Merge branch 'bugfix/litellm-plus-generic-excpetions' into bugfix/drop-user-warnings

* Fix litellm issues to be more broad

* Fix tests

docs/concepts/cli.mdx

@@ -12,7 +12,7 @@ The CrewAI CLI provides a set of commands to interact with CrewAI, allowing you
 
 To use the CrewAI CLI, make sure you have CrewAI installed:
 
-```shell
+```shell Terminal
 pip install crewai
 ```
 
@@ -20,7 +20,7 @@ pip install crewai
 
 The basic structure of a CrewAI CLI command is:
 
-```shell
+```shell Terminal
 crewai [COMMAND] [OPTIONS] [ARGUMENTS]
 ```
 
@@ -30,7 +30,7 @@ crewai [COMMAND] [OPTIONS] [ARGUMENTS]
 
 Create a new crew or flow.
 
-```shell
+```shell Terminal
 crewai create [OPTIONS] TYPE NAME
 ```
 
@@ -38,7 +38,7 @@ crewai create [OPTIONS] TYPE NAME
 - `NAME`: Name of the crew or flow
 
 Example:
-```shell
+```shell Terminal
 crewai create crew my_new_crew
 crewai create flow my_new_flow
 ```
@@ -47,14 +47,14 @@ crewai create flow my_new_flow
 
 Show the installed version of CrewAI.
 
-```shell
+```shell Terminal
 crewai version [OPTIONS]
 ```
 
 - `--tools`: (Optional) Show the installed version of CrewAI tools
 
 Example:
-```shell
+```shell Terminal
 crewai version
 crewai version --tools
 ```
@@ -63,7 +63,7 @@ crewai version --tools
 
 Train the crew for a specified number of iterations.
 
-```shell
+```shell Terminal
 crewai train [OPTIONS]
 ```
 
@@ -71,7 +71,7 @@ crewai train [OPTIONS]
 - `-f, --filename TEXT`: Path to a custom file for training (default: "trained_agents_data.pkl")
 
 Example:
-```shell
+```shell Terminal
 crewai train -n 10 -f my_training_data.pkl
 ```
 
@@ -79,14 +79,14 @@ crewai train -n 10 -f my_training_data.pkl
 
 Replay the crew execution from a specific task.
 
-```shell
+```shell Terminal
 crewai replay [OPTIONS]
 ```
 
 - `-t, --task_id TEXT`: Replay the crew from this task ID, including all subsequent tasks
 
 Example:
-```shell    
+```shell Terminal    
 crewai replay -t task_123456
 ```
 
@@ -94,7 +94,7 @@ crewai replay -t task_123456
 
 Retrieve your latest crew.kickoff() task outputs.
 
-```shell
+```shell Terminal
 crewai log-tasks-outputs
 ```
 
@@ -102,7 +102,7 @@ crewai log-tasks-outputs
 
 Reset the crew memories (long, short, entity, latest_crew_kickoff_outputs).
 
-```shell
+```shell Terminal
 crewai reset-memories [OPTIONS]
 ```
 
@@ -113,7 +113,7 @@ crewai reset-memories [OPTIONS]
 - `-a, --all`: Reset ALL memories
 
 Example:
-```shell
+```shell Terminal
 crewai reset-memories --long --short
 crewai reset-memories --all
 ```
@@ -122,7 +122,7 @@ crewai reset-memories --all
 
 Test the crew and evaluate the results.
 
-```shell
+```shell Terminal
 crewai test [OPTIONS]
 ```
 
@@ -130,7 +130,7 @@ crewai test [OPTIONS]
 - `-m, --model TEXT`: LLM Model to run the tests on the Crew (default: "gpt-4o-mini")
 
 Example:
-```shell
+```shell Terminal    
 crewai test -n 5 -m gpt-3.5-turbo
 ```
 
@@ -138,7 +138,7 @@ crewai test -n 5 -m gpt-3.5-turbo
 
 Run the crew.
 
-```shell
+```shell Terminal
 crewai run
 ```
 <Note>
@@ -147,7 +147,36 @@ Some commands may require additional configuration or setup within your project
 </Note>
 
 
-### 9. API Keys
+### 9. Chat
+
+Starting in version `0.98.0`, when you run the `crewai chat` command, you start an interactive session with your crew. The AI assistant will guide you by asking for necessary inputs to execute the crew. Once all inputs are provided, the crew will execute its tasks.
+
+After receiving the results, you can continue interacting with the assistant for further instructions or questions.
+
+```shell Terminal
+crewai chat
+```
+<Note>
+Ensure you execute these commands from your CrewAI project's root directory.
+</Note>
+<Note>
+IMPORTANT: Set the `chat_llm` property in your `crew.py` file to enable this command.
+
+```python
+@crew
+def crew(self) -> Crew:
+    return Crew(
+        agents=self.agents,
+        tasks=self.tasks,
+        process=Process.sequential,
+        verbose=True,
+        chat_llm="gpt-4o",  # LLM for chat orchestration
+    )
+```
+</Note>
+
+
+### 10. API Keys
 
 When running ```crewai create crew``` command, the CLI will first show you the top 5 most common LLM providers and ask you to select one.
 

docs/concepts/flows.mdx

@@ -323,6 +323,91 @@ flow.kickoff()
 
 By providing both unstructured and structured state management options, CrewAI Flows empowers developers to build AI workflows that are both flexible and robust, catering to a wide range of application requirements.
 
+## Flow Persistence
+
+The @persist decorator enables automatic state persistence in CrewAI Flows, allowing you to maintain flow state across restarts or different workflow executions. This decorator can be applied at either the class level or method level, providing flexibility in how you manage state persistence.
+
+### Class-Level Persistence
+
+When applied at the class level, the @persist decorator automatically persists all flow method states:
+
+```python
+@persist  # Using SQLiteFlowPersistence by default
+class MyFlow(Flow[MyState]):
+    @start()
+    def initialize_flow(self):
+        # This method will automatically have its state persisted
+        self.state.counter = 1
+        print("Initialized flow. State ID:", self.state.id)
+
+    @listen(initialize_flow)
+    def next_step(self):
+        # The state (including self.state.id) is automatically reloaded
+        self.state.counter += 1
+        print("Flow state is persisted. Counter:", self.state.counter)
+```
+
+### Method-Level Persistence
+
+For more granular control, you can apply @persist to specific methods:
+
+```python
+class AnotherFlow(Flow[dict]):
+    @persist  # Persists only this method's state
+    @start()
+    def begin(self):
+        if "runs" not in self.state:
+            self.state["runs"] = 0
+        self.state["runs"] += 1
+        print("Method-level persisted runs:", self.state["runs"])
+```
+
+### How It Works
+
+1. **Unique State Identification**
+   - Each flow state automatically receives a unique UUID
+   - The ID is preserved across state updates and method calls
+   - Supports both structured (Pydantic BaseModel) and unstructured (dictionary) states
+
+2. **Default SQLite Backend**
+   - SQLiteFlowPersistence is the default storage backend
+   - States are automatically saved to a local SQLite database
+   - Robust error handling ensures clear messages if database operations fail
+
+3. **Error Handling**
+   - Comprehensive error messages for database operations
+   - Automatic state validation during save and load
+   - Clear feedback when persistence operations encounter issues
+
+### Important Considerations
+
+- **State Types**: Both structured (Pydantic BaseModel) and unstructured (dictionary) states are supported
+- **Automatic ID**: The `id` field is automatically added if not present
+- **State Recovery**: Failed or restarted flows can automatically reload their previous state
+- **Custom Implementation**: You can provide your own FlowPersistence implementation for specialized storage needs
+
+### Technical Advantages
+
+1. **Precise Control Through Low-Level Access**
+   - Direct access to persistence operations for advanced use cases
+   - Fine-grained control via method-level persistence decorators
+   - Built-in state inspection and debugging capabilities
+   - Full visibility into state changes and persistence operations
+
+2. **Enhanced Reliability**
+   - Automatic state recovery after system failures or restarts
+   - Transaction-based state updates for data integrity
+   - Comprehensive error handling with clear error messages
+   - Robust validation during state save and load operations
+
+3. **Extensible Architecture**
+   - Customizable persistence backend through FlowPersistence interface
+   - Support for specialized storage solutions beyond SQLite
+   - Compatible with both structured (Pydantic) and unstructured (dict) states
+   - Seamless integration with existing CrewAI flow patterns
+
+The persistence system's architecture emphasizes technical precision and customization options, allowing developers to maintain full control over state management while benefiting from built-in reliability features.
+
 ## Flow Control
 
 ### Conditional Logic: `or`

docs/concepts/knowledge.mdx

@@ -93,6 +93,12 @@ 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. 
 
+<Note>
+  You need to install `docling` for the following example to work: `uv add docling`
+</Note>
+
+
+
 ```python Code
 from crewai import LLM, Agent, Crew, Process, Task
 from crewai.knowledge.source.crew_docling_source import CrewDoclingSource
@@ -282,6 +288,7 @@ The `embedder` parameter supports various embedding model providers that include
 - `ollama`: Local embeddings with Ollama
 - `vertexai`: Google Cloud VertexAI embeddings
 - `cohere`: Cohere's embedding models
+- `voyageai`: VoyageAI's embedding models
 - `bedrock`: AWS Bedrock embeddings
 - `huggingface`: Hugging Face models
 - `watson`: IBM Watson embeddings

docs/concepts/llms.mdx

@@ -243,6 +243,9 @@ There are three ways to configure LLMs in CrewAI. Choose the method that best fi
         # llm: bedrock/amazon.titan-text-express-v1
         # llm: bedrock/meta.llama2-70b-chat-v1
 
+        # Amazon SageMaker Models - Enterprise-grade
+        # llm: sagemaker/<my-endpoint>
+
         # Mistral Models - Open source alternative
         # llm: mistral/mistral-large-latest
         # llm: mistral/mistral-medium-latest
@@ -506,6 +509,21 @@ Learn how to get the most out of your LLM configuration:
     )
     ```
   </Accordion>
+  
+  <Accordion title="Amazon SageMaker">
+    ```python Code
+    AWS_ACCESS_KEY_ID=<your-access-key>
+    AWS_SECRET_ACCESS_KEY=<your-secret-key>
+    AWS_DEFAULT_REGION=<your-region>
+    ```
+
+    Example usage:
+    ```python Code
+    llm = LLM(
+        model="sagemaker/<my-endpoint>"
+    )
+    ```
+  </Accordion>
 
   <Accordion title="Mistral">
     ```python Code

docs/concepts/memory.mdx

@@ -293,6 +293,26 @@ my_crew = Crew(
     }
 )
 ```
+### Using VoyageAI embeddings
+
+```python Code
+from crewai import Crew, Agent, Task, Process
+
+my_crew = Crew(
+    agents=[...],
+    tasks=[...],
+    process=Process.sequential,
+    memory=True,
+    verbose=True,
+    embedder={
+        "provider": "voyageai",
+        "config": {
+            "api_key": "YOUR_API_KEY",
+            "model_name": "<model_name>"
+        }
+    }
+)
+```
 ### Using HuggingFace embeddings
 
 ```python Code

docs/how-to/llm-connections.mdx

@@ -23,6 +23,7 @@ LiteLLM supports a wide range of providers, including but not limited to:
 - Azure OpenAI
 - AWS (Bedrock, SageMaker)
 - Cohere
+- VoyageAI
 - Hugging Face
 - Ollama
 - Mistral AI

docs/installation.mdx

@@ -15,10 +15,48 @@ icon: wrench
   If you need to update Python, visit [python.org/downloads](https://python.org/downloads)
 </Note>
 
+# Setting Up Your Environment
+
+Before installing CrewAI, it's recommended to set up a virtual environment. This helps isolate your project dependencies and avoid conflicts.
+
+<Steps>
+    <Step title="Create a Virtual Environment">
+        Choose your preferred method to create a virtual environment:
+
+        **Using venv (Python's built-in tool):**
+        ```shell Terminal
+        python3 -m venv .venv
+        ```
+
+        **Using conda:**
+        ```shell Terminal
+        conda create -n crewai-env python=3.12
+        ```
+    </Step>
+
+    <Step title="Activate the Virtual Environment">
+        Activate your virtual environment based on your platform:
+
+        **On macOS/Linux (venv):**
+        ```shell Terminal
+        source .venv/bin/activate
+        ```
+
+        **On Windows (venv):**
+        ```shell Terminal
+        .venv\Scripts\activate
+        ```
+
+        **Using conda (all platforms):**
+        ```shell Terminal
+        conda activate crewai-env
+        ```
+    </Step>
+</Steps>
+
 # Installing CrewAI
 
-CrewAI is a flexible and powerful AI framework that enables you to create and manage AI agents, tools, and tasks efficiently. 
-Let's get you set up! 🚀
+Now let's get you set up! 🚀
 
 <Steps>
     <Step title="Install CrewAI">
@@ -72,9 +110,9 @@ Let's get you set up! 🚀
 
 # Creating a New Project
 
-<Info>
+<Tip>
   We recommend using the YAML Template scaffolding for a structured approach to defining agents and tasks.
-</Info>
+</Tip>
 
 <Steps>
   <Step title="Generate Project Structure">
@@ -104,7 +142,18 @@ Let's get you set up! 🚀
                 └── tasks.yaml
     ```
     </Frame>
-  </Step>   
+  </Step>
+
+  <Step title="Install Additional Tools">
+    You can install additional tools using UV:
+    ```shell Terminal
+    uv add <tool-name>
+    ```
+
+    <Tip>
+      UV is our preferred package manager as it's significantly faster than pip and provides better dependency resolution.
+    </Tip>
+  </Step>
 
   <Step title="Customize Your Project">
     Your project will contain these essential files:

docs/quickstart.mdx

@@ -278,7 +278,7 @@ email_summarizer:
       Summarize emails into a concise and clear summary
     backstory: >
       You will create a 5 bullet point summary of the report
-    llm: mixtal_llm
+    llm: openai/gpt-4o
 ```
 
