:
+      CF-Cache-Status:
+      - DYNAMIC
+      CF-RAY:
+      - 90cd62c4ba41fa6a-SJC
+      Connection:
+      - keep-alive
+      Content-Encoding:
+      - gzip
+      Content-Type:
+      - application/json
+      Date:
+      - Tue, 04 Feb 2025 20:24:50 GMT
+      Server:
+      - cloudflare
+      Transfer-Encoding:
+      - chunked
+      X-Content-Type-Options:
+      - nosniff
+      access-control-expose-headers:
+      - X-Request-ID
+      alt-svc:
+      - h3=":443"; ma=86400
+      openai-organization:
+      - crewai-iuxna1
+      openai-processing-ms:
+      - '7347'
+      openai-version:
+      - '2020-10-01'
+      strict-transport-security:
+      - max-age=31536000; includeSubDomains; preload
+      x-ratelimit-limit-requests:
+      - '30000'
+      x-ratelimit-limit-tokens:
+      - '150000000'
+      x-ratelimit-remaining-requests:
+      - '29999'
+      x-ratelimit-remaining-tokens:
+      - '149999876'
+      x-ratelimit-reset-requests:
+      - 2ms
+      x-ratelimit-reset-tokens:
+      - 0s
+      x-request-id:
+      - req_aec28dd3fe998d628754e8429623bf9e
+    http_version: HTTP/1.1
+    status_code: 200
+version: 1

tests/utilities/cassettes/test_crew_emits_kickoff_events.yaml

@@ -0,0 +1,245 @@
+interactions:
+- request:
+    body: '{"messages": [{"role": "system", "content": "You are base_agent. You are
+      a helpful assistant that just says hi\nYour personal goal is: Just say hi\nTo
+      give my best complete final answer to the task respond using the exact following
+      format:\n\nThought: I now can give a great answer\nFinal Answer: Your final
+      answer must be the great and the most complete as possible, it must be outcome
+      described.\n\nI MUST use these formats, my job depends on it!"}, {"role": "user",
+      "content": "\nCurrent Task: Just say hi\n\nThis is the expect criteria for your
+      final answer: hi\nyou MUST return the actual complete content as the final answer,
+      not a summary.\n\nBegin! This is VERY important to you, use the tools available
+      and give your best Final Answer, your job depends on it!\n\nThought:"}], "model":
+      "gpt-4o-mini", "stop": ["\nObservation:"]}'
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate
+      connection:
+      - keep-alive
+      content-length:
+      - '836'
+      content-type:
+      - application/json
+      host:
+      - api.openai.com
+      user-agent:
+      - OpenAI/Python 1.61.0
+      x-stainless-arch:
+      - arm64
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - MacOS
+      x-stainless-package-version:
+      - 1.61.0
+      x-stainless-raw-response:
+      - 'true'
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.12.8
+    method: POST
+    uri: https://api.openai.com/v1/chat/completions
+  response:
+    content: "{\n  \"id\": \"chatcmpl-AxJIrSWAFqDEsNtLRhcM8vMHO9Ejw\",\n  \"object\":
+      \"chat.completion\",\n  \"created\": 1738698917,\n  \"model\": \"gpt-4o-mini-2024-07-18\",\n
+      \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
+      \"assistant\",\n        \"content\": \"I now can give a great answer  \\nFinal
+      Answer: hi\",\n        \"refusal\": null\n      },\n      \"logprobs\": null,\n
+      \     \"finish_reason\": \"stop\"\n    }\n  ],\n  \"usage\": {\n    \"prompt_tokens\":
+      161,\n    \"completion_tokens\": 12,\n    \"total_tokens\": 173,\n    \"prompt_tokens_details\":
+      {\n      \"cached_tokens\": 0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\":
+      {\n      \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\":
+      0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"service_tier\":
+      \"default\",\n  \"system_fingerprint\": \"fp_72ed7ab54c\"\n}\n"
+    headers:
+      CF-RAY:
+      - 90cd37a83f5f176a-SJC
+      Connection:
+      - keep-alive
+      Content-Encoding:
+      - gzip
+      Content-Type:
+      - application/json
+      Date:
+      - Tue, 04 Feb 2025 19:55:18 GMT
+      Server:
+      - cloudflare
+      Set-Cookie:
+      - __cf_bm=rKQWp4fbAvcCp4rasEN6DqiTjQfiWYpLfjcLpWcmzi0-1738698918-1.0.1.1-qlcCSdBY3KWbzVms0eLtz5ub5SSLGs_sRLxTdNhDk_purQuz9k6EFp8PHJfN3aP_sLnuyKnFlppM3.2k_dCtPQ;
+        path=/; expires=Tue, 04-Feb-25 20:25:18 GMT; domain=.api.openai.com; HttpOnly;
+        Secure; SameSite=None
+      - _cfuvid=Oi91zDXvjWohBYXSVqK4hFsq3_GZePEIIbi7b7wrjcI-1738698918130-0.0.1.1-604800000;
+        path=/; domain=.api.openai.com; HttpOnly; Secure; SameSite=None
+      Transfer-Encoding:
+      - chunked
+      X-Content-Type-Options:
+      - nosniff
+      access-control-expose-headers:
+      - X-Request-ID
+      alt-svc:
+      - h3=":443"; ma=86400
+      cf-cache-status:
+      - DYNAMIC
+      openai-organization:
+      - crewai-iuxna1
+      openai-processing-ms:
+      - '894'
+      openai-version:
+      - '2020-10-01'
+      strict-transport-security:
+      - max-age=31536000; includeSubDomains; preload
+      x-ratelimit-limit-requests:
+      - '30000'
+      x-ratelimit-limit-tokens:
+      - '150000000'
+      x-ratelimit-remaining-requests:
+      - '29999'
+      x-ratelimit-remaining-tokens:
+      - '149999810'
+      x-ratelimit-reset-requests:
+      - 2ms
+      x-ratelimit-reset-tokens:
+      - 0s
+      x-request-id:
+      - req_864253996bbc0f797f9a2c1b9247a0d5
+    http_version: HTTP/1.1
+    status_code: 200
+- request:
+    body: '{"messages": [{"role": "user", "content": "Assess the quality of the task
+      completed based on the description, expected output, and actual results.\n\nTask
+      Description:\nJust say hi\n\nExpected Output:\nhi\n\nActual Output:\nhi\n\nPlease
+      provide:\n- Bullet points suggestions to improve future similar tasks\n- A score
+      from 0 to 10 evaluating on completion, quality, and overall performance- Entities
+      extracted from the task output, if any, their type, description, and relationships"}],
+      "model": "gpt-4o-mini", "tool_choice": {"type": "function", "function": {"name":
+      "TaskEvaluation"}}, "tools": [{"type": "function", "function": {"name": "TaskEvaluation",
+      "description": "Correctly extracted `TaskEvaluation` with all the required parameters
+      with correct types", "parameters": {"$defs": {"Entity": {"properties": {"name":
+      {"description": "The name of the entity.", "title": "Name", "type": "string"},
+      "type": {"description": "The type of the entity.", "title": "Type", "type":
+      "string"}, "description": {"description": "Description of the entity.", "title":
+      "Description", "type": "string"}, "relationships": {"description": "Relationships
+      of the entity.", "items": {"type": "string"}, "title": "Relationships", "type":
+      "array"}}, "required": ["name", "type", "description", "relationships"], "title":
+      "Entity", "type": "object"}}, "properties": {"suggestions": {"description":
+      "Suggestions to improve future similar tasks.", "items": {"type": "string"},
+      "title": "Suggestions", "type": "array"}, "quality": {"description": "A score
+      from 0 to 10 evaluating on completion, quality, and overall performance, all
+      taking into account the task description, expected output, and the result of
+      the task.", "title": "Quality", "type": "number"}, "entities": {"description":
+      "Entities extracted from the task output.", "items": {"$ref": "#/$defs/Entity"},
+      "title": "Entities", "type": "array"}}, "required": ["entities", "quality",
+      "suggestions"], "type": "object"}}}]}'
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate
+      connection:
+      - keep-alive
+      content-length:
+      - '1962'
+      content-type:
+      - application/json
+      cookie:
+      - __cf_bm=rKQWp4fbAvcCp4rasEN6DqiTjQfiWYpLfjcLpWcmzi0-1738698918-1.0.1.1-qlcCSdBY3KWbzVms0eLtz5ub5SSLGs_sRLxTdNhDk_purQuz9k6EFp8PHJfN3aP_sLnuyKnFlppM3.2k_dCtPQ;
+        _cfuvid=Oi91zDXvjWohBYXSVqK4hFsq3_GZePEIIbi7b7wrjcI-1738698918130-0.0.1.1-604800000
+      host:
+      - api.openai.com
+      user-agent:
+      - OpenAI/Python 1.61.0
+      x-stainless-arch:
+      - arm64
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - MacOS
+      x-stainless-package-version:
+      - 1.61.0
+      x-stainless-raw-response:
+      - 'true'
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.12.8
+    method: POST
+    uri: https://api.openai.com/v1/chat/completions
+  response:
+    content: "{\n  \"id\": \"chatcmpl-AxJIsVEppA04iGQh0k6sanKnVObrO\",\n  \"object\":
+      \"chat.completion\",\n  \"created\": 1738698918,\n  \"model\": \"gpt-4o-mini-2024-07-18\",\n
+      \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
+      \"assistant\",\n        \"content\": null,\n        \"tool_calls\": [\n          {\n
+      \           \"id\": \"call_AQ3iizjGWjEvk1SmhGCzjbf1\",\n            \"type\":
+      \"function\",\n            \"function\": {\n              \"name\": \"TaskEvaluation\",\n
+      \             \"arguments\": \"{\\\"suggestions\\\":[\\\"Provide context for
+      the greeting, like a specific scenario or recipient.\\\",\\\"Encourage responses
+      or follow-ups to promote engagement.\\\",\\\"Specify the tone or formality of
+      the greeting, if relevant.\\\"],\\\"quality\\\":10,\\\"entities\\\":[{\\\"name\\\":\\\"hi\\\",\\\"type\\\":\\\"greeting\\\",\\\"description\\\":\\\"A
+      common informal expression used to initiate conversation or acknowledge someone.\\\",\\\"relationships\\\":[\\\"used
+      in conversation\\\",\\\"expresses friendliness\\\"]}]}\"\n            }\n          }\n
+      \       ],\n        \"refusal\": null\n      },\n      \"logprobs\": null,\n
+      \     \"finish_reason\": \"stop\"\n    }\n  ],\n  \"usage\": {\n    \"prompt_tokens\":
+      273,\n    \"completion_tokens\": 84,\n    \"total_tokens\": 357,\n    \"prompt_tokens_details\":
+      {\n      \"cached_tokens\": 0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\":
+      {\n      \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\":
+      0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"service_tier\":
+      \"default\",\n  \"system_fingerprint\": \"fp_bd83329f63\"\n}\n"
+    headers:
+      CF-Cache-Status:
+      - DYNAMIC
+      CF-RAY:
+      - 90cd37aec8c8176a-SJC
+      Connection:
+      - keep-alive
+      Content-Encoding:
+      - gzip
+      Content-Type:
+      - application/json
+      Date:
+      - Tue, 04 Feb 2025 19:55:21 GMT
+      Server:
+      - cloudflare
+      Transfer-Encoding:
+      - chunked
+      X-Content-Type-Options:
+      - nosniff
+      access-control-expose-headers:
+      - X-Request-ID
+      alt-svc:
+      - h3=":443"; ma=86400
+      openai-organization:
+      - crewai-iuxna1
+      openai-processing-ms:
+      - '3269'
+      openai-version:
+      - '2020-10-01'
+      strict-transport-security:
+      - max-age=31536000; includeSubDomains; preload
+      x-ratelimit-limit-requests:
+      - '30000'
+      x-ratelimit-limit-tokens:
+      - '150000000'
+      x-ratelimit-remaining-requests:
+      - '29999'
+      x-ratelimit-remaining-tokens:
+      - '149999876'
+      x-ratelimit-reset-requests:
+      - 2ms
+      x-ratelimit-reset-tokens:
+      - 0s
+      x-request-id:
+      - req_e6e67a3f5c6f2d48e0351cdce95edd97
+    http_version: HTTP/1.1
+    status_code: 200
+version: 1

tests/utilities/cassettes/test_crew_emits_start_kickoff_event.yaml

@@ -0,0 +1,243 @@
+interactions:
+- request:
+    body: '{"messages": [{"role": "system", "content": "You are base_agent. You are
+      a helpful assistant that just says hi\nYour personal goal is: Just say hi\nTo
+      give my best complete final answer to the task respond using the exact following
+      format:\n\nThought: I now can give a great answer\nFinal Answer: Your final
+      answer must be the great and the most complete as possible, it must be outcome
+      described.\n\nI MUST use these formats, my job depends on it!"}, {"role": "user",
+      "content": "\nCurrent Task: Just say hi\n\nThis is the expect criteria for your
+      final answer: hi\nyou MUST return the actual complete content as the final answer,
+      not a summary.\n\nBegin! This is VERY important to you, use the tools available
+      and give your best Final Answer, your job depends on it!\n\nThought:"}], "model":
+      "gpt-4o-mini", "stop": ["\nObservation:"]}'
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate
+      connection:
+      - keep-alive
+      content-length:
+      - '836'
+      content-type:
+      - application/json
+      host:
+      - api.openai.com
+      user-agent:
+      - OpenAI/Python 1.61.0
+      x-stainless-arch:
+      - arm64
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - MacOS
+      x-stainless-package-version:
+      - 1.61.0
+      x-stainless-raw-response:
+      - 'true'
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.12.8
+    method: POST
+    uri: https://api.openai.com/v1/chat/completions
+  response:
+    content: "{\n  \"id\": \"chatcmpl-AxJJzafmayYpGTsTAWbOyZkmQJNa5\",\n  \"object\":
+      \"chat.completion\",\n  \"created\": 1738698987,\n  \"model\": \"gpt-4o-mini-2024-07-18\",\n
+      \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
+      \"assistant\",\n        \"content\": \"I now can give a great answer  \\nFinal
+      Answer: hi\",\n        \"refusal\": null\n      },\n      \"logprobs\": null,\n
+      \     \"finish_reason\": \"stop\"\n    }\n  ],\n  \"usage\": {\n    \"prompt_tokens\":
+      161,\n    \"completion_tokens\": 12,\n    \"total_tokens\": 173,\n    \"prompt_tokens_details\":
+      {\n      \"cached_tokens\": 0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\":
+      {\n      \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\":
+      0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"service_tier\":
+      \"default\",\n  \"system_fingerprint\": \"fp_72ed7ab54c\"\n}\n"
+    headers:
+      CF-Cache-Status:
+      - DYNAMIC
+      CF-RAY:
+      - 90cd395b0e641698-SJC
+      Connection:
+      - keep-alive
+      Content-Encoding:
+      - gzip
+      Content-Type:
+      - application/json
+      Date:
+      - Tue, 04 Feb 2025 19:56:27 GMT
+      Server:
+      - cloudflare
+      Set-Cookie:
+      - __cf_bm=4s6sWmJ49B9F_wNc1STtdZF1nikfl6uN9_ov3Xzfa8U-1738698987-1.0.1.1-lmbRRS1MHrDbnU93Gh16CP3qNczxxIrQnyBU7vpHSwNf6PdmuWOHKd1mkl5SBx6rg7p1NLaNUMyqDDcE0Mvjzw;
+        path=/; expires=Tue, 04-Feb-25 20:26:27 GMT; domain=.api.openai.com; HttpOnly;
+        Secure; SameSite=None
+      - _cfuvid=Cl48aI8.jSRja0Pqr6Jrh3mAnigd4rDn6lhGicyjMPY-1738698987673-0.0.1.1-604800000;
+        path=/; domain=.api.openai.com; HttpOnly; Secure; SameSite=None
+      Transfer-Encoding:
+      - chunked
+      X-Content-Type-Options:
+      - nosniff
+      access-control-expose-headers:
+      - X-Request-ID
+      alt-svc:
+      - h3=":443"; ma=86400
+      openai-organization:
+      - crewai-iuxna1
+      openai-processing-ms:
+      - '839'
+      openai-version:
+      - '2020-10-01'
+      strict-transport-security:
+      - max-age=31536000; includeSubDomains; preload
+      x-ratelimit-limit-requests:
+      - '30000'
+      x-ratelimit-limit-tokens:
+      - '150000000'
+      x-ratelimit-remaining-requests:
+      - '29999'
+      x-ratelimit-remaining-tokens:
+      - '149999810'
+      x-ratelimit-reset-requests:
+      - 2ms
+      x-ratelimit-reset-tokens:
+      - 0s
+      x-request-id:
+      - req_577b484a927b455c40ed80f9fd4d9106
+    http_version: HTTP/1.1
+    status_code: 200
+- request:
+    body: '{"messages": [{"role": "user", "content": "Assess the quality of the task
+      completed based on the description, expected output, and actual results.\n\nTask
+      Description:\nJust say hi\n\nExpected Output:\nhi\n\nActual Output:\nhi\n\nPlease
+      provide:\n- Bullet points suggestions to improve future similar tasks\n- A score
+      from 0 to 10 evaluating on completion, quality, and overall performance- Entities
+      extracted from the task output, if any, their type, description, and relationships"}],
+      "model": "gpt-4o-mini", "tool_choice": {"type": "function", "function": {"name":
+      "TaskEvaluation"}}, "tools": [{"type": "function", "function": {"name": "TaskEvaluation",
+      "description": "Correctly extracted `TaskEvaluation` with all the required parameters
+      with correct types", "parameters": {"$defs": {"Entity": {"properties": {"name":
+      {"description": "The name of the entity.", "title": "Name", "type": "string"},
+      "type": {"description": "The type of the entity.", "title": "Type", "type":
+      "string"}, "description": {"description": "Description of the entity.", "title":
+      "Description", "type": "string"}, "relationships": {"description": "Relationships
+      of the entity.", "items": {"type": "string"}, "title": "Relationships", "type":
+      "array"}}, "required": ["name", "type", "description", "relationships"], "title":
+      "Entity", "type": "object"}}, "properties": {"suggestions": {"description":
+      "Suggestions to improve future similar tasks.", "items": {"type": "string"},
+      "title": "Suggestions", "type": "array"}, "quality": {"description": "A score
+      from 0 to 10 evaluating on completion, quality, and overall performance, all
+      taking into account the task description, expected output, and the result of
+      the task.", "title": "Quality", "type": "number"}, "entities": {"description":
+      "Entities extracted from the task output.", "items": {"$ref": "#/$defs/Entity"},
+      "title": "Entities", "type": "array"}}, "required": ["entities", "quality",
+      "suggestions"], "type": "object"}}}]}'
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate
+      connection:
+      - keep-alive
+      content-length:
+      - '1962'
+      content-type:
+      - application/json
+      cookie:
+      - __cf_bm=4s6sWmJ49B9F_wNc1STtdZF1nikfl6uN9_ov3Xzfa8U-1738698987-1.0.1.1-lmbRRS1MHrDbnU93Gh16CP3qNczxxIrQnyBU7vpHSwNf6PdmuWOHKd1mkl5SBx6rg7p1NLaNUMyqDDcE0Mvjzw;
+        _cfuvid=Cl48aI8.jSRja0Pqr6Jrh3mAnigd4rDn6lhGicyjMPY-1738698987673-0.0.1.1-604800000
+      host:
+      - api.openai.com
+      user-agent:
+      - OpenAI/Python 1.61.0
+      x-stainless-arch:
+      - arm64
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - MacOS
+      x-stainless-package-version:
+      - 1.61.0
+      x-stainless-raw-response:
+      - 'true'
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.12.8
+    method: POST
+    uri: https://api.openai.com/v1/chat/completions
+  response:
+    content: "{\n  \"id\": \"chatcmpl-AxJJz10KP7iadNPdKsbcsvHBa7cic\",\n  \"object\":
+      \"chat.completion\",\n  \"created\": 1738698987,\n  \"model\": \"gpt-4o-mini-2024-07-18\",\n
+      \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
+      \"assistant\",\n        \"content\": null,\n        \"tool_calls\": [\n          {\n
+      \           \"id\": \"call_czeHQgy5eiOVa0zlrtcfwepe\",\n            \"type\":
+      \"function\",\n            \"function\": {\n              \"name\": \"TaskEvaluation\",\n
+      \             \"arguments\": \"{\\\"suggestions\\\":[\\\"Provide more context
+      or details for similar tasks to enhance output expectations.\\\",\\\"Encourage
+      creativity in responses for simple tasks to engage users more effectively.\\\"],\\\"quality\\\":10,\\\"entities\\\":[]
+      }\"\n            }\n          }\n        ],\n        \"refusal\": null\n      },\n
+      \     \"logprobs\": null,\n      \"finish_reason\": \"stop\"\n    }\n  ],\n
+      \ \"usage\": {\n    \"prompt_tokens\": 273,\n    \"completion_tokens\": 40,\n
+      \   \"total_tokens\": 313,\n    \"prompt_tokens_details\": {\n      \"cached_tokens\":
+      0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\": {\n
+      \     \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\":
+      0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"service_tier\":
+      \"default\",\n  \"system_fingerprint\": \"fp_bd83329f63\"\n}\n"
+    headers:
+      CF-RAY:
+      - 90cd39615b281698-SJC
+      Connection:
+      - keep-alive
+      Content-Encoding:
+      - gzip
+      Content-Type:
+      - application/json
+      Date:
+      - Tue, 04 Feb 2025 19:56:29 GMT
+      Server:
+      - cloudflare
+      Transfer-Encoding:
+      - chunked
+      X-Content-Type-Options:
+      - nosniff
+      access-control-expose-headers:
+      - X-Request-ID
+      alt-svc:
+      - h3=":443"; ma=86400
+      cf-cache-status:
+      - DYNAMIC
+      openai-organization:
+      - crewai-iuxna1
+      openai-processing-ms:
+      - '1411'
+      openai-version:
+      - '2020-10-01'
+      strict-transport-security:
+      - max-age=31536000; includeSubDomains; preload
+      x-ratelimit-limit-requests:
+      - '30000'
+      x-ratelimit-limit-tokens:
+      - '150000000'
+      x-ratelimit-remaining-requests:
+      - '29999'
+      x-ratelimit-remaining-tokens:
+      - '149999876'
+      x-ratelimit-reset-requests:
+      - 2ms
+      x-ratelimit-reset-tokens:
+      - 0s
+      x-request-id:
+      - req_3e717a80c7d9c5ea19893dd990aaae26
+    http_version: HTTP/1.1
+    status_code: 200
+version: 1

