Merge branch 'main' into fix/clone_when_using_knowledge

update litellm for deepseek

docs/concepts/agents.mdx

@@ -43,7 +43,7 @@ Think of an agent as a specialized team member with specific skills, expertise,
 | **Max Retry Limit** _(optional)_        | `max_retry_limit`        | `int`                         | Maximum number of retries when an error occurs. Default is 2.                                                         |
 | **Respect Context Window** _(optional)_ | `respect_context_window` | `bool`                        | Keep messages under context window size by summarizing. Default is True.                                              |
 | **Code Execution Mode** _(optional)_    | `code_execution_mode`    | `Literal["safe", "unsafe"]`   | Mode for code execution: 'safe' (using Docker) or 'unsafe' (direct). Default is 'safe'.                               |
-| **Embedder Config** _(optional)_        | `embedder_config`        | `Optional[Dict[str, Any]]`    | Configuration for the embedder used by the agent.                                                                     |
+| **Embedder** _(optional)_               | `embedder`               | `Optional[Dict[str, Any]]`    | Configuration for the embedder used by the agent.                                                                     |
 | **Knowledge Sources** _(optional)_      | `knowledge_sources`      | `Optional[List[BaseKnowledgeSource]]` | Knowledge sources available to the agent.                                                                     |
 | **Use System Prompt** _(optional)_      | `use_system_prompt`      | `Optional[bool]`              | Whether to use system prompt (for o1 model support). Default is True.                                                 |
 
@@ -152,7 +152,7 @@ agent = Agent(
     use_system_prompt=True,  # Default: True
     tools=[SerperDevTool()],  # Optional: List of tools
     knowledge_sources=None,  # Optional: List of knowledge sources
-    embedder_config=None,  # Optional: Custom embedder configuration
+    embedder=None,  # Optional: Custom embedder configuration
     system_template=None,  # Optional: Custom system prompt template
     prompt_template=None,  # Optional: Custom prompt template
     response_template=None,  # Optional: Custom response template

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
@@ -317,6 +324,13 @@ agent = Agent(
     verbose=True,
     allow_delegation=False,
     llm=gemini_llm,
+    embedder={
+        "provider": "google",
+        "config": {
+            "model": "models/text-embedding-004",
+            "api_key": GEMINI_API_KEY,
+        }
+    }
 )
 
 task = Task(

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 = "0.100.0"
 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"
@@ -11,27 +11,22 @@ dependencies = [
     # Core Dependencies
     "pydantic>=2.4.2",
     "openai>=1.13.3",
-    "litellm==1.57.4",
+    "litellm==1.59.8",
     "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 +35,8 @@ dependencies = [
     "uv>=0.4.25",
     "tomli-w>=1.1.0",
     "tomli>=2.0.2",
-    "blinker>=1.9.0"
+    "blinker>=1.9.0",
+    "json5>=0.10.0",
 ]
 
 [project.urls]
@@ -49,7 +45,7 @@ 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__ = "0.100.0"
 __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
@@ -63,6 +61,7 @@ class Agent(BaseAgent):
             tools: Tools at agents disposal
             step_callback: Callback to be executed after each step of the agent execution.
             knowledge_sources: Knowledge sources for the agent.
+            embedder: Embedder configuration for the agent.
     """
 
     _times_executed: int = PrivateAttr(default=0)
@@ -124,17 +123,10 @@ class Agent(BaseAgent):
         default="safe",
         description="Mode for code execution: 'safe' (using Docker) or 'unsafe' (direct execution).",
     )
-    embedder_config: Optional[Dict[str, Any]] = Field(
+    embedder: Optional[Dict[str, Any]] = Field(
         default=None,
         description="Embedder configuration for the agent.",
     )
-    knowledge_sources: Optional[List[BaseKnowledgeSource]] = Field(
-        default=None,
-        description="Knowledge sources for the agent.",
-    )
-    _knowledge: Optional[Knowledge] = PrivateAttr(
-        default=None,
-    )
 
     @model_validator(mode="after")
     def post_init_setup(self):
@@ -165,10 +157,11 @@ class Agent(BaseAgent):
                 if isinstance(self.knowledge_sources, list) and all(
                     isinstance(k, BaseKnowledgeSource) for k in self.knowledge_sources
                 ):
-                    self._knowledge = Knowledge(
+                    self.knowledge = Knowledge(
                         sources=self.knowledge_sources,
-                        embedder_config=self.embedder_config,
+                        embedder=self.embedder,
                         collection_name=knowledge_agent_name,
+                        storage=self.knowledge_storage or None,
                     )
         except (TypeError, ValueError) as e:
             raise ValueError(f"Invalid Knowledge Configuration: {str(e)}")
@@ -227,8 +220,8 @@ class Agent(BaseAgent):
             if memory.strip() != "":
                 task_prompt += self.i18n.slice("memory").format(memory=memory)
 
-        if self._knowledge:
-            agent_knowledge_snippets = self._knowledge.query([task.prompt()])
+        if self.knowledge:
+            agent_knowledge_snippets = self.knowledge.query([task.prompt()])
             if agent_knowledge_snippets:
                 agent_knowledge_context = extract_knowledge_context(
                     agent_knowledge_snippets
@@ -261,6 +254,9 @@ 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/base_agent.py

@@ -18,6 +18,8 @@ from pydantic_core import PydanticCustomError
 from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
 from crewai.agents.cache.cache_handler import CacheHandler
 from crewai.agents.tools_handler import ToolsHandler
+from crewai.knowledge.knowledge import Knowledge
+from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.tools import BaseTool
 from crewai.tools.base_tool import Tool
 from crewai.utilities import I18N, Logger, RPMController
@@ -48,6 +50,8 @@ class BaseAgent(ABC, BaseModel):
         cache_handler (InstanceOf[CacheHandler]): An instance of the CacheHandler class.
         tools_handler (InstanceOf[ToolsHandler]): An instance of the ToolsHandler class.
         max_tokens: Maximum number of tokens for the agent to generate in a response.
+        knowledge_sources: Knowledge sources for the agent.
+        knowledge_storage: Custom knowledge storage for the agent.
 
 
     Methods:
@@ -130,6 +134,17 @@ class BaseAgent(ABC, BaseModel):
     max_tokens: Optional[int] = Field(
         default=None, description="Maximum number of tokens for the agent's execution."
     )
+    knowledge: Optional[Knowledge] = Field(
+        default=None, description="Knowledge for the agent."
+    )
+    knowledge_sources: Optional[List[BaseKnowledgeSource]] = Field(
+        default=None,
+        description="Knowledge sources for the agent.",
+    )
+    knowledge_storage: Optional[Any] = Field(
+        default=None,
+        description="Custom knowledge storage for the agent.",
+    )
 
     @model_validator(mode="before")
     @classmethod
@@ -256,13 +271,44 @@ class BaseAgent(ABC, BaseModel):
             "tools_handler",
             "cache_handler",
             "llm",
+            "knowledge_sources",
+            "knowledge_storage",
+            "knowledge",
         }
 
-        # Copy llm and clear callbacks
+        # Copy llm
         existing_llm = shallow_copy(self.llm)
+        copied_knowledge = shallow_copy(self.knowledge)
+        copied_knowledge_storage = shallow_copy(self.knowledge_storage)
+        # Properly copy knowledge sources if they exist
+        existing_knowledge_sources = None
+        if self.knowledge_sources:
+            # Create a shared storage instance for all knowledge sources
+            shared_storage = (
+                self.knowledge_sources[0].storage if self.knowledge_sources else None
+            )
+
+            existing_knowledge_sources = []
+            for source in self.knowledge_sources:
+                copied_source = (
+                    source.model_copy()
+                    if hasattr(source, "model_copy")
+                    else shallow_copy(source)
+                )
+                # Ensure all copied sources use the same storage instance
+                copied_source.storage = shared_storage
+                existing_knowledge_sources.append(copied_source)
+
         copied_data = self.model_dump(exclude=exclude)
         copied_data = {k: v for k, v in copied_data.items() if v is not None}
-        copied_agent = type(self)(**copied_data, llm=existing_llm, tools=self.tools)
+        copied_agent = type(self)(
+            **copied_data,
+            llm=existing_llm,
+            tools=self.tools,
+            knowledge_sources=existing_knowledge_sources,
+            knowledge=copied_knowledge,
+            knowledge_storage=copied_knowledge_storage,
+        )
 
         return copied_agent
 

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

@@ -25,7 +25,7 @@ class OutputConverter(BaseModel, ABC):
     llm: Any = Field(description="The language model to be used to convert the text.")
     model: Any = Field(description="The model to be used to convert the text.")
     instructions: str = Field(description="Conversion instructions to the LLM.")
-    max_attempts: Optional[int] = Field(
+    max_attempts: int = Field(
         description="Max number of attempts to try to get the output formatted.",
         default=3,
     )

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

@@ -2,25 +2,26 @@ from crewai.types.usage_metrics import UsageMetrics
 
 
 class TokenProcess:
-    total_tokens: int = 0
-    prompt_tokens: int = 0
-    cached_prompt_tokens: int = 0
-    completion_tokens: int = 0
-    successful_requests: int = 0
+    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

@@ -350,7 +350,10 @@ def chat():
     Start a conversation with the Crew, collecting user-supplied inputs,
     and using the Chat LLM to generate responses.
     """
-    click.echo("Starting a conversation with the Crew")
+    click.secho(
+        "\nStarting a conversation with the Crew\n" "Type 'exit' or Ctrl+C to quit.\n",
+    )
+
     run_chat()
 
 

src/crewai/cli/crew_chat.py

@@ -1,17 +1,52 @@
 import json
+import platform
 import re
 import sys
+import threading
+import time
 from pathlib import Path
 from typing import Any, Dict, List, Optional, Set, Tuple
 
 import click
 import tomli
+from packaging import version
 
+from crewai.cli.utils import read_toml
+from crewai.cli.version import get_crewai_version
 from crewai.crew import Crew
 from crewai.llm import LLM
 from crewai.types.crew_chat import ChatInputField, ChatInputs
 from crewai.utilities.llm_utils import create_llm
 
+MIN_REQUIRED_VERSION = "0.98.0"
+
+
+def check_conversational_crews_version(
+    crewai_version: str, pyproject_data: dict
+) -> bool:
+    """
+    Check if the installed crewAI version supports conversational crews.
+
+    Args:
+        crewai_version: The current version of crewAI.
+        pyproject_data: Dictionary containing pyproject.toml data.
+
+    Returns:
+        bool: True if version check passes, False otherwise.
+    """
+    try:
+        if version.parse(crewai_version) < version.parse(MIN_REQUIRED_VERSION):
+            click.secho(
+                "You are using an older version of crewAI that doesn't support conversational crews. "
+                "Run 'uv upgrade crewai' to get the latest version.",
+                fg="red",
+            )
+            return False
+    except version.InvalidVersion:
+        click.secho("Invalid crewAI version format detected.", fg="red")
+        return False
+    return True
+
 
 def run_chat():
     """
@@ -19,20 +54,47 @@ def run_chat():
     Incorporates crew_name, crew_description, and input fields to build a tool schema.
     Exits if crew_name or crew_description are missing.
     """
+    crewai_version = get_crewai_version()
+    pyproject_data = read_toml()
+
+    if not check_conversational_crews_version(crewai_version, pyproject_data):
+        return
+
     crew, crew_name = load_crew_and_name()
     chat_llm = initialize_chat_llm(crew)
     if not chat_llm:
         return
 
-    crew_chat_inputs = generate_crew_chat_inputs(crew, crew_name, chat_llm)
-    crew_tool_schema = generate_crew_tool_schema(crew_chat_inputs)
-    system_message = build_system_message(crew_chat_inputs)
-
-    # Call the LLM to generate the introductory message
-    introductory_message = chat_llm.call(
-        messages=[{"role": "system", "content": system_message}]
+    # Indicate that the crew is being analyzed
+    click.secho(
+        "\nAnalyzing crew and required inputs - this may take 3 to 30 seconds "
+        "depending on the complexity of your crew.",
+        fg="white",
     )
-    click.secho(f"\nAssistant: {introductory_message}\n", fg="green")
+
+    # Start loading indicator
+    loading_complete = threading.Event()
+    loading_thread = threading.Thread(target=show_loading, args=(loading_complete,))
+    loading_thread.start()
+
+    try:
+        crew_chat_inputs = generate_crew_chat_inputs(crew, crew_name, chat_llm)
+        crew_tool_schema = generate_crew_tool_schema(crew_chat_inputs)
+        system_message = build_system_message(crew_chat_inputs)
+
+        # Call the LLM to generate the introductory message
+        introductory_message = chat_llm.call(
+            messages=[{"role": "system", "content": system_message}]
+        )
+    finally:
+        # Stop loading indicator
+        loading_complete.set()
+        loading_thread.join()
+
+    # Indicate that the analysis is complete
+    click.secho("\nFinished analyzing crew.\n", fg="white")
+
+    click.secho(f"Assistant: {introductory_message}\n", fg="green")
 
     messages = [
         {"role": "system", "content": system_message},
@@ -43,15 +105,17 @@ def run_chat():
         crew_chat_inputs.crew_name: create_tool_function(crew, messages),
     }
 
-    click.secho(
-        "\nEntering an interactive chat loop with function-calling.\n"
-        "Type 'exit' or Ctrl+C to quit.\n",
-        fg="cyan",
-    )
-
     chat_loop(chat_llm, messages, crew_tool_schema, available_functions)
 