 <Tip>

docs/tools/composiotool.mdx

@@ -1,78 +1,118 @@
 ---
 title: Composio Tool
-description: The `ComposioTool` is a wrapper around the composio set of tools and gives your agent access to a wide variety of tools from the Composio SDK.
+description: Composio provides 250+ production-ready tools for AI agents with flexible authentication management.
 icon: gear-code
 ---
 
-# `ComposioTool`
+# `ComposioToolSet`
 
 ## Description
+Composio is an integration platform that allows you to connect your AI agents to 250+ tools. Key features include:
 
-This tools is a wrapper around the composio set of tools and gives your agent access to a wide variety of tools from the Composio SDK.
+- **Enterprise-Grade Authentication**: Built-in support for OAuth, API Keys, JWT with automatic token refresh
+- **Full Observability**: Detailed tool usage logs, execution timestamps, and more
 
 ## Installation
 
-To incorporate this tool into your project, follow the installation instructions below:
+To incorporate Composio tools into your project, follow the instructions below:
 
 ```shell
-pip install composio-core
-pip install 'crewai[tools]'
+pip install composio-crewai
+pip install crewai
 ```
 
-after the installation is complete, either run `composio login` or export your composio API key as `COMPOSIO_API_KEY`.
+After the installation is complete, either run `composio login` or export your composio API key as `COMPOSIO_API_KEY`. Get your Composio API key from [here](https://app.composio.dev)
 
 ## Example
 
 The following example demonstrates how to initialize the tool and execute a github action:
 
-1. Initialize Composio tools
+1. Initialize Composio toolset
 
 ```python Code
-from composio import App
-from crewai_tools import ComposioTool
-from crewai import Agent, Task
+from composio_crewai import ComposioToolSet, App, Action
+from crewai import Agent, Task, Crew
 
-
-tools = [ComposioTool.from_action(action=Action.GITHUB_ACTIVITY_STAR_REPO_FOR_AUTHENTICATED_USER)]
+toolset = ComposioToolSet()
 ```
 
-If you don't know what action you want to use, use `from_app` and `tags` filter to get relevant actions
-
+2. Connect your GitHub account
+<CodeGroup>
+```shell CLI
+composio add github
+```
 ```python Code
-tools = ComposioTool.from_app(App.GITHUB, tags=["important"])
+request = toolset.initiate_connection(app=App.GITHUB)
+print(f"Open this URL to authenticate: {request.redirectUrl}")
 ```
+</CodeGroup>
 
-or use `use_case` to search relevant actions
+3. Get Tools
 
+- Retrieving all the tools from an app (not recommended for production):
 ```python Code
-tools = ComposioTool.from_app(App.GITHUB, use_case="Star a github repository")
+tools = toolset.get_tools(apps=[App.GITHUB])
 ```
 
-2. Define agent
+- Filtering tools based on tags:
+```python Code
+tag = "users"
+
+filtered_action_enums = toolset.find_actions_by_tags(
+    App.GITHUB,
+    tags=[tag], 
+)
+
+tools = toolset.get_tools(actions=filtered_action_enums)
+```
+
+- Filtering tools based on use case:
+```python Code
+use_case = "Star a repository on GitHub"
+
+filtered_action_enums = toolset.find_actions_by_use_case(
+    App.GITHUB, use_case=use_case, advanced=False
+)
+
+tools = toolset.get_tools(actions=filtered_action_enums)
+```
+<Tip>Set `advanced` to True to get actions for complex use cases</Tip>
+
+- Using specific tools:
+
+In this demo, we will use the `GITHUB_STAR_A_REPOSITORY_FOR_THE_AUTHENTICATED_USER` action from the GitHub app.
+```python Code
+tools = toolset.get_tools(
+    actions=[Action.GITHUB_STAR_A_REPOSITORY_FOR_THE_AUTHENTICATED_USER]
+)
+```
+Learn more about filtering actions [here](https://docs.composio.dev/patterns/tools/use-tools/use-specific-actions)
+
+4. Define agent
 
 ```python Code
 crewai_agent = Agent(
-    role="Github Agent",
-    goal="You take action on Github using Github APIs",
-    backstory=(
-        "You are AI agent that is responsible for taking actions on Github "
-        "on users behalf. You need to take action on Github using Github APIs"
-    ),
+    role="GitHub Agent",
+    goal="You take action on GitHub using GitHub APIs",
+    backstory="You are AI agent that is responsible for taking actions on GitHub on behalf of users using GitHub APIs",
     verbose=True,
     tools=tools,
+    llm= # pass an llm
 )
 ```
 
-3. Execute task
+5. Execute task
 
 ```python Code
 task = Task(
-    description="Star a repo ComposioHQ/composio on GitHub",
+    description="Star a repo composiohq/composio on GitHub",
     agent=crewai_agent,
-    expected_output="if the star happened",
+    expected_output="Status of the operation",
 )
 
-task.execute()
+crew = Crew(agents=[crewai_agent], tasks=[task])
+
+crew.kickoff()
 ```
 
-* More detailed list of tools can be found [here](https://app.composio.dev)
+* More detailed list of tools can be found [here](https://app.composio.dev)

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "crewai"
-version = "0.95.0"
+version = "0.98.0"
 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"
@@ -13,25 +13,20 @@ dependencies = [
     "openai>=1.13.3",
     "litellm==1.57.4",
     "instructor>=1.3.3",
-
     # Text Processing
     "pdfplumber>=0.11.4",
     "regex>=2024.9.11",
-
     # Telemetry and Monitoring
     "opentelemetry-api>=1.22.0",
     "opentelemetry-sdk>=1.22.0",
     "opentelemetry-exporter-otlp-proto-http>=1.22.0",
-
     # Data Handling
     "chromadb>=0.5.23",
     "openpyxl>=3.1.5",
     "pyvis>=0.3.2",
-
     # Authentication and Security
     "auth0-python>=4.7.1",
     "python-dotenv>=1.0.0",
-
     # Configuration and Utils
     "click>=8.1.7",
     "appdirs>=1.4.4",
@@ -40,7 +35,7 @@ dependencies = [
     "uv>=0.4.25",
     "tomli-w>=1.1.0",
     "tomli>=2.0.2",
-    "blinker>=1.9.0"
+    "blinker>=1.9.0",
 ]
 
 [project.urls]
@@ -49,7 +44,7 @@ Documentation = "https://docs.crewai.com"
 Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
-tools = ["crewai-tools>=0.25.5"]
+tools = ["crewai-tools>=0.32.1"]
 embeddings = [
     "tiktoken~=0.7.0"
 ]

src/crewai/__init__.py

@@ -14,7 +14,7 @@ warnings.filterwarnings(
     category=UserWarning,
     module="pydantic.main",
 )
-__version__ = "0.95.0"
+__version__ = "0.98.0"
 __all__ = [
     "Agent",
     "Crew",

src/crewai/agent.py

@@ -1,4 +1,3 @@
-import os
 import shutil
 import subprocess
 from typing import Any, Dict, List, Literal, Optional, Union
@@ -8,7 +7,6 @@ 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
@@ -261,6 +259,9 @@ class Agent(BaseAgent):
                 }
             )["output"]
         except Exception as e:
+            if e.__class__.__module__.startswith("litellm"):
+                # Do not retry on litellm errors
+                raise e
             self._times_executed += 1
             if self._times_executed > self.max_retry_limit:
                 raise e

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

@@ -2,26 +2,26 @@ from crewai.types.usage_metrics import UsageMetrics
 
 
 class TokenProcess:
-    def __init__(self):
+    def __init__(self) -> None:
         self.total_tokens: int = 0
         self.prompt_tokens: int = 0
         self.cached_prompt_tokens: int = 0
         self.completion_tokens: int = 0
         self.successful_requests: int = 0
 
-    def sum_prompt_tokens(self, tokens: int):
-        self.prompt_tokens = self.prompt_tokens + tokens
-        self.total_tokens = self.total_tokens + tokens
+    def sum_prompt_tokens(self, tokens: int) -> None:
+        self.prompt_tokens += tokens
+        self.total_tokens += tokens
 
-    def sum_completion_tokens(self, tokens: int):
-        self.completion_tokens = self.completion_tokens + tokens
-        self.total_tokens = self.total_tokens + tokens
+    def sum_completion_tokens(self, tokens: int) -> None:
+        self.completion_tokens += tokens
+        self.total_tokens += tokens
 
-    def sum_cached_prompt_tokens(self, tokens: int):
-        self.cached_prompt_tokens = self.cached_prompt_tokens + tokens
+    def sum_cached_prompt_tokens(self, tokens: int) -> None:
+        self.cached_prompt_tokens += tokens
 
-    def sum_successful_requests(self, requests: int):
-        self.successful_requests = self.successful_requests + requests
+    def sum_successful_requests(self, requests: int) -> None:
+        self.successful_requests += requests
 
     def get_summary(self) -> UsageMetrics:
         return UsageMetrics(

src/crewai/agents/crew_agent_executor.py

@@ -13,6 +13,7 @@ from crewai.agents.parser import (
     OutputParserException,
 )
 from crewai.agents.tools_handler import ToolsHandler
+from crewai.llm import LLM
 from crewai.tools.base_tool import BaseTool
 from crewai.tools.tool_usage import ToolUsage, ToolUsageErrorException
 from crewai.utilities import I18N, Printer
@@ -54,7 +55,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         callbacks: List[Any] = [],
     ):
         self._i18n: I18N = I18N()
-        self.llm = llm
+        self.llm: LLM = llm
         self.task = task
         self.agent = agent
         self.crew = crew
@@ -80,10 +81,8 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self.tool_name_to_tool_map: Dict[str, BaseTool] = {
             tool.name: tool for tool in self.tools
         }
-        if self.llm.stop:
-            self.llm.stop = list(set(self.llm.stop + self.stop))
-        else:
-            self.llm.stop = self.stop
+        self.stop = stop_words
+        self.llm.stop = list(set(self.llm.stop + self.stop))
 
     def invoke(self, inputs: Dict[str, str]) -> Dict[str, Any]:
         if "system" in self.prompt:
@@ -98,7 +97,16 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self._show_start_logs()
 
         self.ask_for_human_input = bool(inputs.get("ask_for_human_input", False))
-        formatted_answer = self._invoke_loop()
+
+        try:
+            formatted_answer = self._invoke_loop()
+        except Exception as 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:
             formatted_answer = self._handle_human_feedback(formatted_answer)
@@ -124,7 +132,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                 self._enforce_rpm_limit()
 
                 answer = self._get_llm_response()
-
                 formatted_answer = self._process_llm_response(answer)
 
                 if isinstance(formatted_answer, AgentAction):
@@ -142,13 +149,32 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                 formatted_answer = self._handle_output_parser_exception(e)
 
             except Exception as e:
+                if e.__class__.__module__.startswith("litellm"):
+                    # Do not retry on litellm errors
+                    raise e
                 if self._is_context_length_exceeded(e):
                     self._handle_context_length()
                     continue
+                else:
+                    self._handle_unknown_error(e)
+                    raise e
+            finally:
+                self.iterations += 1
 
         self._show_logs(formatted_answer)
         return formatted_answer
 
+    def _handle_unknown_error(self, exception: Exception) -> None:
+        """Handle unknown errors by informing the user."""
+        self._printer.print(
+            content="An unknown error occurred. Please check the details below.",
+            color="red",
+        )
+        self._printer.print(
+            content=f"Error details: {exception}",
+            color="red",
+        )
+
     def _has_reached_max_iterations(self) -> bool:
         """Check if the maximum number of iterations has been reached."""
         return self.iterations >= self.max_iter
@@ -160,10 +186,17 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
 
     def _get_llm_response(self) -> str:
         """Call the LLM and return the response, handling any invalid responses."""
-        answer = self.llm.call(
-            self.messages,
-            callbacks=self.callbacks,
-        )
+        try:
+            answer = self.llm.call(
+                self.messages,
+                callbacks=self.callbacks,
+            )
+        except Exception as e:
+            self._printer.print(
+                content=f"Error during LLM call: {e}",
+                color="red",
+            )
+            raise e
 
         if not answer:
             self._printer.print(
@@ -184,7 +217,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                 if FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE in e.error:
                     answer = answer.split("Observation:")[0].strip()
 