tests/utilities/cassettes/test_crew_emits_start_task_event.yaml

@@ -0,0 +1,245 @@
+interactions:
+- request:
+    body: '{"messages": [{"role": "system", "content": "You are base_agent. You are
+      a helpful assistant that just says hi\nYour personal goal is: Just say hi\nTo
+      give my best complete final answer to the task respond using the exact following
+      format:\n\nThought: I now can give a great answer\nFinal Answer: Your final
+      answer must be the great and the most complete as possible, it must be outcome
+      described.\n\nI MUST use these formats, my job depends on it!"}, {"role": "user",
+      "content": "\nCurrent Task: Just say hi\n\nThis is the expect criteria for your
+      final answer: hi\nyou MUST return the actual complete content as the final answer,
+      not a summary.\n\nBegin! This is VERY important to you, use the tools available
+      and give your best Final Answer, your job depends on it!\n\nThought:"}], "model":
+      "gpt-4o-mini", "stop": ["\nObservation:"]}'
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate
+      connection:
+      - keep-alive
+      content-length:
+      - '836'
+      content-type:
+      - application/json
+      cookie:
+      - __cf_bm=4s6sWmJ49B9F_wNc1STtdZF1nikfl6uN9_ov3Xzfa8U-1738698987-1.0.1.1-lmbRRS1MHrDbnU93Gh16CP3qNczxxIrQnyBU7vpHSwNf6PdmuWOHKd1mkl5SBx6rg7p1NLaNUMyqDDcE0Mvjzw;
+        _cfuvid=Cl48aI8.jSRja0Pqr6Jrh3mAnigd4rDn6lhGicyjMPY-1738698987673-0.0.1.1-604800000
+      host:
+      - api.openai.com
+      user-agent:
+      - OpenAI/Python 1.61.0
+      x-stainless-arch:
+      - arm64
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - MacOS
+      x-stainless-package-version:
+      - 1.61.0
+      x-stainless-raw-response:
+      - 'true'
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.12.8
+    method: POST
+    uri: https://api.openai.com/v1/chat/completions
+  response:
+    content: "{\n  \"id\": \"chatcmpl-AxJiiHEQwIXsiG0Sd5wofcuhxVbo9\",\n  \"object\":
+      \"chat.completion\",\n  \"created\": 1738700520,\n  \"model\": \"gpt-4o-mini-2024-07-18\",\n
+      \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
+      \"assistant\",\n        \"content\": \"I now can give a great answer  \\nFinal
+      Answer: hi\",\n        \"refusal\": null\n      },\n      \"logprobs\": null,\n
+      \     \"finish_reason\": \"stop\"\n    }\n  ],\n  \"usage\": {\n    \"prompt_tokens\":
+      161,\n    \"completion_tokens\": 12,\n    \"total_tokens\": 173,\n    \"prompt_tokens_details\":
+      {\n      \"cached_tokens\": 0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\":
+      {\n      \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\":
+      0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"service_tier\":
+      \"default\",\n  \"system_fingerprint\": \"fp_72ed7ab54c\"\n}\n"
+    headers:
+      CF-RAY:
+      - 90cd5ecd0f7667ee-SJC
+      Connection:
+      - keep-alive
+      Content-Encoding:
+      - gzip
+      Content-Type:
+      - application/json
+      Date:
+      - Tue, 04 Feb 2025 20:22:01 GMT
+      Server:
+      - cloudflare
+      Set-Cookie:
+      - __cf_bm=nedOdWE1YnKQYt1kSbrcA.zhwa3bZDzmZqTOjZYER0c-1738700521-1.0.1.1-xQk9iXOvqvyXNhkIOgc8Ws2WYcT1mJFkDCvCC8xA5joFD8QfNrBIAr_Qs6sIxt2EzXyeFwBA6gA8ZgWApCHx0Q;
+        path=/; expires=Tue, 04-Feb-25 20:52:01 GMT; domain=.api.openai.com; HttpOnly;
+        Secure; SameSite=None
+      Transfer-Encoding:
+      - chunked
+      X-Content-Type-Options:
+      - nosniff
+      access-control-expose-headers:
+      - X-Request-ID
+      alt-svc:
+      - h3=":443"; ma=86400
+      cf-cache-status:
+      - DYNAMIC
+      openai-organization:
+      - crewai-iuxna1
+      openai-processing-ms:
+      - '450'
+      openai-version:
+      - '2020-10-01'
+      strict-transport-security:
+      - max-age=31536000; includeSubDomains; preload
+      x-ratelimit-limit-requests:
+      - '30000'
+      x-ratelimit-limit-tokens:
+      - '150000000'
+      x-ratelimit-remaining-requests:
+      - '29999'
+      x-ratelimit-remaining-tokens:
+      - '149999810'
+      x-ratelimit-reset-requests:
+      - 2ms
+      x-ratelimit-reset-tokens:
+      - 0s
+      x-request-id:
+      - req_10eaafc81640a98a0a4789d270dd94d9
+    http_version: HTTP/1.1
+    status_code: 200
+- request:
+    body: '{"messages": [{"role": "user", "content": "Assess the quality of the task
+      completed based on the description, expected output, and actual results.\n\nTask
+      Description:\nJust say hi\n\nExpected Output:\nhi\n\nActual Output:\nhi\n\nPlease
+      provide:\n- Bullet points suggestions to improve future similar tasks\n- A score
+      from 0 to 10 evaluating on completion, quality, and overall performance- Entities
+      extracted from the task output, if any, their type, description, and relationships"}],
+      "model": "gpt-4o-mini", "tool_choice": {"type": "function", "function": {"name":
+      "TaskEvaluation"}}, "tools": [{"type": "function", "function": {"name": "TaskEvaluation",
+      "description": "Correctly extracted `TaskEvaluation` with all the required parameters
+      with correct types", "parameters": {"$defs": {"Entity": {"properties": {"name":
+      {"description": "The name of the entity.", "title": "Name", "type": "string"},
+      "type": {"description": "The type of the entity.", "title": "Type", "type":
+      "string"}, "description": {"description": "Description of the entity.", "title":
+      "Description", "type": "string"}, "relationships": {"description": "Relationships
+      of the entity.", "items": {"type": "string"}, "title": "Relationships", "type":
+      "array"}}, "required": ["name", "type", "description", "relationships"], "title":
+      "Entity", "type": "object"}}, "properties": {"suggestions": {"description":
+      "Suggestions to improve future similar tasks.", "items": {"type": "string"},
+      "title": "Suggestions", "type": "array"}, "quality": {"description": "A score
+      from 0 to 10 evaluating on completion, quality, and overall performance, all
+      taking into account the task description, expected output, and the result of
+      the task.", "title": "Quality", "type": "number"}, "entities": {"description":
+      "Entities extracted from the task output.", "items": {"$ref": "#/$defs/Entity"},
+      "title": "Entities", "type": "array"}}, "required": ["entities", "quality",
+      "suggestions"], "type": "object"}}}]}'
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate
+      connection:
+      - keep-alive
+      content-length:
+      - '1962'
+      content-type:
+      - application/json
+      cookie:
+      - __cf_bm=nedOdWE1YnKQYt1kSbrcA.zhwa3bZDzmZqTOjZYER0c-1738700521-1.0.1.1-xQk9iXOvqvyXNhkIOgc8Ws2WYcT1mJFkDCvCC8xA5joFD8QfNrBIAr_Qs6sIxt2EzXyeFwBA6gA8ZgWApCHx0Q;
+        _cfuvid=Cl48aI8.jSRja0Pqr6Jrh3mAnigd4rDn6lhGicyjMPY-1738698987673-0.0.1.1-604800000
+      host:
+      - api.openai.com
+      user-agent:
+      - OpenAI/Python 1.61.0
+      x-stainless-arch:
+      - arm64
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - MacOS
+      x-stainless-package-version:
+      - 1.61.0
+      x-stainless-raw-response:
+      - 'true'
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.12.8
+    method: POST
+    uri: https://api.openai.com/v1/chat/completions
+  response:
+    content: "{\n  \"id\": \"chatcmpl-AxJijOhk12Ua6lS23IwtZTachfjq9\",\n  \"object\":
+      \"chat.completion\",\n  \"created\": 1738700521,\n  \"model\": \"gpt-4o-mini-2024-07-18\",\n
+      \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
+      \"assistant\",\n        \"content\": null,\n        \"tool_calls\": [\n          {\n
+      \           \"id\": \"call_DSteeMHHPf5RanJb8qjCo4qx\",\n            \"type\":
+      \"function\",\n            \"function\": {\n              \"name\": \"TaskEvaluation\",\n
+      \             \"arguments\": \"{\\\"suggestions\\\":[\\\"Consider adding context
+      for the greeting to make it more engaging.\\\",\\\"Specify if any additional
+      information or tone is desired in the greeting.\\\"],\\\"quality\\\":10,\\\"entities\\\":[{\\\"name\\\":\\\"greeting\\\",\\\"type\\\":\\\"text\\\",\\\"description\\\":\\\"A
+      simple greeting phrase\\\",\\\"relationships\\\":[\\\"is a\\\",\\\"is part of
+      a conversation\\\"]}]}\"\n            }\n          }\n        ],\n        \"refusal\":
+      null\n      },\n      \"logprobs\": null,\n      \"finish_reason\": \"stop\"\n
+      \   }\n  ],\n  \"usage\": {\n    \"prompt_tokens\": 273,\n    \"completion_tokens\":
+      67,\n    \"total_tokens\": 340,\n    \"prompt_tokens_details\": {\n      \"cached_tokens\":
+      0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\": {\n
+      \     \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\":
+      0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"service_tier\":
+      \"default\",\n  \"system_fingerprint\": \"fp_bd83329f63\"\n}\n"
+    headers:
+      CF-RAY:
+      - 90cd5ed20cb267ee-SJC
+      Connection:
+      - keep-alive
+      Content-Encoding:
+      - gzip
+      Content-Type:
+      - application/json
+      Date:
+      - Tue, 04 Feb 2025 20:22:02 GMT
+      Server:
+      - cloudflare
+      Transfer-Encoding:
+      - chunked
+      X-Content-Type-Options:
+      - nosniff
+      access-control-expose-headers:
+      - X-Request-ID
+      alt-svc:
+      - h3=":443"; ma=86400
+      cf-cache-status:
+      - DYNAMIC
+      openai-organization:
+      - crewai-iuxna1
+      openai-processing-ms:
+      - '1624'
+      openai-version:
+      - '2020-10-01'
+      strict-transport-security:
+      - max-age=31536000; includeSubDomains; preload
+      x-ratelimit-limit-requests:
+      - '30000'
+      x-ratelimit-limit-tokens:
+      - '150000000'
+      x-ratelimit-remaining-requests:
+      - '29999'
+      x-ratelimit-remaining-tokens:
+      - '149999876'
+      x-ratelimit-reset-requests:
+      - 2ms
+      x-ratelimit-reset-tokens:
+      - 0s
+      x-request-id:
+      - req_4ee944acdd3928afbf6c5562403b064a
+    http_version: HTTP/1.1
+    status_code: 200
+version: 1

tests/utilities/cassettes/test_crew_emits_task_failed_event.yaml

@@ -0,0 +1,114 @@
+interactions:
+- request:
+    body: '{"messages": [{"role": "system", "content": "You are base_agent. You are
+      a helpful assistant that just says hi\nYour personal goal is: Just say hi\nTo
+      give my best complete final answer to the task respond using the exact following
+      format:\n\nThought: I now can give a great answer\nFinal Answer: Your final
+      answer must be the great and the most complete as possible, it must be outcome
+      described.\n\nI MUST use these formats, my job depends on it!"}, {"role": "user",
+      "content": "\nCurrent Task: Just say hi\n\nThis is the expect criteria for your
+      final answer: hi\nyou MUST return the actual complete content as the final answer,
+      not a summary.\n\nBegin! This is VERY important to you, use the tools available
+      and give your best Final Answer, your job depends on it!\n\nThought:"}], "model":
+      "gpt-4o-mini", "stop": ["\nObservation:"]}'
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate
+      connection:
+      - keep-alive
+      content-length:
+      - '836'
+      content-type:
+      - application/json
+      host:
+      - api.openai.com
+      user-agent:
+      - OpenAI/Python 1.61.0
+      x-stainless-arch:
+      - arm64
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - MacOS
+      x-stainless-package-version:
+      - 1.61.0
+      x-stainless-raw-response:
+      - 'true'
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.12.8
+    method: POST
+    uri: https://api.openai.com/v1/chat/completions
+  response:
+    content: "{\n  \"id\": \"chatcmpl-AzpkZLpCyjKT5d6Udfx4zAme2sOMy\",\n  \"object\":
+      \"chat.completion\",\n  \"created\": 1739300299,\n  \"model\": \"gpt-4o-mini-2024-07-18\",\n
+      \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
+      \"assistant\",\n        \"content\": \"I now can give a great answer  \\nFinal
+      Answer: hi\",\n        \"refusal\": null\n      },\n      \"logprobs\": null,\n
+      \     \"finish_reason\": \"stop\"\n    }\n  ],\n  \"usage\": {\n    \"prompt_tokens\":
+      161,\n    \"completion_tokens\": 12,\n    \"total_tokens\": 173,\n    \"prompt_tokens_details\":
+      {\n      \"cached_tokens\": 0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\":
+      {\n      \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\":
+      0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"service_tier\":
+      \"default\",\n  \"system_fingerprint\": \"fp_72ed7ab54c\"\n}\n"
+    headers:
+      CF-RAY:
+      - 910691d3ab90ebef-SJC
+      Connection:
+      - keep-alive
+      Content-Encoding:
+      - gzip
+      Content-Type:
+      - application/json
+      Date:
+      - Tue, 11 Feb 2025 18:58:20 GMT
+      Server:
+      - cloudflare
+      Set-Cookie:
+      - __cf_bm=MOH5EY6n3p8JKY53.yz7qzLuLYsEB8QdQXH09loUMBM-1739300300-1.0.1.1-hjb4mk04sMygPFhoFyiySKZSqB_fN5PbhbOyn.kipa3.eLvk7EtriDyjvGkBFIAV13DYnc08BfF_l2kxdx9hfQ;
+        path=/; expires=Tue, 11-Feb-25 19:28:20 GMT; domain=.api.openai.com; HttpOnly;
+        Secure; SameSite=None
+      - _cfuvid=uu.cEiV.FfgvSvCdKOooDYJWrwjVEuFeGdQodijGUUI-1739300300232-0.0.1.1-604800000;
+        path=/; domain=.api.openai.com; HttpOnly; Secure; SameSite=None
+      Transfer-Encoding:
+      - chunked
+      X-Content-Type-Options:
+      - nosniff
+      access-control-expose-headers:
+      - X-Request-ID
+      alt-svc:
+      - h3=":443"; ma=86400
+      cf-cache-status:
+      - DYNAMIC
+      openai-organization:
+      - crewai-iuxna1
+      openai-processing-ms:
+      - '1357'
+      openai-version:
+      - '2020-10-01'
+      strict-transport-security:
+      - max-age=31536000; includeSubDomains; preload
+      x-ratelimit-limit-requests:
+      - '30000'
+      x-ratelimit-limit-tokens:
+      - '150000000'
+      x-ratelimit-remaining-requests:
+      - '29999'
+      x-ratelimit-remaining-tokens:
+      - '149999810'
+      x-ratelimit-reset-requests:
+      - 2ms
+      x-ratelimit-reset-tokens:
+      - 0s
+      x-request-id:
+      - req_2277503f851195e7d7a43b66eb044454
+    http_version: HTTP/1.1
+    status_code: 200
+version: 1