 
+def show_loading(event: threading.Event):
+    """Display animated loading dots while processing."""
+    while not event.is_set():
+        print(".", end="", flush=True)
+        time.sleep(1)
+    print()
+
+
 def initialize_chat_llm(crew: Crew) -> Optional[LLM]:
     """Initializes the chat LLM and handles exceptions."""
     try:
@@ -85,7 +149,7 @@ def build_system_message(crew_chat_inputs: ChatInputs) -> str:
         "Please keep your responses concise and friendly. "
         "If a user asks a question outside the crew's scope, provide a brief answer and remind them of the crew's purpose. "
         "After calling the tool, be prepared to take user feedback and make adjustments as needed. "
-        "If you are ever unsure about a user's request or need clarification, ask the user for more information."
+        "If you are ever unsure about a user's request or need clarification, ask the user for more information. "
         "Before doing anything else, introduce yourself with a friendly message like: 'Hey! I'm here to help you with [crew's purpose]. Could you please provide me with [inputs] so we can get started?' "
         "For example: 'Hey! I'm here to help you with uncovering and reporting cutting-edge developments through thorough research and detailed analysis. Could you please provide me with a topic you're interested in? This will help us generate a comprehensive research report and detailed analysis.'"
         f"\nCrew Name: {crew_chat_inputs.crew_name}"
@@ -102,25 +166,33 @@ def create_tool_function(crew: Crew, messages: List[Dict[str, str]]) -> Any:
     return run_crew_tool_with_messages
 
 
+def flush_input():
+    """Flush any pending input from the user."""
+    if platform.system() == "Windows":
+        # Windows platform
+        import msvcrt
+
+        while msvcrt.kbhit():
+            msvcrt.getch()
+    else:
+        # Unix-like platforms (Linux, macOS)
+        import termios
+
+        termios.tcflush(sys.stdin, termios.TCIFLUSH)
+
+
 def chat_loop(chat_llm, messages, crew_tool_schema, available_functions):
     """Main chat loop for interacting with the user."""
     while True:
         try:
-            user_input = click.prompt("You", type=str)
-            if user_input.strip().lower() in ["exit", "quit"]:
-                click.echo("Exiting chat. Goodbye!")
-                break
+            # Flush any pending input before accepting new input
+            flush_input()
 
-            messages.append({"role": "user", "content": user_input})
-            final_response = chat_llm.call(
-                messages=messages,
-                tools=[crew_tool_schema],
-                available_functions=available_functions,
+            user_input = get_user_input()
+            handle_user_input(
+                user_input, chat_llm, messages, crew_tool_schema, available_functions
             )
 
-            messages.append({"role": "assistant", "content": final_response})
-            click.secho(f"\nAssistant: {final_response}\n", fg="green")
-
         except KeyboardInterrupt:
             click.echo("\nExiting chat. Goodbye!")
             break
@@ -129,6 +201,55 @@ def chat_loop(chat_llm, messages, crew_tool_schema, available_functions):
             break
 
 
+def get_user_input() -> str:
+    """Collect multi-line user input with exit handling."""
+    click.secho(
+        "\nYou (type your message below. Press 'Enter' twice when you're done):",
+        fg="blue",
+    )
+    user_input_lines = []
+    while True:
+        line = input()
+        if line.strip().lower() == "exit":
+            return "exit"
+        if line == "":
+            break
+        user_input_lines.append(line)
+    return "\n".join(user_input_lines)
+
+
+def handle_user_input(
+    user_input: str,
+    chat_llm: LLM,
+    messages: List[Dict[str, str]],
+    crew_tool_schema: Dict[str, Any],
+    available_functions: Dict[str, Any],
+) -> None:
+    if user_input.strip().lower() == "exit":
+        click.echo("Exiting chat. Goodbye!")
+        return
+
+    if not user_input.strip():
+        click.echo("Empty message. Please provide input or type 'exit' to quit.")
+        return
+
+    messages.append({"role": "user", "content": user_input})
+
+    # Indicate that assistant is processing
+    click.echo()
+    click.secho("Assistant is processing your input. Please wait...", fg="green")
+
+    # Process assistant's response
+    final_response = chat_llm.call(
+        messages=messages,
+        tools=[crew_tool_schema],
+        available_functions=available_functions,
+    )
+
+    messages.append({"role": "assistant", "content": final_response})
+    click.secho(f"\nAssistant: {final_response}\n", fg="green")
+
+
 def generate_crew_tool_schema(crew_inputs: ChatInputs) -> dict:
     """
     Dynamically build a Littellm 'function' schema for the given crew.
@@ -323,10 +444,10 @@ def generate_input_description_with_ai(input_name: str, crew: Crew, chat_llm) ->
         ):
             # Replace placeholders with input names
             task_description = placeholder_pattern.sub(
-                lambda m: m.group(1), task.description
+                lambda m: m.group(1), task.description or ""
             )
             expected_output = placeholder_pattern.sub(
-                lambda m: m.group(1), task.expected_output
+                lambda m: m.group(1), task.expected_output or ""
             )
             context_texts.append(f"Task Description: {task_description}")
             context_texts.append(f"Expected Output: {expected_output}")
@@ -337,10 +458,10 @@ def generate_input_description_with_ai(input_name: str, crew: Crew, chat_llm) ->
             or f"{{{input_name}}}" in agent.backstory
         ):
             # Replace placeholders with input names
-            agent_role = placeholder_pattern.sub(lambda m: m.group(1), agent.role)
-            agent_goal = placeholder_pattern.sub(lambda m: m.group(1), agent.goal)
+            agent_role = placeholder_pattern.sub(lambda m: m.group(1), agent.role or "")
+            agent_goal = placeholder_pattern.sub(lambda m: m.group(1), agent.goal or "")
             agent_backstory = placeholder_pattern.sub(
-                lambda m: m.group(1), agent.backstory
+                lambda m: m.group(1), agent.backstory or ""
             )
             context_texts.append(f"Agent Role: {agent_role}")
             context_texts.append(f"Agent Goal: {agent_goal}")
@@ -381,18 +502,20 @@ def generate_crew_description_with_ai(crew: Crew, chat_llm) -> str:
     for task in crew.tasks:
         # Replace placeholders with input names
         task_description = placeholder_pattern.sub(
-            lambda m: m.group(1), task.description
+            lambda m: m.group(1), task.description or ""
         )
         expected_output = placeholder_pattern.sub(
-            lambda m: m.group(1), task.expected_output
+            lambda m: m.group(1), task.expected_output or ""
         )
         context_texts.append(f"Task Description: {task_description}")
         context_texts.append(f"Expected Output: {expected_output}")
     for agent in crew.agents:
         # Replace placeholders with input names
-        agent_role = placeholder_pattern.sub(lambda m: m.group(1), agent.role)
-        agent_goal = placeholder_pattern.sub(lambda m: m.group(1), agent.goal)
-        agent_backstory = placeholder_pattern.sub(lambda m: m.group(1), agent.backstory)
+        agent_role = placeholder_pattern.sub(lambda m: m.group(1), agent.role or "")
+        agent_goal = placeholder_pattern.sub(lambda m: m.group(1), agent.goal or "")
+        agent_backstory = placeholder_pattern.sub(
+            lambda m: m.group(1), agent.backstory or ""
+        )
         context_texts.append(f"Agent Role: {agent_role}")
         context_texts.append(f"Agent Goal: {agent_goal}")
         context_texts.append(f"Agent Backstory: {agent_backstory}")

src/crewai/cli/templates/crew/.gitignore

@@ -1,2 +1,3 @@
 .env
 __pycache__/
+.DS_Store

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]>=0.100.0,<1.0.0"
 ]
 
 [project.scripts]

src/crewai/cli/templates/flow/.gitignore

@@ -1,3 +1,4 @@
 .env
 __pycache__/
 lib/
+.DS_Store

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]>=0.100.0,<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]>=0.100.0"
 ]
 
 [tool.crewai]

src/crewai/crew.py

@@ -4,6 +4,7 @@ import re
 import uuid
 import warnings
 from concurrent.futures import Future
+from copy import copy as shallow_copy
 from hashlib import md5
 from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union
 
@@ -37,7 +38,6 @@ 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
@@ -84,6 +84,7 @@ 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
@@ -210,8 +211,9 @@ class Crew(BaseModel):
         default=None,
         description="LLM used to handle chatting with the crew.",
     )
-    _knowledge: Optional[Knowledge] = PrivateAttr(
+    knowledge: Optional[Knowledge] = Field(
         default=None,
+        description="Knowledge for the crew.",
     )
 
     @field_validator("id", mode="before")
@@ -289,7 +291,7 @@ class Crew(BaseModel):
                 if isinstance(self.knowledge_sources, list) and all(
                     isinstance(k, BaseKnowledgeSource) for k in self.knowledge_sources
                 ):
-                    self._knowledge = Knowledge(
+                    self.knowledge = Knowledge(
                         sources=self.knowledge_sources,
                         embedder_config=self.embedder,
                         collection_name="crew",
@@ -991,8 +993,8 @@ class Crew(BaseModel):
         return result
 
     def query_knowledge(self, query: List[str]) -> Union[List[Dict[str, Any]], None]:
-        if self._knowledge:
-            return self._knowledge.query(query)
+        if self.knowledge:
+            return self.knowledge.query(query)
         return None
 
     def fetch_inputs(self) -> Set[str]:
@@ -1036,6 +1038,8 @@ class Crew(BaseModel):
             "_telemetry",
             "agents",
             "tasks",
+            "knowledge_sources",
+            "knowledge",
         }
 
         cloned_agents = [agent.copy() for agent in self.agents]
@@ -1043,6 +1047,9 @@ class Crew(BaseModel):
         task_mapping = {}
 
         cloned_tasks = []
+        existing_knowledge_sources = shallow_copy(self.knowledge_sources)
+        existing_knowledge = shallow_copy(self.knowledge)
+
         for task in self.tasks:
             cloned_task = task.copy(cloned_agents, task_mapping)
             cloned_tasks.append(cloned_task)
@@ -1062,7 +1069,13 @@ class Crew(BaseModel):
         copied_data.pop("agents", None)
         copied_data.pop("tasks", None)
 
-        copied_crew = Crew(**copied_data, agents=cloned_agents, tasks=cloned_tasks)
+        copied_crew = Crew(
+            **copied_data,
+            agents=cloned_agents,
+            tasks=cloned_tasks,
+            knowledge_sources=existing_knowledge_sources,
+            knowledge=existing_knowledge,
+        )
 
         return copied_crew
 

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

@@ -1,5 +1,6 @@
 import asyncio
 import inspect
+import logging
 from typing import (
     Any,
     Callable,
@@ -25,15 +26,70 @@ from crewai.flow.flow_events import (
     MethodExecutionStartedEvent,
 )
 from crewai.flow.flow_visualizer import plot_flow
+from crewai.flow.persistence.base import FlowPersistence
 from crewai.flow.utils import get_possible_return_constants
 from crewai.telemetry import Telemetry
+from crewai.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")
 
-T = TypeVar("T", bound=Union[FlowState, Dict[str, Any]])
+    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
+
+
+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 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__}"
+            )
+        return cast(StateT, state)
+    raise TypeError(f"Invalid expected_type: {expected_type}")
 
 
 def start(condition: Optional[Union[str, dict, Callable]] = None) -> Callable:
@@ -77,6 +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:
@@ -101,6 +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.
@@ -137,6 +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]
@@ -201,6 +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):
@@ -224,6 +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.
@@ -326,21 +387,32 @@ class FlowMeta(type):
         routers = set()
 
         for attr_name, attr_value in dct.items():
-            if hasattr(attr_value, "__is_start_method__"):
-                start_methods.append(attr_name)
+            # 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__")
+            ):
+
+                # 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)
-            elif 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__:
-                    routers.add(attr_name)
-                    possible_returns = get_possible_return_constants(attr_value)
-                    if possible_returns:
-                        router_paths[attr_name] = possible_returns
+
+                    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:
+                            router_paths[attr_name] = possible_returns
 
         setattr(cls, "_start_methods", start_methods)
         setattr(cls, "_listeners", listeners)
@@ -351,7 +423,12 @@ 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]]] = {}
@@ -367,53 +444,130 @@ class Flow(Generic[T], metaclass=FlowMeta):
         _FlowGeneric.__name__ = f"{cls.__name__}[{item.__name__}]"
         return _FlowGeneric
 
-    def __init__(self) -> None:
+    def __init__(
+        self,
+        persistence: Optional[FlowPersistence] = None,
+        **kwargs: Any,
+    ) -> None:
+        """Initialize a new Flow instance.
+
+        Args:
+            persistence: Optional persistence backend for storing flow states
+            **kwargs: Additional state values to initialize or override
+        """
+        # Initialize basic instance attributes
         self._methods: Dict[str, Callable] = {}
-        self._state: T = self._create_initial_state()
         self._method_execution_counts: Dict[str, int] = {}
         self._pending_and_listeners: Dict[str, Set[str]] = {}
         self._method_outputs: List[Any] = []  # List to store all method outputs
+        self._persistence: Optional[FlowPersistence] = persistence
+
+        # 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__)
 
+        # Register all flow-related methods
         for method_name in dir(self):
-            if callable(getattr(self, method_name)) and not method_name.startswith(
-                "__"
-            ):
-                self._methods[method_name] = getattr(self, method_name)
+            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__")
+                ):
+                    # 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
+        """
         # 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 state_type()  # type: ignore
+                    # Create instance without id, then set it
+                    instance = state_type()
+                    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
-                    return StateWithId()  # type: ignore
+
+                    instance = StateWithId()
+                    if not hasattr(instance, "id"):
+                        setattr(instance, "id", str(uuid4()))
+                    return cast(T, instance)
+                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 {"id": str(uuid4())}  # type: ignore
+            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 self.initial_state()  # type: ignore
+                return cast(T, self.initial_state())  # Uses model defaults
             elif issubclass(self.initial_state, BaseModel):
-                # Create a new type that includes the ID field
-                class StateWithId(self.initial_state, FlowState):  # type: ignore
-                    pass
-                return StateWithId()  # type: ignore
+                # 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())  # Uses model defaults
+            elif self.initial_state is dict:
+                return cast(T, {"id": str(uuid4())})
 