-        self.iterations += 1
         return self._format_answer(answer)
 
     def _handle_agent_action(

src/crewai/cli/cli.py

@@ -1,3 +1,11 @@
+import warnings
+
+warnings.filterwarnings(
+    "ignore",
+    message="Valid config keys have changed in V2*",
+    category=UserWarning,
+)
+
 import os
 from importlib.metadata import version as get_version
 from typing import Optional, Tuple

src/crewai/cli/run_crew.py

@@ -11,6 +11,7 @@ def run_crew() -> None:
     """
     Run the crew by running a command in the UV environment.
     """
+    click.echo("Running crew hello...")
     command = ["uv", "run", "run_crew"]
     crewai_version = get_crewai_version()
     min_required_version = "0.71.0"

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]>=0.95.0,<1.0.0"
+    "crewai[tools]>=0.98.0,<1.0.0"
 ]
 
 [project.scripts]

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

@@ -3,7 +3,7 @@ from random import randint
 
 from pydantic import BaseModel
 
-from crewai.flow.flow import Flow, listen, start
+from crewai.flow import Flow, listen, start
 
 from {{folder_name}}.crews.poem_crew.poem_crew import PoemCrew
 

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]>=0.95.0,<1.0.0",
+    "crewai[tools]>=0.98.0,<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]>=0.95.0"
+    "crewai[tools]>=0.98.0"
 ]
 
 [tool.crewai]

src/crewai/crew.py

@@ -37,7 +37,6 @@ 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.types.crew_chat import ChatInputs
 from crewai.types.usage_metrics import UsageMetrics
 from crewai.utilities import I18N, FileHandler, Logger, RPMController
 from crewai.utilities.constants import TRAINING_DATA_FILE
@@ -58,9 +57,6 @@ except ImportError:
     agentops = None
 
 
-warnings.filterwarnings("ignore", category=SyntaxWarning, module="pysbd")
-
-
 class Crew(BaseModel):
     """
     Represents a group of agents, defining how they should collaborate and the tasks they should perform.
@@ -84,6 +80,7 @@ class Crew(BaseModel):
         step_callback: Callback to be executed after each step for every agents execution.
         share_crew: Whether you want to share the complete crew information and execution with crewAI to make the library better, and allow us to train models.
         planning: Plan the crew execution and add the plan to the crew.
+        chat_llm: The language model used for orchestrating chat interactions with the crew.
     """
 
     __hash__ = object.__hash__  # type: ignore

src/crewai/flow/__init__.py

@@ -1,3 +1,5 @@
-from crewai.flow.flow import Flow
+from crewai.flow.flow import Flow, start, listen, or_, and_, router
+from crewai.flow.persistence import persist
+
+__all__ = ["Flow", "start", "listen", "or_", "and_", "router", "persist"]
 
-__all__ = ["Flow"]

src/crewai/flow/flow.py

@@ -1,6 +1,6 @@
 import asyncio
 import inspect
-import uuid
+import logging
 from typing import (
     Any,
     Callable,
@@ -13,7 +13,6 @@ from typing import (
     TypeVar,
     Union,
     cast,
-    overload,
 )
 from uuid import uuid4
 
@@ -27,54 +26,68 @@ from crewai.flow.flow_events import (
     MethodExecutionStartedEvent,
 )
 from crewai.flow.flow_visualizer import plot_flow
-from crewai.flow.persistence import FlowPersistence
 from crewai.flow.persistence.base import FlowPersistence
 from crewai.flow.utils import get_possible_return_constants
 from crewai.telemetry import Telemetry
+from crewai.utilities.printer import Printer
+
+logger = logging.getLogger(__name__)
 
 
 class FlowState(BaseModel):
     """Base model for all flow states, ensuring each state has a unique ID."""
-    id: str = Field(default_factory=lambda: str(uuid4()), description="Unique identifier for the flow state")
+
+    id: str = Field(
+        default_factory=lambda: str(uuid4()),
+        description="Unique identifier for the flow state",
+    )
+
 
 # Type variables with explicit bounds
-T = TypeVar("T", bound=Union[Dict[str, Any], BaseModel])  # Generic flow state type parameter
-StateT = TypeVar("StateT", bound=Union[Dict[str, Any], BaseModel])  # State validation type parameter
+T = TypeVar(
+    "T", bound=Union[Dict[str, Any], BaseModel]
+)  # Generic flow state type parameter
+StateT = TypeVar(
+    "StateT", bound=Union[Dict[str, Any], BaseModel]
+)  # State validation type parameter
+
 
 def ensure_state_type(state: Any, expected_type: Type[StateT]) -> StateT:
     """Ensure state matches expected type with proper validation.
-    
+
     Args:
         state: State instance to validate
         expected_type: Expected type for the state
-        
+
     Returns:
         Validated state instance
-        
+
     Raises:
         TypeError: If state doesn't match expected type
         ValueError: If state validation fails
     """
     """Ensure state matches expected type with proper validation.
-    
+
     Args:
         state: State instance to validate
         expected_type: Expected type for the state
-        
+
     Returns:
         Validated state instance
-        
+
     Raises:
         TypeError: If state doesn't match expected type
         ValueError: If state validation fails
     """
-    if expected_type == dict:
+    if expected_type is dict:
         if not isinstance(state, dict):
             raise TypeError(f"Expected dict, got {type(state).__name__}")
         return cast(StateT, state)
     if isinstance(expected_type, type) and issubclass(expected_type, BaseModel):
         if not isinstance(state, expected_type):
-            raise TypeError(f"Expected {expected_type.__name__}, got {type(state).__name__}")
+            raise TypeError(
+                f"Expected {expected_type.__name__}, got {type(state).__name__}"
+            )
         return cast(StateT, state)
     raise TypeError(f"Invalid expected_type: {expected_type}")
 
@@ -120,6 +133,7 @@ def start(condition: Optional[Union[str, dict, Callable]] = None) -> Callable:
     >>> def complex_start(self):
     ...     pass
     """
+
     def decorator(func):
         func.__is_start_method__ = True
         if condition is not None:
@@ -144,6 +158,7 @@ def start(condition: Optional[Union[str, dict, Callable]] = None) -> Callable:
 
     return decorator
 
+
 def listen(condition: Union[str, dict, Callable]) -> Callable:
     """
     Creates a listener that executes when specified conditions are met.
@@ -180,6 +195,7 @@ def listen(condition: Union[str, dict, Callable]) -> Callable:
     >>> def handle_completion(self):
     ...     pass
     """
+
     def decorator(func):
         if isinstance(condition, str):
             func.__trigger_methods__ = [condition]
@@ -244,6 +260,7 @@ def router(condition: Union[str, dict, Callable]) -> Callable:
     ...         return CONTINUE
     ...     return STOP
     """
+
     def decorator(func):
         func.__is_router__ = True
         if isinstance(condition, str):
@@ -267,6 +284,7 @@ def router(condition: Union[str, dict, Callable]) -> Callable:
 
     return decorator
 
+
 def or_(*conditions: Union[str, dict, Callable]) -> dict:
     """
     Combines multiple conditions with OR logic for flow control.
@@ -370,22 +388,27 @@ class FlowMeta(type):
 
         for attr_name, attr_value in dct.items():
             # Check for any flow-related attributes
-            if (hasattr(attr_value, "__is_flow_method__") or
-                hasattr(attr_value, "__is_start_method__") or
-                hasattr(attr_value, "__trigger_methods__") or
-                hasattr(attr_value, "__is_router__")):
-                
+            if (
+                hasattr(attr_value, "__is_flow_method__")
+                or hasattr(attr_value, "__is_start_method__")
+                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)
-                
+
                 # Register listeners and routers
                 if hasattr(attr_value, "__trigger_methods__"):
                     methods = attr_value.__trigger_methods__
                     condition_type = getattr(attr_value, "__condition_type__", "OR")
                     listeners[attr_name] = (condition_type, methods)
-                    
-                    if hasattr(attr_value, "__is_router__") and attr_value.__is_router__:
+
+                    if (
+                        hasattr(attr_value, "__is_router__")
+                        and attr_value.__is_router__
+                    ):
                         routers.add(attr_name)
                         possible_returns = get_possible_return_constants(attr_value)
                         if possible_returns:
@@ -401,9 +424,11 @@ class FlowMeta(type):
 
 class Flow(Generic[T], metaclass=FlowMeta):
     """Base class for all flows.
-    
+
     Type parameter T must be either Dict[str, Any] or a subclass of BaseModel."""
+
     _telemetry = Telemetry()
+    _printer = Printer()
 
     _start_methods: List[str] = []
     _listeners: Dict[str, tuple[str, List[str]]] = {}
@@ -422,14 +447,12 @@ class Flow(Generic[T], metaclass=FlowMeta):
     def __init__(
         self,
         persistence: Optional[FlowPersistence] = None,
-        restore_uuid: Optional[str] = None,
         **kwargs: Any,
     ) -> None:
         """Initialize a new Flow instance.
-        
+
         Args:
             persistence: Optional persistence backend for storing flow states
-            restore_uuid: Optional UUID to restore state from persistence
             **kwargs: Additional state values to initialize or override
         """
         # Initialize basic instance attributes
@@ -438,54 +461,13 @@ class Flow(Generic[T], metaclass=FlowMeta):
         self._pending_and_listeners: Dict[str, Set[str]] = {}
         self._method_outputs: List[Any] = []  # List to store all method outputs
         self._persistence: Optional[FlowPersistence] = persistence
-        
-        # Validate state model before initialization
-        if isinstance(self.initial_state, type):
-            if issubclass(self.initial_state, BaseModel) and not issubclass(self.initial_state, FlowState):
-                # Check if model has id field
-                model_fields = getattr(self.initial_state, "model_fields", None)
-                if not model_fields or "id" not in model_fields:
-                    raise ValueError("Flow state model must have an 'id' field")
-        
-        # Handle persistence and potential ID conflicts
-        stored_state = None
-        if self._persistence is not None:
-            if restore_uuid and kwargs and "id" in kwargs and restore_uuid != kwargs["id"]:
-                raise ValueError(
-                    f"Conflicting IDs provided: restore_uuid='{restore_uuid}' "
-                    f"vs kwargs['id']='{kwargs['id']}'. Use only one ID for restoration."
-                )
-            
-            # Attempt to load state, prioritizing restore_uuid
-            if restore_uuid:
-                stored_state = self._persistence.load_state(restore_uuid)
-                if not stored_state:
-                    raise ValueError(f"No state found for restore_uuid='{restore_uuid}'")
-            elif kwargs and "id" in kwargs:
-                stored_state = self._persistence.load_state(kwargs["id"])
-                if not stored_state:
-                    # For kwargs["id"], we allow creating new state if not found
-                    self._state = self._create_initial_state()
-                    if kwargs:
-                        self._initialize_state(kwargs)
-                    return
-        
-        # Initialize state based on persistence and kwargs
-        if stored_state:
-            # Create initial state and restore from persistence
-            self._state = self._create_initial_state()
-            self._restore_state(stored_state)
-            # Apply any additional kwargs to override specific fields
-            if kwargs:
-                filtered_kwargs = {k: v for k, v in kwargs.items() if k != "id"}
-                if filtered_kwargs:
-                    self._initialize_state(filtered_kwargs)
-        else:
-            # No stored state, create new state with initial values
-            self._state = self._create_initial_state()
-            # Apply any additional kwargs
-            if kwargs:
-                self._initialize_state(kwargs)
+
+        # Initialize state with initial values
+        self._state = self._create_initial_state()
+
+        # Apply any additional kwargs
+        if kwargs:
+            self._initialize_state(kwargs)
 
         self._telemetry.flow_creation_span(self.__class__.__name__)
 
@@ -494,23 +476,23 @@ class Flow(Generic[T], metaclass=FlowMeta):
             if not method_name.startswith("_"):
                 method = getattr(self, method_name)
                 # Check for any flow-related attributes
-                if (hasattr(method, "__is_flow_method__") or
-                    hasattr(method, "__is_start_method__") or
-                    hasattr(method, "__trigger_methods__") or
-                    hasattr(method, "__is_router__")):
+                if (
+                    hasattr(method, "__is_flow_method__")
+                    or hasattr(method, "__is_start_method__")
+                    or hasattr(method, "__trigger_methods__")
+                    or hasattr(method, "__is_router__")
+                ):
                     # Ensure method is bound to this instance
                     if not hasattr(method, "__self__"):
                         method = method.__get__(self, self.__class__)
                     self._methods[method_name] = method
 