tests/utilities/cassettes/test_multiple_handlers_for_same_event.yaml

@@ -0,0 +1,111 @@
+interactions:
+- request:
+    body: '{"messages": [{"role": "system", "content": "You are base_agent. You are
+      a helpful assistant that just says hi\nYour personal goal is: Just say hi\nTo
+      give my best complete final answer to the task respond using the exact following
+      format:\n\nThought: I now can give a great answer\nFinal Answer: Your final
+      answer must be the great and the most complete as possible, it must be outcome
+      described.\n\nI MUST use these formats, my job depends on it!"}, {"role": "user",
+      "content": "\nCurrent Task: Just say hi\n\nThis is the expect criteria for your
+      final answer: hi\nyou MUST return the actual complete content as the final answer,
+      not a summary.\n\nBegin! This is VERY important to you, use the tools available
+      and give your best Final Answer, your job depends on it!\n\nThought:"}], "model":
+      "gpt-4o-mini", "stop": ["\nObservation:"]}'
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate
+      connection:
+      - keep-alive
+      content-length:
+      - '836'
+      content-type:
+      - application/json
+      cookie:
+      - _cfuvid=gsNyCo_jrDOolzf8SXHDaxQQrEgdR3jgv4OAH8MziDE-1739291824699-0.0.1.1-604800000;
+        __cf_bm=cRijYuylMGzRGxv3udQL5PhHOR5mRN_9_eLLwevlM_o-1739299455-1.0.1.1-Fszr_Msw0B1.IBMkiunP.VF2ilul1YGZZV8TqMcO3Q2SHvSlqfgm9NHgns1bJrm0wWRvHiCE7wdZfUAOx7T3Lg
+      host:
+      - api.openai.com
+      user-agent:
+      - OpenAI/Python 1.61.0
+      x-stainless-arch:
+      - arm64
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - MacOS
+      x-stainless-package-version:
+      - 1.61.0
+      x-stainless-raw-response:
+      - 'true'
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.12.8
+    method: POST
+    uri: https://api.openai.com/v1/chat/completions
+  response:
+    content: "{\n  \"id\": \"chatcmpl-AzpWx6pctOvzu6xsbyg0XfSAc0q9V\",\n  \"object\":
+      \"chat.completion\",\n  \"created\": 1739299455,\n  \"model\": \"gpt-4o-mini-2024-07-18\",\n
+      \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
+      \"assistant\",\n        \"content\": \"I now can give a great answer  \\nFinal
+      Answer: hi\",\n        \"refusal\": null\n      },\n      \"logprobs\": null,\n
+      \     \"finish_reason\": \"stop\"\n    }\n  ],\n  \"usage\": {\n    \"prompt_tokens\":
+      161,\n    \"completion_tokens\": 12,\n    \"total_tokens\": 173,\n    \"prompt_tokens_details\":
+      {\n      \"cached_tokens\": 0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\":
+      {\n      \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\":
+      0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"service_tier\":
+      \"default\",\n  \"system_fingerprint\": \"fp_72ed7ab54c\"\n}\n"
+    headers:
+      CF-Cache-Status:
+      - DYNAMIC
+      CF-RAY:
+      - 91067d3ddc68fa16-SJC
+      Connection:
+      - keep-alive
+      Content-Encoding:
+      - gzip
+      Content-Type:
+      - application/json
+      Date:
+      - Tue, 11 Feb 2025 18:44:16 GMT
+      Server:
+      - cloudflare
+      Transfer-Encoding:
+      - chunked
+      X-Content-Type-Options:
+      - nosniff
+      access-control-expose-headers:
+      - X-Request-ID
+      alt-svc:
+      - h3=":443"; ma=86400
+      openai-organization:
+      - crewai-iuxna1
+      openai-processing-ms:
+      - '703'
+      openai-version:
+      - '2020-10-01'
+      strict-transport-security:
+      - max-age=31536000; includeSubDomains; preload
+      x-ratelimit-limit-requests:
+      - '30000'
+      x-ratelimit-limit-tokens:
+      - '150000000'
+      x-ratelimit-remaining-requests:
+      - '29999'
+      x-ratelimit-remaining-tokens:
+      - '149999810'
+      x-ratelimit-reset-requests:
+      - 2ms
+      x-ratelimit-reset-tokens:
+      - 0s
+      x-request-id:
+      - req_89222c00e4608e8557a135e91b223556
+    http_version: HTTP/1.1
+    status_code: 200
+version: 1

tests/utilities/cassettes/test_register_handler_adds_new_handler.yaml

@@ -0,0 +1,114 @@
+interactions:
+- request:
+    body: '{"messages": [{"role": "system", "content": "You are base_agent. You are
+      a helpful assistant that just says hi\nYour personal goal is: Just say hi\nTo
+      give my best complete final answer to the task respond using the exact following
+      format:\n\nThought: I now can give a great answer\nFinal Answer: Your final
+      answer must be the great and the most complete as possible, it must be outcome
+      described.\n\nI MUST use these formats, my job depends on it!"}, {"role": "user",
+      "content": "\nCurrent Task: Just say hi\n\nThis is the expect criteria for your
+      final answer: hi\nyou MUST return the actual complete content as the final answer,
+      not a summary.\n\nBegin! This is VERY important to you, use the tools available
+      and give your best Final Answer, your job depends on it!\n\nThought:"}], "model":
+      "gpt-4o-mini", "stop": ["\nObservation:"]}'
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate
+      connection:
+      - keep-alive
+      content-length:
+      - '836'
+      content-type:
+      - application/json
+      cookie:
+      - _cfuvid=gsNyCo_jrDOolzf8SXHDaxQQrEgdR3jgv4OAH8MziDE-1739291824699-0.0.1.1-604800000
+      host:
+      - api.openai.com
+      user-agent:
+      - OpenAI/Python 1.61.0
+      x-stainless-arch:
+      - arm64
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - MacOS
+      x-stainless-package-version:
+      - 1.61.0
+      x-stainless-raw-response:
+      - 'true'
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.12.8
+    method: POST
+    uri: https://api.openai.com/v1/chat/completions
+  response:
+    content: "{\n  \"id\": \"chatcmpl-AzpWxLzAcRzigZuIGmjP3ckQgxAom\",\n  \"object\":
+      \"chat.completion\",\n  \"created\": 1739299455,\n  \"model\": \"gpt-4o-mini-2024-07-18\",\n
+      \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
+      \"assistant\",\n        \"content\": \"I now can give a great answer  \\nFinal
+      Answer: hi\",\n        \"refusal\": null\n      },\n      \"logprobs\": null,\n
+      \     \"finish_reason\": \"stop\"\n    }\n  ],\n  \"usage\": {\n    \"prompt_tokens\":
+      161,\n    \"completion_tokens\": 12,\n    \"total_tokens\": 173,\n    \"prompt_tokens_details\":
+      {\n      \"cached_tokens\": 0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\":
+      {\n      \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\":
+      0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"service_tier\":
+      \"default\",\n  \"system_fingerprint\": \"fp_72ed7ab54c\"\n}\n"
+    headers:
+      CF-RAY:
+      - 91067d389e90fa16-SJC
+      Connection:
+      - keep-alive
+      Content-Encoding:
+      - gzip
+      Content-Type:
+      - application/json
+      Date:
+      - Tue, 11 Feb 2025 18:44:15 GMT
+      Server:
+      - cloudflare
+      Set-Cookie:
+      - __cf_bm=cRijYuylMGzRGxv3udQL5PhHOR5mRN_9_eLLwevlM_o-1739299455-1.0.1.1-Fszr_Msw0B1.IBMkiunP.VF2ilul1YGZZV8TqMcO3Q2SHvSlqfgm9NHgns1bJrm0wWRvHiCE7wdZfUAOx7T3Lg;
+        path=/; expires=Tue, 11-Feb-25 19:14:15 GMT; domain=.api.openai.com; HttpOnly;
+        Secure; SameSite=None
+      Transfer-Encoding:
+      - chunked
+      X-Content-Type-Options:
+      - nosniff
+      access-control-expose-headers:
+      - X-Request-ID
+      alt-svc:
+      - h3=":443"; ma=86400
+      cf-cache-status:
+      - DYNAMIC
+      openai-organization:
+      - crewai-iuxna1
+      openai-processing-ms:
+      - '716'
+      openai-version:
+      - '2020-10-01'
+      strict-transport-security:
+      - max-age=31536000; includeSubDomains; preload
+      x-ratelimit-limit-requests:
+      - '30000'
+      x-ratelimit-limit-tokens:
+      - '150000000'
+      x-ratelimit-remaining-requests:
+      - '29999'
+      x-ratelimit-remaining-tokens:
+      - '149999810'
+      x-ratelimit-reset-requests:
+      - 2ms
+      x-ratelimit-reset-tokens:
+      - 0s
+      x-request-id:
+      - req_ef807dc3223d40332aae8a313e96ef3a
+    http_version: HTTP/1.1
+    status_code: 200
+version: 1