-        # Handle dictionary case
-        if isinstance(self.initial_state, dict) and "id" not in self.initial_state:
-            self.initial_state["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)
 
-        return self.initial_state  # type: ignore
+        # 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
+                state_dict = model.model_dump()
+            elif hasattr(model, "dict"):
+                # Pydantic v1
+                state_dict = model.dict()
+            else:
+                # Fallback for other BaseModel implementations
+                state_dict = {
+                    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)}"
+        )
 
     @property
     def state(self) -> T:
@@ -424,53 +578,158 @@ 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
+        """
         if isinstance(self._state, dict):
-            # Preserve the ID when updating unstructured state
+            # For dict states, preserve existing fields unless overridden
             current_id = self._state.get("id")
-            self._state.update(inputs)
+            # Only update specified fields
+            for k, v in inputs.items():
+                self._state[k] = v
+            # Ensure ID is preserved or generated
             if current_id:
                 self._state["id"] = current_id
             elif "id" not in self._state:
                 self._state["id"] = str(uuid4())
         elif isinstance(self._state, BaseModel):
-            # Structured state
+            # For BaseModel states, preserve existing fields unless overridden
             try:
-                def create_model_with_extra_forbid(
-                    base_model: Type[BaseModel],
-                ) -> Type[BaseModel]:
-                    class ModelWithExtraForbid(base_model):  # type: ignore
-                        model_config = base_model.model_config.copy()
-                        model_config["extra"] = "forbid"
-
-                    return ModelWithExtraForbid
-
-                # Get current state as dict, preserving the ID if it exists
-                state_model = cast(BaseModel, self._state)
-                current_state = (
-                    state_model.model_dump()
-                    if hasattr(state_model, "model_dump")
-                    else state_model.dict()
-                    if hasattr(state_model, "dict")
-                    else {
-                        k: v
-                        for k, v in state_model.__dict__.items()
-                        if not k.startswith("_")
+                model = cast(BaseModel, self._state)
+                # Get current state as dict
+                if hasattr(model, "model_dump"):
+                    current_state = model.model_dump()
+                elif hasattr(model, "dict"):
+                    current_state = model.dict()
+                else:
+                    current_state = {
+                        k: v for k, v in model.__dict__.items() if not k.startswith("_")
                     }
-                )
 
-                ModelWithExtraForbid = create_model_with_extra_forbid(
-                    self._state.__class__
-                )
-                self._state = cast(
-                    T, ModelWithExtraForbid(**{**current_state, **inputs})
-                )
+                # 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"):
+                    # Pydantic v2
+                    self._state = cast(T, model_class.model_validate(new_state))
+                elif hasattr(model_class, "parse_obj"):
+                    # Pydantic v1
+                    self._state = cast(T, model_class.parse_obj(new_state))
+                else:
+                    # Fallback for other BaseModel implementations
+                    self._state = cast(T, model_class(**new_state))
             except ValidationError as e:
                 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
+        """
+        # When restoring from persistence, use the stored ID
+        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()
+            self._state.update(stored_state)
+        elif isinstance(self._state, BaseModel):
+            # For BaseModel states, create new instance with stored values
+            model = cast(BaseModel, self._state)
+            if hasattr(model, "model_validate"):
+                # Pydantic v2
+                self._state = cast(T, type(model).model_validate(stored_state))
+            elif hasattr(model, "parse_obj"):
+                # Pydantic v1
+                self._state = cast(T, type(model).parse_obj(stored_state))
+            else:
+                # 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)}")
+
     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(
@@ -478,9 +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:
@@ -723,6 +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/__init__.py

@@ -0,0 +1,18 @@
+"""
+CrewAI Flow Persistence.
+
+This module provides interfaces and implementations for persisting flow states.
+"""
+
+from typing import Any, Dict, TypeVar, Union
+
+from pydantic import BaseModel
+
+from crewai.flow.persistence.base import FlowPersistence
+from crewai.flow.persistence.decorators import persist
+from crewai.flow.persistence.sqlite import SQLiteFlowPersistence
+
+__all__ = ["FlowPersistence", "persist", "SQLiteFlowPersistence"]
+
+StateType = TypeVar('StateType', bound=Union[Dict[str, Any], BaseModel])
+DictStateType = Dict[str, Any]

src/crewai/flow/persistence/base.py

@@ -0,0 +1,53 @@
+"""Base class for flow state persistence."""
+
+import abc
+from typing import Any, Dict, Optional, Union
+
+from pydantic import BaseModel
+
+
+class FlowPersistence(abc.ABC):
+    """Abstract base class for flow state persistence.
+    
+    This class defines the interface that all persistence implementations must follow.
+    It supports both structured (Pydantic BaseModel) and unstructured (dict) states.
+    """
+    
+    @abc.abstractmethod
+    def init_db(self) -> None:
+        """Initialize the persistence backend.
+        
+        This method should handle any necessary setup, such as:
+        - Creating tables
+        - Establishing connections
+        - Setting up indexes
+        """
+        pass
+    
+    @abc.abstractmethod
+    def save_state(
+        self,
+        flow_uuid: str,
+        method_name: str,
+        state_data: Union[Dict[str, Any], BaseModel]
+    ) -> None:
+        """Persist the flow state after method completion.
+        
+        Args:
+            flow_uuid: Unique identifier for the flow instance
+            method_name: Name of the method that just completed
+            state_data: Current state data (either dict or Pydantic model)
+        """
+        pass
+    
+    @abc.abstractmethod
+    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
+        """
+        pass

