wip

.github/workflows/tests.yml

@@ -1,32 +1,60 @@
 name: Run Tests
 
-on: [pull_request]
+on:
+  pull_request:
+  push:
+    branches:
+      - main
 
 permissions:
   contents: write
 
-env:
-  OPENAI_API_KEY: fake-api-key
-
 jobs:
   tests:
     runs-on: ubuntu-latest
     timeout-minutes: 15
+    env:
+      OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+      MODEL: gpt-4o-mini
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
 
-      - name: Install uv
+      - name: Install UV
         uses: astral-sh/setup-uv@v3
         with:
           enable-cache: true
 
-
       - name: Set up Python
         run: uv python install 3.12.8
 
       - name: Install the project
         run: uv sync --dev --all-extras
 
-      - name: Run tests
-        run: uv run pytest tests -vv
+      - name: Run General Tests
+        run: uv run pytest tests -k "not main_branch_tests" -vv
+
+  main_branch_tests:
+    if: github.ref == 'refs/heads/main'
+    runs-on: ubuntu-latest
+    needs: tests
+    timeout-minutes: 15
+    env:
+      OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Install UV
+        uses: astral-sh/setup-uv@v3
+        with:
+          enable-cache: true
+
+      - name: Set up Python
+        run: uv python install 3.12.8
+
+      - name: Install the project
+        run: uv sync --dev --all-extras
+
+      - name: Run Main Branch Specific Tests
+        run: uv run pytest tests/main_branch_tests -vv

README.md

@@ -1,18 +1,10 @@
 <div align="center">
 
-![Logo of CrewAI](./docs/crewai_logo.png)
+![Logo of CrewAI, two people rowing on a boat](./docs/crewai_logo.png)
 
 # **CrewAI**
 
-**CrewAI**: Production-grade framework for orchestrating sophisticated AI agent systems. From simple automations to complex real-world applications, CrewAI provides precise control and deep customization. By fostering collaborative intelligence through flexible, production-ready architecture, CrewAI empowers agents to work together seamlessly, tackling complex business challenges with predictable, consistent results.
-
-**CrewAI Enterprise**
-Want to plan, build (+ no code), deploy, monitor and interare your agents: [CrewAI Enterprise](https://www.crewai.com/enterprise). Designed for complex, real-world applications, our enterprise solution offers:
-
-- **Seamless Integrations**
-- **Scalable & Secure Deployment**
-- **Actionable Insights**
-- **24/7 Support**
+🤖 **CrewAI**: Production-grade framework for orchestrating sophisticated AI agent systems. From simple automations to complex real-world applications, CrewAI provides precise control and deep customization. By fostering collaborative intelligence through flexible, production-ready architecture, CrewAI empowers agents to work together seamlessly, tackling complex business challenges with predictable, consistent results.
 
 <h3>
 
@@ -198,7 +190,7 @@ research_task:
   description: >
     Conduct a thorough research about {topic}
     Make sure you find any interesting and relevant information given
-    the current year is 2025.
+    the current year is 2024.
   expected_output: >
     A list with 10 bullet points of the most relevant information about {topic}
   agent: researcher
@@ -400,7 +392,7 @@ class AdvancedAnalysisFlow(Flow[MarketState]):
             goal="Gather and validate supporting market data",
             backstory="You excel at finding and correlating multiple data sources"
         )
-
+        
         analysis_task = Task(
             description="Analyze {sector} sector data for the past {timeframe}",
             expected_output="Detailed market analysis with confidence score",
@@ -411,7 +403,7 @@ class AdvancedAnalysisFlow(Flow[MarketState]):
             expected_output="Corroborating evidence and potential contradictions",
             agent=researcher
         )
-
+        
         # Demonstrate crew autonomy
         analysis_crew = Crew(
             agents=[analyst, researcher],

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** _(optional)_               | `embedder`               | `Optional[Dict[str, Any]]`    | Configuration for the embedder used by the agent.                                                                     |
+| **Embedder Config** _(optional)_        | `embedder_config`        | `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=None,  # Optional: Custom embedder configuration
+    embedder_config=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 Terminal
+```shell
 pip install crewai
 ```
 
@@ -20,7 +20,7 @@ pip install crewai
 
 The basic structure of a CrewAI CLI command is:
 
-```shell Terminal
+```shell
 crewai [COMMAND] [OPTIONS] [ARGUMENTS]
 ```
 
@@ -30,7 +30,7 @@ crewai [COMMAND] [OPTIONS] [ARGUMENTS]
 
 Create a new crew or flow.
 