-
-    
     def _create_initial_state(self) -> T:
         """Create and initialize flow state with UUID and default values.
-        
+
         Returns:
             New state instance with UUID and default values initialized
-            
+
         Raises:
             ValueError: If structured state model lacks 'id' field
             TypeError: If state is neither BaseModel nor dictionary
@@ -522,24 +504,25 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 if issubclass(state_type, FlowState):
                     # Create instance without id, then set it
                     instance = state_type()
-                    if not hasattr(instance, 'id'):
-                        setattr(instance, 'id', str(uuid4()))
+                    if not hasattr(instance, "id"):
+                        setattr(instance, "id", str(uuid4()))
                     return cast(T, instance)
                 elif issubclass(state_type, BaseModel):
                     # Create a new type that includes the ID field
                     class StateWithId(state_type, FlowState):  # type: ignore
                         pass
+
                     instance = StateWithId()
-                    if not hasattr(instance, 'id'):
-                        setattr(instance, 'id', str(uuid4()))
+                    if not hasattr(instance, "id"):
+                        setattr(instance, "id", str(uuid4()))
                     return cast(T, instance)
-                elif state_type == dict:
-                    return cast(T, {"id": str(uuid4())})  # Minimal dict state
-        
+                elif state_type is dict:
+                    return cast(T, {"id": str(uuid4())})
+
         # Handle case where no initial state is provided
         if self.initial_state is None:
             return cast(T, {"id": str(uuid4())})
-        
+
         # Handle case where initial_state is a type (class)
         if isinstance(self.initial_state, type):
             if issubclass(self.initial_state, FlowState):
@@ -550,22 +533,22 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 if not model_fields or "id" not in model_fields:
                     raise ValueError("Flow state model must have an 'id' field")
                 return cast(T, self.initial_state())  # Uses model defaults
-            elif self.initial_state == dict:
+            elif self.initial_state is dict:
                 return cast(T, {"id": str(uuid4())})
-        
+
         # Handle dictionary instance case
         if isinstance(self.initial_state, dict):
             new_state = dict(self.initial_state)  # Copy to avoid mutations
             if "id" not in new_state:
                 new_state["id"] = str(uuid4())
             return cast(T, new_state)
-        
+
         # Handle BaseModel instance case
         if isinstance(self.initial_state, BaseModel):
             model = cast(BaseModel, self.initial_state)
             if not hasattr(model, "id"):
                 raise ValueError("Flow state model must have an 'id' field")
-            
+
             # Create new instance with same values to avoid mutations
             if hasattr(model, "model_dump"):
                 # Pydantic v2
@@ -576,60 +559,12 @@ class Flow(Generic[T], metaclass=FlowMeta):
             else:
                 # Fallback for other BaseModel implementations
                 state_dict = {
-                    k: v for k, v in model.__dict__.items()
-                    if not k.startswith("_")
+                    k: v for k, v in model.__dict__.items() if not k.startswith("_")
                 }
-            
+
             # Create new instance of the same class
             model_class = type(model)
             return cast(T, model_class(**state_dict))
-            
-        raise TypeError(
-            f"Initial state must be dict or BaseModel, got {type(self.initial_state)}"
-        )
-        # Handle case where initial_state is None but we have a type parameter
-        if self.initial_state is None and hasattr(self, "_initial_state_T"):
-            state_type = getattr(self, "_initial_state_T")
-            if isinstance(state_type, type):
-                if issubclass(state_type, FlowState):
-                    return cast(T, state_type())
-                elif issubclass(state_type, BaseModel):
-                    # Create a new type that includes the ID field
-                    class StateWithId(state_type, FlowState):  # type: ignore
-                        pass
-                    return cast(T, StateWithId())
-                elif state_type == dict:
-                    return cast(T, {"id": str(uuid4())})
-
-        # Handle case where no initial state is provided
-        if self.initial_state is None:
-            return cast(T, {"id": str(uuid4())})
-
-        # Handle case where initial_state is a type (class)
-        if isinstance(self.initial_state, type):
-            if issubclass(self.initial_state, FlowState):
-                return cast(T, self.initial_state())
-            elif issubclass(self.initial_state, BaseModel):
-                # Validate that the model has an id field
-                model_fields = getattr(self.initial_state, "model_fields", None)
-                if not model_fields or "id" not in model_fields:
-                    raise ValueError("Flow state model must have an 'id' field")
-                return cast(T, self.initial_state())
-            elif self.initial_state == dict:
-                return cast(T, {"id": str(uuid4())})
-
-        # Handle dictionary instance case
-        if isinstance(self.initial_state, dict):
-            if "id" not in self.initial_state:
-                self.initial_state["id"] = str(uuid4())
-            return cast(T, dict(self.initial_state))  # Create new dict to avoid mutations
-
-        # Handle BaseModel instance case
-        if isinstance(self.initial_state, BaseModel):
-            if not hasattr(self.initial_state, "id"):
-                raise ValueError("Flow state model must have an 'id' field")
-            return cast(T, self.initial_state)
-            
         raise TypeError(
             f"Initial state must be dict or BaseModel, got {type(self.initial_state)}"
         )
@@ -643,12 +578,45 @@ class Flow(Generic[T], metaclass=FlowMeta):
         """Returns the list of all outputs from executed methods."""
         return self._method_outputs
 
+    @property
+    def flow_id(self) -> str:
+        """Returns the unique identifier of this flow instance.
+
+        This property provides a consistent way to access the flow's unique identifier
+        regardless of the underlying state implementation (dict or BaseModel).
+
+        Returns:
+            str: The flow's unique identifier, or an empty string if not found
+
+        Note:
+            This property safely handles both dictionary and BaseModel state types,
+            returning an empty string if the ID cannot be retrieved rather than raising
+            an exception.
+
+        Example:
+            ```python
+            flow = MyFlow()
+            print(f"Current flow ID: {flow.flow_id}")  # Safely get flow ID
+            ```
+        """
+        try:
+            if not hasattr(self, '_state'):
+                return ""
+
+            if isinstance(self._state, dict):
+                return str(self._state.get("id", ""))
+            elif isinstance(self._state, BaseModel):
+                return str(getattr(self._state, "id", ""))
+            return ""
+        except (AttributeError, TypeError):
+            return ""  # Safely handle any unexpected attribute access issues
+
     def _initialize_state(self, inputs: Dict[str, Any]) -> None:
         """Initialize or update flow state with new inputs.
-        
+
         Args:
             inputs: Dictionary of state values to set/update
-            
+
         Raises:
             ValueError: If validation fails for structured state
             TypeError: If state is neither BaseModel nor dictionary
@@ -675,13 +643,12 @@ class Flow(Generic[T], metaclass=FlowMeta):
                     current_state = model.dict()
                 else:
                     current_state = {
-                        k: v for k, v in model.__dict__.items()
-                        if not k.startswith("_")
+                        k: v for k, v in model.__dict__.items() if not k.startswith("_")
                     }
-                
+
                 # Create new state with preserved fields and updates
                 new_state = {**current_state, **inputs}
-                
+
                 # Create new instance with merged state
                 model_class = type(model)
                 if hasattr(model_class, "model_validate"):
@@ -697,13 +664,13 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 raise ValueError(f"Invalid inputs for structured state: {e}") from e
         else:
             raise TypeError("State must be a BaseModel instance or a dictionary.")
-            
+
     def _restore_state(self, stored_state: Dict[str, Any]) -> None:
         """Restore flow state from persistence.
-        
+
         Args:
             stored_state: Previously stored state to restore
-            
+
         Raises:
             ValueError: If validation fails for structured state
             TypeError: If state is neither BaseModel nor dictionary
@@ -712,7 +679,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
         stored_id = stored_state.get("id")
         if not stored_id:
             raise ValueError("Stored state must have an 'id' field")
-        
+
         if isinstance(self._state, dict):
             # For dict states, update all fields from stored state
             self._state.clear()
@@ -730,11 +697,39 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 # Fallback for other BaseModel implementations
                 self._state = cast(T, type(model)(**stored_state))
         else:
-            raise TypeError(
-                f"State must be dict or BaseModel, got {type(self._state)}"
-            )
+            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.
+
+        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)
+
+            # Override the id in the state if it exists in inputs
+            if 'id' in inputs:
+                if isinstance(self._state, dict):
+                    self._state['id'] = inputs['id']
+                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")
+
+            # Apply any additional inputs after restoration
+            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(
             self,
             event=FlowStartedEvent(
@@ -742,9 +737,11 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 flow_name=self.__class__.__name__,
             ),
         )
+        self._log_flow_event(f"Flow started with ID: {self.flow_id}", color="bold_magenta")
 
-        if inputs is not None:
+        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:
@@ -987,6 +984,30 @@ class Flow(Generic[T], metaclass=FlowMeta):
 
             traceback.print_exc()
 
+    def _log_flow_event(self, message: str, color: str = "yellow", level: str = "info") -> None:
+        """Centralized logging method for flow events.
+
+        This method provides a consistent interface for logging flow-related events,
+        combining both console output with colors and proper logging levels.
+
+        Args:
+            message: The message to log
+            color: Color to use for console output (default: yellow)
+                  Available colors: purple, red, bold_green, bold_purple,
+                  bold_blue, yellow, yellow
+            level: Log level to use (default: info)
+                  Supported levels: info, warning
+
+        Note:
+            This method uses the Printer utility for colored console output
+            and the standard logging module for log level support.
+        """
+        self._printer.print(message, color=color)
+        if level == "info":
+            logger.info(message)
+        elif level == "warning":
+            logger.warning(message)
+
     def plot(self, filename: str = "crewai_flow") -> None:
         self._telemetry.flow_plotting_span(
             self.__class__.__name__, list(self._methods.keys())

src/crewai/flow/persistence/decorators.py

@@ -5,14 +5,14 @@ Example:
     ```python
     from crewai.flow.flow import Flow, start
     from crewai.flow.persistence import persist, SQLiteFlowPersistence
-    
+
     class MyFlow(Flow):
         @start()
         @persist(SQLiteFlowPersistence())
         def sync_method(self):
             # Synchronous method implementation
             pass
-            
+
         @start()
         @persist(SQLiteFlowPersistence())
         async def async_method(self):
@@ -23,48 +23,117 @@ Example:
 
 import asyncio
 import functools
-import inspect
 import logging
 from typing import (
     Any,
     Callable,
-    Dict,
     Optional,
     Type,
     TypeVar,
     Union,
     cast,
-    get_type_hints,
 )
 
 from pydantic import BaseModel
 
 from crewai.flow.persistence.base import FlowPersistence
 from crewai.flow.persistence.sqlite import SQLiteFlowPersistence
+from crewai.utilities.printer import Printer
 
 logger = logging.getLogger(__name__)
 T = TypeVar("T")
 
+# Constants for log messages
+LOG_MESSAGES = {
+    "save_state": "Saving flow state to memory for ID: {}",
+    "save_error": "Failed to persist state for method {}: {}",
+    "state_missing": "Flow instance has no state",
+    "id_missing": "Flow state must have an 'id' field for persistence"
+}
+
+
+class PersistenceDecorator:
+    """Class to handle flow state persistence with consistent logging."""
+
+    _printer = Printer()  # Class-level printer instance
+
+    @classmethod
+    def persist_state(cls, flow_instance: Any, method_name: str, persistence_instance: FlowPersistence) -> None:
+        """Persist flow state with proper error handling and logging.
+
+        This method handles the persistence of flow state data, including proper
+        error handling and colored console output for status updates.
+
+        Args:
+            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
+
+        Raises:
+            ValueError: If flow has no state or state lacks an ID
+            RuntimeError: If state persistence fails
+            AttributeError: If flow instance lacks required state attributes
+        """
+        try:
+            state = getattr(flow_instance, 'state', None)
+            if state is None:
+                raise ValueError("Flow instance has no state")
+
+            flow_uuid: Optional[str] = None
+            if isinstance(state, dict):
+                flow_uuid = state.get('id')
+            elif isinstance(state, BaseModel):
+                flow_uuid = getattr(state, 'id', None)
+
+            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))
+
+            try:
+                persistence_instance.save_state(
+                    flow_uuid=flow_uuid,
+                    method_name=method_name,
+                    state_data=state,
+                )
+            except Exception as e:
+                error_msg = LOG_MESSAGES["save_error"].format(method_name, str(e))
+                cls._printer.print(error_msg, color="red")
+                logger.error(error_msg)
+                raise RuntimeError(f"State persistence failed: {str(e)}") from e
+        except AttributeError:
+            error_msg = LOG_MESSAGES["state_missing"]
+            cls._printer.print(error_msg, color="red")
+            logger.error(error_msg)
+            raise ValueError(error_msg)
+        except (TypeError, ValueError) as e:
+            error_msg = LOG_MESSAGES["id_missing"]
+            cls._printer.print(error_msg, color="red")
+            logger.error(error_msg)
+            raise ValueError(error_msg) from e
+
 