tests/utilities/cassettes/test_tools_emits_finished_events.yaml

@@ -0,0 +1,512 @@
+interactions:
+- request:
+    body: '{"messages": [{"role": "system", "content": "You are base_agent. You are
+      a helpful assistant that just says hi\nYour personal goal is: Just say hi\nYou
+      ONLY have access to the following tools, and should NEVER make up tools that
+      are not listed here:\n\nTool Name: say_hi\nTool Arguments: {}\nTool Description:
+      Say hi\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought:
+      you should always think about what to do\nAction: the action to take, only one
+      name of [say_hi], just the name, exactly as it''s written.\nAction Input: the
+      input to the action, just a simple JSON object, enclosed in curly braces, using
+      \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce
+      all necessary information is gathered, return the following format:\n\n```\nThought:
+      I now know the final answer\nFinal Answer: the final answer to the original
+      input question\n```"}, {"role": "user", "content": "\nCurrent Task: Just say
+      hi\n\nThis is the expect criteria for your final answer: hi\nyou MUST return
+      the actual complete content as the final answer, not a summary.\n\nBegin! This
+      is VERY important to you, use the tools available and give your best Final Answer,
+      your job depends on it!\n\nThought:"}], "model": "gpt-4o-mini", "stop": ["\nObservation:"]}'
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate
+      connection:
+      - keep-alive
+      content-length:
+      - '1275'
+      content-type:
+      - application/json
+      cookie:
+      - _cfuvid=efIHP1NUsh1dFewGJBu4YoBu6hhGa8vjOOKQglYQGno-1739214901306-0.0.1.1-604800000
+      host:
+      - api.openai.com
+      user-agent:
+      - OpenAI/Python 1.61.0
+      x-stainless-arch:
+      - arm64
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - MacOS
+      x-stainless-package-version:
+      - 1.61.0
+      x-stainless-raw-response:
+      - 'true'
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.12.8
+    method: POST
+    uri: https://api.openai.com/v1/chat/completions
+  response:
+    content: "{\n  \"id\": \"chatcmpl-AzUA6kJQfpUvB4CGot4gSfAIR0foh\",\n  \"object\":
+      \"chat.completion\",\n  \"created\": 1739217314,\n  \"model\": \"gpt-4o-mini-2024-07-18\",\n
+      \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
+      \"assistant\",\n        \"content\": \"you should always think about what to
+      do  \\nAction: say_hi  \\nAction Input: {}  \",\n        \"refusal\": null\n
+      \     },\n      \"logprobs\": null,\n      \"finish_reason\": \"stop\"\n    }\n
+      \ ],\n  \"usage\": {\n    \"prompt_tokens\": 257,\n    \"completion_tokens\":
+      19,\n    \"total_tokens\": 276,\n    \"prompt_tokens_details\": {\n      \"cached_tokens\":
+      0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\": {\n
+      \     \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\":
+      0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"service_tier\":
+      \"default\",\n  \"system_fingerprint\": \"fp_72ed7ab54c\"\n}\n"
+    headers:
+      CF-RAY:
+      - 90fea7d78e1fceb9-SJC
+      Connection:
+      - keep-alive
+      Content-Encoding:
+      - gzip
+      Content-Type:
+      - application/json
+      Date:
+      - Mon, 10 Feb 2025 19:55:15 GMT
+      Server:
+      - cloudflare
+      Set-Cookie:
+      - __cf_bm=fmlg1wjOwuOwZhUUOEtL1tQYluAPumn7AHLF8s0EU2Y-1739217315-1.0.1.1-PQDvxn8TOhzaznlHjwVsqPZUzbAyJWFkvzCubfNJydTu2_AyA1cJ8hkM0khsEE4UY_xp8iPe2gSGmH1ydrDa0Q;
+        path=/; expires=Mon, 10-Feb-25 20:25:15 GMT; domain=.api.openai.com; HttpOnly;
+        Secure; SameSite=None
+      Transfer-Encoding:
+      - chunked
+      X-Content-Type-Options:
+      - nosniff
+      access-control-expose-headers:
+      - X-Request-ID
+      alt-svc:
+      - h3=":443"; ma=86400
+      cf-cache-status:
+      - DYNAMIC
+      openai-organization:
+      - crewai-iuxna1
+      openai-processing-ms:
+      - '526'
+      openai-version:
+      - '2020-10-01'
+      strict-transport-security:
+      - max-age=31536000; includeSubDomains; preload
+      x-ratelimit-limit-requests:
+      - '30000'
+      x-ratelimit-limit-tokens:
+      - '150000000'
+      x-ratelimit-remaining-requests:
+      - '29999'
+      x-ratelimit-remaining-tokens:
+      - '149999703'
+      x-ratelimit-reset-requests:
+      - 2ms
+      x-ratelimit-reset-tokens:
+      - 0s
+      x-request-id:
+      - req_f6358ff0cc7a2b8d2e167ab00a40f2a4
+    http_version: HTTP/1.1
+    status_code: 200
+- request:
+    body: '{"messages": [{"role": "system", "content": "You are base_agent. You are
+      a helpful assistant that just says hi\nYour personal goal is: Just say hi\nYou
+      ONLY have access to the following tools, and should NEVER make up tools that
+      are not listed here:\n\nTool Name: say_hi\nTool Arguments: {}\nTool Description:
+      Say hi\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought:
+      you should always think about what to do\nAction: the action to take, only one
+      name of [say_hi], just the name, exactly as it''s written.\nAction Input: the
+      input to the action, just a simple JSON object, enclosed in curly braces, using
+      \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce
+      all necessary information is gathered, return the following format:\n\n```\nThought:
+      I now know the final answer\nFinal Answer: the final answer to the original
+      input question\n```"}, {"role": "user", "content": "\nCurrent Task: Just say
+      hi\n\nThis is the expect criteria for your final answer: hi\nyou MUST return
+      the actual complete content as the final answer, not a summary.\n\nBegin! This
+      is VERY important to you, use the tools available and give your best Final Answer,
+      your job depends on it!\n\nThought:"}, {"role": "assistant", "content": "you
+      should always think about what to do  \nAction: say_hi  \nAction Input: {}  \nObservation:
+      hi"}], "model": "gpt-4o-mini", "stop": ["\nObservation:"]}'
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate
+      connection:
+      - keep-alive
+      content-length:
+      - '1410'
+      content-type:
+      - application/json
+      cookie:
+      - _cfuvid=efIHP1NUsh1dFewGJBu4YoBu6hhGa8vjOOKQglYQGno-1739214901306-0.0.1.1-604800000;
+        __cf_bm=fmlg1wjOwuOwZhUUOEtL1tQYluAPumn7AHLF8s0EU2Y-1739217315-1.0.1.1-PQDvxn8TOhzaznlHjwVsqPZUzbAyJWFkvzCubfNJydTu2_AyA1cJ8hkM0khsEE4UY_xp8iPe2gSGmH1ydrDa0Q
+      host:
+      - api.openai.com
+      user-agent:
+      - OpenAI/Python 1.61.0
+      x-stainless-arch:
+      - arm64
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - MacOS
+      x-stainless-package-version:
+      - 1.61.0
+      x-stainless-raw-response:
+      - 'true'
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.12.8
+    method: POST
+    uri: https://api.openai.com/v1/chat/completions
+  response:
+    content: "{\n  \"id\": \"chatcmpl-AzUA7QdlQy1WZZijxNWUv25sZycg0\",\n  \"object\":
+      \"chat.completion\",\n  \"created\": 1739217315,\n  \"model\": \"gpt-4o-mini-2024-07-18\",\n
+      \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
+      \"assistant\",\n        \"content\": \"```\\nThought: I now know the final answer\\nFinal
+      Answer: hi\\n```\",\n        \"refusal\": null\n      },\n      \"logprobs\":
+      null,\n      \"finish_reason\": \"stop\"\n    }\n  ],\n  \"usage\": {\n    \"prompt_tokens\":
+      283,\n    \"completion_tokens\": 17,\n    \"total_tokens\": 300,\n    \"prompt_tokens_details\":
+      {\n      \"cached_tokens\": 0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\":
+      {\n      \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\":
+      0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"service_tier\":
+      \"default\",\n  \"system_fingerprint\": \"fp_72ed7ab54c\"\n}\n"
+    headers:
+      CF-RAY:
+      - 90fea7dc5ba6ceb9-SJC
+      Connection:
+      - keep-alive
+      Content-Encoding:
+      - gzip
+      Content-Type:
+      - application/json
+      Date:
+      - Mon, 10 Feb 2025 19:55:15 GMT
+      Server:
+      - cloudflare
+      Transfer-Encoding:
+      - chunked
+      X-Content-Type-Options:
+      - nosniff
+      access-control-expose-headers:
+      - X-Request-ID
+      alt-svc:
+      - h3=":443"; ma=86400
+      cf-cache-status:
+      - DYNAMIC
+      openai-organization:
+      - crewai-iuxna1
+      openai-processing-ms:
+      - '388'
+      openai-version:
+      - '2020-10-01'
+      strict-transport-security:
+      - max-age=31536000; includeSubDomains; preload
+      x-ratelimit-limit-requests:
+      - '30000'
+      x-ratelimit-limit-tokens:
+      - '150000000'
+      x-ratelimit-remaining-requests:
+      - '29999'
+      x-ratelimit-remaining-tokens:
+      - '149999680'
+      x-ratelimit-reset-requests:
+      - 2ms
+      x-ratelimit-reset-tokens:
+      - 0s
+      x-request-id:
+      - req_7d7c68b90b3a9c3ac6092fe17ac1185a
+    http_version: HTTP/1.1
+    status_code: 200
+- request:
+    body: !!binary |
+      CoMzCiQKIgoMc2VydmljZS5uYW1lEhIKEGNyZXdBSS10ZWxlbWV0cnkS2jIKEgoQY3Jld2FpLnRl
+      bGVtZXRyeRKOAgoQ2EINIGZRoXD589od63oHmBIIMfUgEWudUbIqDFRhc2sgQ3JlYXRlZDABOcjI
+      7lbu8CIYQZB471bu8CIYSi4KCGNyZXdfa2V5EiIKIGU1ODA3MDFkNTJlYjY1YWZmMjRlZWZlNzhj
+      NzQ2MjhjSjEKB2NyZXdfaWQSJgokNTE4ODdiOTktY2FlMy00Yjc4LWJjMGEtMDY4MmVmNWEzNGQ0
+      Si4KCHRhc2tfa2V5EiIKIDFiMTVlZjIzOTE1YjI3NTVlODlhMGVjM2IyNmExM2QySjEKB3Rhc2tf
+      aWQSJgokMzlmMDlmMWUtOTJmOC00ZGJiLTgzNDAtNjU2ZmVkMDk3ZjM0egIYAYUBAAEAABKkBwoQ
+      RzhWoF6ewSTS/qUc9yeFRhIIM3SNZCwjz5AqDENyZXcgQ3JlYXRlZDABOQjrGlru8CIYQdgbKVru
+      8CIYShsKDmNyZXdhaV92ZXJzaW9uEgkKBzAuMTAwLjBKGgoOcHl0aG9uX3ZlcnNpb24SCAoGMy4x
+      Mi44Si4KCGNyZXdfa2V5EiIKIGU1ODA3MDFkNTJlYjY1YWZmMjRlZWZlNzhjNzQ2MjhjSjEKB2Ny
+      ZXdfaWQSJgokYzk4ODFkY2YtMmM0MS00ZjRlLTgzMjctNjJjYjFhYjJkOTg4ShwKDGNyZXdfcHJv
+      Y2VzcxIMCgpzZXF1ZW50aWFsShEKC2NyZXdfbWVtb3J5EgIQAEoaChRjcmV3X251bWJlcl9vZl90
+      YXNrcxICGAFKGwoVY3Jld19udW1iZXJfb2ZfYWdlbnRzEgIYAUrRAgoLY3Jld19hZ2VudHMSwQIK
+      vgJbeyJrZXkiOiAiYWQxNTMxNjFjNWM1YTg1NmFhMGQwNmIyNDljNGM2NGEiLCAiaWQiOiAiNTU2
+      NzJiMDgtOTU4ZC00MjljLWE3ZTctY2ZlN2U4Y2MwOGZkIiwgInJvbGUiOiAiYmFzZV9hZ2VudCIs
+      ICJ2ZXJib3NlPyI6IGZhbHNlLCAibWF4X2l0ZXIiOiAyMCwgIm1heF9ycG0iOiBudWxsLCAiZnVu
+      Y3Rpb25fY2FsbGluZ19sbG0iOiAiIiwgImxsbSI6ICJncHQtNG8tbWluaSIsICJkZWxlZ2F0aW9u
+      X2VuYWJsZWQ/IjogZmFsc2UsICJhbGxvd19jb2RlX2V4ZWN1dGlvbj8iOiBmYWxzZSwgIm1heF9y
+      ZXRyeV9saW1pdCI6IDIsICJ0b29sc19uYW1lcyI6IFtdfV1K/wEKCmNyZXdfdGFza3MS8AEK7QFb
+      eyJrZXkiOiAiMWIxNWVmMjM5MTViMjc1NWU4OWEwZWMzYjI2YTEzZDIiLCAiaWQiOiAiMzlmMDlm
+      MWUtOTJmOC00ZGJiLTgzNDAtNjU2ZmVkMDk3ZjM0IiwgImFzeW5jX2V4ZWN1dGlvbj8iOiBmYWxz
+      ZSwgImh1bWFuX2lucHV0PyI6IGZhbHNlLCAiYWdlbnRfcm9sZSI6ICJiYXNlX2FnZW50IiwgImFn
+      ZW50X2tleSI6ICJhZDE1MzE2MWM1YzVhODU2YWEwZDA2YjI0OWM0YzY0YSIsICJ0b29sc19uYW1l
+      cyI6IFtdfV16AhgBhQEAAQAAEo4CChB8AxWkb2Uwpdc8RpyCRqw5EggJAxbgNu81XyoMVGFzayBD
+      cmVhdGVkMAE5+HQ8Wu7wIhhB+PE8Wu7wIhhKLgoIY3Jld19rZXkSIgogZTU4MDcwMWQ1MmViNjVh
+      ZmYyNGVlZmU3OGM3NDYyOGNKMQoHY3Jld19pZBImCiRjOTg4MWRjZi0yYzQxLTRmNGUtODMyNy02
+      MmNiMWFiMmQ5ODhKLgoIdGFza19rZXkSIgogMWIxNWVmMjM5MTViMjc1NWU4OWEwZWMzYjI2YTEz
+      ZDJKMQoHdGFza19pZBImCiQzOWYwOWYxZS05MmY4LTRkYmItODM0MC02NTZmZWQwOTdmMzR6AhgB
+      hQEAAQAAEqQHChCcXvdbsgYC+gzCMrXs3LN/EgijKwJLCRIiHioMQ3JldyBDcmVhdGVkMAE5iJqz
+      vu7wIhhBqKC/vu7wIhhKGwoOY3Jld2FpX3ZlcnNpb24SCQoHMC4xMDAuMEoaCg5weXRob25fdmVy
+      c2lvbhIICgYzLjEyLjhKLgoIY3Jld19rZXkSIgogZTU4MDcwMWQ1MmViNjVhZmYyNGVlZmU3OGM3
+      NDYyOGNKMQoHY3Jld19pZBImCiQ2Zjk1ZWI3Yy0wOWM5LTQxOTYtYWFiYi1kOWIxNmMxMzZjODdK
+      HAoMY3Jld19wcm9jZXNzEgwKCnNlcXVlbnRpYWxKEQoLY3Jld19tZW1vcnkSAhAAShoKFGNyZXdf
+      bnVtYmVyX29mX3Rhc2tzEgIYAUobChVjcmV3X251bWJlcl9vZl9hZ2VudHMSAhgBStECCgtjcmV3
+      X2FnZW50cxLBAgq+Alt7ImtleSI6ICJhZDE1MzE2MWM1YzVhODU2YWEwZDA2YjI0OWM0YzY0YSIs
+      ICJpZCI6ICI1NTY3MmIwOC05NThkLTQyOWMtYTdlNy1jZmU3ZThjYzA4ZmQiLCAicm9sZSI6ICJi
+      YXNlX2FnZW50IiwgInZlcmJvc2U/IjogZmFsc2UsICJtYXhfaXRlciI6IDIwLCAibWF4X3JwbSI6
+      IG51bGwsICJmdW5jdGlvbl9jYWxsaW5nX2xsbSI6ICIiLCAibGxtIjogImdwdC00by1taW5pIiwg
+      ImRlbGVnYXRpb25fZW5hYmxlZD8iOiBmYWxzZSwgImFsbG93X2NvZGVfZXhlY3V0aW9uPyI6IGZh
+      bHNlLCAibWF4X3JldHJ5X2xpbWl0IjogMiwgInRvb2xzX25hbWVzIjogW119XUr/AQoKY3Jld190
+      YXNrcxLwAQrtAVt7ImtleSI6ICIxYjE1ZWYyMzkxNWIyNzU1ZTg5YTBlYzNiMjZhMTNkMiIsICJp
+      ZCI6ICIzOWYwOWYxZS05MmY4LTRkYmItODM0MC02NTZmZWQwOTdmMzQiLCAiYXN5bmNfZXhlY3V0
+      aW9uPyI6IGZhbHNlLCAiaHVtYW5faW5wdXQ/IjogZmFsc2UsICJhZ2VudF9yb2xlIjogImJhc2Vf
+      YWdlbnQiLCAiYWdlbnRfa2V5IjogImFkMTUzMTYxYzVjNWE4NTZhYTBkMDZiMjQ5YzRjNjRhIiwg
+      InRvb2xzX25hbWVzIjogW119XXoCGAGFAQABAAASjgIKEExDo5nPLyHb2H8DfYjPoX4SCLEYs+24
+      8EenKgxUYXNrIENyZWF0ZWQwATmI4NG+7vAiGEFYZdK+7vAiGEouCghjcmV3X2tleRIiCiBlNTgw
+      NzAxZDUyZWI2NWFmZjI0ZWVmZTc4Yzc0NjI4Y0oxCgdjcmV3X2lkEiYKJDZmOTVlYjdjLTA5Yzkt
+      NDE5Ni1hYWJiLWQ5YjE2YzEzNmM4N0ouCgh0YXNrX2tleRIiCiAxYjE1ZWYyMzkxNWIyNzU1ZTg5
+      YTBlYzNiMjZhMTNkMkoxCgd0YXNrX2lkEiYKJDM5ZjA5ZjFlLTkyZjgtNGRiYi04MzQwLTY1NmZl
+      ZDA5N2YzNHoCGAGFAQABAAASpAcKEBBQzR2bcR/7woQ+VkaJ4kQSCD1LFx3SNPPPKgxDcmV3IENy
+      ZWF0ZWQwATlotsW/7vAiGEEgA9C/7vAiGEobCg5jcmV3YWlfdmVyc2lvbhIJCgcwLjEwMC4wShoK
+      DnB5dGhvbl92ZXJzaW9uEggKBjMuMTIuOEouCghjcmV3X2tleRIiCiBlNTgwNzAxZDUyZWI2NWFm
+      ZjI0ZWVmZTc4Yzc0NjI4Y0oxCgdjcmV3X2lkEiYKJDJiMWI2MGYzLTNlZTMtNGNjYi05MDM2LTdk
+      MzE4OTJiYjVkZkocCgxjcmV3X3Byb2Nlc3MSDAoKc2VxdWVudGlhbEoRCgtjcmV3X21lbW9yeRIC
+      EABKGgoUY3Jld19udW1iZXJfb2ZfdGFza3MSAhgBShsKFWNyZXdfbnVtYmVyX29mX2FnZW50cxIC
+      GAFK0QIKC2NyZXdfYWdlbnRzEsECCr4CW3sia2V5IjogImFkMTUzMTYxYzVjNWE4NTZhYTBkMDZi
+      MjQ5YzRjNjRhIiwgImlkIjogIjU1NjcyYjA4LTk1OGQtNDI5Yy1hN2U3LWNmZTdlOGNjMDhmZCIs
+      ICJyb2xlIjogImJhc2VfYWdlbnQiLCAidmVyYm9zZT8iOiBmYWxzZSwgIm1heF9pdGVyIjogMjAs
+      ICJtYXhfcnBtIjogbnVsbCwgImZ1bmN0aW9uX2NhbGxpbmdfbGxtIjogIiIsICJsbG0iOiAiZ3B0
+      LTRvLW1pbmkiLCAiZGVsZWdhdGlvbl9lbmFibGVkPyI6IGZhbHNlLCAiYWxsb3dfY29kZV9leGVj
+      dXRpb24/IjogZmFsc2UsICJtYXhfcmV0cnlfbGltaXQiOiAyLCAidG9vbHNfbmFtZXMiOiBbXX1d
+      Sv8BCgpjcmV3X3Rhc2tzEvABCu0BW3sia2V5IjogIjFiMTVlZjIzOTE1YjI3NTVlODlhMGVjM2Iy
+      NmExM2QyIiwgImlkIjogIjM5ZjA5ZjFlLTkyZjgtNGRiYi04MzQwLTY1NmZlZDA5N2YzNCIsICJh
+      c3luY19leGVjdXRpb24/IjogZmFsc2UsICJodW1hbl9pbnB1dD8iOiBmYWxzZSwgImFnZW50X3Jv
+      bGUiOiAiYmFzZV9hZ2VudCIsICJhZ2VudF9rZXkiOiAiYWQxNTMxNjFjNWM1YTg1NmFhMGQwNmIy
+      NDljNGM2NGEiLCAidG9vbHNfbmFtZXMiOiBbXX1degIYAYUBAAEAABKOAgoQmT07KMiFRgzOOPQf
+      I4bJPhIIqzN+pCYM6IUqDFRhc2sgQ3JlYXRlZDABOYjr3r/u8CIYQehY37/u8CIYSi4KCGNyZXdf
+      a2V5EiIKIGU1ODA3MDFkNTJlYjY1YWZmMjRlZWZlNzhjNzQ2MjhjSjEKB2NyZXdfaWQSJgokMmIx
+      YjYwZjMtM2VlMy00Y2NiLTkwMzYtN2QzMTg5MmJiNWRmSi4KCHRhc2tfa2V5EiIKIDFiMTVlZjIz
+      OTE1YjI3NTVlODlhMGVjM2IyNmExM2QySjEKB3Rhc2tfaWQSJgokMzlmMDlmMWUtOTJmOC00ZGJi
+      LTgzNDAtNjU2ZmVkMDk3ZjM0egIYAYUBAAEAABKkBwoQE53vZNAWshkoNK1bqTvovRII83djkBUL
+      EbcqDENyZXcgQ3JlYXRlZDABORBBzsDu8CIYQbAU2MDu8CIYShsKDmNyZXdhaV92ZXJzaW9uEgkK
+      BzAuMTAwLjBKGgoOcHl0aG9uX3ZlcnNpb24SCAoGMy4xMi44Si4KCGNyZXdfa2V5EiIKIGU1ODA3
+      MDFkNTJlYjY1YWZmMjRlZWZlNzhjNzQ2MjhjSjEKB2NyZXdfaWQSJgokNTQ0MWY0MWYtOTVjMC00
+      YzdkLTkxM2QtNDUxODcwY2YyZjYzShwKDGNyZXdfcHJvY2VzcxIMCgpzZXF1ZW50aWFsShEKC2Ny
+      ZXdfbWVtb3J5EgIQAEoaChRjcmV3X251bWJlcl9vZl90YXNrcxICGAFKGwoVY3Jld19udW1iZXJf
+      b2ZfYWdlbnRzEgIYAUrRAgoLY3Jld19hZ2VudHMSwQIKvgJbeyJrZXkiOiAiYWQxNTMxNjFjNWM1
+      YTg1NmFhMGQwNmIyNDljNGM2NGEiLCAiaWQiOiAiNTU2NzJiMDgtOTU4ZC00MjljLWE3ZTctY2Zl
+      N2U4Y2MwOGZkIiwgInJvbGUiOiAiYmFzZV9hZ2VudCIsICJ2ZXJib3NlPyI6IGZhbHNlLCAibWF4
+      X2l0ZXIiOiAyMCwgIm1heF9ycG0iOiBudWxsLCAiZnVuY3Rpb25fY2FsbGluZ19sbG0iOiAiIiwg
+      ImxsbSI6ICJncHQtNG8tbWluaSIsICJkZWxlZ2F0aW9uX2VuYWJsZWQ/IjogZmFsc2UsICJhbGxv
+      d19jb2RlX2V4ZWN1dGlvbj8iOiBmYWxzZSwgIm1heF9yZXRyeV9saW1pdCI6IDIsICJ0b29sc19u
+      YW1lcyI6IFtdfV1K/wEKCmNyZXdfdGFza3MS8AEK7QFbeyJrZXkiOiAiMWIxNWVmMjM5MTViMjc1
+      NWU4OWEwZWMzYjI2YTEzZDIiLCAiaWQiOiAiMzlmMDlmMWUtOTJmOC00ZGJiLTgzNDAtNjU2ZmVk
+      MDk3ZjM0IiwgImFzeW5jX2V4ZWN1dGlvbj8iOiBmYWxzZSwgImh1bWFuX2lucHV0PyI6IGZhbHNl
+      LCAiYWdlbnRfcm9sZSI6ICJiYXNlX2FnZW50IiwgImFnZW50X2tleSI6ICJhZDE1MzE2MWM1YzVh
+      ODU2YWEwZDA2YjI0OWM0YzY0YSIsICJ0b29sc19uYW1lcyI6IFtdfV16AhgBhQEAAQAAEo4CChBV
+      JNEz3VIdOlQM9VT3bctVEgisogN707a2AioMVGFzayBDcmVhdGVkMAE5kGbnwO7wIhhBaMDnwO7w
+      IhhKLgoIY3Jld19rZXkSIgogZTU4MDcwMWQ1MmViNjVhZmYyNGVlZmU3OGM3NDYyOGNKMQoHY3Jl
+      d19pZBImCiQ1NDQxZjQxZi05NWMwLTRjN2QtOTEzZC00NTE4NzBjZjJmNjNKLgoIdGFza19rZXkS
+      IgogMWIxNWVmMjM5MTViMjc1NWU4OWEwZWMzYjI2YTEzZDJKMQoHdGFza19pZBImCiQzOWYwOWYx
+      ZS05MmY4LTRkYmItODM0MC02NTZmZWQwOTdmMzR6AhgBhQEAAQAAErQHChDA7zaLCfy56rd5t3oS
+      rDPZEgjYoSW3mq6WJyoMQ3JldyBDcmVhdGVkMAE5cP/5we7wIhhBIH0Dwu7wIhhKGwoOY3Jld2Fp
+      X3ZlcnNpb24SCQoHMC4xMDAuMEoaCg5weXRob25fdmVyc2lvbhIICgYzLjEyLjhKLgoIY3Jld19r
+      ZXkSIgogZTU4MDcwMWQ1MmViNjVhZmYyNGVlZmU3OGM3NDYyOGNKMQoHY3Jld19pZBImCiRmNjcz
+      MTc1ZS04Y2Q1LTQ1ZWUtYTZiOS0xYWFjMTliODQxZWJKHAoMY3Jld19wcm9jZXNzEgwKCnNlcXVl
+      bnRpYWxKEQoLY3Jld19tZW1vcnkSAhAAShoKFGNyZXdfbnVtYmVyX29mX3Rhc2tzEgIYAUobChVj
+      cmV3X251bWJlcl9vZl9hZ2VudHMSAhgBStkCCgtjcmV3X2FnZW50cxLJAgrGAlt7ImtleSI6ICJh
+      ZDE1MzE2MWM1YzVhODU2YWEwZDA2YjI0OWM0YzY0YSIsICJpZCI6ICJmMGUwMGIzZi0wZWNmLTQ2
+      OGQtYjdjMC0yZmJhN2I5OTc5YjMiLCAicm9sZSI6ICJiYXNlX2FnZW50IiwgInZlcmJvc2U/Ijog
+      ZmFsc2UsICJtYXhfaXRlciI6IDIwLCAibWF4X3JwbSI6IG51bGwsICJmdW5jdGlvbl9jYWxsaW5n
+      X2xsbSI6ICIiLCAibGxtIjogImdwdC00by1taW5pIiwgImRlbGVnYXRpb25fZW5hYmxlZD8iOiBm
+      YWxzZSwgImFsbG93X2NvZGVfZXhlY3V0aW9uPyI6IGZhbHNlLCAibWF4X3JldHJ5X2xpbWl0Ijog
+      MiwgInRvb2xzX25hbWVzIjogWyJzYXlfaGkiXX1dSocCCgpjcmV3X3Rhc2tzEvgBCvUBW3sia2V5
+      IjogIjFiMTVlZjIzOTE1YjI3NTVlODlhMGVjM2IyNmExM2QyIiwgImlkIjogImFhMGFmMmE2LTdm
+      MTktNDZmNi1iMjMxLTg1M2JjYzYxYzhiZiIsICJhc3luY19leGVjdXRpb24/IjogZmFsc2UsICJo
+      dW1hbl9pbnB1dD8iOiBmYWxzZSwgImFnZW50X3JvbGUiOiAiYmFzZV9hZ2VudCIsICJhZ2VudF9r
+      ZXkiOiAiYWQxNTMxNjFjNWM1YTg1NmFhMGQwNmIyNDljNGM2NGEiLCAidG9vbHNfbmFtZXMiOiBb
+      InNheV9oaSJdfV16AhgBhQEAAQAAEo4CChBH8NUZY1Cv8sM2lfQLaEogEgiFlW7Wp7QpdyoMVGFz
+      ayBDcmVhdGVkMAE5MNkPwu7wIhhBUCcQwu7wIhhKLgoIY3Jld19rZXkSIgogZTU4MDcwMWQ1MmVi
+      NjVhZmYyNGVlZmU3OGM3NDYyOGNKMQoHY3Jld19pZBImCiRmNjczMTc1ZS04Y2Q1LTQ1ZWUtYTZi
+      OS0xYWFjMTliODQxZWJKLgoIdGFza19rZXkSIgogMWIxNWVmMjM5MTViMjc1NWU4OWEwZWMzYjI2
+      YTEzZDJKMQoHdGFza19pZBImCiRhYTBhZjJhNi03ZjE5LTQ2ZjYtYjIzMS04NTNiY2M2MWM4YmZ6
+      AhgBhQEAAQAAEooBChCJg/wSACw+HIDy4vvYISP/EgjoC/oI/1V0cCoKVG9vbCBVc2FnZTABOWA0
+      ifTu8CIYQTD0lPTu8CIYShsKDmNyZXdhaV92ZXJzaW9uEgkKBzAuMTAwLjBKFQoJdG9vbF9uYW1l
+      EggKBnNheV9oaUoOCghhdHRlbXB0cxICGAF6AhgBhQEAAQAA
+    headers:
+      Accept:
+      - '*/*'
+      Accept-Encoding:
+      - gzip, deflate
+      Connection:
+      - keep-alive
+      Content-Length:
+      - '6534'
+      Content-Type:
+      - application/x-protobuf
+      User-Agent:
+      - OTel-OTLP-Exporter-Python/1.27.0
+    method: POST
+    uri: https://telemetry.crewai.com:4319/v1/traces
+  response:
+    body:
+      string: "\n\0"
+    headers:
+      Content-Length:
+      - '2'
+      Content-Type:
+      - application/x-protobuf
+      Date:
+      - Mon, 10 Feb 2025 19:55:17 GMT
+    status:
+      code: 200
+      message: OK
+- request:
+    body: '{"messages": [{"role": "user", "content": "Assess the quality of the task
+      completed based on the description, expected output, and actual results.\n\nTask
+      Description:\nJust say hi\n\nExpected Output:\nhi\n\nActual Output:\nhi\n```\n\nPlease
+      provide:\n- Bullet points suggestions to improve future similar tasks\n- A score
+      from 0 to 10 evaluating on completion, quality, and overall performance- Entities
+      extracted from the task output, if any, their type, description, and relationships"}],
+      "model": "gpt-4o-mini", "tool_choice": {"type": "function", "function": {"name":
+      "TaskEvaluation"}}, "tools": [{"type": "function", "function": {"name": "TaskEvaluation",
+      "description": "Correctly extracted `TaskEvaluation` with all the required parameters
+      with correct types", "parameters": {"$defs": {"Entity": {"properties": {"name":
+      {"description": "The name of the entity.", "title": "Name", "type": "string"},
+      "type": {"description": "The type of the entity.", "title": "Type", "type":
+      "string"}, "description": {"description": "Description of the entity.", "title":
+      "Description", "type": "string"}, "relationships": {"description": "Relationships
+      of the entity.", "items": {"type": "string"}, "title": "Relationships", "type":
+      "array"}}, "required": ["name", "type", "description", "relationships"], "title":
+      "Entity", "type": "object"}}, "properties": {"suggestions": {"description":
+      "Suggestions to improve future similar tasks.", "items": {"type": "string"},
+      "title": "Suggestions", "type": "array"}, "quality": {"description": "A score
+      from 0 to 10 evaluating on completion, quality, and overall performance, all
+      taking into account the task description, expected output, and the result of
+      the task.", "title": "Quality", "type": "number"}, "entities": {"description":
+      "Entities extracted from the task output.", "items": {"$ref": "#/$defs/Entity"},
+      "title": "Entities", "type": "array"}}, "required": ["entities", "quality",
+      "suggestions"], "type": "object"}}}]}'
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate
+      connection:
+      - keep-alive
+      content-length:
+      - '1967'
+      content-type:
+      - application/json
+      cookie:
+      - _cfuvid=efIHP1NUsh1dFewGJBu4YoBu6hhGa8vjOOKQglYQGno-1739214901306-0.0.1.1-604800000;
+        __cf_bm=fmlg1wjOwuOwZhUUOEtL1tQYluAPumn7AHLF8s0EU2Y-1739217315-1.0.1.1-PQDvxn8TOhzaznlHjwVsqPZUzbAyJWFkvzCubfNJydTu2_AyA1cJ8hkM0khsEE4UY_xp8iPe2gSGmH1ydrDa0Q
+      host:
+      - api.openai.com
+      user-agent:
+      - OpenAI/Python 1.61.0
+      x-stainless-arch:
+      - arm64
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - MacOS
+      x-stainless-package-version:
+      - 1.61.0
+      x-stainless-raw-response:
+      - 'true'
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.12.8
+    method: POST
+    uri: https://api.openai.com/v1/chat/completions
+  response:
+    content: "{\n  \"id\": \"chatcmpl-AzUA8oE0A2d99i1Khpu0CI7fSgRtZ\",\n  \"object\":
+      \"chat.completion\",\n  \"created\": 1739217316,\n  \"model\": \"gpt-4o-mini-2024-07-18\",\n
+      \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
+      \"assistant\",\n        \"content\": null,\n        \"tool_calls\": [\n          {\n
+      \           \"id\": \"call_bk3duHRErK1qCyvWJ1uVmmGl\",\n            \"type\":
+      \"function\",\n            \"function\": {\n              \"name\": \"TaskEvaluation\",\n
+      \             \"arguments\": \"{\\\"suggestions\\\":[\\\"Provide more context
+      or details for similar tasks to enhance clarity.\\\",\\\"Specify desired tone
+      or style for the output.\\\",\\\"Consider adding more variety in tasks to keep
+      engagement high.\\\"],\\\"quality\\\":10,\\\"entities\\\":[{\\\"name\\\":\\\"hi\\\",\\\"type\\\":\\\"greeting\\\",\\\"description\\\":\\\"A
+      casual way to say hello or acknowledge someone's presence.\\\",\\\"relationships\\\":[\\\"used
+      as a greeting\\\",\\\"expresses friendliness\\\"]}]}\"\n            }\n          }\n
+      \       ],\n        \"refusal\": null\n      },\n      \"logprobs\": null,\n
+      \     \"finish_reason\": \"stop\"\n    }\n  ],\n  \"usage\": {\n    \"prompt_tokens\":
+      275,\n    \"completion_tokens\": 80,\n    \"total_tokens\": 355,\n    \"prompt_tokens_details\":
+      {\n      \"cached_tokens\": 0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\":
+      {\n      \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\":
+      0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"service_tier\":
+      \"default\",\n  \"system_fingerprint\": \"fp_72ed7ab54c\"\n}\n"
+    headers:
+      CF-RAY:
+      - 90fea7dfef41ceb9-SJC
+      Connection:
+      - keep-alive
+      Content-Encoding:
+      - gzip
+      Content-Type:
+      - application/json
+      Date:
+      - Mon, 10 Feb 2025 19:55:17 GMT
+      Server:
+      - cloudflare
+      Transfer-Encoding:
+      - chunked
+      X-Content-Type-Options:
+      - nosniff
+      access-control-expose-headers:
+      - X-Request-ID
+      alt-svc:
+      - h3=":443"; ma=86400
+      cf-cache-status:
+      - DYNAMIC
+      openai-organization:
+      - crewai-iuxna1
+      openai-processing-ms:
+      - '1535'
+      openai-version:
+      - '2020-10-01'
+      strict-transport-security:
+      - max-age=31536000; includeSubDomains; preload
+      x-ratelimit-limit-requests:
+      - '30000'
+      x-ratelimit-limit-tokens:
+      - '150000000'
+      x-ratelimit-remaining-requests:
+      - '29999'
+      x-ratelimit-remaining-tokens:
+      - '149999874'
+      x-ratelimit-reset-requests:
+      - 2ms
+      x-ratelimit-reset-tokens:
+      - 0s
+      x-request-id:
+      - req_55d8eb91b4318245556b73d3f4c1e7c4
+    http_version: HTTP/1.1
+    status_code: 200
+version: 1