src/crewai/flow/persistence/decorators.py

@@ -0,0 +1,252 @@
+"""
+Decorators for flow state persistence.
+
+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):
+            # Asynchronous method implementation
+            await some_async_operation()
+    ```
+"""
+
+import asyncio
+import functools
+import logging
+from typing import (
+    Any,
+    Callable,
+    Optional,
+    Type,
+    TypeVar,
+    Union,
+    cast,
+)
+
+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]):
+            @start()
+            def begin(self):
+                pass
+    """
+
+    def decorator(target: Union[Type, Callable[..., T]]) -> Union[Type, Callable[..., T]]:
+        """Decorator that handles both class and method decoration."""
+        actual_persistence = persistence or SQLiteFlowPersistence()
+
+        if isinstance(target, type):
+            # Class decoration
+            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_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
+                        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(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:
+                    method_coro = method(flow_instance, *args, **kwargs)
+                    if asyncio.iscoroutine(method_coro):
+                        result = await method_coro
+                    else:
+                        result = method_coro
+                    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))
+                setattr(method_async_wrapper, "__is_flow_method__", True)
+                return cast(Callable[..., T], method_async_wrapper)
+            else:
+                @functools.wraps(method)
+                def method_sync_wrapper(flow_instance: Any, *args: Any, **kwargs: Any) -> T:
+                    result = method(flow_instance, *args, **kwargs)
+                    PersistenceDecorator.persist_state(flow_instance, method.__name__, actual_persistence)
+                    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

@@ -0,0 +1,123 @@
+"""
+SQLite-based implementation of flow state persistence.
+"""
+
+import json
+import sqlite3
+from datetime import datetime
+from pathlib import Path
+from typing import Any, Dict, Optional, Union
+
+from pydantic import BaseModel
+
+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 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:
+            conn.execute("""
+            CREATE TABLE IF NOT EXISTS flow_states (
+                id INTEGER PRIMARY KEY AUTOINCREMENT,
+                flow_uuid TEXT NOT NULL,
+                method_name TEXT NOT NULL,
+                timestamp DATETIME NOT NULL,
+                state_json TEXT NOT NULL
+            )
+            """)
+            # Add index for faster UUID lookups
+            conn.execute("""
+            CREATE INDEX IF NOT EXISTS idx_flow_states_uuid
+            ON flow_states(flow_uuid)
+            """)
+
+    def save_state(
+        self,
+        flow_uuid: str,
+        method_name: str,
+        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
+            state_data: Current state data (either dict or Pydantic model)
+        """
+        # Convert state_data to dict, handling both Pydantic and dict cases
+        if isinstance(state_data, BaseModel):
+            state_dict = dict(state_data)  # Use dict() for better type compatibility
+        elif isinstance(state_data, dict):
+            state_dict = state_data
+        else:
+            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 (
+                flow_uuid,
+                method_name,
+                timestamp,
+                state_json
+            ) VALUES (?, ?, ?, ?)
+            """, (
+                flow_uuid,
+                method_name,
+                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
+        """
+        with sqlite3.connect(self.db_path) as conn:
+            cursor = conn.execute("""
+            SELECT state_json
+            FROM flow_states
+            WHERE flow_uuid = ?
+            ORDER BY id DESC
+            LIMIT 1
+            """, (flow_uuid,))
+            row = cursor.fetchone()
+
+        if row:
+            return json.loads(row[0])
+        return None

src/crewai/knowledge/knowledge.py

@@ -15,20 +15,20 @@ class Knowledge(BaseModel):
     Args:
         sources: List[BaseKnowledgeSource] = Field(default_factory=list)
         storage: Optional[KnowledgeStorage] = Field(default=None)
-        embedder_config: Optional[Dict[str, Any]] = None
+        embedder: Optional[Dict[str, Any]] = None
     """
 
     sources: List[BaseKnowledgeSource] = Field(default_factory=list)
     model_config = ConfigDict(arbitrary_types_allowed=True)
     storage: Optional[KnowledgeStorage] = Field(default=None)
-    embedder_config: Optional[Dict[str, Any]] = None
+    embedder: Optional[Dict[str, Any]] = None
     collection_name: Optional[str] = None
 
     def __init__(
         self,
         collection_name: str,
         sources: List[BaseKnowledgeSource],
-        embedder_config: Optional[Dict[str, Any]] = None,
+        embedder: Optional[Dict[str, Any]] = None,
         storage: Optional[KnowledgeStorage] = None,
         **data,
     ):
@@ -37,25 +37,23 @@ class Knowledge(BaseModel):
             self.storage = storage
         else:
             self.storage = KnowledgeStorage(
-                embedder_config=embedder_config, collection_name=collection_name
+                embedder=embedder, collection_name=collection_name
             )
         self.sources = sources
         self.storage.initialize_knowledge_storage()
-        for source in sources:
-            source.storage = self.storage
-            source.add()
+        self._add_sources()
 
     def query(self, query: List[str], limit: int = 3) -> List[Dict[str, Any]]:
         """
         Query across all knowledge sources to find the most relevant information.
         Returns the top_k most relevant chunks.
-        
+
         Raises:
             ValueError: If storage is not initialized.
         """
         if self.storage is None:
             raise ValueError("Storage is not initialized.")
-            
+
         results = self.storage.search(
             query,
             limit,
@@ -63,6 +61,9 @@ class Knowledge(BaseModel):
         return results
 
     def _add_sources(self):
-        for source in self.sources:
-            source.storage = self.storage
-            source.add()
+        try:
+            for source in self.sources:
+                source.storage = self.storage
+                source.add()
+        except Exception as e:
+            raise e

src/crewai/knowledge/source/base_file_knowledge_source.py

@@ -29,7 +29,13 @@ class BaseFileKnowledgeSource(BaseKnowledgeSource, ABC):
     def validate_file_path(cls, v, info):
         """Validate that at least one of file_path or file_paths is provided."""
         # Single check if both are None, O(1) instead of nested conditions
-        if v is None and info.data.get("file_path" if info.field_name == "file_paths" else "file_paths") is None:
+        if (
+            v is None
+            and info.data.get(
+                "file_path" if info.field_name == "file_paths" else "file_paths"
+            )
+            is None
+        ):
             raise ValueError("Either file_path or file_paths must be provided")
         return v
 

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/knowledge/storage/knowledge_storage.py

@@ -48,11 +48,11 @@ class KnowledgeStorage(BaseKnowledgeStorage):
 
     def __init__(
         self,
-        embedder_config: Optional[Dict[str, Any]] = None,
+        embedder: Optional[Dict[str, Any]] = None,
         collection_name: Optional[str] = None,
     ):
         self.collection_name = collection_name
-        self._set_embedder_config(embedder_config)
+        self._set_embedder_config(embedder)
 
     def search(
         self,
@@ -99,7 +99,7 @@ class KnowledgeStorage(BaseKnowledgeStorage):
             )
             if self.app:
                 self.collection = self.app.get_or_create_collection(
-                    name=collection_name, embedding_function=self.embedder_config
+                    name=collection_name, embedding_function=self.embedder
                 )
             else:
                 raise Exception("Vector Database Client not initialized")
@@ -187,17 +187,15 @@ class KnowledgeStorage(BaseKnowledgeStorage):
             api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"
         )
 
-    def _set_embedder_config(
-        self, embedder_config: Optional[Dict[str, Any]] = None
-    ) -> None:
+    def _set_embedder_config(self, embedder: Optional[Dict[str, Any]] = None) -> None:
         """Set the embedding configuration for the knowledge storage.
 
         Args:
             embedder_config (Optional[Dict[str, Any]]): Configuration dictionary for the embedder.
                 If None or empty, defaults to the default embedding function.
         """
-        self.embedder_config = (
-            EmbeddingConfigurator().configure_embedder(embedder_config)
-            if embedder_config
+        self.embedder = (
+            EmbeddingConfigurator().configure_embedder(embedder)
+            if embedder
             else self._create_default_embedding_function()
         )

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,
@@ -211,11 +236,13 @@ 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
@@ -223,11 +250,24 @@ class LLM:
                 text_response = response_message.content or ""
                 tool_calls = getattr(response_message, "tool_calls", [])
 
-                # --- 2) If no tool calls, return the text response
+                # --- 3) Handle callbacks with usage info
+                if callbacks and len(callbacks) > 0:
+                    for callback in callbacks:
+                        if hasattr(callback, "log_success_event"):
+                            usage_info = getattr(response, "usage", None)
+                            if usage_info:
+                                callback.log_success_event(
+                                    kwargs=params,
+                                    response_obj={"usage": usage_info},
+                                    start_time=0,
+                                    end_time=0,
+                                )
+
+                # --- 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
 
@@ -242,7 +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

@@ -1,12 +1,17 @@
 import json
+import logging
 import sqlite3
+from pathlib import Path
 from typing import Any, Dict, List, Optional
 
 from crewai.task import Task
 from crewai.utilities import Printer
 from crewai.utilities.crew_json_encoder import CrewJSONEncoder
+from crewai.utilities.errors import DatabaseError, DatabaseOperationError
 from crewai.utilities.paths import db_storage_path
 
+logger = logging.getLogger(__name__)
+
 
 class KickoffTaskOutputsSQLiteStorage:
     """
@@ -14,15 +19,24 @@ class KickoffTaskOutputsSQLiteStorage:
     """
 
     def __init__(
-        self, db_path: str = f"{db_storage_path()}/latest_kickoff_task_outputs.db"
+        self, db_path: Optional[str] = None
     ) -> 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()) / "latest_kickoff_task_outputs.db")
         self.db_path = db_path
         self._printer: Printer = Printer()
         self._initialize_db()
 
-    def _initialize_db(self):
-        """
-        Initializes the SQLite database and creates LTM table
+    def _initialize_db(self) -> None:
+        """Initialize the SQLite database and create the latest_kickoff_task_outputs table.
+
+        This method sets up the database schema for storing task outputs. It creates
+        a table with columns for task_id, expected_output, output (as JSON),
+        task_index, inputs (as JSON), was_replayed flag, and timestamp.
+
+        Raises:
+            DatabaseOperationError: If database initialization fails due to SQLite errors.
         """
         try:
             with sqlite3.connect(self.db_path) as conn:
@@ -43,10 +57,9 @@ class KickoffTaskOutputsSQLiteStorage:
 
                 conn.commit()
         except sqlite3.Error as e:
-            self._printer.print(
-                content=f"SAVING KICKOFF TASK OUTPUTS ERROR: An error occurred during database initialization: {e}",
-                color="red",
-            )
+            error_msg = DatabaseError.format_error(DatabaseError.INIT_ERROR, e)
+            logger.error(error_msg)
+            raise DatabaseOperationError(error_msg, e)
 
     def add(
         self,
@@ -55,9 +68,22 @@ class KickoffTaskOutputsSQLiteStorage:
         task_index: int,
         was_replayed: bool = False,
         inputs: Dict[str, Any] = {},
-    ):
+    ) -> None:
+        """Add a new task output record to the database.
+
+        Args:
+            task: The Task object containing task details.
+            output: Dictionary containing the task's output data.
+            task_index: Integer index of the task in the sequence.
+            was_replayed: Boolean indicating if this was a replay execution.
+            inputs: Dictionary of input parameters used for the task.
+
+        Raises:
+            DatabaseOperationError: If saving the task output fails due to SQLite errors.
+        """
         try:
             with sqlite3.connect(self.db_path) as conn:
+                conn.execute("BEGIN TRANSACTION")
                 cursor = conn.cursor()
                 cursor.execute(
                     """
@@ -76,21 +102,31 @@ class KickoffTaskOutputsSQLiteStorage:
                 )
                 conn.commit()
         except sqlite3.Error as e:
-            self._printer.print(
-                content=f"SAVING KICKOFF TASK OUTPUTS ERROR: An error occurred during database initialization: {e}",
-                color="red",
-            )
+            error_msg = DatabaseError.format_error(DatabaseError.SAVE_ERROR, e)
+            logger.error(error_msg)
+            raise DatabaseOperationError(error_msg, e)
 
     def update(
         self,
         task_index: int,
-        **kwargs,
-    ):
-        """
-        Updates an existing row in the latest_kickoff_task_outputs table based on task_index.
+        **kwargs: Any,
+    ) -> None:
+        """Update an existing task output record in the database.
+
+        Updates fields of a task output record identified by task_index. The fields
+        to update are provided as keyword arguments.
+
+        Args:
+            task_index: Integer index of the task to update.
+            **kwargs: Arbitrary keyword arguments representing fields to update.
+                     Values that are dictionaries will be JSON encoded.
+
+        Raises:
+            DatabaseOperationError: If updating the task output fails due to SQLite errors.
         """
         try:
             with sqlite3.connect(self.db_path) as conn:
+                conn.execute("BEGIN TRANSACTION")
                 cursor = conn.cursor()
 
                 fields = []
@@ -110,14 +146,23 @@ class KickoffTaskOutputsSQLiteStorage:
                 conn.commit()
 
                 if cursor.rowcount == 0:
-                    self._printer.print(
-                        f"No row found with task_index {task_index}. No update performed.",
-                        color="red",
-                    )
+                    logger.warning(f"No row found with task_index {task_index}. No update performed.")
         except sqlite3.Error as e:
-            self._printer.print(f"UPDATE KICKOFF TASK OUTPUTS ERROR: {e}", color="red")
+            error_msg = DatabaseError.format_error(DatabaseError.UPDATE_ERROR, e)
+            logger.error(error_msg)
+            raise DatabaseOperationError(error_msg, e)
 
-    def load(self) -> Optional[List[Dict[str, Any]]]:
+    def load(self) -> List[Dict[str, Any]]:
+        """Load all task output records from the database.
+
+        Returns:
+            List of dictionaries containing task output records, ordered by task_index.
+            Each dictionary contains: task_id, expected_output, output, task_index,
+            inputs, was_replayed, and timestamp.
+
+        Raises:
+            DatabaseOperationError: If loading task outputs fails due to SQLite errors.
+        """
         try:
             with sqlite3.connect(self.db_path) as conn:
                 cursor = conn.cursor()
@@ -144,23 +189,26 @@ class KickoffTaskOutputsSQLiteStorage:
                 return results
 
         except sqlite3.Error as e:
-            self._printer.print(
-                content=f"LOADING KICKOFF TASK OUTPUTS ERROR: An error occurred while querying kickoff task outputs: {e}",
-                color="red",
-            )
-            return None
+            error_msg = DatabaseError.format_error(DatabaseError.LOAD_ERROR, e)
+            logger.error(error_msg)
+            raise DatabaseOperationError(error_msg, e)
 