-```shell Terminal
+```shell
 crewai create [OPTIONS] TYPE NAME
 ```
 
@@ -38,7 +38,7 @@ crewai create [OPTIONS] TYPE NAME
 - `NAME`: Name of the crew or flow
 
 Example:
-```shell Terminal
+```shell
 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 Terminal
+```shell
 crewai version [OPTIONS]
 ```
 
 - `--tools`: (Optional) Show the installed version of CrewAI tools
 
 Example:
-```shell Terminal
+```shell
 crewai version
 crewai version --tools
 ```
@@ -63,7 +63,7 @@ crewai version --tools
 
 Train the crew for a specified number of iterations.
 
-```shell Terminal
+```shell
 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 Terminal
+```shell
 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 Terminal
+```shell
 crewai replay [OPTIONS]
 ```
 
 - `-t, --task_id TEXT`: Replay the crew from this task ID, including all subsequent tasks
 
 Example:
-```shell Terminal    
+```shell    
 crewai replay -t task_123456
 ```
 
@@ -94,7 +94,7 @@ crewai replay -t task_123456
 
 Retrieve your latest crew.kickoff() task outputs.
 
-```shell Terminal
+```shell
 crewai log-tasks-outputs
 ```
 
@@ -102,7 +102,7 @@ crewai log-tasks-outputs
 
 Reset the crew memories (long, short, entity, latest_crew_kickoff_outputs).
 
-```shell Terminal
+```shell
 crewai reset-memories [OPTIONS]
 ```
 
@@ -113,7 +113,7 @@ crewai reset-memories [OPTIONS]
 - `-a, --all`: Reset ALL memories
 
 Example:
-```shell Terminal
+```shell
 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 Terminal
+```shell
 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 Terminal    
+```shell
 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 Terminal
+```shell
 crewai run
 ```
 <Note>
@@ -147,36 +147,7 @@ Some commands may require additional configuration or setup within your project
 </Note>
 
 
-### 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
+### 9. 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/crews.mdx