tests/utilities/test_converter.py

@@ -1,4 +1,5 @@
 import json
+import os
 from typing import Dict, List, Optional
 from unittest.mock import MagicMock, Mock, patch
 
@@ -220,10 +221,13 @@ def test_get_conversion_instructions_gpt():
         supports_function_calling.return_value = True
         instructions = get_conversion_instructions(SimpleModel, llm)
         model_schema = PydanticSchemaParser(model=SimpleModel).get_schema()
-        assert (
-            instructions
-            == f"Please convert the following text into valid JSON.\n\nThe JSON should follow this schema:\n```json\n{model_schema}\n```"
+        expected_instructions = (
+            "Please convert the following text into valid JSON.\n\n"
+            "Output ONLY the valid JSON and nothing else.\n\n"
+            "The JSON must follow this schema exactly:\n```json\n"
+            f"{model_schema}\n```"
         )
+        assert instructions == expected_instructions
 
 
 def test_get_conversion_instructions_non_gpt():
@@ -346,12 +350,17 @@ def test_convert_with_instructions():
     assert output.age == 30
 
 
-@pytest.mark.vcr(filter_headers=["authorization"])
+# Skip tests that call external APIs when running in CI/CD
+skip_external_api = pytest.mark.skipif(
+    os.getenv("CI") is not None, reason="Skipping tests that call external API in CI/CD"
+)
+
+
+@skip_external_api
+@pytest.mark.vcr(filter_headers=["authorization"], record_mode="once")
 def test_converter_with_llama3_2_model():
     llm = LLM(model="ollama/llama3.2:3b", base_url="http://localhost:11434")
-
     sample_text = "Name: Alice Llama, Age: 30"
-
     instructions = get_conversion_instructions(SimpleModel, llm)
     converter = Converter(
         llm=llm,
@@ -359,19 +368,17 @@ def test_converter_with_llama3_2_model():
         model=SimpleModel,
         instructions=instructions,
     )
-
     output = converter.to_pydantic()
-
     assert isinstance(output, SimpleModel)
     assert output.name == "Alice Llama"
     assert output.age == 30
 
 
-@pytest.mark.vcr(filter_headers=["authorization"])
+@skip_external_api
+@pytest.mark.vcr(filter_headers=["authorization"], record_mode="once")
 def test_converter_with_llama3_1_model():
     llm = LLM(model="ollama/llama3.1", base_url="http://localhost:11434")
     sample_text = "Name: Alice Llama, Age: 30"
-
     instructions = get_conversion_instructions(SimpleModel, llm)
     converter = Converter(
         llm=llm,
@@ -379,14 +386,19 @@ def test_converter_with_llama3_1_model():
         model=SimpleModel,
         instructions=instructions,
     )
-
     output = converter.to_pydantic()
-
     assert isinstance(output, SimpleModel)
     assert output.name == "Alice Llama"
     assert output.age == 30
 
 
+# Skip tests that call external APIs when running in CI/CD
+skip_external_api = pytest.mark.skipif(
+    os.getenv("CI") is not None, reason="Skipping tests that call external API in CI/CD"
+)
+
+
+@skip_external_api
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_converter_with_nested_model():
     llm = LLM(model="gpt-4o-mini")
@@ -563,7 +575,7 @@ def test_converter_with_ambiguous_input():
     with pytest.raises(ConverterError) as exc_info:
         output = converter.to_pydantic()
 
-    assert "validation error" in str(exc_info.value).lower()
+    assert "failed to convert text into a pydantic model" in str(exc_info.value).lower()
 