-    def delete_all(self):
-        """
-        Deletes all rows from the latest_kickoff_task_outputs table.
+    def delete_all(self) -> None:
+        """Delete all task output records from the database.
+
+        This method removes all records from the latest_kickoff_task_outputs table.
+        Use with caution as this operation cannot be undone.
+
+        Raises:
+            DatabaseOperationError: If deleting task outputs fails due to SQLite errors.
         """
         try:
             with sqlite3.connect(self.db_path) as conn:
+                conn.execute("BEGIN TRANSACTION")
                 cursor = conn.cursor()
                 cursor.execute("DELETE FROM latest_kickoff_task_outputs")
                 conn.commit()
         except sqlite3.Error as e:
-            self._printer.print(
-                content=f"ERROR: Failed to delete all kickoff task outputs: {e}",
-                color="red",
-            )
+            error_msg = DatabaseError.format_error(DatabaseError.DELETE_ERROR, e)
+            logger.error(error_msg)
+            raise DatabaseOperationError(error_msg, e)

src/crewai/memory/storage/ltm_sqlite_storage.py

@@ -1,5 +1,6 @@
 import json
 import sqlite3
+from pathlib import Path
 from typing import Any, Dict, List, Optional, Union
 
 from crewai.utilities import Printer
@@ -12,10 +13,15 @@ class LTMSQLiteStorage:
     """
 
     def __init__(
-        self, db_path: str = f"{db_storage_path()}/long_term_memory_storage.db"
+        self, db_path: Optional[str] = None
     ) -> 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()) / "long_term_memory_storage.db")
         self.db_path = db_path
         self._printer: Printer = Printer()
+        # Ensure parent directory exists
+        Path(self.db_path).parent.mkdir(parents=True, exist_ok=True)
         self._initialize_db()
 
     def _initialize_db(self):

src/crewai/tools/tool_usage.py

@@ -1,12 +1,13 @@
 import ast
 import datetime
 import json
-import re
 import time
 from difflib import SequenceMatcher
+from json import JSONDecodeError
 from textwrap import dedent
-from typing import Any, Dict, List, Union
+from typing import Any, Dict, List, Optional, Union
 
+import json5
 from json_repair import repair_json
 
 import crewai.utilities.events as events
@@ -407,28 +408,55 @@ class ToolUsage:
                 )
             return self._tool_calling(tool_string)
 
-    def _validate_tool_input(self, tool_input: str) -> Dict[str, Any]:
+    def _validate_tool_input(self, tool_input: Optional[str]) -> Dict[str, Any]:
+        if tool_input is None:
+            return {}
+
+        if not isinstance(tool_input, str) or not tool_input.strip():
+            raise Exception(
+                "Tool input must be a valid dictionary in JSON or Python literal format"
+            )
+
+        # Attempt 1: Parse as JSON
         try:
-            # Replace Python literals with JSON equivalents
-            replacements = {
-                r"'": '"',
-                r"None": "null",
-                r"True": "true",
-                r"False": "false",
-            }
-            for pattern, replacement in replacements.items():
-                tool_input = re.sub(pattern, replacement, tool_input)
-
             arguments = json.loads(tool_input)
-        except json.JSONDecodeError:
-            # Attempt to repair JSON string
-            repaired_input = repair_json(tool_input)
-            try:
-                arguments = json.loads(repaired_input)
-            except json.JSONDecodeError as e:
-                raise Exception(f"Invalid tool input JSON: {e}")
+            if isinstance(arguments, dict):
+                return arguments
+        except (JSONDecodeError, TypeError):
+            pass  # Continue to the next parsing attempt
 
-        return arguments
+        # Attempt 2: Parse as Python literal
+        try:
+            arguments = ast.literal_eval(tool_input)
+            if isinstance(arguments, dict):
+                return arguments
+        except (ValueError, SyntaxError):
+            pass  # Continue to the next parsing attempt
+
+        # Attempt 3: Parse as JSON5
+        try:
+            arguments = json5.loads(tool_input)
+            if isinstance(arguments, dict):
+                return arguments
+        except (JSONDecodeError, ValueError, TypeError):
+            pass  # Continue to the next parsing attempt
+
+        # Attempt 4: Repair JSON
+        try:
+            repaired_input = repair_json(tool_input)
+            self._printer.print(
+                content=f"Repaired JSON: {repaired_input}", color="blue"
+            )
+            arguments = json.loads(repaired_input)
+            if isinstance(arguments, dict):
+                return arguments
+        except Exception as e:
+            self._printer.print(content=f"Failed to repair JSON: {e}", color="red")
+
+        # If all parsing attempts fail, raise an error
+        raise Exception(
+            "Tool input must be a valid dictionary in JSON or Python literal format"
+        )
 
     def on_tool_error(self, tool: Any, tool_calling: ToolCalling, e: Exception) -> None:
         event_data = self._prepare_event_data(tool, tool_calling)

src/crewai/utilities/converter.py

@@ -26,17 +26,24 @@ class Converter(OutputConverter):
             if self.llm.supports_function_calling():
                 return self._create_instructor().to_pydantic()
             else:
-                return self.llm.call(
+                response = self.llm.call(
                     [
                         {"role": "system", "content": self.instructions},
                         {"role": "user", "content": self.text},
                     ]
                 )
+                return self.model.model_validate_json(response)
+        except ValidationError as e:
+            if current_attempt < self.max_attempts:
+                return self.to_pydantic(current_attempt + 1)
+            raise ConverterError(
+                f"Failed to convert text into a Pydantic model due to the following validation error: {e}"
+            )
         except Exception as e:
             if current_attempt < self.max_attempts:
                 return self.to_pydantic(current_attempt + 1)
-            return ConverterError(
-                f"Failed to convert text into a pydantic model due to the following error: {e}"
+            raise ConverterError(
+                f"Failed to convert text into a Pydantic model due to the following error: {e}"
             )
 
     def to_json(self, current_attempt=1):
@@ -66,7 +73,6 @@ class Converter(OutputConverter):
             llm=self.llm,
             model=self.model,
             content=self.text,
-            instructions=self.instructions,
         )
         return inst
 
@@ -187,10 +193,15 @@ def convert_with_instructions(
 
 
 def get_conversion_instructions(model: Type[BaseModel], llm: Any) -> str:
-    instructions = "I'm gonna convert this raw text into valid JSON."
+    instructions = "Please convert the following text into valid JSON."
     if llm.supports_function_calling():
         model_schema = PydanticSchemaParser(model=model).get_schema()
-        instructions = f"{instructions}\n\nThe json should have the following structure, with the following keys:\n{model_schema}"
+        instructions += (
+            f"\n\nThe JSON should follow this schema:\n```json\n{model_schema}\n```"
+        )
+    else:
+        model_description = generate_model_description(model)
+        instructions += f"\n\nThe JSON should follow this format:\n{model_description}"
     return instructions
 
 
@@ -230,9 +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:
@@ -241,8 +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,
@@ -42,7 +43,6 @@ class EmbeddingConfigurator:
             raise Exception(
                 f"Unsupported embedding provider: {provider}, supported providers: {list(self.embedding_functions.keys())}"
             )
-
         return self.embedding_functions[provider](config, model_name)
 
     @staticmethod
@@ -124,6 +124,17 @@ 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/errors.py

@@ -0,0 +1,39 @@
+"""Error message definitions for CrewAI database operations."""
+from typing import Optional
+
+
+class DatabaseOperationError(Exception):
+    """Base exception class for database operation errors."""
+
+    def __init__(self, message: str, original_error: Optional[Exception] = None):
+        """Initialize the database operation error.
+
+        Args:
+            message: The error message to display
+            original_error: The original exception that caused this error, if any
+        """
+        super().__init__(message)
+        self.original_error = original_error
+
+
+class DatabaseError:
+    """Standardized error message templates for database operations."""
+
+    INIT_ERROR: str = "Database initialization error: {}"
+    SAVE_ERROR: str = "Error saving task outputs: {}"
+    UPDATE_ERROR: str = "Error updating task outputs: {}"
+    LOAD_ERROR: str = "Error loading task outputs: {}"
+    DELETE_ERROR: str = "Error deleting task outputs: {}"
+
+    @classmethod
+    def format_error(cls, template: str, error: Exception) -> str:
+        """Format an error message with the given template and error.
+
+        Args:
+            template: The error message template to use
+            error: The exception to format into the template
+
+        Returns:
+            The formatted error message
+        """
+        return template.format(str(error))

src/crewai/utilities/evaluators/task_evaluator.py

@@ -96,9 +96,9 @@ class TaskEvaluator:
         final_aggregated_data = ""
         for _, data in output_training_data.items():
             final_aggregated_data += (
-                f"Initial Output:\n{data['initial_output']}\n\n"
-                f"Human Feedback:\n{data['human_feedback']}\n\n"
-                f"Improved Output:\n{data['improved_output']}\n\n"
+                f"Initial Output:\n{data.get('initial_output', '')}\n\n"
+                f"Human Feedback:\n{data.get('human_feedback', '')}\n\n"
+                f"Improved Output:\n{data.get('improved_output', '')}\n\n"
             )
 
         evaluation_query = (

src/crewai/utilities/internal_instructor.py

@@ -11,12 +11,10 @@ class InternalInstructor:
         model: Type,
         agent: Optional[Any] = None,
         llm: Optional[str] = None,
-        instructions: Optional[str] = None,
     ):
         self.content = content
         self.agent = agent
         self.llm = llm
-        self.instructions = instructions
         self.model = model
         self._client = None
         self.set_instructor()
@@ -31,10 +29,7 @@ class InternalInstructor:
             import instructor
             from litellm import completion
 
-            self._client = instructor.from_litellm(
-                completion,
-                mode=instructor.Mode.TOOLS,
-            )
+            self._client = instructor.from_litellm(completion)
 
     def to_json(self):
         model = self.to_pydantic()
@@ -42,8 +37,6 @@ class InternalInstructor:
 
     def to_pydantic(self):
         messages = [{"role": "user", "content": self.content}]
-        if self.instructions:
-            messages.append({"role": "system", "content": self.instructions})
         model = self._client.chat.completions.create(
             model=self.llm.model, response_model=self.model, messages=messages
         )

src/crewai/utilities/paths.py

@@ -5,14 +5,18 @@ import appdirs
 
 """Path management utilities for CrewAI storage and configuration."""
 
-def db_storage_path():
-    """Returns the path for database storage."""
+def db_storage_path() -> str:
+    """Returns the path for SQLite database storage.
+
+    Returns:
+        str: Full path to the SQLite database file
+    """
     app_name = get_project_directory_name()
     app_author = "CrewAI"
 
     data_dir = Path(appdirs.user_data_dir(app_name, app_author))
     data_dir.mkdir(parents=True, exist_ok=True)
-    return data_dir
+    return str(data_dir)
 
 
 def get_project_directory_name():
@@ -24,4 +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))

src/crewai/utilities/pydantic_schema_parser.py

@@ -1,4 +1,4 @@
-from typing import Type, Union, get_args, get_origin
+from typing import Dict, List, Type, Union, get_args, get_origin
 
 from pydantic import BaseModel
 
@@ -10,40 +10,83 @@ class PydanticSchemaParser(BaseModel):
         """
         Public method to get the schema of a Pydantic model.
 
-        :param model: The Pydantic model class to generate schema for.
         :return: String representation of the model schema.
         """
-        return self._get_model_schema(self.model)
+        return "{\n" + self._get_model_schema(self.model) + "\n}"
 
-    def _get_model_schema(self, model, depth=0) -> str:
-        indent = "    " * depth
-        lines = [f"{indent}{{"]
-        for field_name, field in model.model_fields.items():
-            field_type_str = self._get_field_type(field, depth + 1)
-            lines.append(f"{indent}    {field_name}: {field_type_str},")
-        lines[-1] = lines[-1].rstrip(",")  # Remove trailing comma from last item
-        lines.append(f"{indent}}}")
-        return "\n".join(lines)
+    def _get_model_schema(self, model: Type[BaseModel], depth: int = 0) -> str:
+        indent = " " * 4 * depth
+        lines = [
+            f"{indent}    {field_name}: {self._get_field_type(field, depth + 1)}"
+            for field_name, field in model.model_fields.items()
+        ]
+        return ",\n".join(lines)
 
-    def _get_field_type(self, field, depth) -> str:
+    def _get_field_type(self, field, depth: int) -> str:
         field_type = field.annotation
-        if get_origin(field_type) is list:
+        origin = get_origin(field_type)
+
+        if origin in {list, List}:
             list_item_type = get_args(field_type)[0]
-            if isinstance(list_item_type, type) and issubclass(
-                list_item_type, BaseModel
-            ):
-                nested_schema = self._get_model_schema(list_item_type, depth + 1)
-                return f"List[\n{nested_schema}\n{' ' * 4 * depth}]"
+            return self._format_list_type(list_item_type, depth)
+
+        if origin in {dict, Dict}:
+            key_type, value_type = get_args(field_type)
+            return f"Dict[{key_type.__name__}, {value_type.__name__}]"
+
+        if origin is Union:
+            return self._format_union_type(field_type, depth)
+
+        if isinstance(field_type, type) and issubclass(field_type, BaseModel):
+            nested_schema = self._get_model_schema(field_type, depth)
+            nested_indent = " " * 4 * depth
+            return f"{field_type.__name__}\n{nested_indent}{{\n{nested_schema}\n{nested_indent}}}"
+
+        return field_type.__name__
+
+    def _format_list_type(self, list_item_type, depth: int) -> str:
+        if isinstance(list_item_type, type) and issubclass(list_item_type, BaseModel):
+            nested_schema = self._get_model_schema(list_item_type, depth + 1)
+            nested_indent = " " * 4 * (depth)
+            return f"List[\n{nested_indent}{{\n{nested_schema}\n{nested_indent}}}\n{nested_indent}]"
+        return f"List[{list_item_type.__name__}]"
+
+    def _format_union_type(self, field_type, depth: int) -> str:
+        args = get_args(field_type)
+        if type(None) in args:
+            # It's an Optional type
+            non_none_args = [arg for arg in args if arg is not type(None)]
+            if len(non_none_args) == 1:
+                inner_type = self._get_field_type_for_annotation(
+                    non_none_args[0], depth
+                )
+                return f"Optional[{inner_type}]"
             else:
-                return f"List[{list_item_type.__name__}]"
-        elif get_origin(field_type) is Union:
-            union_args = get_args(field_type)
-            if type(None) in union_args:
-                non_none_type = next(arg for arg in union_args if arg is not type(None))
-                return f"Optional[{self._get_field_type(field.__class__(annotation=non_none_type), depth)}]"
-            else:
-                return f"Union[{', '.join(arg.__name__ for arg in union_args)}]"
-        elif isinstance(field_type, type) and issubclass(field_type, BaseModel):
-            return self._get_model_schema(field_type, depth)
+                # Union with None and multiple other types
+                inner_types = ", ".join(
+                    self._get_field_type_for_annotation(arg, depth)
+                    for arg in non_none_args
+                )
+                return f"Optional[Union[{inner_types}]]"
         else:
-            return getattr(field_type, "__name__", str(field_type))
+            # General Union type
+            inner_types = ", ".join(
+                self._get_field_type_for_annotation(arg, depth) for arg in args
+            )