@@ -23,14 +23,14 @@ A crew in crewAI represents a collaborative group of agents working together to
 | **Language** _(optional)_             | `language`             | Language used for the crew, defaults to English.                                                                                                                                                                                                          |
 | **Language File** _(optional)_        | `language_file`        | Path to the language file to be used for the crew.                                                                                                                                                                                                        |
 | **Memory** _(optional)_               | `memory`               | Utilized for storing execution memories (short-term, long-term, entity memory).                                                                                                                                                                           |
-| **Memory Config** _(optional)_        | `memory_config`        | Configuration for the memory provider to be used by the crew.                                                                                                                                                                                             |
-| **Cache** _(optional)_                | `cache`                | Specifies whether to use a cache for storing the results of tools' execution. Defaults to `True`.                                                                                                                                                         |
-| **Embedder** _(optional)_             | `embedder`             | Configuration for the embedder to be used by the crew. Mostly used by memory for now. Default is `{"provider": "openai"}`.                                                                                                                                |
-| **Full Output** _(optional)_          | `full_output`          | Whether the crew should return the full output with all tasks outputs or just the final output. Defaults to `False`.                                                                                                                                      |
+| **Memory Config** _(optional)_        | `memory_config`        | Configuration for the memory provider to be used by the crew.                                                                                                                                                                                          |
+| **Cache** _(optional)_                | `cache`                | Specifies whether to use a cache for storing the results of tools' execution. Defaults to `True`.                                                                                                                                                          |
+| **Embedder** _(optional)_             | `embedder`             | Configuration for the embedder to be used by the crew. Mostly used by memory for now. Default is `{"provider": "openai"}`.                                                                                                                                                                     |
+| **Full Output** _(optional)_          | `full_output`          | Whether the crew should return the full output with all tasks outputs or just the final output. Defaults to `False`.                                                                                                                                       |
 | **Step Callback** _(optional)_        | `step_callback`        | A function that is called after each step of every agent. This can be used to log the agent's actions or to perform other operations; it won't override the agent-specific `step_callback`.                                                               |
 | **Task Callback** _(optional)_        | `task_callback`        | A function that is called after the completion of each task. Useful for monitoring or additional operations post-task execution.                                                                                                                          |
 | **Share Crew** _(optional)_           | `share_crew`           | Whether you want to share the complete crew information and execution with the crewAI team to make the library better, and allow us to train models.                                                                                                      |
-| **Output Log File** _(optional)_      | `output_log_file`      | Set to True to save logs as logs.txt in the current directory or provide a file path. Logs will be in JSON format if the filename ends in .json, otherwise .txt. Defautls to `None`.                                                                      |
+| **Output Log File** _(optional)_      | `output_log_file`      | Whether you want to have a file with the complete crew output and execution. You can set it using True and it will default to the folder you are currently in and it will be called logs.txt or passing a string with the full path and name of the file. |
 | **Manager Agent** _(optional)_        | `manager_agent`        | `manager` sets a custom agent that will be used as a manager.                                                                                                                                                                                             |
 | **Prompt File** _(optional)_          | `prompt_file`          | Path to the prompt JSON file to be used for the crew.                                                                                                                                                                                                     |
 | **Planning** *(optional)*             | `planning`             | Adds planning ability to the Crew. When activated before each Crew iteration, all Crew data is sent to an AgentPlanner that will plan the tasks and this plan will be added to each task description.                                                     |
@@ -240,23 +240,6 @@ print(f"Tasks Output: {crew_output.tasks_output}")
 print(f"Token Usage: {crew_output.token_usage}")
 ```
 
-## Accessing Crew Logs
-
-You can see real time log of the crew execution, by setting `output_log_file` as a `True(Boolean)` or a `file_name(str)`. Supports logging of events as both `file_name.txt` and `file_name.json`.
-In case of `True(Boolean)` will save as `logs.txt`.
-
-In case of `output_log_file` is set as `False(Booelan)` or `None`, the logs will not be populated.
-
-```python Code
-# Save crew logs
-crew = Crew(output_log_file = True)  # Logs will be saved as logs.txt
-crew = Crew(output_log_file = file_name)  # Logs will be saved as file_name.txt
-crew = Crew(output_log_file = file_name.txt)  # Logs will be saved as file_name.txt
-crew = Crew(output_log_file = file_name.json)  # Logs will be saved as file_name.json
-```
-
-
-
 ## Memory Utilization
 
 Crews can utilize memory (short-term, long-term, and entity memory) to enhance their execution and learning over time. This feature allows crews to store and recall execution memories, aiding in decision-making and task execution strategies.
@@ -296,9 +279,9 @@ print(result)
 Once your crew is assembled, initiate the workflow with the appropriate kickoff method. CrewAI provides several methods for better control over the kickoff process: `kickoff()`, `kickoff_for_each()`, `kickoff_async()`, and `kickoff_for_each_async()`.
 
 - `kickoff()`: Starts the execution process according to the defined process flow.
-- `kickoff_for_each()`: Executes tasks sequentially for each provided input event or item in the collection.
+- `kickoff_for_each()`: Executes tasks for each agent individually.
 - `kickoff_async()`: Initiates the workflow asynchronously.
-- `kickoff_for_each_async()`: Executes tasks concurrently for each provided input event or item, leveraging asynchronous processing.
+- `kickoff_for_each_async()`: Executes tasks for each agent individually in an asynchronous manner.
 
 ```python Code
 # Start the crew's task execution

docs/concepts/flows.mdx

@@ -35,8 +35,6 @@ class ExampleFlow(Flow):
     @start()
     def generate_city(self):
         print("Starting flow")
-        # Each flow state automatically gets a unique ID
-        print(f"Flow State ID: {self.state['id']}")
 
         response = completion(
             model=self.model,
@@ -49,8 +47,6 @@ class ExampleFlow(Flow):
         )
 
         random_city = response["choices"][0]["message"]["content"]
-        # Store the city in our state
-        self.state["city"] = random_city
         print(f"Random City: {random_city}")
 
         return random_city
@@ -68,8 +64,6 @@ class ExampleFlow(Flow):
         )
 
         fun_fact = response["choices"][0]["message"]["content"]
-        # Store the fun fact in our state
-        self.state["fun_fact"] = fun_fact
         return fun_fact
 
 
@@ -82,15 +76,7 @@ print(f"Generated fun fact: {result}")
 
 In the above example, we have created a simple Flow that generates a random city using OpenAI and then generates a fun fact about that city. The Flow consists of two tasks: `generate_city` and `generate_fun_fact`. The `generate_city` task is the starting point of the Flow, and the `generate_fun_fact` task listens for the output of the `generate_city` task.
 