 def persist(persistence: Optional[FlowPersistence] = None):
     """Decorator to persist flow state.
-    
+
     This decorator can be applied at either the class level or method level.
     When applied at the class level, it automatically persists all flow method
     states. When applied at the method level, it persists only that method's
     state.
-    
+
     Args:
         persistence: Optional FlowPersistence implementation to use.
                     If not provided, uses SQLiteFlowPersistence.
-    
+
     Returns:
         A decorator that can be applied to either a class or method
-    
+
     Raises:
         ValueError: If the flow state doesn't have an 'id' field
         RuntimeError: If state persistence fails
-        
+
     Example:
         @persist  # Class-level persistence with default SQLite
         class MyFlow(Flow[MyState]):
@@ -72,81 +141,85 @@ def persist(persistence: Optional[FlowPersistence] = None):
             def begin(self):
                 pass
     """
-    def _persist_state(flow_instance: Any, method_name: str, persistence_instance: FlowPersistence) -> None:
-        """Helper to persist state with error handling."""
-        try:
-            # Get flow UUID from state
-            state = getattr(flow_instance, 'state', None)
-            if state is None:
-                raise ValueError("Flow instance has no state")
-                
-            flow_uuid: Optional[str] = None
-            if isinstance(state, dict):
-                flow_uuid = state.get('id')
-            elif isinstance(state, BaseModel):
-                flow_uuid = getattr(state, 'id', None)
-                
-            if not flow_uuid:
-                raise ValueError(
-                    "Flow state must have an 'id' field for persistence"
-                )
-                
-            # Persist the state
-            persistence_instance.save_state(
-                flow_uuid=flow_uuid,
-                method_name=method_name,
-                state_data=state,
-            )
-        except Exception as e:
-            logger.error(
-                f"Failed to persist state for method {method_name}: {str(e)}"
-            )
-            raise RuntimeError(f"State persistence failed: {str(e)}") from e
-    
+
     def decorator(target: Union[Type, Callable[..., T]]) -> Union[Type, Callable[..., T]]:
         """Decorator that handles both class and method decoration."""
         actual_persistence = persistence or SQLiteFlowPersistence()
-        
+
         if isinstance(target, type):
             # Class decoration
-            class_methods = {}
+            original_init = getattr(target, "__init__")
+
+            @functools.wraps(original_init)
+            def new_init(self: Any, *args: Any, **kwargs: Any) -> None:
+                if 'persistence' not in kwargs:
+                    kwargs['persistence'] = actual_persistence
+                original_init(self, *args, **kwargs)
+
+            setattr(target, "__init__", new_init)
+
+            # Store original methods to preserve their decorators
+            original_methods = {}
+
             for name, method in target.__dict__.items():
-                if callable(method) and hasattr(method, "__is_flow_method__"):
-                    # Wrap each flow method with persistence
-                    if asyncio.iscoroutinefunction(method):
-                        @functools.wraps(method)
-                        async def class_async_wrapper(self: Any, *args: Any, **kwargs: Any) -> Any:
-                            method_coro = method(self, *args, **kwargs)
-                            if asyncio.iscoroutine(method_coro):
-                                result = await method_coro
-                            else:
-                                result = method_coro
-                            _persist_state(self, method.__name__, actual_persistence)
+                if callable(method) and (
+                    hasattr(method, "__is_start_method__") or
+                    hasattr(method, "__trigger_methods__") or
+                    hasattr(method, "__condition_type__") or
+                    hasattr(method, "__is_flow_method__") or
+                    hasattr(method, "__is_router__")
+                ):
+                    original_methods[name] = method
+
+            # Create wrapped versions of the methods that include persistence
+            for name, method in original_methods.items():
+                if asyncio.iscoroutinefunction(method):
+                    # Create a closure to capture the current name and method
+                    def create_async_wrapper(method_name: str, original_method: Callable):
+                        @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)
                             return result
-                        class_methods[name] = class_async_wrapper
-                    else:
-                        @functools.wraps(method)
-                        def class_sync_wrapper(self: Any, *args: Any, **kwargs: Any) -> Any:
-                            result = method(self, *args, **kwargs)
-                            _persist_state(self, method.__name__, actual_persistence)
-                            return result
-                        class_methods[name] = class_sync_wrapper
-                    
-                    # Preserve flow-specific attributes
+                        return method_wrapper
+
+                    wrapped = create_async_wrapper(name, method)
+
+                    # Preserve all original decorators and attributes
                     for attr in ["__is_start_method__", "__trigger_methods__", "__condition_type__", "__is_router__"]:
                         if hasattr(method, attr):
-                            setattr(class_methods[name], attr, getattr(method, attr))
-                    setattr(class_methods[name], "__is_flow_method__", True)
-            
-            # Update class with wrapped methods
-            for name, method in class_methods.items():
-                setattr(target, name, method)
+                            setattr(wrapped, attr, getattr(method, attr))
+                    setattr(wrapped, "__is_flow_method__", True)
+
+                    # Update the class with the wrapped method
+                    setattr(target, name, wrapped)
+                else:
+                    # Create a closure to capture the current name and method
+                    def create_sync_wrapper(method_name: str, original_method: Callable):
+                        @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)
+                            return result
+                        return method_wrapper
+
+                    wrapped = create_sync_wrapper(name, method)
+
+                    # Preserve all original decorators and attributes
+                    for attr in ["__is_start_method__", "__trigger_methods__", "__condition_type__", "__is_router__"]:
+                        if hasattr(method, attr):
+                            setattr(wrapped, attr, getattr(method, attr))
+                    setattr(wrapped, "__is_flow_method__", True)
+
+                    # Update the class with the wrapped method
+                    setattr(target, name, wrapped)
+
             return target
         else:
             # Method decoration
             method = target
             setattr(method, "__is_flow_method__", True)
-            
+
             if asyncio.iscoroutinefunction(method):
                 @functools.wraps(method)
                 async def method_async_wrapper(flow_instance: Any, *args: Any, **kwargs: Any) -> T:
@@ -155,8 +228,9 @@ def persist(persistence: Optional[FlowPersistence] = None):
                         result = await method_coro
                     else:
                         result = method_coro
-                    _persist_state(flow_instance, method.__name__, actual_persistence)
+                    PersistenceDecorator.persist_state(flow_instance, method.__name__, actual_persistence)
                     return result
+
                 for attr in ["__is_start_method__", "__trigger_methods__", "__condition_type__", "__is_router__"]:
                     if hasattr(method, attr):
                         setattr(method_async_wrapper, attr, getattr(method, attr))
@@ -166,12 +240,13 @@ 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)
-                    _persist_state(flow_instance, method.__name__, actual_persistence)
+                    PersistenceDecorator.persist_state(flow_instance, method.__name__, actual_persistence)
                     return result
+
                 for attr in ["__is_start_method__", "__trigger_methods__", "__condition_type__", "__is_router__"]:
                     if hasattr(method, attr):
                         setattr(method_sync_wrapper, attr, getattr(method, attr))
                 setattr(method_sync_wrapper, "__is_flow_method__", True)
                 return cast(Callable[..., T], method_sync_wrapper)
-            
+
     return decorator

src/crewai/flow/persistence/sqlite.py

@@ -3,10 +3,9 @@ SQLite-based implementation of flow state persistence.
 """
 
 import json
-import os
 import sqlite3
-import tempfile
 from datetime import datetime
+from pathlib import Path
 from typing import Any, Dict, Optional, Union
 
 from pydantic import BaseModel
@@ -16,34 +15,34 @@ from crewai.flow.persistence.base import FlowPersistence
 
 class SQLiteFlowPersistence(FlowPersistence):
     """SQLite-based implementation of flow state persistence.
-    
+
     This class provides a simple, file-based persistence implementation using SQLite.
     It's suitable for development and testing, or for production use cases with
     moderate performance requirements.
     """
-    
+
     db_path: str  # Type annotation for instance variable
-    
+
     def __init__(self, db_path: Optional[str] = None):
         """Initialize SQLite persistence.
-        
+
         Args:
             db_path: Path to the SQLite database file. If not provided, uses
                     db_storage_path() from utilities.paths.
-                    
+
         Raises:
             ValueError: If db_path is invalid
         """
         from crewai.utilities.paths import db_storage_path
         # Get path from argument or default location
-        path = db_path or db_storage_path()
-        
+        path = db_path or str(Path(db_storage_path()) / "flow_states.db")
+
         if not path:
             raise ValueError("Database path must be provided")
-            
+
         self.db_path = path  # Now mypy knows this is str
         self.init_db()
-    
+
     def init_db(self) -> None:
         """Create the necessary tables if they don't exist."""
         with sqlite3.connect(self.db_path) as conn:
@@ -58,10 +57,10 @@ class SQLiteFlowPersistence(FlowPersistence):
             """)
             # Add index for faster UUID lookups
             conn.execute("""
-            CREATE INDEX IF NOT EXISTS idx_flow_states_uuid 
+            CREATE INDEX IF NOT EXISTS idx_flow_states_uuid
             ON flow_states(flow_uuid)
             """)
-    
+
     def save_state(
         self,
         flow_uuid: str,
@@ -69,7 +68,7 @@ class SQLiteFlowPersistence(FlowPersistence):
         state_data: Union[Dict[str, Any], BaseModel],
     ) -> None:
         """Save the current flow state to SQLite.
-        
+
         Args:
             flow_uuid: Unique identifier for the flow instance
             method_name: Name of the method that just completed
@@ -84,7 +83,7 @@ class SQLiteFlowPersistence(FlowPersistence):
             raise ValueError(
                 f"state_data must be either a Pydantic BaseModel or dict, got {type(state_data)}"
             )
-            
+
         with sqlite3.connect(self.db_path) as conn:
             conn.execute("""
             INSERT INTO flow_states (
@@ -99,13 +98,13 @@ class SQLiteFlowPersistence(FlowPersistence):
                 datetime.utcnow().isoformat(),
                 json.dumps(state_dict),
             ))
-    
+
     def load_state(self, flow_uuid: str) -> Optional[Dict[str, Any]]:
         """Load the most recent state for a given flow UUID.
-        
+
         Args:
             flow_uuid: Unique identifier for the flow instance
-            
+
         Returns:
             The most recent state as a dictionary, or None if no state exists
         """
@@ -118,7 +117,7 @@ class SQLiteFlowPersistence(FlowPersistence):
             LIMIT 1
             """, (flow_uuid,))
             row = cursor.fetchone()
-            
+
         if row:
             return json.loads(row[0])
         return None

src/crewai/knowledge/source/crew_docling_source.py

@@ -8,6 +8,7 @@ try:
     from docling.exceptions import ConversionError
     from docling_core.transforms.chunker.hierarchical_chunker import HierarchicalChunker
     from docling_core.types.doc.document import DoclingDocument
+
     DOCLING_AVAILABLE = True
 except ImportError:
     DOCLING_AVAILABLE = False
@@ -38,8 +39,8 @@ class CrewDoclingSource(BaseKnowledgeSource):
     file_paths: List[Union[Path, str]] = Field(default_factory=list)
     chunks: List[str] = Field(default_factory=list)
     safe_file_paths: List[Union[Path, str]] = Field(default_factory=list)
-    content: List[DoclingDocument] = Field(default_factory=list)
-    document_converter: DocumentConverter = Field(
+    content: List["DoclingDocument"] = Field(default_factory=list)
+    document_converter: "DocumentConverter" = Field(
         default_factory=lambda: DocumentConverter(
             allowed_formats=[
                 InputFormat.MD,
@@ -65,7 +66,7 @@ class CrewDoclingSource(BaseKnowledgeSource):
         self.safe_file_paths = self.validate_content()
         self.content = self._load_content()
 
-    def _load_content(self) -> List[DoclingDocument]:
+    def _load_content(self) -> List["DoclingDocument"]:
         try:
             return self._convert_source_to_docling_documents()
         except ConversionError as e:
@@ -87,11 +88,11 @@ class CrewDoclingSource(BaseKnowledgeSource):
             self.chunks.extend(list(new_chunks_iterable))
         self._save_documents()
 
-    def _convert_source_to_docling_documents(self) -> List[DoclingDocument]:
+    def _convert_source_to_docling_documents(self) -> List["DoclingDocument"]:
         conv_results_iter = self.document_converter.convert_all(self.safe_file_paths)
         return [result.document for result in conv_results_iter]
 
-    def _chunk_doc(self, doc: DoclingDocument) -> Iterator[str]:
+    def _chunk_doc(self, doc: "DoclingDocument") -> Iterator[str]:
         chunker = HierarchicalChunker()
         for chunk in chunker.chunk(doc):
             yield chunk.text

src/crewai/llm.py

@@ -142,7 +142,6 @@ class LLM:
         self.temperature = temperature
         self.top_p = top_p
         self.n = n
-        self.stop = stop
         self.max_completion_tokens = max_completion_tokens
         self.max_tokens = max_tokens
         self.presence_penalty = presence_penalty
@@ -160,37 +159,63 @@ class LLM:
 
         litellm.drop_params = True
 
+        # Normalize self.stop to always be a List[str]
+        if stop is None:
+            self.stop: List[str] = []
+        elif isinstance(stop, str):
+            self.stop = [stop]
+        else:
+            self.stop = stop
+
         self.set_callbacks(callbacks)
         self.set_env_callbacks()
 
     def call(
         self,
-        messages: List[Dict[str, str]],
+        messages: Union[str, List[Dict[str, str]]],
         tools: Optional[List[dict]] = None,
         callbacks: Optional[List[Any]] = None,
         available_functions: Optional[Dict[str, Any]] = None,
     ) -> str:
         """
-        High-level call method that:
-          1) Calls litellm.completion
-          2) Checks for function/tool calls
-          3) If a tool call is found:
-               a) executes the function
-               b) returns the result
-          4) If no tool call, returns the text response
+        High-level llm call method that:
+          1) Accepts either a string or a list of messages
+          2) Converts string input to the required message format
+          3) Calls litellm.completion
+          4) Handles function/tool calls if any
+          5) Returns the final text response or tool result
 