+            return f"Union[{inner_types}]"
+
+    def _get_field_type_for_annotation(self, annotation, depth: int) -> str:
+        origin = get_origin(annotation)
+        if origin in {list, List}:
+            list_item_type = get_args(annotation)[0]
+            return self._format_list_type(list_item_type, depth)
+        if origin in {dict, Dict}:
+            key_type, value_type = get_args(annotation)
+            return f"Dict[{key_type.__name__}, {value_type.__name__}]"
+        if origin is Union:
+            return self._format_union_type(annotation, depth)
+        if isinstance(annotation, type) and issubclass(annotation, BaseModel):
+            nested_schema = self._get_model_schema(annotation, depth)
+            nested_indent = " " * 4 * depth
+            return f"{annotation.__name__}\n{nested_indent}{{\n{nested_schema}\n{nested_indent}}}"
+        return annotation.__name__

src/crewai/utilities/token_counter_callback.py

@@ -23,11 +23,15 @@ class TokenCalcHandler(CustomLogger):
 
         with warnings.catch_warnings():
             warnings.simplefilter("ignore", UserWarning)
-            usage: Usage = response_obj["usage"]
-            self.token_cost_process.sum_successful_requests(1)
-            self.token_cost_process.sum_prompt_tokens(usage.prompt_tokens)
-            self.token_cost_process.sum_completion_tokens(usage.completion_tokens)
-            if usage.prompt_tokens_details:
-                self.token_cost_process.sum_cached_prompt_tokens(
-                    usage.prompt_tokens_details.cached_tokens
-                )
+            if isinstance(response_obj, dict) and "usage" in response_obj:
+                usage: Usage = response_obj["usage"]
+                if usage:
+                    self.token_cost_process.sum_successful_requests(1)
+                    if hasattr(usage, "prompt_tokens"):
+                        self.token_cost_process.sum_prompt_tokens(usage.prompt_tokens)
+                    if hasattr(usage, "completion_tokens"):
+                        self.token_cost_process.sum_completion_tokens(usage.completion_tokens)
+                    if hasattr(usage, "prompt_tokens_details") and usage.prompt_tokens_details:
+                        self.token_cost_process.sum_cached_prompt_tokens(
+                            usage.prompt_tokens_details.cached_tokens
+                        )

tests/agent_test.py

@@ -10,6 +10,7 @@ from crewai import Agent, Crew, Task
 from crewai.agents.cache import CacheHandler
 from crewai.agents.crew_agent_executor import CrewAgentExecutor
 from crewai.agents.parser import AgentAction, CrewAgentParser, OutputParserException
+from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
 from crewai.llm import LLM
 from crewai.tools import tool
@@ -114,35 +115,6 @@ 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 +1601,181 @@ 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_agent_with_knowledge_sources_works_with_copy():
+    content = "Brandon's favorite color is red and he likes Mexican food."
+    string_source = StringKnowledgeSource(content=content)
+
+    with patch(
+        "crewai.knowledge.source.base_knowledge_source.BaseKnowledgeSource",
+        autospec=True,
+    ) as MockKnowledgeSource:
+        mock_knowledge_source_instance = MockKnowledgeSource.return_value
+        mock_knowledge_source_instance.__class__ = BaseKnowledgeSource
+        mock_knowledge_source_instance.sources = [string_source]
+
+        agent = Agent(
+            role="Information Agent",
+            goal="Provide information based on knowledge sources",
+            backstory="You have access to specific knowledge sources.",
+            llm=LLM(model="gpt-4o-mini"),
+            knowledge_sources=[string_source],
+        )
+
+        with patch(
+            "crewai.knowledge.storage.knowledge_storage.KnowledgeStorage"
+        ) as MockKnowledgeStorage:
+            mock_knowledge_storage = MockKnowledgeStorage.return_value
+            agent.knowledge_storage = mock_knowledge_storage
+
+            agent_copy = agent.copy()
+
+            assert agent_copy.role == agent.role
+            assert agent_copy.goal == agent.goal
+            assert agent_copy.backstory == agent.backstory
+            assert agent_copy.knowledge_sources is not None
+            assert len(agent_copy.knowledge_sources) == 1
+            assert isinstance(agent_copy.knowledge_sources[0], StringKnowledgeSource)
+            assert agent_copy.knowledge_sources[0].content == content
+            assert isinstance(agent_copy.llm, LLM)
+
+
+@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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tests/cassettes/test_llm_call_with_tool_and_string_input.yaml

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tests/conftest.py

@@ -1,4 +1,37 @@
 # conftest.py
+import os
+import tempfile
+from pathlib import Path
+
+import pytest
 from dotenv import load_dotenv
 
 load_result = load_dotenv(override=True)
+
+@pytest.fixture(autouse=True)
+def setup_test_environment():
+    """Set up test environment with a temporary directory for SQLite storage."""
+    with tempfile.TemporaryDirectory() as temp_dir:
+        # Create the directory with proper permissions
+        storage_dir = Path(temp_dir) / "crewai_test_storage"
+        storage_dir.mkdir(parents=True, exist_ok=True)
+        
+        # Validate that the directory was created successfully
+        if not storage_dir.exists() or not storage_dir.is_dir():
+            raise RuntimeError(f"Failed to create test storage directory: {storage_dir}")
+        
+        # Verify directory permissions
+        try:
+            # Try to create a test file to verify write permissions
+            test_file = storage_dir / ".permissions_test"
+            test_file.touch()
+            test_file.unlink()
+        except (OSError, IOError) as e:
+            raise RuntimeError(f"Test storage directory {storage_dir} is not writable: {e}")
+        
+        # Set environment variable to point to the test storage directory
+        os.environ["CREWAI_STORAGE_DIR"] = str(storage_dir)
+        
+        yield
+        
+        # Cleanup is handled automatically when tempfile context exits

tests/crew_test.py

@@ -14,6 +14,7 @@ from crewai.agent import Agent
 from crewai.agents.cache import CacheHandler
 from crewai.crew import Crew
 from crewai.crews.crew_output import CrewOutput
+from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
 from crewai.memory.contextual.contextual_memory import ContextualMemory
 from crewai.process import Process
 from crewai.project import crew
@@ -555,12 +556,12 @@ def test_crew_with_delegating_agents_should_not_override_task_tools():
         _, kwargs = mock_execute_sync.call_args
         tools = kwargs["tools"]
 
-        assert any(
-            isinstance(tool, TestTool) for tool in tools
-        ), "TestTool should be present"
-        assert any(
-            "delegate" in tool.name.lower() for tool in tools
-        ), "Delegation tool should be present"
+        assert any(isinstance(tool, TestTool) for tool in tools), (
+            "TestTool should be present"
+        )
+        assert any("delegate" in tool.name.lower() for tool in tools), (
+            "Delegation tool should be present"
+        )
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -619,12 +620,12 @@ def test_crew_with_delegating_agents_should_not_override_agent_tools():
         _, kwargs = mock_execute_sync.call_args
         tools = kwargs["tools"]
 
-        assert any(
-            isinstance(tool, TestTool) for tool in new_ceo.tools
-        ), "TestTool should be present"
-        assert any(
-            "delegate" in tool.name.lower() for tool in tools
-        ), "Delegation tool should be present"
+        assert any(isinstance(tool, TestTool) for tool in new_ceo.tools), (
+            "TestTool should be present"
+        )
+        assert any("delegate" in tool.name.lower() for tool in tools), (
+            "Delegation tool should be present"
+        )
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -748,17 +749,17 @@ def test_task_tools_override_agent_tools_with_allow_delegation():
         used_tools = kwargs["tools"]
 
         # Confirm AnotherTestTool is present but TestTool is not
-        assert any(
-            isinstance(tool, AnotherTestTool) for tool in used_tools
-        ), "AnotherTestTool should be present"
-        assert not any(
-            isinstance(tool, TestTool) for tool in used_tools
-        ), "TestTool should not be present among used tools"
+        assert any(isinstance(tool, AnotherTestTool) for tool in used_tools), (
+            "AnotherTestTool should be present"
+        )
+        assert not any(isinstance(tool, TestTool) for tool in used_tools), (
+            "TestTool should not be present among used tools"
+        )
 
         # Confirm delegation tool(s) are present
-        assert any(
-            "delegate" in tool.name.lower() for tool in used_tools
-        ), "Delegation tool should be present"
+        assert any("delegate" in tool.name.lower() for tool in used_tools), (
+            "Delegation tool should be present"
+        )
 
     # Finally, make sure the agent's original tools remain unchanged
     assert len(researcher_with_delegation.tools) == 1
@@ -1228,6 +1229,7 @@ def test_kickoff_for_each_empty_input():
     assert results == []
 
 
+@pytest.mark.vcr(filter_headers=["authorization"])
 def test_kickoff_for_each_invalid_input():
     """Tests if kickoff_for_each raises TypeError for invalid input types."""
 
@@ -1465,7 +1467,6 @@ def test_dont_set_agents_step_callback_if_already_set():
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_crew_function_calling_llm():
-
     from crewai import LLM
     from crewai.tools import tool
 
@@ -1559,9 +1560,9 @@ def test_code_execution_flag_adds_code_tool_upon_kickoff():
 
         # Verify that exactly one tool was used and it was a CodeInterpreterTool
         assert len(used_tools) == 1, "Should have exactly one tool"
-        assert isinstance(
-            used_tools[0], CodeInterpreterTool
-        ), "Tool should be CodeInterpreterTool"
+        assert isinstance(used_tools[0], CodeInterpreterTool), (
+            "Tool should be CodeInterpreterTool"
+        )
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -3106,9 +3107,9 @@ def test_fetch_inputs():
     expected_placeholders = {"role_detail", "topic", "field"}
     actual_placeholders = crew.fetch_inputs()
 
-    assert (
-        actual_placeholders == expected_placeholders
-    ), f"Expected {expected_placeholders}, but got {actual_placeholders}"
+    assert actual_placeholders == expected_placeholders, (
+        f"Expected {expected_placeholders}, but got {actual_placeholders}"
+    )
 
 
 def test_task_tools_preserve_code_execution_tools():
@@ -3181,20 +3182,20 @@ def test_task_tools_preserve_code_execution_tools():
         used_tools = kwargs["tools"]
 
         # Verify all expected tools are present
-        assert any(
-            isinstance(tool, TestTool) for tool in used_tools
-        ), "Task's TestTool should be present"
-        assert any(
-            isinstance(tool, CodeInterpreterTool) for tool in used_tools
-        ), "CodeInterpreterTool should be present"
-        assert any(
-            "delegate" in tool.name.lower() for tool in used_tools
-        ), "Delegation tool should be present"
+        assert any(isinstance(tool, TestTool) for tool in used_tools), (
+            "Task's TestTool should be present"
+        )
+        assert any(isinstance(tool, CodeInterpreterTool) for tool in used_tools), (
+            "CodeInterpreterTool should be present"
+        )
+        assert any("delegate" in tool.name.lower() for tool in used_tools), (
+            "Delegation tool should be present"
+        )
 
         # Verify the total number of tools (TestTool + CodeInterpreter + 2 delegation tools)
-        assert (
-            len(used_tools) == 4
-        ), "Should have TestTool, CodeInterpreter, and 2 delegation tools"
+        assert len(used_tools) == 4, (
+            "Should have TestTool, CodeInterpreter, and 2 delegation tools"
+        )
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -3238,9 +3239,9 @@ def test_multimodal_flag_adds_multimodal_tools():
         used_tools = kwargs["tools"]
 
         # Check that the multimodal tool was added
-        assert any(
-            isinstance(tool, AddImageTool) for tool in used_tools
-        ), "AddImageTool should be present when agent is multimodal"
+        assert any(isinstance(tool, AddImageTool) for tool in used_tools), (
+            "AddImageTool should be present when agent is multimodal"
+        )
 
         # Verify we have exactly one tool (just the AddImageTool)
         assert len(used_tools) == 1, "Should only have the AddImageTool"
@@ -3466,9 +3467,9 @@ def test_crew_guardrail_feedback_in_context():
     assert len(execution_contexts) > 1, "Task should have been executed multiple times"
 
     # Verify that the second execution included the guardrail feedback
-    assert (
-        "Output must contain the keyword 'IMPORTANT'" in execution_contexts[1]
-    ), "Guardrail feedback should be included in retry context"
+    assert "Output must contain the keyword 'IMPORTANT'" in execution_contexts[1], (
+        "Guardrail feedback should be included in retry context"
+    )
 
     # Verify final output meets guardrail requirements
     assert "IMPORTANT" in result.raw, "Final output should contain required keyword"
@@ -3479,10 +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
 
@@ -3491,7 +3494,6 @@ def test_before_kickoff_callback():
 
         @before_kickoff
         def modify_inputs(self, inputs):
-
             self.inputs_modified = True
             inputs["modified"] = True
             return inputs
@@ -3509,7 +3511,7 @@ 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
 
@@ -3519,28 +3521,30 @@ 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
 
@@ -3578,12 +3582,12 @@ 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
@@ -3591,3 +3595,21 @@ def test_before_kickoff_without_inputs():
     # Verify that the inputs were initialized and modified inside the before_kickoff method
     assert test_crew_instance.received_inputs is not None
     assert test_crew_instance.received_inputs.get("modified") is True
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_crew_with_knowledge_sources_works_with_copy():
+    content = "Brandon's favorite color is red and he likes Mexican food."
+    string_source = StringKnowledgeSource(content=content)
+
+    crew = Crew(
+        agents=[researcher, writer],
+        tasks=[Task(description="test", expected_output="test", agent=researcher)],
+        knowledge_sources=[string_source],
+    )
+
+    crew_copy = crew.copy()
+
+    assert crew_copy.knowledge_sources == crew.knowledge_sources
+    assert len(crew_copy.agents) == len(crew.agents)
+    assert len(crew_copy.tasks) == len(crew.tasks)

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

@@ -0,0 +1,176 @@
+"""Test flow state persistence functionality."""
+
+import os
+from typing import Dict
+
+import pytest
+from pydantic import BaseModel
+
+from crewai.flow.flow import Flow, FlowState, listen, start
+from crewai.flow.persistence import persist
+from crewai.flow.persistence.sqlite import SQLiteFlowPersistence
+
+
+class TestState(FlowState):
+    """Test state model with required id field."""
+    counter: int = 0
+    message: str = ""
+
+
+def test_persist_decorator_saves_state(tmp_path):
+    """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
+    assert saved_state["message"] == "Hello, World!"
+
+
+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
+    assert saved_state["counter"] == 1
+    assert saved_state["message"] == "Count is 1"
+
+
+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]):
+
+        @start()
+        @persist(persistence)
+        def set_message(self):
+            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)
+    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)
+    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 == 43  # Preserved
+    assert flow3.state.message == "Updated message"  # Overridden
+
+
+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]):
+        @start()
+        @persist(persistence)
+        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)
+        @persist(persistence)
+        def step_2(self):
+            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 = flow2.state
+    assert final_state is not None
+    assert final_state.counter == 2
+    assert final_state.message == "Step 2"
+
+    class NoPersistenceMultiStepFlow(Flow[TestState]):
+        @start()
+        @persist(persistence)
+        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/test_manager_llm_delegation.py

@@ -1,51 +0,0 @@
-import pytest
-
-from crewai import Agent