 
 # Tests for function calling support

tests/utilities/test_events.py

@@ -0,0 +1,497 @@
+import json
+from datetime import datetime
+from unittest.mock import MagicMock, patch
+
+import pytest
+from pydantic import Field
+
+from crewai.agent import Agent
+from crewai.agents.crew_agent_executor import CrewAgentExecutor
+from crewai.crew import Crew
+from crewai.flow.flow import Flow, listen, start
+from crewai.task import Task
+from crewai.tools.base_tool import BaseTool
+from crewai.tools.tool_usage import ToolUsage
+from crewai.utilities.events.agent_events import (
+    AgentExecutionCompletedEvent,
+    AgentExecutionErrorEvent,
+    AgentExecutionStartedEvent,
+)
+from crewai.utilities.events.crew_events import (
+    CrewKickoffCompletedEvent,
+    CrewKickoffFailedEvent,
+    CrewKickoffStartedEvent,
+)
+from crewai.utilities.events.crewai_event_bus import crewai_event_bus
+from crewai.utilities.events.event_types import ToolUsageFinishedEvent
+from crewai.utilities.events.flow_events import (
+    FlowCreatedEvent,
+    FlowFinishedEvent,
+    FlowStartedEvent,
+    MethodExecutionFailedEvent,
+    MethodExecutionStartedEvent,
+)
+from crewai.utilities.events.task_events import (
+    TaskCompletedEvent,
+    TaskFailedEvent,
+    TaskStartedEvent,
+)
+from crewai.utilities.events.tool_usage_events import (
+    ToolUsageErrorEvent,
+)
+
+base_agent = Agent(
+    role="base_agent",
+    llm="gpt-4o-mini",
+    goal="Just say hi",
+    backstory="You are a helpful assistant that just says hi",
+)
+
+base_task = Task(
+    description="Just say hi",
+    expected_output="hi",
+    agent=base_agent,
+)
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_crew_emits_start_kickoff_event():
+    received_events = []
+
+    with crewai_event_bus.scoped_handlers():
+
+        @crewai_event_bus.on(CrewKickoffStartedEvent)
+        def handle_crew_start(source, event):
+            received_events.append(event)
+
+        crew = Crew(agents=[base_agent], tasks=[base_task], name="TestCrew")
+
+        crew.kickoff()
+
+        assert len(received_events) == 1
+        assert received_events[0].crew_name == "TestCrew"
+        assert isinstance(received_events[0].timestamp, datetime)
+        assert received_events[0].type == "crew_kickoff_started"
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_crew_emits_end_kickoff_event():
+    received_events = []
+
+    @crewai_event_bus.on(CrewKickoffCompletedEvent)
+    def handle_crew_end(source, event):
+        received_events.append(event)
+
+    crew = Crew(agents=[base_agent], tasks=[base_task], name="TestCrew")
+
+    crew.kickoff()
+
+    assert len(received_events) == 1
+    assert received_events[0].crew_name == "TestCrew"
+    assert isinstance(received_events[0].timestamp, datetime)
+    assert received_events[0].type == "crew_kickoff_completed"
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_crew_emits_kickoff_failed_event():
+    received_events = []
+
+    with crewai_event_bus.scoped_handlers():
+
+        @crewai_event_bus.on(CrewKickoffFailedEvent)
+        def handle_crew_failed(source, event):
+            received_events.append(event)
+
+        crew = Crew(agents=[base_agent], tasks=[base_task], name="TestCrew")
+
+        with patch.object(Crew, "_execute_tasks") as mock_execute:
+            error_message = "Simulated crew kickoff failure"
+            mock_execute.side_effect = Exception(error_message)
+
+            with pytest.raises(Exception):
+                crew.kickoff()
+
+        assert len(received_events) == 1
+        assert received_events[0].error == error_message
+        assert isinstance(received_events[0].timestamp, datetime)
+        assert received_events[0].type == "crew_kickoff_failed"
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_crew_emits_start_task_event():
+    received_events = []
+
+    @crewai_event_bus.on(TaskStartedEvent)
+    def handle_task_start(source, event):
+        received_events.append(event)
+
+    crew = Crew(agents=[base_agent], tasks=[base_task], name="TestCrew")
+
+    crew.kickoff()
+
+    assert len(received_events) == 1
+    assert isinstance(received_events[0].timestamp, datetime)
+    assert received_events[0].type == "task_started"
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_crew_emits_end_task_event():
+    received_events = []
+
+    @crewai_event_bus.on(TaskCompletedEvent)
+    def handle_task_end(source, event):
+        received_events.append(event)
+
+    crew = Crew(agents=[base_agent], tasks=[base_task], name="TestCrew")
+
+    crew.kickoff()
+
+    assert len(received_events) == 1
+    assert isinstance(received_events[0].timestamp, datetime)
+    assert received_events[0].type == "task_completed"
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_task_emits_failed_event_on_execution_error():
+    received_events = []
+    received_sources = []
+
+    @crewai_event_bus.on(TaskFailedEvent)
+    def handle_task_failed(source, event):
+        received_events.append(event)
+        received_sources.append(source)
+
+    with patch.object(
+        Task,
+        "_execute_core",
+    ) as mock_execute:
+        error_message = "Simulated task failure"
+        mock_execute.side_effect = Exception(error_message)
+        agent = Agent(
+            role="base_agent",
+            goal="Just say hi",
+            backstory="You are a helpful assistant that just says hi",
+        )
+        task = Task(
+            description="Just say hi",
+            expected_output="hi",
+            agent=agent,
+        )
+
+        with pytest.raises(Exception):
+            agent.execute_task(task=task)
+
+            assert len(received_events) == 1
+            assert received_sources[0] == task
+            assert received_events[0].error == error_message
+            assert isinstance(received_events[0].timestamp, datetime)
+            assert received_events[0].type == "task_failed"
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_agent_emits_execution_started_and_completed_events():
+    received_events = []
+
+    @crewai_event_bus.on(AgentExecutionStartedEvent)
+    def handle_agent_start(source, event):
+        received_events.append(event)
+
+    @crewai_event_bus.on(AgentExecutionCompletedEvent)
+    def handle_agent_completed(source, event):
+        received_events.append(event)
+
+    crew = Crew(agents=[base_agent], tasks=[base_task], name="TestCrew")
+    crew.kickoff()
+    assert len(received_events) == 2
+    assert received_events[0].agent == base_agent
+    assert received_events[0].task == base_task
+    assert received_events[0].tools == []
+    assert isinstance(received_events[0].task_prompt, str)
+    assert (
+        received_events[0].task_prompt
+        == "Just say hi\n\nThis is the expected criteria for your final answer: hi\nyou MUST return the actual complete content as the final answer, not a summary."
+    )
+    assert isinstance(received_events[0].timestamp, datetime)
+    assert received_events[0].type == "agent_execution_started"
+    assert isinstance(received_events[1].timestamp, datetime)
+    assert received_events[1].type == "agent_execution_completed"
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_agent_emits_execution_error_event():
+    received_events = []
+
+    @crewai_event_bus.on(AgentExecutionErrorEvent)
+    def handle_agent_start(source, event):
+        received_events.append(event)
+
+    error_message = "Error happening while sending prompt to model."
+    base_agent.max_retry_limit = 0
+    with patch.object(
+        CrewAgentExecutor, "invoke", wraps=base_agent.agent_executor.invoke
+    ) as invoke_mock:
+        invoke_mock.side_effect = Exception(error_message)
+
+        with pytest.raises(Exception) as e:
+            base_agent.execute_task(
+                task=base_task,
+            )
+
+        assert len(received_events) == 1
+        assert received_events[0].agent == base_agent
+        assert received_events[0].task == base_task
+        assert received_events[0].error == error_message
+        assert isinstance(received_events[0].timestamp, datetime)
+        assert received_events[0].type == "agent_execution_error"
+
+
+class SayHiTool(BaseTool):
+    name: str = Field(default="say_hi", description="The name of the tool")
+    description: str = Field(
+        default="Say hi", description="The description of the tool"
+    )
+
+    def _run(self) -> str:
+        return "hi"
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_tools_emits_finished_events():
+    received_events = []
+
+    @crewai_event_bus.on(ToolUsageFinishedEvent)
+    def handle_tool_end(source, event):
+        received_events.append(event)
+
+    agent = Agent(
+        role="base_agent",
+        goal="Just say hi",
+        backstory="You are a helpful assistant that just says hi",
+        tools=[SayHiTool()],
+    )
+
+    task = Task(
+        description="Just say hi",
+        expected_output="hi",
+        agent=agent,
+    )
+    crew = Crew(agents=[agent], tasks=[task], name="TestCrew")
+    crew.kickoff()
+    assert len(received_events) == 1
+    assert received_events[0].agent_key == agent.key
+    assert received_events[0].agent_role == agent.role
+    assert received_events[0].tool_name == SayHiTool().name
+    assert received_events[0].tool_args == {}
+    assert received_events[0].type == "tool_usage_finished"
+    assert isinstance(received_events[0].timestamp, datetime)
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_tools_emits_error_events():
+    received_events = []
+
+    @crewai_event_bus.on(ToolUsageErrorEvent)
+    def handle_tool_end(source, event):
+        received_events.append(event)
+
+    class ErrorTool(BaseTool):
+        name: str = Field(
+            default="error_tool", description="A tool that raises an error"
+        )
+        description: str = Field(
+            default="This tool always raises an error",
+            description="The description of the tool",
+        )
+
+        def _run(self) -> str:
+            raise Exception("Simulated tool error")
+
+    agent = Agent(
+        role="base_agent",
+        goal="Try to use the error tool",
+        backstory="You are an assistant that tests error handling",
+        tools=[ErrorTool()],
+    )
+
+    task = Task(
+        description="Use the error tool",
+        expected_output="This should error",
+        agent=agent,
+    )
+
+    crew = Crew(agents=[agent], tasks=[task], name="TestCrew")
+    crew.kickoff()
+
+    assert len(received_events) == 75
+    assert received_events[0].agent_key == agent.key
+    assert received_events[0].agent_role == agent.role
+    assert received_events[0].tool_name == "error_tool"
+    assert received_events[0].tool_args == {}
+    assert str(received_events[0].error) == "Simulated tool error"
+    assert received_events[0].type == "tool_usage_error"
+    assert isinstance(received_events[0].timestamp, datetime)
+
+
+def test_flow_emits_start_event():
+    received_events = []
+
+    with crewai_event_bus.scoped_handlers():
+
+        @crewai_event_bus.on(FlowStartedEvent)
+        def handle_flow_start(source, event):
+            received_events.append(event)
+
+        class TestFlow(Flow[dict]):
+            @start()
+            def begin(self):
+                return "started"
+
+        flow = TestFlow()
+        flow.kickoff()
+
+        assert len(received_events) == 1
+        assert received_events[0].flow_name == "TestFlow"
+        assert received_events[0].type == "flow_started"
+
+
+def test_flow_emits_finish_event():
+    received_events = []
+
+    with crewai_event_bus.scoped_handlers():
+
+        @crewai_event_bus.on(FlowFinishedEvent)
+        def handle_flow_finish(source, event):
+            received_events.append(event)
+
+        class TestFlow(Flow[dict]):
+            @start()
+            def begin(self):
+                return "completed"
+
+        flow = TestFlow()
+        result = flow.kickoff()
+
+        assert len(received_events) == 1
+        assert received_events[0].flow_name == "TestFlow"
+        assert received_events[0].type == "flow_finished"
+        assert received_events[0].result == "completed"
+        assert result == "completed"
+
+
+def test_flow_emits_method_execution_started_event():
+    received_events = []
+
+    with crewai_event_bus.scoped_handlers():
+
+        @crewai_event_bus.on(MethodExecutionStartedEvent)
+        def handle_method_start(source, event):
+            print("event in method name", event.method_name)
+            received_events.append(event)
+
+        class TestFlow(Flow[dict]):
+            @start()
+            def begin(self):
+                return "started"
+
+            @listen("begin")
+            def second_method(self):
+                return "executed"
+
+        flow = TestFlow()
+        flow.kickoff()
+
+        assert len(received_events) == 2
+
+        assert received_events[0].method_name == "begin"
+        assert received_events[0].flow_name == "TestFlow"
+        assert received_events[0].type == "method_execution_started"
+
+        assert received_events[1].method_name == "second_method"
+        assert received_events[1].flow_name == "TestFlow"
+        assert received_events[1].type == "method_execution_started"
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_register_handler_adds_new_handler():
+    received_events = []
+
+    def custom_handler(source, event):
+        received_events.append(event)
+
+    with crewai_event_bus.scoped_handlers():
+        crewai_event_bus.register_handler(CrewKickoffStartedEvent, custom_handler)
+
+        crew = Crew(agents=[base_agent], tasks=[base_task], name="TestCrew")
+        crew.kickoff()
+
+        assert len(received_events) == 1
+        assert isinstance(received_events[0].timestamp, datetime)
+        assert received_events[0].type == "crew_kickoff_started"
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_multiple_handlers_for_same_event():
+    received_events_1 = []
+    received_events_2 = []
+
+    def handler_1(source, event):
+        received_events_1.append(event)
+
+    def handler_2(source, event):
+        received_events_2.append(event)
+
+    with crewai_event_bus.scoped_handlers():
+        crewai_event_bus.register_handler(CrewKickoffStartedEvent, handler_1)
+        crewai_event_bus.register_handler(CrewKickoffStartedEvent, handler_2)
+
+        crew = Crew(agents=[base_agent], tasks=[base_task], name="TestCrew")
+        crew.kickoff()
+
+        assert len(received_events_1) == 1
+        assert len(received_events_2) == 1
+        assert received_events_1[0].type == "crew_kickoff_started"
+        assert received_events_2[0].type == "crew_kickoff_started"
+
+
+def test_flow_emits_created_event():
+    received_events = []
+
+    @crewai_event_bus.on(FlowCreatedEvent)
+    def handle_flow_created(source, event):
+        received_events.append(event)
+
+    class TestFlow(Flow[dict]):
+        @start()
+        def begin(self):
+            return "started"
+
+    flow = TestFlow()
+    flow.kickoff()
+
+    assert len(received_events) == 1
+    assert received_events[0].flow_name == "TestFlow"
+    assert received_events[0].type == "flow_created"
+
+
+def test_flow_emits_method_execution_failed_event():
+    received_events = []
+    error = Exception("Simulated method failure")
+
+    @crewai_event_bus.on(MethodExecutionFailedEvent)
+    def handle_method_failed(source, event):
+        received_events.append(event)
+
+    class TestFlow(Flow[dict]):
+        @start()
+        def begin(self):
+            raise error
+
+    flow = TestFlow()
+    with pytest.raises(Exception):
+        flow.kickoff()
+
+    assert len(received_events) == 1
+    assert received_events[0].method_name == "begin"
+    assert received_events[0].flow_name == "TestFlow"
+    assert received_events[0].type == "method_execution_failed"
+    assert received_events[0].error == error

uv.lock

@@ -198,15 +198,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/39/e3/893e8757be2612e6c266d9bb58ad2e3651524b5b40cf56761e985a28b13e/asgiref-3.8.1-py3-none-any.whl", hash = "sha256:3e1e3ecc849832fe52ccf2cb6686b7a55f82bb1d6aee72a58826471390335e47", size = 23828 },
 ]
 
-[[package]]
-name = "asn1crypto"
-version = "1.5.1"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/de/cf/d547feed25b5244fcb9392e288ff9fdc3280b10260362fc45d37a798a6ee/asn1crypto-1.5.1.tar.gz", hash = "sha256:13ae38502be632115abf8a24cbe5f4da52e3b5231990aff31123c805306ccb9c", size = 121080 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/c9/7f/09065fd9e27da0eda08b4d6897f1c13535066174cc023af248fc2a8d5e5a/asn1crypto-1.5.1-py2.py3-none-any.whl", hash = "sha256:db4e40728b728508912cbb3d44f19ce188f218e9eba635821bb4b68564f8fd67", size = 105045 },
-]
-
 [[package]]
 name = "asttokens"
 version = "2.4.1"
@@ -228,15 +219,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/a7/fa/e01228c2938de91d47b307831c62ab9e4001e747789d0b05baf779a6488c/async_timeout-4.0.3-py3-none-any.whl", hash = "sha256:7405140ff1230c310e51dc27b3145b9092d659ce68ff733fb0cefe3ee42be028", size = 5721 },
 ]
 
-[[package]]
-name = "atpublic"
-version = "5.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/5d/18/b1d247792440378abeeb0853f9daa2a127284b68776af6815990be7fcdb0/atpublic-5.0.tar.gz", hash = "sha256:d5cb6cbabf00ec1d34e282e8ce7cbc9b74ba4cb732e766c24e2d78d1ad7f723f", size = 14646 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/6b/03/2cb0e5326e19b7d877bc9c3a7ef436a30a06835b638580d1f5e21a0409ed/atpublic-5.0-py3-none-any.whl", hash = "sha256:b651dcd886666b1042d1e38158a22a4f2c267748f4e97fde94bc492a4a28a3f3", size = 5207 },
-]
-
 [[package]]
 name = "attrs"
 version = "24.2.0"
@@ -262,18 +244,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/e4/0e/38cb7b781371e79e9c697fb78f3ccd18fda8bd547d0a2e76e616561a3792/auth0_python-4.7.2-py3-none-any.whl", hash = "sha256:df2224f9b1e170b3aa12d8bc7ff02eadb7cc229307a09ec6b8a55fd1e0e05dc8", size = 131834 },
 ]
 
-[[package]]
-name = "authlib"
-version = "1.3.1"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "cryptography" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/09/47/df70ecd34fbf86d69833fe4e25bb9ecbaab995c8e49df726dd416f6bb822/authlib-1.3.1.tar.gz", hash = "sha256:7ae843f03c06c5c0debd63c9db91f9fda64fa62a42a77419fa15fbb7e7a58917", size = 146074 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/87/1f/bc95e43ffb57c05b8efcc376dd55a0240bf58f47ddf5a0f92452b6457b75/Authlib-1.3.1-py2.py3-none-any.whl", hash = "sha256:d35800b973099bbadc49b42b256ecb80041ad56b7fe1216a362c7943c088f377", size = 223827 },
-]
-
 [[package]]
 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'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/96/d3/f04c7bfcf5c1862a2a5b845c6b2b360488cf47af55dfa79c98f6a6bf98b5/click-8.1.7.tar.gz", hash = "sha256:ca9853ad459e787e2192211578cc907e7594e294c7ccc834310722b41b9ca6de", size = 336121 }
+sdist = { url = "https://files.pythonhosted.org/packages/b9/2e/0090cbf739cee7d23781ad4b89a9894a41538e4fcf4c31dcdd705b78eb8b/click-8.1.8.tar.gz", hash = "sha256:ed53c9d8990d83c2a27deae68e4ee337473f6330c040a31d4225c9574d16096a", size = 226593 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/00/2e/d53fa4befbf2cfa713304affc7ca780ce4fc1fd8710527771b58311a3229/click-8.1.7-py3-none-any.whl", hash = "sha256:ae74fb96c20a0277a1d615f1e4d73c8414f5a98db8b799a7931d1582f3390c28", size = 97941 },
+    { url = "https://files.pythonhosted.org/packages/7e/d4/7ebdbd03970677812aac39c869717059dbb71a4cfc033ca6e5221787892c/click-8.1.8-py3-none-any.whl", hash = "sha256:63c132bbbed01578a06712a2d1f497bb62d9c1c0d329b7903a866228027263b2", size = 98188 },
 ]
 
 [[package]]
@@ -649,7 +619,7 @@ wheels = [
 
 [[package]]
 name = "crewai"
-version = "0.100.1"
+version = "0.102.0"
 source = { editable = "." }
 dependencies = [
     { name = "appdirs" },
@@ -733,7 +703,7 @@ requires-dist = [
     { name = "blinker", specifier = ">=1.9.0" },
     { 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" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/e9/e7/fb07f0089028f7c9003770641d21f5844d4fa22bf5cc4c4b3676bfa0e1fe/crewai_tools-0.32.1.tar.gz", hash = "sha256:41acea9243b17a463f355d48dfe7d73bd59738c8862a8da780eae008e0136414", size = 887378 }
+sdist = { url = "https://files.pythonhosted.org/packages/4d/e1/d65778cf4aea106f3f60a4208521f04bc7f1d26be4e34eeb63cae6297d50/crewai_tools-0.36.0.tar.gz", hash = "sha256:761b396ee6a4019a988720dd6a14e1409f5de9d0cdc2a8662b487d87efb1a6bf", size = 900178 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/36/f0/8f98f1a2b90b9b989bd01cf48b5e3bb2d842be2062bfd3177a77561e7b61/crewai_tools-0.32.1-py3-none-any.whl", hash = "sha256:6cb436dc66e19e35285a4fce501158a13bce99b244370574f568ec33c5513351", size = 537264 },
+    { url = "https://files.pythonhosted.org/packages/bd/b6/533632a6c2a2e623fc4a1677458aff3539413a196fb220a7fece4ead3f71/crewai_tools-0.36.0-py3-none-any.whl", hash = "sha256:dbd0d95a080acfb281e105f4376e1e98576dae6d53d94f7b883c57af893668b3", size = 545937 },
 ]
 
 [[package]]
@@ -1099,12 +1060,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/d5/7c/e9fcff7623954d86bdc17782036cbf715ecab1bec4847c008557affe1ca8/docstring_parser-0.16-py3-none-any.whl", hash = "sha256:bf0a1387354d3691d102edef7ec124f219ef639982d096e26e3b60aeffa90637", size = 36533 },
 ]
 
-[[package]]
-name = "docx2txt"
-version = "0.8"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/7d/7d/60ee3f2b16d9bfdfa72e8599470a2c1a5b759cb113c6fe1006be28359327/docx2txt-0.8.tar.gz", hash = "sha256:2c06d98d7cfe2d3947e5760a57d924e3ff07745b379c8737723922e7009236e5", size = 2814 }
-
 [[package]]
 name = "durationpy"
 version = "0.9"
@@ -1646,19 +1601,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/1d/1f/acf03ee901313446d52c3916d527d4981de9f6f3edc69267d05509dcfa7b/grpcio-1.67.0-cp312-cp312-win_amd64.whl", hash = "sha256:985b2686f786f3e20326c4367eebdaed3e7aa65848260ff0c6644f817042cb15", size = 4343545 },
 ]
 
-[[package]]
-name = "grpcio-health-checking"
-version = "1.62.3"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "grpcio" },
-    { name = "protobuf" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/eb/9f/09df9b02fc8eafa3031d878c8a4674a0311293c8c6f1c942cdaeec204126/grpcio-health-checking-1.62.3.tar.gz", hash = "sha256:5074ba0ce8f0dcfe328408ec5c7551b2a835720ffd9b69dade7fa3e0dc1c7a93", size = 15640 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/40/4c/ee3173906196b741ac6ba55a9788ba9ebf2cd05f91715a49b6c3bfbb9d73/grpcio_health_checking-1.62.3-py3-none-any.whl", hash = "sha256:f29da7dd144d73b4465fe48f011a91453e9ff6c8af0d449254cf80021cab3e0d", size = 18547 },
-]
-
 [[package]]
 name = "grpcio-status"
 version = "1.62.3"
@@ -1870,52 +1812,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/76/c6/c88e154df9c4e1a2a66ccf0005a88dfb2650c1dffb6f5ce603dfbd452ce3/idna-3.10-py3-none-any.whl", hash = "sha256:946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3", size = 70442 },
 ]
 