-Each Flow instance automatically receives a unique identifier (UUID) in its state, which helps track and manage flow executions. The state can also store additional data (like the generated city and fun fact) that persists throughout the flow's execution.
-
-When you run the Flow, it will:
-1. Generate a unique ID for the flow state
-2. Generate a random city and store it in the state
-3. Generate a fun fact about that city and store it in the state
-4. Print the results to the console
-
-The state's unique ID and stored data can be useful for tracking flow executions and maintaining context between tasks.
+When you run the Flow, it will generate a random city and then generate a fun fact about that city. The output will be printed to the console.
 
 **Note:** Ensure you have set up your `.env` file to store your `OPENAI_API_KEY`. This key is necessary for authenticating requests to the OpenAI API.
 
@@ -221,39 +207,34 @@ allowing developers to choose the approach that best fits their application's ne
 
 In unstructured state management, all state is stored in the `state` attribute of the `Flow` class.
 This approach offers flexibility, enabling developers to add or modify state attributes on the fly without defining a strict schema.
-Even with unstructured states, CrewAI Flows automatically generates and maintains a unique identifier (UUID) for each state instance.
 
 ```python Code
 from crewai.flow.flow import Flow, listen, start
 
-class UnstructuredExampleFlow(Flow):
+class UntructuredExampleFlow(Flow):
 
     @start()
     def first_method(self):
-        # The state automatically includes an 'id' field
-        print(f"State ID: {self.state['id']}")
-        self.state['counter'] = 0
-        self.state['message'] = "Hello from structured flow"
+        self.state.message = "Hello from structured flow"
+        self.state.counter = 0
 
     @listen(first_method)
     def second_method(self):
-        self.state['counter'] += 1
-        self.state['message'] += " - updated"
+        self.state.counter += 1
+        self.state.message += " - updated"
 
     @listen(second_method)
     def third_method(self):
-        self.state['counter'] += 1
-        self.state['message'] += " - updated again"
+        self.state.counter += 1
+        self.state.message += " - updated again"
 
         print(f"State after third_method: {self.state}")
 
 
-flow = UnstructuredExampleFlow()
+flow = UntructuredExampleFlow()
 flow.kickoff()
 ```
 
-**Note:** The `id` field is automatically generated and preserved throughout the flow's execution. You don't need to manage or set it manually, and it will be maintained even when updating the state with new data.
-
 **Key Points:**
 
 - **Flexibility:** You can dynamically add attributes to `self.state` without predefined constraints.
@@ -264,15 +245,12 @@ flow.kickoff()
 Structured state management leverages predefined schemas to ensure consistency and type safety across the workflow.
 By using models like Pydantic's `BaseModel`, developers can define the exact shape of the state, enabling better validation and auto-completion in development environments.
 