-from crewai.tools.agent_tools.base_agent_tools import BaseAgentTool
-
-
-class InternalAgentTool(BaseAgentTool):
-    """Concrete implementation of BaseAgentTool for testing."""
-
-    def _run(self, *args, **kwargs):
-        """Implement required _run method."""
-        return "Test response"
-
-
-@pytest.mark.parametrize(
-    "role_name,should_match",
-    [
-        ("Futel Official Infopoint", True),  # exact match
-        ('  "Futel Official Infopoint"  ', True),  # extra quotes and spaces
-        ("Futel Official Infopoint\n", True),  # trailing newline
-        ('"Futel Official Infopoint"', True),  # embedded quotes
-        (" FUTEL\nOFFICIAL   INFOPOINT ", True),  # multiple whitespace and newline
-    ],
-)
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_agent_tool_role_matching(role_name, should_match):
-    """Test that agent tools can match roles regardless of case, whitespace, and special characters."""
-    # Create test agent
-    test_agent = Agent(
-        role="Futel Official Infopoint",
-        goal="Answer questions about Futel",
-        backstory="Futel Football Club info",
-        allow_delegation=False,
-    )
-
-    # Create test agent tool
-    agent_tool = InternalAgentTool(
-        name="test_tool", description="Test tool", agents=[test_agent]
-    )
-
-    # Test role matching
-    result = agent_tool._execute(agent_name=role_name, task="Test task", context=None)
-
-    if should_match:
-        assert (
-            "coworker mentioned not found" not in result.lower()
-        ), f"Should find agent with role name: {role_name}"
-    else:
-        assert (
-            "coworker mentioned not found" in result.lower()
-        ), f"Should not find agent with role name: {role_name}"

tests/tools/test_tool_usage.py

@@ -231,3 +231,255 @@ def test_validate_tool_input_with_special_characters():
 
     arguments = tool_usage._validate_tool_input(tool_input)
     assert arguments == expected_arguments
+
+
+def test_validate_tool_input_none_input():
+    tool_usage = ToolUsage(
+        tools_handler=MagicMock(),
+        tools=[],
+        original_tools=[],
+        tools_description="",
+        tools_names="",
+        task=MagicMock(),
+        function_calling_llm=None,
+        agent=MagicMock(),
+        action=MagicMock(),
+    )
+
+    arguments = tool_usage._validate_tool_input(None)
+    assert arguments == {}
+
+
+def test_validate_tool_input_valid_json():
+    tool_usage = ToolUsage(
+        tools_handler=MagicMock(),
+        tools=[],
+        original_tools=[],
+        tools_description="",
+        tools_names="",
+        task=MagicMock(),
+        function_calling_llm=None,
+        agent=MagicMock(),
+        action=MagicMock(),
+    )
+
+    tool_input = '{"key": "value", "number": 42, "flag": true}'
+    expected_arguments = {"key": "value", "number": 42, "flag": True}
+
+    arguments = tool_usage._validate_tool_input(tool_input)
+    assert arguments == expected_arguments
+
+
+def test_validate_tool_input_python_dict():
+    tool_usage = ToolUsage(
+        tools_handler=MagicMock(),
+        tools=[],
+        original_tools=[],
+        tools_description="",
+        tools_names="",
+        task=MagicMock(),
+        function_calling_llm=None,
+        agent=MagicMock(),
+        action=MagicMock(),
+    )
+
+    tool_input = "{'key': 'value', 'number': 42, 'flag': True}"
+    expected_arguments = {"key": "value", "number": 42, "flag": True}
+
+    arguments = tool_usage._validate_tool_input(tool_input)
+    assert arguments == expected_arguments
+
+
+def test_validate_tool_input_json5_unquoted_keys():
+    tool_usage = ToolUsage(
+        tools_handler=MagicMock(),
+        tools=[],
+        original_tools=[],
+        tools_description="",
+        tools_names="",
+        task=MagicMock(),
+        function_calling_llm=None,
+        agent=MagicMock(),
+        action=MagicMock(),
+    )
+
+    tool_input = "{key: 'value', number: 42, flag: true}"
+    expected_arguments = {"key": "value", "number": 42, "flag": True}
+
+    arguments = tool_usage._validate_tool_input(tool_input)
+    assert arguments == expected_arguments
+
+
+def test_validate_tool_input_with_trailing_commas():
+    tool_usage = ToolUsage(
+        tools_handler=MagicMock(),
+        tools=[],
+        original_tools=[],
+        tools_description="",
+        tools_names="",
+        task=MagicMock(),
+        function_calling_llm=None,
+        agent=MagicMock(),
+        action=MagicMock(),
+    )
+
+    tool_input = '{"key": "value", "number": 42, "flag": true,}'
+    expected_arguments = {"key": "value", "number": 42, "flag": True}
+
+    arguments = tool_usage._validate_tool_input(tool_input)
+    assert arguments == expected_arguments
+
+
+def test_validate_tool_input_invalid_input():
+    tool_usage = ToolUsage(
+        tools_handler=MagicMock(),
+        tools=[],
+        original_tools=[],
+        tools_description="",
+        tools_names="",
+        task=MagicMock(),
+        function_calling_llm=None,
+        agent=MagicMock(),
+        action=MagicMock(),
+    )
+
+    invalid_inputs = [
+        "Just a string",
+        "['list', 'of', 'values']",
+        "12345",
+        "",
+    ]
+
+    for invalid_input in invalid_inputs:
+        with pytest.raises(Exception) as e_info:
+            tool_usage._validate_tool_input(invalid_input)
+        assert (
+            "Tool input must be a valid dictionary in JSON or Python literal format"
+            in str(e_info.value)
+        )
+
+    # Test for None input separately
+    arguments = tool_usage._validate_tool_input(None)
+    assert arguments == {}  # Expecting an empty dictionary
+
+
+def test_validate_tool_input_complex_structure():
+    tool_usage = ToolUsage(
+        tools_handler=MagicMock(),
+        tools=[],
+        original_tools=[],
+        tools_description="",
+        tools_names="",
+        task=MagicMock(),
+        function_calling_llm=None,
+        agent=MagicMock(),
+        action=MagicMock(),
+    )
+
+    tool_input = """
+    {
+        "user": {
+            "name": "Alice",
+            "age": 30
+        },
+        "items": [
+            {"id": 1, "value": "Item1"},
+            {"id": 2, "value": "Item2",}
+        ],
+        "active": true,
+    }
+    """
+    expected_arguments = {
+        "user": {"name": "Alice", "age": 30},
+        "items": [
+            {"id": 1, "value": "Item1"},
+            {"id": 2, "value": "Item2"},
+        ],
+        "active": True,
+    }
+
+    arguments = tool_usage._validate_tool_input(tool_input)
+    assert arguments == expected_arguments
+
+
+def test_validate_tool_input_code_content():
+    tool_usage = ToolUsage(
+        tools_handler=MagicMock(),
+        tools=[],
+        original_tools=[],
+        tools_description="",
+        tools_names="",
+        task=MagicMock(),
+        function_calling_llm=None,
+        agent=MagicMock(),
+        action=MagicMock(),
+    )
+
+    tool_input = '{"filename": "script.py", "content": "def hello():\\n    print(\'Hello, world!\')"}'
+    expected_arguments = {
+        "filename": "script.py",
+        "content": "def hello():\n    print('Hello, world!')",
+    }
+
+    arguments = tool_usage._validate_tool_input(tool_input)
+    assert arguments == expected_arguments
+
+
+def test_validate_tool_input_with_escaped_quotes():
+    tool_usage = ToolUsage(
+        tools_handler=MagicMock(),
+        tools=[],
+        original_tools=[],
+        tools_description="",
+        tools_names="",
+        task=MagicMock(),
+        function_calling_llm=None,
+        agent=MagicMock(),
+        action=MagicMock(),
+    )
+
+    tool_input = '{"text": "He said, \\"Hello, world!\\""}'
+    expected_arguments = {"text": 'He said, "Hello, world!"'}
+
+    arguments = tool_usage._validate_tool_input(tool_input)
+    assert arguments == expected_arguments
+
+
+def test_validate_tool_input_large_json_content():
+    tool_usage = ToolUsage(
+        tools_handler=MagicMock(),
+        tools=[],
+        original_tools=[],
+        tools_description="",
+        tools_names="",
+        task=MagicMock(),
+        function_calling_llm=None,
+        agent=MagicMock(),
+        action=MagicMock(),
+    )
+
+    # Simulate a large JSON content
+    tool_input = (
+        '{"data": ' + json.dumps([{"id": i, "value": i * 2} for i in range(1000)]) + "}"
+    )
+    expected_arguments = {"data": [{"id": i, "value": i * 2} for i in range(1000)]}
+
+    arguments = tool_usage._validate_tool_input(tool_input)
+    assert arguments == expected_arguments
+
+
+def test_validate_tool_input_none_input():
+    tool_usage = ToolUsage(
+        tools_handler=MagicMock(),
+        tools=[],
+        original_tools=[],
+        tools_description="",
+        tools_names="",
+        task=MagicMock(),
+        function_calling_llm=None,
+        agent=MagicMock(),
+        action=MagicMock(),
+    )
+
+    arguments = tool_usage._validate_tool_input(None)
+    assert arguments == {}  # Expecting an empty dictionary

tests/utilities/cassettes/test_convert_with_instructions.yaml

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+      Llama Materials set forth herein.\\n\\n\u201CDocumentation\u201D means the specifications,
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+      (if you are \\nentering into this Agreement on such person or entity\u2019s
+      behalf), of the age required under\\napplicable laws, rules or regulations to
+      provide legal consent and that has legal authority\\nto bind your employer or
+      such other person or entity if you are entering in this Agreement\\non their
+      behalf.\\n\\n\u201CLlama 3.2\u201D means the foundational large language models
+      and software and algorithms, including\\nmachine-learning model code, trained
+      model weights, inference-enabling code, training-enabling code,\\nfine-tuning
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+      or \u201Cwe\u201D means Meta Platforms Ireland Limited (if you are located in
+      or, \\nif you are an entity, your principal place of business is in the EEA
+      or Switzerland) \\nand Meta Platforms, Inc. (if you are located outside of the
+      EEA or Switzerland). \\n\\n\\nBy clicking \u201CI Accept\u201D below or by using
+      or distributing any portion or element of the Llama Materials,\\nyou agree to
+      be bound by this Agreement.\\n\\n\\n1. License Rights and Redistribution.\\n\\n
+      \   a. Grant of Rights. You are granted a non-exclusive, worldwide, \\nnon-transferable
+      and royalty-free limited license under Meta\u2019s intellectual property or
+      other rights \\nowned by Meta embodied in the Llama Materials to use, reproduce,
+      distribute, copy, create derivative works \\nof, and make modifications to the
+      Llama Materials.  \\n\\n    b. Redistribution and Use.  \\n\\n        i. If
+      you distribute or make available the Llama Materials (or any derivative works
+      thereof), \\nor a product or service (including another AI model) that contains
+      any of them, you shall (A) provide\\na copy of this Agreement with any such
+      Llama Materials; and (B) prominently display \u201CBuilt with Llama\u201D\\non
+      a related website, user interface, blogpost, about page, or product documentation.
+      If you use the\\nLlama Materials or any outputs or results of the Llama Materials
+      to create, train, fine tune, or\\notherwise improve an AI model, which is distributed
+      or made available, you shall also include \u201CLlama\u201D\\nat the beginning
+      of any such AI model name.\\n\\n        ii. If you receive Llama Materials,
+      or any derivative works thereof, from a Licensee as part\\nof an integrated
+      end user product, then Section 2 of this Agreement will not apply to you. \\n\\n
+      \       iii. You must retain in all copies of the Llama Materials that you distribute
+      the \\nfollowing attribution notice within a \u201CNotice\u201D text file distributed
+      as a part of such copies: \\n\u201CLlama 3.2 is licensed under the Llama 3.2
+      Community License, Copyright \xA9 Meta Platforms,\\nInc. All Rights Reserved.\u201D\\n\\n
+      \       iv. Your use of the Llama Materials must comply with applicable laws
+      and regulations\\n(including trade compliance laws and regulations) and adhere
+      to the Acceptable Use Policy for\\nthe Llama Materials (available at https://www.llama.com/llama3_2/use-policy),
+      which is hereby \\nincorporated by reference into this Agreement.\\n  \\n2.
+      Additional Commercial Terms. If, on the Llama 3.2 version release date, the
+      monthly active users\\nof the products or services made available by or for
+      Licensee, or Licensee\u2019s affiliates, \\nis greater than 700 million monthly
+      active users in the preceding calendar month, you must request \\na license
+      from Meta, which Meta may grant to you in its sole discretion, and you are not
+      authorized to\\nexercise any of the rights under this Agreement unless or until
+      Meta otherwise expressly grants you such rights.\\n\\n3. Disclaimer of Warranty.
+      UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND \\nRESULTS
+      THEREFROM ARE PROVIDED ON AN \u201CAS IS\u201D BASIS, WITHOUT WARRANTIES OF
+      ANY KIND, AND META DISCLAIMS\\nALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND
+      IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES\\nOF TITLE, NON-INFRINGEMENT,
+      MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE\\nFOR
+      DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS
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+      OUTPUT AND RESULTS.\\n\\n4. Limitation of Liability. IN NO EVENT WILL META OR
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+      TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT,
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+      EXEMPLARY OR PUNITIVE DAMAGES, EVEN \\nIF META OR ITS AFFILIATES HAVE BEEN ADVISED
+      OF THE POSSIBILITY OF ANY OF THE FOREGOING.\\n\\n5. Intellectual Property.\\n\\n
+      \   a. No trademark licenses are granted under this Agreement, and in connection
+      with the Llama Materials, \\nneither Meta nor Licensee may use any name or mark
+      owned by or associated with the other or any of its affiliates, \\nexcept as
+      required for reasonable and customary use in describing and redistributing the
+      Llama Materials or as \\nset forth in this Section 5(a). Meta hereby grants
+      you a license to use \u201CLlama\u201D (the \u201CMark\u201D) solely as required
+      \\nto comply with the last sentence of Section 1.b.i. You will comply with Meta\u2019s
+      brand guidelines (currently accessible \\nat https://about.meta.com/brand/resources/meta/company-brand/).
+      All goodwill arising out of your use of the Mark \\nwill inure to the benefit
+      of Meta.\\n\\n    b. Subject to Meta\u2019s ownership of Llama Materials and
+      derivatives made by or for Meta, with respect to any\\n    derivative works
+      and modifications of the Llama Materials that are made by you, as between you
+      and Meta,\\n    you are and will be the owner of such derivative works and modifications.\\n\\n
+      \   c. If you institute litigation or other proceedings against Meta or any
+      entity (including a cross-claim or\\n    counterclaim in a lawsuit) alleging
+      that the Llama Materials or Llama 3.2 outputs or results, or any portion\\n
+      \   of any of the foregoing, constitutes infringement of intellectual property
+      or other rights owned or licensable\\n    by you, then any licenses granted
+      to you under this Agreement shall terminate as of the date such litigation or\\n
+      \   claim is filed or instituted. You will indemnify and hold harmless Meta
+      from and against any claim by any third\\n    party arising out of or related
+      to your use or distribution of the Llama Materials.\\n\\n6. Term and Termination.
+      The term of this Agreement will commence upon your acceptance of this Agreement
+      or access\\nto the Llama Materials and will continue in full force and effect
+      until terminated in accordance with the terms\\nand conditions herein. Meta
+      may terminate this Agreement if you are in breach of any term or condition of
+      this\\nAgreement. Upon termination of this Agreement, you shall delete and cease
+      use of the Llama Materials. Sections 3,\\n4 and 7 shall survive the termination
+      of this Agreement. \\n\\n7. Governing Law and Jurisdiction. This Agreement will
+      be governed and construed under the laws of the State of \\nCalifornia without
+      regard to choice of law principles, and the UN Convention on Contracts for the
+      International\\nSale of Goods does not apply to this Agreement. The courts of
+      California shall have exclusive jurisdiction of\\nany dispute arising out of