-[[package]]
-name = "ijson"
-version = "3.3.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/6c/83/28e9e93a3a61913e334e3a2e78ea9924bb9f9b1ac45898977f9d9dd6133f/ijson-3.3.0.tar.gz", hash = "sha256:7f172e6ba1bee0d4c8f8ebd639577bfe429dee0f3f96775a067b8bae4492d8a0", size = 60079 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/ad/89/96e3608499b4a500b9bc27aa8242704e675849dd65bdfa8682b00a92477e/ijson-3.3.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:7f7a5250599c366369fbf3bc4e176f5daa28eb6bc7d6130d02462ed335361675", size = 85009 },
-    { url = "https://files.pythonhosted.org/packages/e4/7e/1098503500f5316c5f7912a51c91aca5cbc609c09ce4ecd9c4809983c560/ijson-3.3.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:f87a7e52f79059f9c58f6886c262061065eb6f7554a587be7ed3aa63e6b71b34", size = 57796 },
-    { url = "https://files.pythonhosted.org/packages/78/f7/27b8c27a285628719ff55b68507581c86b551eb162ce810fe51e3e1a25f2/ijson-3.3.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:b73b493af9e947caed75d329676b1b801d673b17481962823a3e55fe529c8b8b", size = 57218 },
-    { url = "https://files.pythonhosted.org/packages/0c/c5/1698094cb6a336a223c30e1167cc1b15cdb4bfa75399c1a2eb82fa76cc3c/ijson-3.3.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d5576415f3d76290b160aa093ff968f8bf6de7d681e16e463a0134106b506f49", size = 117153 },
-    { url = "https://files.pythonhosted.org/packages/4b/21/c206dda0945bd832cc9b0894596b0efc2cb1819a0ac61d8be1429ac09494/ijson-3.3.0-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:4e9ffe358d5fdd6b878a8a364e96e15ca7ca57b92a48f588378cef315a8b019e", size = 110781 },
-    { url = "https://files.pythonhosted.org/packages/f4/f5/2d733e64577109a9b255d14d031e44a801fa20df9ccc58b54a31e8ecf9e6/ijson-3.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8643c255a25824ddd0895c59f2319c019e13e949dc37162f876c41a283361527", size = 114527 },
-    { url = "https://files.pythonhosted.org/packages/8d/a8/78bfee312aa23417b86189a65f30b0edbceaee96dc6a616cc15f611187d1/ijson-3.3.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:df3ab5e078cab19f7eaeef1d5f063103e1ebf8c26d059767b26a6a0ad8b250a3", size = 116824 },
-    { url = "https://files.pythonhosted.org/packages/5d/a4/aff410f7d6aa1a77ee2ab2d6a2d2758422726270cb149c908a9baf33cf58/ijson-3.3.0-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:3dc1fb02c6ed0bae1b4bf96971258bf88aea72051b6e4cebae97cff7090c0607", size = 112647 },
-    { url = "https://files.pythonhosted.org/packages/77/ee/2b5122dc4713f5a954267147da36e7156240ca21b04ed5295bc0cabf0fbe/ijson-3.3.0-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:e9afd97339fc5a20f0542c971f90f3ca97e73d3050cdc488d540b63fae45329a", size = 114156 },
-    { url = "https://files.pythonhosted.org/packages/b3/d7/ad3b266490b60c6939e8a07fd8e4b7e2002aea08eaa9572a016c3e3a9129/ijson-3.3.0-cp310-cp310-win32.whl", hash = "sha256:844c0d1c04c40fd1b60f148dc829d3f69b2de789d0ba239c35136efe9a386529", size = 48931 },
-    { url = "https://files.pythonhosted.org/packages/0b/68/b9e1c743274c8a23dddb12d2ed13b5f021f6d21669d51ff7fa2e9e6c19df/ijson-3.3.0-cp310-cp310-win_amd64.whl", hash = "sha256:d654d045adafdcc6c100e8e911508a2eedbd2a1b5f93f930ba13ea67d7704ee9", size = 50965 },
-    { url = "https://files.pythonhosted.org/packages/fd/df/565ba72a6f4b2c833d051af8e2228cfa0b1fef17bb44995c00ad27470c52/ijson-3.3.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:501dce8eaa537e728aa35810656aa00460a2547dcb60937c8139f36ec344d7fc", size = 85041 },
-    { url = "https://files.pythonhosted.org/packages/f0/42/1361eaa57ece921d0239881bae6a5e102333be5b6e0102a05ec3caadbd5a/ijson-3.3.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:658ba9cad0374d37b38c9893f4864f284cdcc7d32041f9808fba8c7bcaadf134", size = 57829 },
-    { url = "https://files.pythonhosted.org/packages/f5/b0/143dbfe12e1d1303ea8d8cd6f40e95cea8f03bcad5b79708614a7856c22e/ijson-3.3.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:2636cb8c0f1023ef16173f4b9a233bcdb1df11c400c603d5f299fac143ca8d70", size = 57217 },
-    { url = "https://files.pythonhosted.org/packages/0d/80/b3b60c5e5be2839365b03b915718ca462c544fdc71e7a79b7262837995ef/ijson-3.3.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:cd174b90db68c3bcca273e9391934a25d76929d727dc75224bf244446b28b03b", size = 121878 },
-    { url = "https://files.pythonhosted.org/packages/8d/eb/7560fafa4d40412efddf690cb65a9bf2d3429d6035e544103acbf5561dc4/ijson-3.3.0-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:97a9aea46e2a8371c4cf5386d881de833ed782901ac9f67ebcb63bb3b7d115af", size = 115620 },
-    { url = "https://files.pythonhosted.org/packages/51/2b/5a34c7841388dce161966e5286931518de832067cd83e6f003d93271e324/ijson-3.3.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c594c0abe69d9d6099f4ece17763d53072f65ba60b372d8ba6de8695ce6ee39e", size = 119200 },
-    { url = "https://files.pythonhosted.org/packages/3e/b7/1d64fbec0d0a7b0c02e9ad988a89614532028ead8bb52a2456c92e6ee35a/ijson-3.3.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:8e0ff16c224d9bfe4e9e6bd0395826096cda4a3ef51e6c301e1b61007ee2bd24", size = 121107 },
-    { url = "https://files.pythonhosted.org/packages/d4/b9/01044f09850bc545ffc85b35aaec473d4f4ca2b6667299033d252c1b60dd/ijson-3.3.0-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:0015354011303175eae7e2ef5136414e91de2298e5a2e9580ed100b728c07e51", size = 116658 },
-    { url = "https://files.pythonhosted.org/packages/fb/0d/53856b61f3d952d299d1695c487e8e28058d01fa2adfba3d6d4b4660c242/ijson-3.3.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:034642558afa57351a0ffe6de89e63907c4cf6849070cc10a3b2542dccda1afe", size = 118186 },
-    { url = "https://files.pythonhosted.org/packages/95/2d/5bd86e2307dd594840ee51c4e32de953fee837f028acf0f6afb08914cd06/ijson-3.3.0-cp311-cp311-win32.whl", hash = "sha256:192e4b65495978b0bce0c78e859d14772e841724d3269fc1667dc6d2f53cc0ea", size = 48938 },
-    { url = "https://files.pythonhosted.org/packages/55/e1/4ba2b65b87f67fb19d698984d92635e46d9ce9dd748ce7d009441a586710/ijson-3.3.0-cp311-cp311-win_amd64.whl", hash = "sha256:72e3488453754bdb45c878e31ce557ea87e1eb0f8b4fc610373da35e8074ce42", size = 50972 },
-    { url = "https://files.pythonhosted.org/packages/8a/4d/3992f7383e26a950e02dc704bc6c5786a080d5c25fe0fc5543ef477c1883/ijson-3.3.0-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:988e959f2f3d59ebd9c2962ae71b97c0df58323910d0b368cc190ad07429d1bb", size = 84550 },
-    { url = "https://files.pythonhosted.org/packages/1b/cc/3d4372e0d0b02a821b982f1fdf10385512dae9b9443c1597719dd37769a9/ijson-3.3.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:b2f73f0d0fce5300f23a1383d19b44d103bb113b57a69c36fd95b7c03099b181", size = 57572 },
-    { url = "https://files.pythonhosted.org/packages/02/de/970d48b1ff9da5d9513c86fdd2acef5cb3415541c8069e0d92a151b84adb/ijson-3.3.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:0ee57a28c6bf523d7cb0513096e4eb4dac16cd935695049de7608ec110c2b751", size = 56902 },
-    { url = "https://files.pythonhosted.org/packages/5e/a0/4537722c8b3b05e82c23dfe09a3a64dd1e44a013a5ca58b1e77dfe48b2f1/ijson-3.3.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e0155a8f079c688c2ccaea05de1ad69877995c547ba3d3612c1c336edc12a3a5", size = 127400 },
-    { url = "https://files.pythonhosted.org/packages/b2/96/54956062a99cf49f7a7064b573dcd756da0563ce57910dc34e27a473d9b9/ijson-3.3.0-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7ab00721304af1ae1afa4313ecfa1bf16b07f55ef91e4a5b93aeaa3e2bd7917c", size = 118786 },
-    { url = "https://files.pythonhosted.org/packages/07/74/795319531c5b5504508f595e631d592957f24bed7ff51a15bc4c61e7b24c/ijson-3.3.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:40ee3821ee90be0f0e95dcf9862d786a7439bd1113e370736bfdf197e9765bfb", size = 126288 },
-    { url = "https://files.pythonhosted.org/packages/69/6a/e0cec06fbd98851d5d233b59058c1dc2ea767c9bb6feca41aa9164fff769/ijson-3.3.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:da3b6987a0bc3e6d0f721b42c7a0198ef897ae50579547b0345f7f02486898f5", size = 129569 },
-    { url = "https://files.pythonhosted.org/packages/2a/4f/82c0d896d8dcb175f99ced7d87705057bcd13523998b48a629b90139a0dc/ijson-3.3.0-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:63afea5f2d50d931feb20dcc50954e23cef4127606cc0ecf7a27128ed9f9a9e6", size = 121508 },
-    { url = "https://files.pythonhosted.org/packages/2b/b6/8973474eba4a917885e289d9e138267d3d1f052c2d93b8c968755661a42d/ijson-3.3.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:b5c3e285e0735fd8c5a26d177eca8b52512cdd8687ca86ec77a0c66e9c510182", size = 127896 },
-    { url = "https://files.pythonhosted.org/packages/94/25/00e66af887adbbe70002e0479c3c2340bdfa17a168e25d4ab5a27b53582d/ijson-3.3.0-cp312-cp312-win32.whl", hash = "sha256:907f3a8674e489abdcb0206723e5560a5cb1fa42470dcc637942d7b10f28b695", size = 49272 },
-    { url = "https://files.pythonhosted.org/packages/25/a2/e187beee237808b2c417109ae0f4f7ee7c81ecbe9706305d6ac2a509cc45/ijson-3.3.0-cp312-cp312-win_amd64.whl", hash = "sha256:8f890d04ad33262d0c77ead53c85f13abfb82f2c8f078dfbf24b78f59534dfdd", size = 51272 },
-    { url = "https://files.pythonhosted.org/packages/c3/28/2e1cf00abe5d97aef074e7835b86a94c9a06be4629a0e2c12600792b51ba/ijson-3.3.0-pp310-pypy310_pp73-macosx_10_9_x86_64.whl", hash = "sha256:2af323a8aec8a50fa9effa6d640691a30a9f8c4925bd5364a1ca97f1ac6b9b5c", size = 54308 },
-    { url = "https://files.pythonhosted.org/packages/04/d2/8c541c28da4f931bac8177e251efe2b6902f7c486d2d4bdd669eed4ff5c0/ijson-3.3.0-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f64f01795119880023ba3ce43072283a393f0b90f52b66cc0ea1a89aa64a9ccb", size = 66010 },
-    { url = "https://files.pythonhosted.org/packages/d0/02/8fec0b9037a368811dba7901035e8e0973ebda308f57f30c42101a16a5f7/ijson-3.3.0-pp310-pypy310_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a716e05547a39b788deaf22725490855337fc36613288aa8ae1601dc8c525553", size = 66770 },
-    { url = "https://files.pythonhosted.org/packages/47/23/90c61f978c83647112460047ea0137bde9c7fe26600ce255bb3e17ea7a21/ijson-3.3.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:473f5d921fadc135d1ad698e2697025045cd8ed7e5e842258295012d8a3bc702", size = 64159 },
-    { url = "https://files.pythonhosted.org/packages/20/af/aab1a36072590af62d848f03981f1c587ca40a391fc61e418e388d8b0d46/ijson-3.3.0-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:dd26b396bc3a1e85f4acebeadbf627fa6117b97f4c10b177d5779577c6607744", size = 51095 },
-]
-
 [[package]]
 name = "imageio"
 version = "2.36.1"
@@ -2359,19 +2255,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/83/60/d497a310bde3f01cb805196ac61b7ad6dc5dcf8dce66634dc34364b20b4f/lazy_loader-0.4-py3-none-any.whl", hash = "sha256:342aa8e14d543a154047afb4ba8ef17f5563baad3fc610d7b15b213b0f119efc", size = 12097 },
 ]
 
-[[package]]
-name = "linkup-sdk"
-version = "0.2.1"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "httpx" },
-    { name = "pydantic" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/2e/ba/b06e8f2ca2f0ce255a40ee4505637536acfe83ec997cd8b61bd5cd031513/linkup_sdk-0.2.1.tar.gz", hash = "sha256:b00ba7cb0117358e975d50196501ac49b247509fd236121e40abe40e6a2a3e9a", size = 8918 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/4f/90/2903b9e2eba501ceb6c6b4fc57bbeddde7e8964921a05d424f5a6125cbd0/linkup_sdk-0.2.1-py3-none-any.whl", hash = "sha256:bf50c88e659c6d9291cbd5e3e99b6a20a14c9b1eb2dc7acca763a3ae6f84b26e", size = 7961 },
-]
-
 [[package]]
 name = "litellm"
 version = "1.60.2"
@@ -3424,18 +3307,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/9f/8a/ce7c28e4ea337f6d95261345d7c61322f8561c52f57b263a3ad7025984f4/orjson-3.10.10-cp312-none-win_amd64.whl", hash = "sha256:384cd13579a1b4cd689d218e329f459eb9ddc504fa48c5a83ef4889db7fd7a4f", size = 139389 },
 ]
 
-[[package]]
-name = "outcome"
-version = "1.3.0.post0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "attrs" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/98/df/77698abfac98571e65ffeb0c1fba8ffd692ab8458d617a0eed7d9a8d38f2/outcome-1.3.0.post0.tar.gz", hash = "sha256:9dcf02e65f2971b80047b377468e72a268e15c0af3cf1238e6ff14f7f91143b8", size = 21060 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/55/8b/5ab7257531a5d830fc8000c476e63c935488d74609b50f9384a643ec0a62/outcome-1.3.0.post0-py2.py3-none-any.whl", hash = "sha256:e771c5ce06d1415e356078d3bdd68523f284b4ce5419828922b6871e65eda82b", size = 10692 },
-]
-
 [[package]]
 name = "overrides"
 version = "7.7.0"
@@ -3525,24 +3396,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/cc/20/ff623b09d963f88bfde16306a54e12ee5ea43e9b597108672ff3a408aad6/pathspec-0.12.1-py3-none-any.whl", hash = "sha256:a0d503e138a4c123b27490a4f7beda6a01c6f288df0e4a8b79c7eb0dc7b4cc08", size = 31191 },
 ]
 
-[[package]]
-name = "patronus"
-version = "0.0.17"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "httpx" },
-    { name = "pandas" },
-    { name = "pydantic" },
-    { name = "pydantic-settings" },
-    { name = "pyyaml" },
-    { name = "tqdm" },
-    { name = "typing-extensions" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/c5/a0/d5218ff6f2eab18c5a90266d21cdac673c85070e82e3f8aba538b3200f54/patronus-0.0.17.tar.gz", hash = "sha256:7298f770d4f6774b955806fb319c2c872fda3551bd7fa63d975bbeedc14b28de", size = 27377 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/0e/9e/717c4508d675549ff081a7fecf25af7d70f9d7ad87ea0d4825e02de3b801/patronus-0.0.17-py3-none-any.whl", hash = "sha256:1f322eeee838974515fdb7cbf8530ad25c6c59686abbcb28c1fdbf23d34eb10d", size = 31516 },
-]
-
 [[package]]
 name = "pdfminer-six"
 version = "20231228"
@@ -4103,18 +3956,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/c2/35/c0edf199257ef0a7d407d29cd51c4e70d1dad4370a5f44deb65a7a5475e2/pymdown_extensions-10.11.2-py3-none-any.whl", hash = "sha256:41cdde0a77290e480cf53892f5c5e50921a7ee3e5cd60ba91bf19837b33badcf", size = 259044 },
 ]
 
-[[package]]
-name = "pyopenssl"
-version = "24.3.0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "cryptography" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/c1/d4/1067b82c4fc674d6f6e9e8d26b3dff978da46d351ca3bac171544693e085/pyopenssl-24.3.0.tar.gz", hash = "sha256:49f7a019577d834746bc55c5fce6ecbcec0f2b4ec5ce1cf43a9a173b8138bb36", size = 178944 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/42/22/40f9162e943f86f0fc927ebc648078be87def360d9d8db346619fb97df2b/pyOpenSSL-24.3.0-py3-none-any.whl", hash = "sha256:e474f5a473cd7f92221cc04976e48f4d11502804657a08a989fb3be5514c904a", size = 56111 },
-]
-
 [[package]]
 name = "pypdf"
 version = "5.0.1"
@@ -4192,15 +4033,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/48/0a/c99fb7d7e176f8b176ef19704a32e6a9c6aafdf19ef75a187f701fc15801/pysbd-0.3.4-py3-none-any.whl", hash = "sha256:cd838939b7b0b185fcf86b0baf6636667dfb6e474743beeff878e9f42e022953", size = 71082 },
 ]
 
-[[package]]
-name = "pysocks"
-version = "1.7.1"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/bd/11/293dd436aea955d45fc4e8a35b6ae7270f5b8e00b53cf6c024c83b657a11/PySocks-1.7.1.tar.gz", hash = "sha256:3f8804571ebe159c380ac6de37643bb4685970655d3bba243530d6558b799aa0", size = 284429 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/8d/59/b4572118e098ac8e46e399a1dd0f2d85403ce8bbaad9ec79373ed6badaf9/PySocks-1.7.1-py3-none-any.whl", hash = "sha256:2725bd0a9925919b9b51739eea5f9e2bae91e83288108a9ad338b2e3a4435ee5", size = 16725 },
-]
-
 [[package]]
 name = "pytest"
 version = "8.3.3"
@@ -4860,39 +4692,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/aa/7d/43ab67228ef98c6b5dd42ab386eae2d7877036970a0d7e3dd3eb47a0d530/scipy-1.14.1-cp312-cp312-win_amd64.whl", hash = "sha256:2ff38e22128e6c03ff73b6bb0f85f897d2362f8c052e3b8ad00532198fbdae3f", size = 44521212 },
 ]
 
-[[package]]
-name = "scrapegraph-py"
-version = "1.8.0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "aiohttp" },
-    { name = "beautifulsoup4" },
-    { name = "pydantic" },
-    { name = "python-dotenv" },
-    { name = "requests" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/33/90/2388754061394a6c95fd5ad48cf4550208ce081c99cbc883672d52ccc360/scrapegraph_py-1.8.0.tar.gz", hash = "sha256:e075f6e6012a14a038537d0664609229069d9d2c2956bcbf9362f0c5c48de786", size = 108112 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/f7/80/14aeb7ba092cfc6928844a6726855f0c33489107f344e71dd8071f6433ed/scrapegraph_py-1.8.0-py3-none-any.whl", hash = "sha256:279176c972a770bac37a284e0bc25e34793797f30ff24dfba8fbcbfda79c8c88", size = 14460 },
-]
-
-[[package]]
-name = "selenium"
-version = "4.25.0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "certifi" },
-    { name = "trio" },
-    { name = "trio-websocket" },
-    { name = "typing-extensions" },
-    { name = "urllib3", extra = ["socks"] },
-    { name = "websocket-client" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/0e/5a/d3735b189b91715fd0f5a9b8d55e2605061309849470e96ab830f02cba40/selenium-4.25.0.tar.gz", hash = "sha256:95d08d3b82fb353f3c474895154516604c7f0e6a9a565ae6498ef36c9bac6921", size = 957765 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/aa/85/fa44f23dd5d5066a72f7c4304cce4b5ff9a6e7fd92431a48b2c63fbf63ec/selenium-4.25.0-py3-none-any.whl", hash = "sha256:3798d2d12b4a570bc5790163ba57fef10b2afee958bf1d80f2a3cf07c4141f33", size = 9693127 },
-]
-
 [[package]]
 name = "semchunk"
 version = "2.2.0"
@@ -4906,18 +4705,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/f8/85/3940bb4c586e10603d169d13ffccd59ed32fcb8d1b8104c3aef0e525b3b2/semchunk-2.2.0-py3-none-any.whl", hash = "sha256:7db19ca90ddb48f99265e789e07a7bb111ae25185f9cc3d44b94e1e61b9067fc", size = 10243 },
 ]
 