-        :param messages: The conversation messages
-        :param tools: Optional list of function schemas for function calling
-        :param callbacks: Optional list of callbacks
-        :param available_functions: A dictionary mapping function_name -> actual Python function
-        :return: Final text response from the LLM or the tool result
+        Parameters:
+        - messages (Union[str, List[Dict[str, str]]]): The input messages for the LLM.
+          - If a string is provided, it will be converted into a message list with a single entry.
+          - If a list of dictionaries is provided, each dictionary should have 'role' and 'content' keys.
+        - tools (Optional[List[dict]]): A list of tool schemas for function calling.
+        - callbacks (Optional[List[Any]]): A list of callback functions to be executed.
+        - available_functions (Optional[Dict[str, Any]]): A dictionary mapping function names to actual Python functions.
+
+        Returns:
+        - str: The final text response from the LLM or the result of a tool function call.
+
+        Examples:
+        ---------
+        # Example 1: Using a string input
+        response = llm.call("Return the name of a random city in the world.")
+        print(response)
+
+        # Example 2: Using a list of messages
+        messages = [{"role": "user", "content": "What is the capital of France?"}]
+        response = llm.call(messages)
+        print(response)
         """
+        if isinstance(messages, str):
+            messages = [{"role": "user", "content": messages}]
+
         with suppress_warnings():
             if callbacks and len(callbacks) > 0:
                 self.set_callbacks(callbacks)
 
             try:
-                # --- 1) Make the completion call
+                # --- 1) Prepare the parameters for the completion call
                 params = {
                     "model": self.model,
                     "messages": messages,
@@ -211,19 +236,21 @@ class LLM:
                     "api_version": self.api_version,
                     "api_key": self.api_key,
                     "stream": False,
-                    "tools": tools,  # pass the tool schema
+                    "tools": tools,
                 }
 
+                # Remove None values from params
                 params = {k: v for k, v in params.items() if v is not None}
 
+                # --- 2) Make the completion call
                 response = litellm.completion(**params)
                 response_message = cast(Choices, cast(ModelResponse, response).choices)[
                     0
                 ].message
                 text_response = response_message.content or ""
                 tool_calls = getattr(response_message, "tool_calls", [])
-                
-                # Ensure callbacks get the full response object with usage info
+
+                # --- 3) Handle callbacks with usage info
                 if callbacks and len(callbacks) > 0:
                     for callback in callbacks:
                         if hasattr(callback, "log_success_event"):
@@ -236,11 +263,11 @@ class LLM:
                                     end_time=0,
                                 )
 
-                # --- 2) If no tool calls, return the text response
+                # --- 4) If no tool calls, return the text response
                 if not tool_calls or not available_functions:
                     return text_response
 
-                # --- 3) Handle the tool call
+                # --- 5) Handle the tool call
                 tool_call = tool_calls[0]
                 function_name = tool_call.function.name
 
@@ -255,7 +282,6 @@ class LLM:
                     try:
                         # Call the actual tool function
                         result = fn(**function_args)
-
                         return result
 
                     except Exception as e:

src/crewai/memory/storage/kickoff_task_outputs_storage.py

@@ -23,7 +23,7 @@ class KickoffTaskOutputsSQLiteStorage:
     ) -> None:
         if db_path is None:
             # Get the parent directory of the default db path and create our db file there
-            db_path = str(Path(db_storage_path()).parent / "latest_kickoff_task_outputs.db")
+            db_path = str(Path(db_storage_path()) / "latest_kickoff_task_outputs.db")
         self.db_path = db_path
         self._printer: Printer = Printer()
         self._initialize_db()

src/crewai/memory/storage/ltm_sqlite_storage.py

@@ -17,7 +17,7 @@ class LTMSQLiteStorage:
     ) -> None:
         if db_path is None:
             # Get the parent directory of the default db path and create our db file there
-            db_path = str(Path(db_storage_path()).parent / "long_term_memory_storage.db")
+            db_path = str(Path(db_storage_path()) / "long_term_memory_storage.db")
         self.db_path = db_path
         self._printer: Printer = Printer()
         # Ensure parent directory exists

src/crewai/utilities/converter.py

@@ -241,9 +241,13 @@ def generate_model_description(model: Type[BaseModel]) -> str:
         origin = get_origin(field_type)
         args = get_args(field_type)
 
-        if origin is Union and type(None) in args:
+        if origin is Union or (origin is None and len(args) > 0):
+            # Handle both Union and the new '|' syntax
             non_none_args = [arg for arg in args if arg is not type(None)]
-            return f"Optional[{describe_field(non_none_args[0])}]"
+            if len(non_none_args) == 1:
+                return f"Optional[{describe_field(non_none_args[0])}]"
+            else:
+                return f"Optional[Union[{', '.join(describe_field(arg) for arg in non_none_args)}]]"
         elif origin is list:
             return f"List[{describe_field(args[0])}]"
         elif origin is dict:
@@ -252,8 +256,10 @@ def generate_model_description(model: Type[BaseModel]) -> str:
             return f"Dict[{key_type}, {value_type}]"
         elif isinstance(field_type, type) and issubclass(field_type, BaseModel):
             return generate_model_description(field_type)
-        else:
+        elif hasattr(field_type, "__name__"):
             return field_type.__name__
+        else:
+            return str(field_type)
 
     fields = model.__annotations__
     field_descriptions = [

src/crewai/utilities/embedding_configurator.py

@@ -14,6 +14,7 @@ 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,
@@ -124,6 +125,17 @@ class EmbeddingConfigurator:
             api_key=config.get("api_key"),
         )
 
+    @staticmethod
+    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_bedrock(config, model_name):
         from chromadb.utils.embedding_functions.amazon_bedrock_embedding_function import (

src/crewai/utilities/paths.py

@@ -7,7 +7,7 @@ import appdirs
 
 def db_storage_path() -> str:
     """Returns the path for SQLite database storage.
-    
+
     Returns:
         str: Full path to the SQLite database file
     """
@@ -16,7 +16,7 @@ def db_storage_path() -> str:
 
     data_dir = Path(appdirs.user_data_dir(app_name, app_author))
     data_dir.mkdir(parents=True, exist_ok=True)
-    return str(data_dir / "crewai_flows.db")
+    return str(data_dir)
 
 
 def get_project_directory_name():
@@ -28,4 +28,4 @@ def get_project_directory_name():
     else:
         cwd = Path.cwd()
         project_directory_name = cwd.name
-        return project_directory_name
+        return project_directory_name

src/crewai/utilities/printer.py

@@ -21,6 +21,16 @@ class Printer:
             self._print_yellow(content)
         elif color == "bold_yellow":
             self._print_bold_yellow(content)
+        elif color == "cyan":
+            self._print_cyan(content)
+        elif color == "bold_cyan":
+            self._print_bold_cyan(content)
+        elif color == "magenta":
+            self._print_magenta(content)
+        elif color == "bold_magenta":
+            self._print_bold_magenta(content)
+        elif color == "green":
+            self._print_green(content)
         else:
             print(content)
 
@@ -44,3 +54,18 @@ class Printer:
 
     def _print_bold_yellow(self, content):
         print("\033[1m\033[93m {}\033[00m".format(content))
+
+    def _print_cyan(self, content):
+        print("\033[96m {}\033[00m".format(content))
+
+    def _print_bold_cyan(self, content):
+        print("\033[1m\033[96m {}\033[00m".format(content))
+
+    def _print_magenta(self, content):
+        print("\033[35m {}\033[00m".format(content))
+
+    def _print_bold_magenta(self, content):
+        print("\033[1m\033[35m {}\033[00m".format(content))
+
+    def _print_green(self, content):
+        print("\033[32m {}\033[00m".format(content))

tests/agent_test.py

@@ -16,7 +16,7 @@ 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 import Printer, RPMController
 from crewai.utilities.events import Emitter
 
 
@@ -114,35 +114,6 @@ def test_custom_llm_temperature_preservation():
     assert agent.llm.temperature == 0.7
 
 
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_agent_execute_task():
-    from langchain_openai import ChatOpenAI
-
-    from crewai import Task
-
-    agent = Agent(
-        role="Math Tutor",
-        goal="Solve math problems accurately",
-        backstory="You are an experienced math tutor with a knack for explaining complex concepts simply.",
-        llm=ChatOpenAI(temperature=0.7, model="gpt-4o-mini"),
-    )
-
-    task = Task(
-        description="Calculate the area of a circle with radius 5 cm.",
-        expected_output="The calculated area of the circle in square centimeters.",
-        agent=agent,
-    )
-
-    result = agent.execute_task(task)
-
-    assert result is not None
-    assert (
-        result
-        == "The calculated area of the circle is approximately 78.5 square centimeters."
-    )
-    assert "square centimeters" in result.lower()
-
-
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_agent_execution():
     agent = Agent(
@@ -1629,3 +1600,142 @@ def test_agent_with_knowledge_sources():
 
         # Assert that the agent provides the correct information
         assert "red" in result.raw.lower()
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_litellm_auth_error_handling():
+    """Test that LiteLLM authentication errors are handled correctly and not retried."""
+    from litellm import AuthenticationError as LiteLLMAuthenticationError
+
+    # Create an agent with a mocked LLM and max_retry_limit=0
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        llm=LLM(model="gpt-4"),
+        max_retry_limit=0,  # Disable retries for authentication errors
+    )
+
+    # Create a task
+    task = Task(
+        description="Test task",
+        expected_output="Test output",
+        agent=agent,
+    )
+
+    # Mock the LLM call to raise AuthenticationError
+    with (
+        patch.object(LLM, "call") as mock_llm_call,
+        pytest.raises(LiteLLMAuthenticationError, match="Invalid API key"),
+    ):
+        mock_llm_call.side_effect = LiteLLMAuthenticationError(
+            message="Invalid API key", llm_provider="openai", model="gpt-4"
+        )
+        agent.execute_task(task)
+
+    # Verify the call was only made once (no retries)
+    mock_llm_call.assert_called_once()
+
+
+def test_crew_agent_executor_litellm_auth_error():
+    """Test that CrewAgentExecutor handles LiteLLM authentication errors by raising them."""
+    from litellm.exceptions import AuthenticationError
+
+    from crewai.agents.tools_handler import ToolsHandler
+    from crewai.utilities import Printer
+
+    # Create an agent and executor
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        llm=LLM(model="gpt-4", api_key="invalid_api_key"),
+    )
+    task = Task(
+        description="Test task",
+        expected_output="Test output",
+        agent=agent,
+    )
+
+    # Create executor with all required parameters
+    executor = CrewAgentExecutor(
+        agent=agent,
+        task=task,
+        llm=agent.llm,
+        crew=None,
+        prompt={"system": "You are a test agent", "user": "Execute the task: {input}"},
+        max_iter=5,
+        tools=[],
+        tools_names="",
+        stop_words=[],
+        tools_description="",
+        tools_handler=ToolsHandler(),
+    )
+
+    # Mock the LLM call to raise AuthenticationError
+    with (
+        patch.object(LLM, "call") as mock_llm_call,
+        patch.object(Printer, "print") as mock_printer,
+        pytest.raises(AuthenticationError) as exc_info,
+    ):
+        mock_llm_call.side_effect = AuthenticationError(
+            message="Invalid API key", llm_provider="openai", model="gpt-4"
+        )
+        executor.invoke(
+            {
+                "input": "test input",
+                "tool_names": "",
+                "tools": "",
+            }
+        )
+
+    # Verify error handling messages
+    error_message = f"Error during LLM call: {str(mock_llm_call.side_effect)}"
+    mock_printer.assert_any_call(
+        content=error_message,
+        color="red",
+    )
+
+    # Verify the call was only made once (no retries)
+    mock_llm_call.assert_called_once()
+
+    # Assert that the exception was raised and has the expected attributes
+    assert exc_info.type is AuthenticationError
+    assert "Invalid API key".lower() in exc_info.value.message.lower()
+    assert exc_info.value.llm_provider == "openai"
+    assert exc_info.value.model == "gpt-4"
+
+
+def test_litellm_anthropic_error_handling():
+    """Test that AnthropicError from LiteLLM is handled correctly and not retried."""
+    from litellm.llms.anthropic.common_utils import AnthropicError
+
+    # Create an agent with a mocked LLM that uses an Anthropic model
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        llm=LLM(model="claude-3.5-sonnet-20240620"),
+        max_retry_limit=0,
+    )
+
+    # Create a task
+    task = Task(
+        description="Test task",
+        expected_output="Test output",
+        agent=agent,
+    )
+
+    # Mock the LLM call to raise AnthropicError
+    with (
+        patch.object(LLM, "call") as mock_llm_call,
+        pytest.raises(AnthropicError, match="Test Anthropic error"),
+    ):
+        mock_llm_call.side_effect = AnthropicError(
+            status_code=500,
+            message="Test Anthropic error",
+        )
+        agent.execute_task(task)
+
+    # Verify the LLM call was only made once (no retries)
+    mock_llm_call.assert_called_once()

tests/cassettes/test_agent_error_on_parsing_tool.yaml

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+      Input: the input to the action, dictionary enclosed in curly braces\nObservation:
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+      N times. Once I know the final answer, I must return the following format:\n\n```\nThought:
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+      and the most complete as possible, it must be outcome described\n\n```"}, {"role":
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tests/cassettes/test_llm_call_with_tool_and_message_list.yaml

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tests/cassettes/test_llm_call_with_tool_and_string_input.yaml