+      this Agreement.\\n**Llama 3.2** **Acceptable Use Policy**\\n\\nMeta is committed
+      to promoting safe and fair use of its tools and features, including Llama 3.2.
+      If you access or use Llama 3.2, you agree to this Acceptable Use Policy (\u201C**Policy**\u201D).
+      The most recent copy of this policy can be found at [https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy).\\n\\n**Prohibited
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tests/utilities/cassettes/test_converter_with_nested_model.yaml

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+      "function": {"name": "Person", "description": "Correctly extracted `Person`
+      with all the required parameters with correct types", "parameters": {"$defs":
+      {"Address": {"properties": {"street": {"title": "Street", "type": "string"},
+      "city": {"title": "City", "type": "string"}, "zip_code": {"title": "Zip Code",
+      "type": "string"}}, "required": ["street", "city", "zip_code"], "title": "Address",
+      "type": "object"}}, "properties": {"name": {"title": "Name", "type": "string"},
+      "age": {"title": "Age", "type": "integer"}, "address": {"$ref": "#/$defs/Address"}},
+      "required": ["address", "age", "name"], "type": "object"}}}]}'
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tests/utilities/test_converter.py

@@ -39,6 +39,22 @@ class NestedModel(BaseModel):
     data: SimpleModel
 
 
+class Address(BaseModel):
+    street: str
+    city: str
+    zip_code: str
+
+
+class Person(BaseModel):
+    name: str
+    age: int
+    address: Address
+
+
+class CustomConverter(Converter):
+    pass
+
+
 # Fixtures
 @pytest.fixture
 def mock_agent():
@@ -199,26 +215,23 @@ def test_convert_with_instructions_failure(
 
 # Tests for get_conversion_instructions
 def test_get_conversion_instructions_gpt():
-    mock_llm = Mock()
-    mock_llm.openai_api_base = None
+    llm = LLM(model="gpt-4o-mini")
     with patch.object(LLM, "supports_function_calling") as supports_function_calling:
         supports_function_calling.return_value = True
-        instructions = get_conversion_instructions(SimpleModel, mock_llm)
+        instructions = get_conversion_instructions(SimpleModel, llm)
         model_schema = PydanticSchemaParser(model=SimpleModel).get_schema()
         assert (
             instructions
-            == f"I'm gonna convert this raw text into valid JSON.\n\nThe json should have the following structure, with the following keys:\n{model_schema}"
+            == f"Please convert the following text into valid JSON.\n\nThe JSON should follow this schema:\n```json\n{model_schema}\n```"
         )
 
 
 def test_get_conversion_instructions_non_gpt():
-    mock_llm = Mock()
-    with patch.object(LLM, "supports_function_calling") as supports_function_calling:
-        supports_function_calling.return_value = False
-        with patch("crewai.utilities.converter.PydanticSchemaParser") as mock_parser:
-            mock_parser.return_value.get_schema.return_value = "Sample schema"
-            instructions = get_conversion_instructions(SimpleModel, mock_llm)
-            assert "Sample schema" in instructions
+    llm = LLM(model="ollama/llama3.1", base_url="http://localhost:11434")
+    with patch.object(LLM, "supports_function_calling", return_value=False):
+        instructions = get_conversion_instructions(SimpleModel, llm)
+        assert '"name": str' in instructions
+        assert '"age": int' in instructions
 
 
 # Tests for is_gpt
@@ -232,10 +245,6 @@ def test_supports_function_calling_false():
     assert llm.supports_function_calling() is False
 
 
-class CustomConverter(Converter):
-    pass
-
-
 def test_create_converter_with_mock_agent():
     mock_agent = MagicMock()
     mock_agent.get_output_converter.return_value = MagicMock(spec=Converter)
@@ -255,7 +264,7 @@ def test_create_converter_with_mock_agent():
 def test_create_converter_with_custom_converter():
     converter = create_converter(
         converter_cls=CustomConverter,
-        llm=Mock(),
+        llm=LLM(model="gpt-4o-mini"),
         text="Sample",
         model=SimpleModel,
         instructions="Convert",
@@ -313,3 +322,278 @@ def test_generate_model_description_dict_field():
     description = generate_model_description(ModelWithDictField)
     expected_description = '{\n  "attributes": Dict[str, int]\n}'
     assert description == expected_description
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_convert_with_instructions():
+    llm = LLM(model="gpt-4o-mini")
+    sample_text = "Name: Alice, Age: 30"
+
+    instructions = get_conversion_instructions(SimpleModel, llm)
+    converter = Converter(
+        llm=llm,
+        text=sample_text,
+        model=SimpleModel,
+        instructions=instructions,
+    )
+
+    # Act
+    output = converter.to_pydantic()
+
+    # Assert
+    assert isinstance(output, SimpleModel)
+    assert output.name == "Alice"
+    assert output.age == 30
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_converter_with_llama3_2_model():
+    llm = LLM(model="ollama/llama3.2:3b", base_url="http://localhost:11434")
+
+    sample_text = "Name: Alice Llama, Age: 30"
+
+    instructions = get_conversion_instructions(SimpleModel, llm)
+    converter = Converter(
+        llm=llm,
+        text=sample_text,
+        model=SimpleModel,
+        instructions=instructions,
+    )
+
+    output = converter.to_pydantic()
+
+    assert isinstance(output, SimpleModel)
+    assert output.name == "Alice Llama"
+    assert output.age == 30
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_converter_with_llama3_1_model():
+    llm = LLM(model="ollama/llama3.1", base_url="http://localhost:11434")
+    sample_text = "Name: Alice Llama, Age: 30"
+
+    instructions = get_conversion_instructions(SimpleModel, llm)
+    converter = Converter(
+        llm=llm,
+        text=sample_text,
+        model=SimpleModel,
+        instructions=instructions,
+    )
+
+    output = converter.to_pydantic()
+
+    assert isinstance(output, SimpleModel)
+    assert output.name == "Alice Llama"
+    assert output.age == 30
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_converter_with_nested_model():
+    llm = LLM(model="gpt-4o-mini")
+    sample_text = "Name: John Doe\nAge: 30\nAddress: 123 Main St, Anytown, 12345"
+
+    instructions = get_conversion_instructions(Person, llm)
+    converter = Converter(
+        llm=llm,
+        text=sample_text,
+        model=Person,
+        instructions=instructions,
+    )
+
+    output = converter.to_pydantic()
+
+    assert isinstance(output, Person)
+    assert output.name == "John Doe"
+    assert output.age == 30
+    assert isinstance(output.address, Address)
+    assert output.address.street == "123 Main St"
+    assert output.address.city == "Anytown"
+    assert output.address.zip_code == "12345"
+
+
+# Tests for error handling
+def test_converter_error_handling():
+    llm = Mock(spec=LLM)
+    llm.supports_function_calling.return_value = False
+    llm.call.return_value = "Invalid JSON"
+    sample_text = "Name: Alice, Age: 30"
+
+    instructions = get_conversion_instructions(SimpleModel, llm)
+    converter = Converter(
+        llm=llm,
+        text=sample_text,
+        model=SimpleModel,
+        instructions=instructions,
+    )
+
+    with pytest.raises(ConverterError) as exc_info:
+        output = converter.to_pydantic()
+
+    assert "Failed to convert text into a Pydantic model" in str(exc_info.value)
+
+
+# Tests for retry logic
+def test_converter_retry_logic():
+    llm = Mock(spec=LLM)
+    llm.supports_function_calling.return_value = False
+    llm.call.side_effect = [
+        "Invalid JSON",
+        "Still invalid",
+        '{"name": "Retry Alice", "age": 30}',
+    ]
+    sample_text = "Name: Retry Alice, Age: 30"
+
+    instructions = get_conversion_instructions(SimpleModel, llm)
+    converter = Converter(
+        llm=llm,
+        text=sample_text,
+        model=SimpleModel,
+        instructions=instructions,
+        max_attempts=3,
+    )
+
+    output = converter.to_pydantic()
+
+    assert isinstance(output, SimpleModel)
+    assert output.name == "Retry Alice"
+    assert output.age == 30
+    assert llm.call.call_count == 3
+
+
+# Tests for optional fields
+def test_converter_with_optional_fields():
+    class OptionalModel(BaseModel):
+        name: str
+        age: Optional[int]
+
+    llm = Mock(spec=LLM)
+    llm.supports_function_calling.return_value = False
+    # Simulate the LLM's response with 'age' explicitly set to null
+    llm.call.return_value = '{"name": "Bob", "age": null}'
+    sample_text = "Name: Bob, age: None"
+
+    instructions = get_conversion_instructions(OptionalModel, llm)
+    converter = Converter(
+        llm=llm,
+        text=sample_text,
+        model=OptionalModel,
+        instructions=instructions,
+    )
+
+    output = converter.to_pydantic()
+
+    assert isinstance(output, OptionalModel)
+    assert output.name == "Bob"
+    assert output.age is None
+
+
+# Tests for list fields
+def test_converter_with_list_field():
+    class ListModel(BaseModel):
+        items: List[int]
+
+    llm = Mock(spec=LLM)
+    llm.supports_function_calling.return_value = False
+    llm.call.return_value = '{"items": [1, 2, 3]}'
+    sample_text = "Items: 1, 2, 3"
+
+    instructions = get_conversion_instructions(ListModel, llm)
+    converter = Converter(
+        llm=llm,
+        text=sample_text,
+        model=ListModel,
+        instructions=instructions,
+    )
+
+    output = converter.to_pydantic()
+
+    assert isinstance(output, ListModel)
+    assert output.items == [1, 2, 3]
+
+
+# Tests for enums
+from enum import Enum
+
+
+def test_converter_with_enum():
+    class Color(Enum):
+        RED = "red"
+        GREEN = "green"
+        BLUE = "blue"
+
+    class EnumModel(BaseModel):
+        name: str
+        color: Color
+
+    llm = Mock(spec=LLM)
+    llm.supports_function_calling.return_value = False
+    llm.call.return_value = '{"name": "Alice", "color": "red"}'
+    sample_text = "Name: Alice, Color: Red"
+
+    instructions = get_conversion_instructions(EnumModel, llm)
+    converter = Converter(
+        llm=llm,
+        text=sample_text,
+        model=EnumModel,
+        instructions=instructions,
+    )
+
+    output = converter.to_pydantic()
+
+    assert isinstance(output, EnumModel)
+    assert output.name == "Alice"
+    assert output.color == Color.RED
+
+
+# Tests for ambiguous input
+def test_converter_with_ambiguous_input():
+    llm = Mock(spec=LLM)
+    llm.supports_function_calling.return_value = False
+    llm.call.return_value = '{"name": "Charlie", "age": "Not an age"}'
+    sample_text = "Charlie is thirty years old"
+
+    instructions = get_conversion_instructions(SimpleModel, llm)
+    converter = Converter(
+        llm=llm,
+        text=sample_text,
+        model=SimpleModel,
+        instructions=instructions,
+    )
+
+    with pytest.raises(ConverterError) as exc_info:
+        output = converter.to_pydantic()
+
+    assert "validation error" in str(exc_info.value).lower()
+
+
+# Tests for function calling support
+def test_converter_with_function_calling():
+    llm = Mock(spec=LLM)
+    llm.supports_function_calling.return_value = True
+
+    instructor = Mock()
+    instructor.to_pydantic.return_value = SimpleModel(name="Eve", age=35)
+
+    converter = Converter(
+        llm=llm,
+        text="Name: Eve, Age: 35",
+        model=SimpleModel,
+        instructions="Convert this text.",
+    )
+    converter._create_instructor = Mock(return_value=instructor)
+
+    output = converter.to_pydantic()
+
+    assert isinstance(output, SimpleModel)
+    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

tests/utilities/test_pydantic_schema_parser.py

@@ -0,0 +1,94 @@
+from typing import Any, Dict, List, Optional, Set, Tuple, Union
+
+import pytest
+from pydantic import BaseModel, Field
+
+from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
+
+
+def test_simple_model():
+    class SimpleModel(BaseModel):
+        field1: int
+        field2: str
+
+    parser = PydanticSchemaParser(model=SimpleModel)
+    schema = parser.get_schema()
+
+    expected_schema = """{
+    field1: int,
+    field2: str
+}"""
+    assert schema.strip() == expected_schema.strip()
+
+
+def test_nested_model():
+    class NestedModel(BaseModel):
+        nested_field: int
+
+    class ParentModel(BaseModel):
+        parent_field: str
+        nested: NestedModel
+
+    parser = PydanticSchemaParser(model=ParentModel)
+    schema = parser.get_schema()
+
+    expected_schema = """{
+    parent_field: str,
+    nested: NestedModel
+    {
+        nested_field: int
+    }
+}"""
+    assert schema.strip() == expected_schema.strip()
+
+
+def test_model_with_list():
+    class ListModel(BaseModel):
+        list_field: List[int]
+
+    parser = PydanticSchemaParser(model=ListModel)
+    schema = parser.get_schema()
+
+    expected_schema = """{
+    list_field: List[int]
+}"""
+    assert schema.strip() == expected_schema.strip()
+
+
+def test_model_with_optional_field():
+    class OptionalModel(BaseModel):
+        optional_field: Optional[str]
+
+    parser = PydanticSchemaParser(model=OptionalModel)
+    schema = parser.get_schema()
+
+    expected_schema = """{
+    optional_field: Optional[str]
+}"""
+    assert schema.strip() == expected_schema.strip()
+
+
+def test_model_with_union():
+    class UnionModel(BaseModel):
+        union_field: Union[int, str]
+
+    parser = PydanticSchemaParser(model=UnionModel)
+    schema = parser.get_schema()
+
+    expected_schema = """{
+    union_field: Union[int, str]
+}"""
+    assert schema.strip() == expected_schema.strip()
+
+
+def test_model_with_dict():
+    class DictModel(BaseModel):
+        dict_field: Dict[str, int]
+
+    parser = PydanticSchemaParser(model=DictModel)
+    schema = parser.get_schema()
+
+    expected_schema = """{
+    dict_field: Dict[str, int]
+}"""
+    assert schema.strip() == expected_schema.strip()

uv.lock

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+[[package]]
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+version = "1.5.1"
+source = { registry = "https://pypi.org/simple" }
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+[[package]]
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+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 = [
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+]
+
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 name = "attrs"
 version = "24.2.0"
@@ -631,7 +649,7 @@ wheels = [
 
 [[package]]
 name = "crewai"
-version = "0.95.0"
+version = "0.100.0"
 source = { editable = "." }
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     { name = "appdirs" },
@@ -641,6 +659,7 @@ dependencies = [
     { name = "click" },
     { name = "instructor" },
     { name = "json-repair" },
+    { name = "json5" },
     { name = "jsonref" },
     { name = "litellm" },
     { name = "openai" },
@@ -714,13 +733,14 @@ 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" },
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     { name = "instructor", specifier = ">=1.3.3" },
     { name = "json-repair", specifier = ">=0.25.2" },
+    { name = "json5", specifier = ">=0.10.0" },
     { name = "jsonref", specifier = ">=1.1.0" },
-    { name = "litellm", specifier = "==1.57.4" },
+    { name = "litellm", specifier = "==1.59.8" },
     { name = "mem0ai", marker = "extra == 'mem0'", specifier = ">=0.1.29" },
     { name = "openai", specifier = ">=1.13.3" },
     { name = "openpyxl", specifier = ">=3.1.5" },
@@ -762,7 +782,7 @@ dev = [
 
 [[package]]
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-version = "0.25.6"
+version = "0.32.1"
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     { name = "beautifulsoup4" },
@@ -774,20 +794,21 @@ 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" },
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+]
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@@ -2359,9 +2389,9 @@ dependencies = [
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+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" },
+]
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+[[package]]
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+version = "24.3.0"
+source = { registry = "https://pypi.org/simple" }
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+]
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+    { name = "certifi" },
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+    { name = "charset-normalizer" },
+    { name = "cryptography" },
+    { name = "filelock" },
+    { name = "idna" },
+    { name = "packaging" },
+    { name = "platformdirs" },
+    { name = "pyjwt" },
+    { name = "pyopenssl" },
+    { name = "pytz" },
+    { name = "requests" },
+    { name = "sortedcontainers" },
+    { name = "tomlkit" },
+    { name = "typing-extensions" },
+]
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