-[[package]]
-name = "serpapi"
-version = "0.1.5"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "requests" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/f0/fa/3fd8809287f3977a3e752bb88610e918d49cb1038b14f4bc51e13e594197/serpapi-0.1.5.tar.gz", hash = "sha256:b9707ed54750fdd2f62dc3a17c6a3fb7fa421dc37902fd65b2263c0ac765a1a5", size = 14191 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/df/6a/21deade04100d64844e494353a5d65e7971fbdfddf78eb1f248423593ad0/serpapi-0.1.5-py2.py3-none-any.whl", hash = "sha256:6467b6adec1231059f754ccaa952b229efeaa8b9cae6e71f879703ec9e5bb3d1", size = 10966 },
-]
-
 [[package]]
 name = "setuptools"
 version = "75.2.0"
@@ -4983,96 +4770,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/e9/44/75a9c9421471a6c4805dbf2356f7c181a29c1879239abab1ea2cc8f38b40/sniffio-1.3.1-py3-none-any.whl", hash = "sha256:2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2", size = 10235 },
 ]
 
-[[package]]
-name = "snowflake"
-version = "1.0.2"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "snowflake-core" },
-    { name = "snowflake-legacy" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/80/d1/830929fb7b54586f4ee601f409e80343e16f32b9b579246cd6fa9984bcff/snowflake-1.0.2.tar.gz", hash = "sha256:4009e59af24e444de4a9e9d340fff0979cca8a02a4feee4665da97eb9c76d958", size = 6033 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/b6/25/4cbba4da3f9b333d132680a66221d1a101309cce330fa8be38b674ceafd0/snowflake-1.0.2-py3-none-any.whl", hash = "sha256:6bb0fc70aa10234769202861ccb4b091f5e9fb1bbc61a1e708db93baa3f221f4", size = 5623 },
-]
-
-[[package]]
-name = "snowflake-connector-python"
-version = "3.12.4"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "asn1crypto" },
-    { name = "certifi" },
-    { name = "cffi" },
-    { name = "charset-normalizer" },
-    { name = "cryptography" },
-    { name = "filelock" },
-    { name = "idna" },
-    { name = "packaging" },
-    { name = "platformdirs" },
-    { name = "pyjwt" },
-    { name = "pyopenssl" },
-    { name = "pytz" },
-    { name = "requests" },
-    { name = "sortedcontainers" },
-    { name = "tomlkit" },
-    { name = "typing-extensions" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/6b/de/f43d9c827ccc1974696ffd3c0495e2d4e98b0414b2353b7de932621f23dd/snowflake_connector_python-3.12.4.tar.gz", hash = "sha256:289e0691dfbf8ec8b7a8f58bcbb95a819890fe5e5b278fdbfc885059a63a946f", size = 743445 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/53/6c/edc8909e424654a7a3c18cbf804d8a35c17a65a2131f866a87ed8e762bd0/snowflake_connector_python-3.12.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:6f141c159e3244bd660279f87f32e39351b2845fcb75f8138f31d2219f983b05", size = 958038 },
-    { url = "https://files.pythonhosted.org/packages/93/a3/34c5082dfb9b555c914f4233224b8bc1f2c4d5668bc71bb587680b8dcd73/snowflake_connector_python-3.12.4-cp310-cp310-macosx_11_0_x86_64.whl", hash = "sha256:091458ba777c24adff659c5c28f0f5bb0bcca8a9b6ecc5641ae25b7c20a8f43d", size = 970665 },
-    { url = "https://files.pythonhosted.org/packages/f8/87/9eceaaba58b2ec4f9094fc3a04d953bbabbfdcc05a6b14ef12610c1039f9/snowflake_connector_python-3.12.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:23049d341da681ec7131cead71cdf7b1761ae5bcc08bcbdb931dcef6c25e8a5f", size = 2496731 },
-    { url = "https://files.pythonhosted.org/packages/66/0a/e35e9e0a142f3779007b0246166a245305858b198ed0dd3a41a3d2405512/snowflake_connector_python-3.12.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cc88a09d77a8ce7e445094b2409b606ddb208b5fc9f7c7a379d0255a8d566e9d", size = 2520041 },
-    { url = "https://files.pythonhosted.org/packages/79/77/9a238c153600adff8fbd1136d9f4be1e42cb827cbe1865924bfe84653e85/snowflake_connector_python-3.12.4-cp310-cp310-win_amd64.whl", hash = "sha256:3c33fbba036805c1767ea48eb40ffc3fb79d61f2a4bb4e77b571ea6f6a998be8", size = 918272 },
-    { url = "https://files.pythonhosted.org/packages/0d/95/e8aac28d6913e4b59f96e6d361f31b9576b5f0abe4d2c4f7decf9f075932/snowflake_connector_python-3.12.4-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:2ec5cfaa1526084cf4d0e7849d5ace601245cb4ad9675ab3cd7d799b3abea481", size = 958125 },
-    { url = "https://files.pythonhosted.org/packages/67/b6/a847a94e03bdf39010048feacd57f250a91a655eed333d7d32b165f65201/snowflake_connector_python-3.12.4-cp311-cp311-macosx_11_0_x86_64.whl", hash = "sha256:ff225824b3a0fa5e822442de72172f97028f04ae183877f1305d538d8d6c5d11", size = 970770 },
-    { url = "https://files.pythonhosted.org/packages/0e/91/f97812ae9946944bcd9bfe1965af1cb9b1844919da879d90b90dfd3e5086/snowflake_connector_python-3.12.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a9beced2789dc75e8f1e749aa637e7ec9b03302b4ed4b793ae0f1ff32823370e", size = 2519875 },
-    { url = "https://files.pythonhosted.org/packages/37/52/500d72079bfb322ebdf3892180ecf3dc73c117b3a966ee8d4bb1378882b2/snowflake_connector_python-3.12.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5ea47450a04ff713f3adf28053e34103bd990291e62daee9721c76597af4b2b5", size = 2542320 },
-    { url = "https://files.pythonhosted.org/packages/59/92/74ead6bee8dd29fe372002ce59477221e04b9da96ad7aafe584afce02937/snowflake_connector_python-3.12.4-cp311-cp311-win_amd64.whl", hash = "sha256:748f9125854dca07ea471bb2bb3c5bb932a53f9b8a77ba348b50b738c77203ce", size = 918363 },
-    { url = "https://files.pythonhosted.org/packages/a5/a3/1cbe0b52b810f069bdc96c372b2d91ac51aeac32986c2832aa3fe0b0b0e5/snowflake_connector_python-3.12.4-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:4bcd0371b20d199f15e6a3c0b489bf18e27f2a88c84cf3194b2569ca039fa7d1", size = 957561 },
-    { url = "https://files.pythonhosted.org/packages/f4/05/8a5e16bd908a89f36d59686d356890c4bd6a976a487f86274181010f4b49/snowflake_connector_python-3.12.4-cp312-cp312-macosx_11_0_x86_64.whl", hash = "sha256:7900d82a450b206fa2ed6c42cd65d9b3b9fd4547eca1696937175fac2a03ba37", size = 969045 },
-    { url = "https://files.pythonhosted.org/packages/79/1b/8f5ab15d224d7bf76533c55cfd8ce73b185ce94d84241f0e900739ce3f37/snowflake_connector_python-3.12.4-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:300f0562aeea55e40ee03b45205dbef7b78f5ba2f1787a278c7b807e7d8db22c", size = 2533969 },
-    { url = "https://files.pythonhosted.org/packages/6e/d9/2e2fd72e0251691b5c54a219256c455141a2d3c104e411b82de598c62553/snowflake_connector_python-3.12.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a6762a00948f003be55d7dc5de9de690315d01951a94371ec3db069d9303daba", size = 2558052 },
-    { url = "https://files.pythonhosted.org/packages/e8/cb/e0ab230ad5adc9932e595bdbec693b2499d446666daf6cb9cae306a41dd2/snowflake_connector_python-3.12.4-cp312-cp312-win_amd64.whl", hash = "sha256:83ca896790a7463b6c8cd42e1a29b8ea197cc920839ae6ee96a467475eab4ec2", size = 916627 },
-]
-
-[[package]]
-name = "snowflake-core"
-version = "1.0.2"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "atpublic" },
-    { name = "pydantic" },
-    { name = "python-dateutil" },
-    { name = "pyyaml" },
-    { name = "requests" },
-    { name = "snowflake-connector-python" },
-    { name = "urllib3" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/1d/cf/6f91e5b2daaf3df9ae666a65f5ba3938f11a40784e4ada5218ecf154b29a/snowflake_core-1.0.2.tar.gz", hash = "sha256:8bf267ff1efcd17f157432c6e24f6d2eb6c2aeed66f43ab34b215aa76d8edf02", size = 1092618 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/75/3c/ec228b7325b32781081c72254dd0ef793943e853d82616e862e231909c6c/snowflake_core-1.0.2-py3-none-any.whl", hash = "sha256:55c37cf526a0d78dd3359ad96b9ecd7130bbbbc2f5a2fec77bb3da0dac2dc688", size = 1555690 },
-]
-
-[[package]]
-name = "snowflake-legacy"
-version = "1.0.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/94/41/a6211bd2109913eee1506d37865ab13cf9a8cc2faa41833da3d1ffec654b/snowflake_legacy-1.0.0.tar.gz", hash = "sha256:2044661c79ba01841ab279c5e74b994532244c9d103224eba16eb159c8ed6033", size = 4043 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/aa/8c/64f9b5ee0c3f376a733584c480b31addbf2baff7bb41f655e5e3f3719d3b/snowflake_legacy-1.0.0-py3-none-any.whl", hash = "sha256:25f9678f180d7d5f5b60d17f8112f0ee8a7a77b82c67fd599ed6e27bd502be5a", size = 3059 },
-]
-
-[[package]]
-name = "sortedcontainers"
-version = "2.4.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/e8/c4/ba2f8066cceb6f23394729afe52f3bf7adec04bf9ed2c820b39e19299111/sortedcontainers-2.4.0.tar.gz", hash = "sha256:25caa5a06cc30b6b83d11423433f65d1f9d76c4c6a0c90e3379eaa43b9bfdb88", size = 30594 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/32/46/9cb0e58b2deb7f82b84065f37f3bffeb12413f947f9388e4cac22c4621ce/sortedcontainers-2.4.0-py2.py3-none-any.whl", hash = "sha256:a163dcaede0f1c021485e957a39245190e74249897e2ae4b2aa38595db237ee0", size = 29575 },
-]
-
 [[package]]
 name = "soupsieve"
 version = "2.6"
@@ -5082,18 +4779,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/d1/c2/fe97d779f3ef3b15f05c94a2f1e3d21732574ed441687474db9d342a7315/soupsieve-2.6-py3-none-any.whl", hash = "sha256:e72c4ff06e4fb6e4b5a9f0f55fe6e81514581fca1515028625d0f299c602ccc9", size = 36186 },
 ]
 
-[[package]]
-name = "spider-client"
-version = "0.1.25"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "aiohttp" },
-    { name = "ijson" },
-    { name = "requests" },
-    { name = "tenacity" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/b8/f2/06d89322f0054ea72e8d5580199f580e29df23476cb3cfe83a70a2a58a1b/spider-client-0.1.25.tar.gz", hash = "sha256:92ca4ce1d9d715dd8db52684ea417653940d8f3bbc13383d78683bc4fbb899a2", size = 15412 }
-
 [[package]]
 name = "sqlalchemy"
 version = "2.0.36"
@@ -5325,15 +5010,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/c4/ac/ce90573ba446a9bbe65838ded066a805234d159b4446ae9f8ec5bbd36cbd/tomli_w-1.1.0-py3-none-any.whl", hash = "sha256:1403179c78193e3184bfaade390ddbd071cba48a32a2e62ba11aae47490c63f7", size = 6440 },
 ]
 
-[[package]]
-name = "tomlkit"
-version = "0.13.2"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/b1/09/a439bec5888f00a54b8b9f05fa94d7f901d6735ef4e55dcec9bc37b5d8fa/tomlkit-0.13.2.tar.gz", hash = "sha256:fff5fe59a87295b278abd31bec92c15d9bc4a06885ab12bcea52c71119392e79", size = 192885 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/f9/b6/a447b5e4ec71e13871be01ba81f5dfc9d0af7e473da256ff46bc0e24026f/tomlkit-0.13.2-py3-none-any.whl", hash = "sha256:7a974427f6e119197f670fbbbeae7bef749a6c14e793db934baefc1b5f03efde", size = 37955 },
-]
-
 [[package]]
 name = "torch"
 version = "2.4.1"
@@ -5439,38 +5115,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/51/51/b87caa939fedf307496e4dbf412f4b909af3d9ca8b189fc3b65c1faa456f/transformers-4.46.3-py3-none-any.whl", hash = "sha256:a12ef6f52841fd190a3e5602145b542d03507222f2c64ebb7ee92e8788093aef", size = 10034536 },
 ]
 
-[[package]]
-name = "trio"
-version = "0.27.0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "attrs" },
-    { name = "cffi", marker = "implementation_name != 'pypy' and os_name == 'nt'" },
-    { name = "exceptiongroup", marker = "python_full_version < '3.11'" },
-    { name = "idna" },
-    { name = "outcome" },
-    { name = "sniffio" },
-    { name = "sortedcontainers" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/17/d1/a83dee5be404da7afe5a71783a33b8907bacb935a6dc8c69ab785e4a3eed/trio-0.27.0.tar.gz", hash = "sha256:1dcc95ab1726b2da054afea8fd761af74bad79bd52381b84eae408e983c76831", size = 568064 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/3c/83/ec3196c360afffbc5b342ead48d1eb7393dd74fa70bca75d33905a86f211/trio-0.27.0-py3-none-any.whl", hash = "sha256:68eabbcf8f457d925df62da780eff15ff5dc68fd6b367e2dde59f7aaf2a0b884", size = 481734 },
-]
-
-[[package]]
-name = "trio-websocket"
-version = "0.11.1"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "exceptiongroup", marker = "python_full_version < '3.11'" },
-    { name = "trio" },
-    { name = "wsproto" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/dd/36/abad2385853077424a11b818d9fd8350d249d9e31d583cb9c11cd4c85eda/trio-websocket-0.11.1.tar.gz", hash = "sha256:18c11793647703c158b1f6e62de638acada927344d534e3c7628eedcb746839f", size = 26511 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/48/be/a9ae5f50cad5b6f85bd2574c2c923730098530096e170c1ce7452394d7aa/trio_websocket-0.11.1-py3-none-any.whl", hash = "sha256:520d046b0d030cf970b8b2b2e00c4c2245b3807853ecd44214acd33d74581638", size = 17408 },
-]
-
 [[package]]
 name = "triton"
 version = "3.0.0"
@@ -5551,11 +5195,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/ce/d9/5f4c13cecde62396b0d3fe530a50ccea91e7dfc1ccf0e09c228841bb5ba8/urllib3-2.2.3-py3-none-any.whl", hash = "sha256:ca899ca043dcb1bafa3e262d73aa25c465bfb49e0bd9dd5d59f1d0acba2f8fac", size = 126338 },
 ]
 
-[package.optional-dependencies]
-socks = [
-    { name = "pysocks" },
-]
-
 [[package]]
 name = "uv"
 version = "0.4.26"
@@ -5632,15 +5271,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/8f/eb/f7032be105877bcf924709c97b1bf3b90255b4ec251f9340cef912559f28/uvloop-0.21.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:183aef7c8730e54c9a3ee3227464daed66e37ba13040bb3f350bc2ddc040f22f", size = 4659022 },
 ]
 
-[[package]]
-name = "validators"
-version = "0.34.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/64/07/91582d69320f6f6daaf2d8072608a4ad8884683d4840e7e4f3a9dbdcc639/validators-0.34.0.tar.gz", hash = "sha256:647fe407b45af9a74d245b943b18e6a816acf4926974278f6dd617778e1e781f", size = 70955 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/6e/78/36828a4d857b25896f9774c875714ba4e9b3bc8a92d2debe3f4df3a83d4f/validators-0.34.0-py3-none-any.whl", hash = "sha256:c804b476e3e6d3786fa07a30073a4ef694e617805eb1946ceee3fe5a9b8b1321", size = 43536 },
-]
-
 [[package]]
 name = "vcrpy"
 version = "5.1.0"
@@ -5760,25 +5390,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/fd/84/fd2ba7aafacbad3c4201d395674fc6348826569da3c0937e75505ead3528/wcwidth-0.2.13-py2.py3-none-any.whl", hash = "sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859", size = 34166 },
 ]
 
-[[package]]
-name = "weaviate-client"
-version = "4.9.6"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "authlib" },
-    { name = "grpcio" },
-    { name = "grpcio-health-checking" },
-    { name = "grpcio-tools" },
-    { name = "httpx" },
-    { name = "pydantic" },
-    { name = "requests" },
-    { name = "validators" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/5d/7d/3894d12065d006743271b0b6bcc3bf911910473e91179d5966966816d694/weaviate_client-4.9.6.tar.gz", hash = "sha256:56d67c40fc94b0d53e81e0aa4477baaebbf3646fbec26551df66e396a72adcb6", size = 696813 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/2f/40/e3550e743b92ddd8dc69ebfd69cceb6de45b7d9a1cd439995454b499e9a3/weaviate_client-4.9.6-py3-none-any.whl", hash = "sha256:1d3b551939c0f7314f25e417cbcf4cf34e7adf942627993eef36ae6b4a044673", size = 386998 },
-]
-
 [[package]]
 name = "webencodings"
 version = "0.5.1"
@@ -5893,18 +5504,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/ff/21/abdedb4cdf6ff41ebf01a74087740a709e2edb146490e4d9beea054b0b7a/wrapt-1.16.0-py3-none-any.whl", hash = "sha256:6906c4100a8fcbf2fa735f6059214bb13b97f75b1a61777fcf6432121ef12ef1", size = 23362 },
 ]
 
-[[package]]
-name = "wsproto"
-version = "1.2.0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "h11" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/c9/4a/44d3c295350d776427904d73c189e10aeae66d7f555bb2feee16d1e4ba5a/wsproto-1.2.0.tar.gz", hash = "sha256:ad565f26ecb92588a3e43bc3d96164de84cd9902482b130d0ddbaa9664a85065", size = 53425 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/78/58/e860788190eba3bcce367f74d29c4675466ce8dddfba85f7827588416f01/wsproto-1.2.0-py3-none-any.whl", hash = "sha256:b9acddd652b585d75b20477888c56642fdade28bdfd3579aa24a4d2c037dd736", size = 24226 },
-]
-
 [[package]]
 name = "xlsxwriter"
 version = "3.2.0"