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tests/crew_test.py

@@ -3480,10 +3480,12 @@ def test_crew_guardrail_feedback_in_context():
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_before_kickoff_callback():
-    from crewai.project import CrewBase, agent, before_kickoff, crew, task
+    from crewai.project import CrewBase, agent, before_kickoff, task
 
     @CrewBase
     class TestCrewClass:
+        from crewai.project import crew
+
         agents_config = None
         tasks_config = None
 
@@ -3510,7 +3512,7 @@ def test_before_kickoff_callback():
             task = Task(
                 description="Test task description",
                 expected_output="Test expected output",
-                agent=self.my_agent(),  # Use the agent instance
+                agent=self.my_agent(),
             )
             return task
 
@@ -3520,28 +3522,30 @@ def test_before_kickoff_callback():
 
     test_crew_instance = TestCrewClass()
 
-    crew = test_crew_instance.crew()
+    test_crew = test_crew_instance.crew()
 
     # Verify that the before_kickoff_callbacks are set
-    assert len(crew.before_kickoff_callbacks) == 1
+    assert len(test_crew.before_kickoff_callbacks) == 1
 
     # Prepare inputs
     inputs = {"initial": True}
 
     # Call kickoff
-    crew.kickoff(inputs=inputs)
+    test_crew.kickoff(inputs=inputs)
 
     # Check that the before_kickoff function was called and modified inputs
     assert test_crew_instance.inputs_modified
-    assert inputs.get("modified") == True
+    assert inputs.get("modified")
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_before_kickoff_without_inputs():
-    from crewai.project import CrewBase, agent, before_kickoff, crew, task
+    from crewai.project import CrewBase, agent, before_kickoff, task
 
     @CrewBase
     class TestCrewClass:
+        from crewai.project import crew
+
         agents_config = None
         tasks_config = None
 
@@ -3579,12 +3583,12 @@ def test_before_kickoff_without_inputs():
     # Instantiate the class
     test_crew_instance = TestCrewClass()
     # Build the crew
-    crew = test_crew_instance.crew()
+    test_crew = test_crew_instance.crew()
     # Verify that the before_kickoff_callback is registered
-    assert len(crew.before_kickoff_callbacks) == 1
+    assert len(test_crew.before_kickoff_callbacks) == 1
 
     # Call kickoff without passing inputs
-    output = crew.kickoff()
+    test_crew.kickoff()
 
     # Check that the before_kickoff function was called
     assert test_crew_instance.inputs_modified

tests/llm_test.py

@@ -4,6 +4,7 @@ import pytest
 
 from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
 from crewai.llm import LLM
+from crewai.tools import tool
 from crewai.utilities.token_counter_callback import TokenCalcHandler
 
 
@@ -37,3 +38,119 @@ def test_llm_callback_replacement():
     assert usage_metrics_1.successful_requests == 1
     assert usage_metrics_2.successful_requests == 1
     assert usage_metrics_1 == calc_handler_1.token_cost_process.get_summary()
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_llm_call_with_string_input():
+    llm = LLM(model="gpt-4o-mini")
+
+    # Test the call method with a string input
+    result = llm.call("Return the name of a random city in the world.")
+    assert isinstance(result, str)
+    assert len(result.strip()) > 0  # Ensure the response is not empty
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_llm_call_with_string_input_and_callbacks():
+    llm = LLM(model="gpt-4o-mini")
+    calc_handler = TokenCalcHandler(token_cost_process=TokenProcess())
+
+    # Test the call method with a string input and callbacks
+    result = llm.call(
+        "Tell me a joke.",
+        callbacks=[calc_handler],
+    )
+    usage_metrics = calc_handler.token_cost_process.get_summary()
+
+    assert isinstance(result, str)
+    assert len(result.strip()) > 0
+    assert usage_metrics.successful_requests == 1
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_llm_call_with_message_list():
+    llm = LLM(model="gpt-4o-mini")
+    messages = [{"role": "user", "content": "What is the capital of France?"}]
+
+    # Test the call method with a list of messages
+    result = llm.call(messages)
+    assert isinstance(result, str)
+    assert "Paris" in result
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_llm_call_with_tool_and_string_input():
+    llm = LLM(model="gpt-4o-mini")
+
+    def get_current_year() -> str:
+        """Returns the current year as a string."""
+        from datetime import datetime
+
+        return str(datetime.now().year)
+
+    # Create tool schema
+    tool_schema = {
+        "type": "function",
+        "function": {
+            "name": "get_current_year",
+            "description": "Returns the current year as a string.",
+            "parameters": {
+                "type": "object",
+                "properties": {},
+                "required": [],
+            },
+        },
+    }
+
+    # Available functions mapping
+    available_functions = {"get_current_year": get_current_year}
+
+    # Test the call method with a string input and tool
+    result = llm.call(
+        "What is the current year?",
+        tools=[tool_schema],
+        available_functions=available_functions,
+    )
+
+    assert isinstance(result, str)
+    assert result == get_current_year()
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_llm_call_with_tool_and_message_list():
+    llm = LLM(model="gpt-4o-mini")
+
+    def square_number(number: int) -> int:
+        """Returns the square of a number."""
+        return number * number
+
+    # Create tool schema
+    tool_schema = {
+        "type": "function",
+        "function": {
+            "name": "square_number",
+            "description": "Returns the square of a number.",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "number": {"type": "integer", "description": "The number to square"}
+                },
+                "required": ["number"],
+            },
+        },
+    }
+
+    # Available functions mapping
+    available_functions = {"square_number": square_number}
+
+    messages = [{"role": "user", "content": "What is the square of 5?"}]
+
+    # Test the call method with messages and tool
+    result = llm.call(
+        messages,
+        tools=[tool_schema],
+        available_functions=available_functions,
+    )
+
+    assert isinstance(result, int)
+    assert result == 25

tests/test_flow_default_override.py

@@ -0,0 +1,112 @@
+"""Test that persisted state properly overrides default values."""
+
+from crewai.flow.flow import Flow, FlowState, listen, start
+from crewai.flow.persistence import persist
+
+
+class PoemState(FlowState):
+    """Test state model with default values that should be overridden."""
+    sentence_count: int = 1000  # Default that should be overridden
+    has_set_count: bool = False  # Track whether we've set the count
+    poem_type: str = ""
+
+
+def test_default_value_override():
+    """Test that persisted state values override class defaults."""
+
+    @persist()
+    class PoemFlow(Flow[PoemState]):
+        initial_state = PoemState
+
+        @start()
+        def set_sentence_count(self):
+            if self.state.has_set_count and self.state.sentence_count == 2:
+                self.state.sentence_count = 3
+
+            elif self.state.has_set_count and self.state.sentence_count == 1000:
+                self.state.sentence_count = 1000
+
+            elif self.state.has_set_count and self.state.sentence_count == 5:
+                self.state.sentence_count = 5
+
+            else:
+                self.state.sentence_count = 2
+                self.state.has_set_count = True
+
+    # First run - should set sentence_count to 2
+    flow1 = PoemFlow()
+    flow1.kickoff()
+    original_uuid = flow1.state.id
+    assert flow1.state.sentence_count == 2
+
+    # Second run - should load sentence_count=2 instead of default 1000
+    flow2 = PoemFlow()
+    flow2.kickoff(inputs={"id": original_uuid})
+    assert flow2.state.sentence_count == 3  # Should load 2, not default 1000
+
+    # Fourth run - explicit override should work
+    flow3 = PoemFlow()
+    flow3.kickoff(inputs={
+        "id": original_uuid,
+        "has_set_count": True,
+        "sentence_count": 5,  # Override persisted value
+    })
+    assert flow3.state.sentence_count == 5  # Should use override value
+
+    # Third run - should not load sentence_count=2 instead of default 1000
+    flow4 = PoemFlow()
+    flow4.kickoff(inputs={"has_set_count": True})
+    assert flow4.state.sentence_count == 1000  # Should load 1000, not 2
+
+
+def test_multi_step_default_override():
+    """Test default value override with multiple start methods."""
+
+    @persist()
+    class MultiStepPoemFlow(Flow[PoemState]):
+        initial_state = PoemState
+
+        @start()
+        def set_sentence_count(self):
+            print("Setting sentence count")
+            if not self.state.has_set_count:
+                self.state.sentence_count = 3
+                self.state.has_set_count = True
+
+        @listen(set_sentence_count)
+        def set_poem_type(self):
+            print("Setting poem type")
+            if self.state.sentence_count == 3:
+                self.state.poem_type = "haiku"
+            elif self.state.sentence_count == 5:
+                self.state.poem_type = "limerick"
+            else:
+                self.state.poem_type = "free_verse"
+
+        @listen(set_poem_type)
+        def finished(self):
+            print("finished")
+
+    # First run - should set both sentence count and poem type
+    flow1 = MultiStepPoemFlow()
+    flow1.kickoff()
+    original_uuid = flow1.state.id
+    assert flow1.state.sentence_count == 3
+    assert flow1.state.poem_type == "haiku"
+
+    # Second run - should load persisted state and update poem type
+    flow2 = MultiStepPoemFlow()
+    flow2.kickoff(inputs={
+        "id": original_uuid,
+        "sentence_count": 5
+    })
+    assert flow2.state.sentence_count == 5
+    assert flow2.state.poem_type == "limerick"
+
+    # Third run - new flow without persisted state should use defaults
+    flow3 = MultiStepPoemFlow()
+    flow3.kickoff(inputs={
+        "id": original_uuid
+    })
+    assert flow3.state.sentence_count == 5
+    assert flow3.state.poem_type == "limerick"

tests/test_flow_persistence.py

@@ -1,13 +1,13 @@
 """Test flow state persistence functionality."""
 
 import os
-from typing import Dict, Optional
+from typing import Dict
 
 import pytest
 from pydantic import BaseModel
 
-from crewai.flow.flow import Flow, FlowState, start
-from crewai.flow.persistence import FlowPersistence, persist
+from crewai.flow.flow import Flow, FlowState, listen, start
+from crewai.flow.persistence import persist
 from crewai.flow.persistence.sqlite import SQLiteFlowPersistence
 
 
@@ -21,20 +21,20 @@ def test_persist_decorator_saves_state(tmp_path):
     """Test that @persist decorator saves state in SQLite."""
     db_path = os.path.join(tmp_path, "test_flows.db")
     persistence = SQLiteFlowPersistence(db_path)
-    
+
     class TestFlow(Flow[Dict[str, str]]):
         initial_state = dict()  # Use dict instance as initial state
-        
+
         @start()
         @persist(persistence)
         def init_step(self):
             self.state["message"] = "Hello, World!"
             self.state["id"] = "test-uuid"  # Ensure we have an ID for persistence
-    
+
     # Run flow and verify state is saved
     flow = TestFlow(persistence=persistence)
     flow.kickoff()
-    
+
     # Load state from DB and verify
     saved_state = persistence.load_state(flow.state["id"])
     assert saved_state is not None
@@ -45,20 +45,20 @@ def test_structured_state_persistence(tmp_path):
     """Test persistence with Pydantic model state."""
     db_path = os.path.join(tmp_path, "test_flows.db")
     persistence = SQLiteFlowPersistence(db_path)
-    
+
     class StructuredFlow(Flow[TestState]):
         initial_state = TestState
-        
+
         @start()
         @persist(persistence)
         def count_up(self):
             self.state.counter += 1
             self.state.message = f"Count is {self.state.counter}"
-    
+
     # Run flow and verify state changes are saved
     flow = StructuredFlow(persistence=persistence)
     flow.kickoff()
-    
+
     # Load and verify state
     saved_state = persistence.load_state(flow.state.id)
     assert saved_state is not None
@@ -70,126 +70,107 @@ def test_flow_state_restoration(tmp_path):
     """Test restoring flow state from persistence with various restoration methods."""
     db_path = os.path.join(tmp_path, "test_flows.db")
     persistence = SQLiteFlowPersistence(db_path)
-    
+
     # First flow execution to create initial state
     class RestorableFlow(Flow[TestState]):
-        initial_state = TestState
-        
+
         @start()
         @persist(persistence)
         def set_message(self):
-            self.state.message = "Original message"
-            self.state.counter = 42
-    
+            if self.state.message == "":
+                self.state.message = "Original message"
+            if self.state.counter == 0:
+                self.state.counter = 42
+
     # Create and persist initial state
     flow1 = RestorableFlow(persistence=persistence)
     flow1.kickoff()
     original_uuid = flow1.state.id
-    
+
     # Test case 1: Restore using restore_uuid with field override
-    flow2 = RestorableFlow(
-        persistence=persistence,
-        restore_uuid=original_uuid,
-        counter=43,  # Override counter
-    )
-    
+    flow2 = RestorableFlow(persistence=persistence)
+    flow2.kickoff(inputs={
+        "id": original_uuid,
+        "counter": 43
+    })
+
     # Verify state restoration and selective field override
     assert flow2.state.id == original_uuid
     assert flow2.state.message == "Original message"  # Preserved
     assert flow2.state.counter == 43  # Overridden
-    
+
     # Test case 2: Restore using kwargs['id']
-    flow3 = RestorableFlow(
-        persistence=persistence,
-        id=original_uuid,
-        message="Updated message",  # Override message
-    )
-    
+    flow3 = RestorableFlow(persistence=persistence)
+    flow3.kickoff(inputs={
+        "id": original_uuid,
+        "message": "Updated message"
+    })
+
     # Verify state restoration and selective field override
     assert flow3.state.id == original_uuid
-    assert flow3.state.counter == 42  # Preserved
+    assert flow3.state.counter == 43  # Preserved
     assert flow3.state.message == "Updated message"  # Overridden
-    
-    # Test case 3: Verify error on conflicting IDs
-    with pytest.raises(ValueError) as exc_info:
-        RestorableFlow(
-            persistence=persistence,
-            restore_uuid=original_uuid,
-            id="different-id",  # Conflict with restore_uuid
-        )
-    assert "Conflicting IDs provided" in str(exc_info.value)
-    
-    # Test case 4: Verify error on non-existent restore_uuid
-    with pytest.raises(ValueError) as exc_info:
-        RestorableFlow(
-            persistence=persistence,
-            restore_uuid="non-existent-uuid",
-        )
-    assert "No state found" in str(exc_info.value)
-    
-    # Test case 5: Allow new state creation with kwargs['id']
-    new_uuid = "new-flow-id"
-    flow4 = RestorableFlow(
-        persistence=persistence,
-        id=new_uuid,
-        message="New message",
-        counter=100,
-    )
-    
-    # Verify new state creation with provided ID
-    assert flow4.state.id == new_uuid
-    assert flow4.state.message == "New message"
-    assert flow4.state.counter == 100
 
 
 def test_multiple_method_persistence(tmp_path):
     """Test state persistence across multiple method executions."""
     db_path = os.path.join(tmp_path, "test_flows.db")
     persistence = SQLiteFlowPersistence(db_path)
-    
+
     class MultiStepFlow(Flow[TestState]):
-        initial_state = TestState
-        
         @start()
         @persist(persistence)
         def step_1(self):
-            self.state.counter = 1
-            self.state.message = "Step 1"
-        
-        @start()
+            if self.state.counter == 1:
+                self.state.counter = 99999
+                self.state.message = "Step 99999"
+            else:
+                self.state.counter = 1
+                self.state.message = "Step 1"
+
+        @listen(step_1)
         @persist(persistence)
         def step_2(self):
-            self.state.counter = 2
-            self.state.message = "Step 2"
-    
+            if self.state.counter == 1:
+                self.state.counter = 2
+                self.state.message = "Step 2"
+
     flow = MultiStepFlow(persistence=persistence)
     flow.kickoff()
-    
+
+    flow2 = MultiStepFlow(persistence=persistence)
+    flow2.kickoff(inputs={"id": flow.state.id})
+
     # Load final state
-    final_state = persistence.load_state(flow.state.id)
+    final_state = flow2.state
     assert final_state is not None
-    assert final_state["counter"] == 2
-    assert final_state["message"] == "Step 2"
+    assert final_state.counter == 2
+    assert final_state.message == "Step 2"
 
-
-def test_persistence_error_handling(tmp_path):
-    """Test error handling in persistence operations."""
-    db_path = os.path.join(tmp_path, "test_flows.db")
-    persistence = SQLiteFlowPersistence(db_path)
-    
-    class InvalidFlow(Flow[TestState]):
-        # Missing id field in initial state
-        class InvalidState(BaseModel):
-            value: str = ""
-            
-        initial_state = InvalidState
-        
+    class NoPersistenceMultiStepFlow(Flow[TestState]):
         @start()
         @persist(persistence)
-        def will_fail(self):
-            self.state.value = "test"
-    
-    with pytest.raises(ValueError) as exc_info:
-        flow = InvalidFlow(persistence=persistence)
-    
-    assert "must have an 'id' field" in str(exc_info.value)
+        def step_1(self):
+            if self.state.counter == 1:
+                self.state.counter = 99999
+                self.state.message = "Step 99999"
+            else:
+                self.state.counter = 1
+                self.state.message = "Step 1"
+
+        @listen(step_1)
+        def step_2(self):
+            if self.state.counter == 1:
+                self.state.counter = 2
+                self.state.message = "Step 2"
+
+    flow = NoPersistenceMultiStepFlow(persistence=persistence)
+    flow.kickoff()
+
+    flow2 = NoPersistenceMultiStepFlow(persistence=persistence)
+    flow2.kickoff(inputs={"id": flow.state.id})
+
+    # Load final state
+    final_state = flow2.state
+    assert final_state.counter == 99999
+    assert final_state.message == "Step 99999"

tests/utilities/test_converter.py

@@ -588,3 +588,12 @@ def test_converter_with_function_calling():
     assert output.name == "Eve"
     assert output.age == 35
     instructor.to_pydantic.assert_called_once()
+
+
+def test_generate_model_description_union_field():
+    class UnionModel(BaseModel):
+        field: int | str | None
+
+    description = generate_model_description(UnionModel)
+    expected_description = '{\n  "field": int | str | None\n}'
+    assert description == expected_description

uv.lock

@@ -1,7 +1,6 @@
 version = 1
 requires-python = ">=3.10, <3.13"
 resolution-markers = [
-
     "python_full_version < '3.11' and platform_system == 'Darwin' and sys_platform == 'darwin'",
     "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'darwin'",
     "(python_full_version < '3.11' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform == 'darwin') or (python_full_version < '3.11' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform == 'darwin')",
@@ -37,7 +36,7 @@ resolution-markers = [
     "python_full_version >= '3.12.4' and platform_machine == 'aarch64' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform == 'linux'",
     "(python_full_version >= '3.12.4' and platform_machine != 'aarch64' and platform_system == 'Darwin' and sys_platform != 'darwin') or (python_full_version >= '3.12.4' and platform_system == 'Darwin' and sys_platform != 'darwin' and sys_platform != 'linux')",
     "python_full_version >= '3.12.4' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform != 'darwin' and sys_platform != 'linux'",
-    "(python_full_version >= '3.12.4' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform != 'darwin') or (python_full_version >= '3.12.4' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform != 'darwin' and sys_platform != 'linux')"
+    "(python_full_version >= '3.12.4' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform != 'darwin') or (python_full_version >= '3.12.4' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform != 'darwin' and sys_platform != 'linux')",
 ]
 
 [[package]]
@@ -199,6 +198,15 @@ 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"
@@ -220,6 +228,15 @@ 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"
@@ -346,7 +363,7 @@ name = "build"
 version = "1.2.2.post1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "colorama", marker = "(os_name == 'nt' and platform_machine != 'aarch64' and sys_platform == 'linux') or (os_name == 'nt' and sys_platform != 'darwin' and sys_platform != 'linux')" },
+    { name = "colorama", marker = "os_name == 'nt'" },
     { name = "importlib-metadata", marker = "python_full_version < '3.10.2'" },
     { name = "packaging" },
     { name = "pyproject-hooks" },
@@ -581,7 +598,7 @@ name = "click"
 version = "8.1.7"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "colorama", marker = "sys_platform == 'win32'" },
+    { name = "colorama", marker = "platform_system == 'Windows'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/96/d3/f04c7bfcf5c1862a2a5b845c6b2b360488cf47af55dfa79c98f6a6bf98b5/click-8.1.7.tar.gz", hash = "sha256:ca9853ad459e787e2192211578cc907e7594e294c7ccc834310722b41b9ca6de", size = 336121 }
 wheels = [
@@ -632,7 +649,7 @@ wheels = [
 
 [[package]]
 name = "crewai"
-version = "0.95.0"
+version = "0.98.0"
 source = { editable = "." }
 dependencies = [
     { name = "appdirs" },
@@ -715,7 +732,7 @@ 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 = ">=0.25.5" },
+    { name = "crewai-tools", marker = "extra == 'tools'", specifier = ">=0.32.1" },
     { name = "docling", marker = "extra == 'docling'", specifier = ">=2.12.0" },
     { name = "fastembed", marker = "extra == 'fastembed'", specifier = ">=0.4.1" },
     { name = "instructor", specifier = ">=1.3.3" },
@@ -763,7 +780,7 @@ dev = [
 
 [[package]]
 name = "crewai-tools"
-version = "0.25.6"
+version = "0.32.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "beautifulsoup4" },
@@ -775,20 +792,21 @@ dependencies = [
     { name = "lancedb" },
     { name = "linkup-sdk" },
     { name = "openai" },
+    { name = "patronus" },
     { name = "pydantic" },
     { name = "pyright" },
-    { name = "pytest" },
     { 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/23/2f/fbfd0dc8912d375a2d1272c503f79c83c25f3d2b4b72c230b0672278a1bd/crewai_tools-0.25.6.tar.gz", hash = "sha256:442a7e7e579cb3c671a53c5b7afce645cd31d2db913ecc6d1e22a4c5e1baa840", size = 883175 }
+sdist = { url = "https://files.pythonhosted.org/packages/e9/e7/fb07f0089028f7c9003770641d21f5844d4fa22bf5cc4c4b3676bfa0e1fe/crewai_tools-0.32.1.tar.gz", hash = "sha256:41acea9243b17a463f355d48dfe7d73bd59738c8862a8da780eae008e0136414", size = 887378 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/ce/21/561a81b4f8cfcc2ac6a0c3db3ec86b70a7db6dabb0dd7d13c96be981b2fc/crewai_tools-0.25.6-py3-none-any.whl", hash = "sha256:463e0ee8d780ab7a801992e3960471fb8e64d038866429f70995ddd0a83e0679", size = 514758 },
+    { url = "https://files.pythonhosted.org/packages/36/f0/8f98f1a2b90b9b989bd01cf48b5e3bb2d842be2062bfd3177a77561e7b61/crewai_tools-0.32.1-py3-none-any.whl", hash = "sha256:6cb436dc66e19e35285a4fce501158a13bce99b244370574f568ec33c5513351", size = 537264 },
 ]
 
 [[package]]
@@ -2588,7 +2606,7 @@ version = "1.6.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "click" },
-    { name = "colorama", marker = "sys_platform == 'win32'" },
+    { name = "colorama", marker = "platform_system == 'Windows'" },
     { name = "ghp-import" },
     { name = "jinja2" },
     { name = "markdown" },
@@ -2769,7 +2787,7 @@ version = "2.10.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "pygments" },
-    { name = "pywin32", marker = "sys_platform == 'win32'" },
+    { name = "pywin32", marker = "platform_system == 'Windows'" },
     { name = "tqdm" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/3a/93/80ac75c20ce54c785648b4ed363c88f148bf22637e10c9863db4fbe73e74/mpire-2.10.2.tar.gz", hash = "sha256:f66a321e93fadff34585a4bfa05e95bd946cf714b442f51c529038eb45773d97", size = 271270 }
@@ -3016,7 +3034,7 @@ name = "nvidia-cudnn-cu12"
 version = "9.1.0.70"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-cublas-cu12", marker = "(platform_machine != 'aarch64' and platform_system != 'Darwin') or (platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform != 'linux')" }
+    { name = "nvidia-cublas-cu12", marker = "(platform_machine != 'aarch64' and platform_system != 'Darwin') or (platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform != 'linux')" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/9f/fd/713452cd72343f682b1c7b9321e23829f00b842ceaedcda96e742ea0b0b3/nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl", hash = "sha256:165764f44ef8c61fcdfdfdbe769d687e06374059fbb388b6c89ecb0e28793a6f", size = 664752741 },
@@ -3045,7 +3063,7 @@ source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "nvidia-cublas-cu12", marker = "(platform_machine != 'aarch64' and platform_system != 'Darwin') or (platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform != 'linux')" },
     { name = "nvidia-cusparse-cu12", marker = "(platform_machine != 'aarch64' and platform_system != 'Darwin') or (platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform != 'linux')" },
-    { name = "nvidia-nvjitlink-cu12", marker = "(platform_machine != 'aarch64' and platform_system != 'Darwin') or (platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform != 'linux')" }
+    { name = "nvidia-nvjitlink-cu12", marker = "(platform_machine != 'aarch64' and platform_system != 'Darwin') or (platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform != 'linux')" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/bc/1d/8de1e5c67099015c834315e333911273a8c6aaba78923dd1d1e25fc5f217/nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl", hash = "sha256:8a7ec542f0412294b15072fa7dab71d31334014a69f953004ea7a118206fe0dd", size = 124161928 },
@@ -3056,7 +3074,7 @@ name = "nvidia-cusparse-cu12"
 version = "12.1.0.106"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-nvjitlink-cu12", marker = "(platform_machine != 'aarch64' and platform_system != 'Darwin') or (platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform != 'linux')" }
+    { name = "nvidia-nvjitlink-cu12", marker = "(platform_machine != 'aarch64' and platform_system != 'Darwin') or (platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform != 'linux')" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/65/5b/cfaeebf25cd9fdec14338ccb16f6b2c4c7fa9163aefcf057d86b9cc248bb/nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl", hash = "sha256:f3b50f42cf363f86ab21f720998517a659a48131e8d538dc02f8768237bd884c", size = 195958278 },
@@ -3496,6 +3514,24 @@ 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"
@@ -3606,7 +3642,7 @@ name = "portalocker"
 version = "2.10.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "pywin32", marker = "sys_platform == 'win32'" },
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