-Each state in CrewAI Flows automatically receives a unique identifier (UUID) to help track and manage state instances. This ID is automatically generated and managed by the Flow system.
-
 ```python Code
 from crewai.flow.flow import Flow, listen, start
 from pydantic import BaseModel
 
 
 class ExampleState(BaseModel):
-    # Note: 'id' field is automatically added to all states
     counter: int = 0
     message: str = ""
 
@@ -281,8 +259,6 @@ class StructuredExampleFlow(Flow[ExampleState]):
 
     @start()
     def first_method(self):
-        # Access the auto-generated ID if needed
-        print(f"State ID: {self.state.id}")
         self.state.message = "Hello from structured flow"
 
     @listen(first_method)
@@ -323,91 +299,6 @@ 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`
@@ -737,4 +628,4 @@ Also, check out our YouTube video on how to use flows in CrewAI below!
   allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
   referrerpolicy="strict-origin-when-cross-origin"
   allowfullscreen
-></iframe>
+></iframe>

docs/concepts/knowledge.mdx

@@ -93,12 +93,6 @@ 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
@@ -288,7 +282,6 @@ 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
@@ -324,13 +317,6 @@ 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

@@ -38,7 +38,6 @@ Here's a detailed breakdown of supported models and their capabilities, you can
     | GPT-4 | 8,192 tokens | High-accuracy tasks, complex reasoning |
     | GPT-4 Turbo | 128,000 tokens | Long-form content, document analysis |
     | GPT-4o & GPT-4o-mini | 128,000 tokens | Cost-effective large context processing |
-    | o3-mini | 200,000 tokens | Fast reasoning, complex reasoning |
 
     <Note>
       1 token ≈ 4 characters in English. For example, 8,192 tokens ≈ 32,768 characters or about 6,000 words.
@@ -163,8 +162,7 @@ Here's a detailed breakdown of supported models and their capabilities, you can
   <Tab title="Others">
     | Provider | Context Window | Key Features |
     |----------|---------------|--------------|
-    | Deepseek Chat | 64,000 tokens | Specialized in technical discussions |
-    | Deepseek R1 | 64,000 tokens | Affordable reasoning model |
+    | Deepseek Chat | 128,000 tokens | Specialized in technical discussions |
     | Claude 3 | Up to 200K tokens | Strong reasoning, code understanding |
     | Gemma Series | 8,192 tokens | Efficient, smaller-scale tasks |
 
@@ -245,9 +243,6 @@ 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
@@ -298,10 +293,6 @@ There are three ways to configure LLMs in CrewAI. Choose the method that best fi
         # llm: sambanova/Meta-Llama-3.1-8B-Instruct
         # llm: sambanova/BioMistral-7B
         # llm: sambanova/Falcon-180B
-
-        # Open Router Models - Affordable reasoning
-        # llm: openrouter/deepseek/deepseek-r1
-        # llm: openrouter/deepseek/deepseek-chat
     ```
 
     <Info>
@@ -463,36 +454,19 @@ Learn how to get the most out of your LLM configuration:
 
   <Accordion title="Google">
     ```python Code
-    # Option 1: Gemini accessed with an API key.
+    # Option 1. Gemini accessed with an API key.
     # https://ai.google.dev/gemini-api/docs/api-key
     GEMINI_API_KEY=<your-api-key>
 
-    # Option 2: Vertex AI IAM credentials for Gemini, Anthropic, and Model Garden.
+    # Option 2. Vertex AI IAM credentials for Gemini, Anthropic, and anything in the Model Garden.
     # https://cloud.google.com/vertex-ai/generative-ai/docs/overview
     ```
 
-    Get credentials:
-    ```python Code
-    import json
-
-    file_path = 'path/to/vertex_ai_service_account.json'
-
-    # Load the JSON file
-    with open(file_path, 'r') as file:
-        vertex_credentials = json.load(file)
-
-    # Convert the credentials to a JSON string
-    vertex_credentials_json = json.dumps(vertex_credentials)
-    ```
-
     Example usage:
     ```python Code
-    from crewai import LLM
-
     llm = LLM(
         model="gemini/gemini-1.5-pro-latest",
-        temperature=0.7,
-        vertex_credentials=vertex_credentials_json
+        temperature=0.7
     )
     ```
   </Accordion>
@@ -532,21 +506,6 @@ 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
@@ -703,53 +662,8 @@ Learn how to get the most out of your LLM configuration:
       - Support for long context windows
     </Info>
   </Accordion>
-
-  <Accordion title="Open Router">
-    ```python Code
-    OPENROUTER_API_KEY=<your-api-key>
-    ```
-    
-    Example usage:
-    ```python Code
-    llm = LLM(
-        model="openrouter/deepseek/deepseek-r1",
-        base_url="https://openrouter.ai/api/v1",
-        api_key=OPENROUTER_API_KEY
-    )
-    ```
-
-    <Info>
-      Open Router models:
-      - openrouter/deepseek/deepseek-r1
-      - openrouter/deepseek/deepseek-chat
-    </Info>
-  </Accordion>
 </AccordionGroup>
 
-## Structured LLM Calls
-
-CrewAI supports structured responses from LLM calls by allowing you to define a `response_format` using a Pydantic model. This enables the framework to automatically parse and validate the output, making it easier to integrate the response into your application without manual post-processing.
-
-For example, you can define a Pydantic model to represent the expected response structure and pass it as the `response_format` when instantiating the LLM. The model will then be used to convert the LLM output into a structured Python object.
-
-```python Code
-from crewai import LLM
-
-class Dog(BaseModel):
-    name: str
-    age: int
-    breed: str
-
-
-llm = LLM(model="gpt-4o", response_format=Dog)
-
-response = llm.call(
-    "Analyze the following messages and return the name, age, and breed. "
-    "Meet Kona! She is 3 years old and is a black german shepherd."
-)
-print(response)
-```
-
 ## Common Issues and Solutions
 
 <Tabs>

docs/concepts/memory.mdx

@@ -58,107 +58,41 @@ my_crew = Crew(
 ### Example: Use Custom Memory Instances e.g FAISS as the VectorDB
 
 ```python Code
-from crewai import Crew, Process
-from crewai.memory import LongTermMemory, ShortTermMemory, EntityMemory
-from crewai.memory.storage import LTMSQLiteStorage, RAGStorage
-from typing import List, Optional
+from crewai import Crew, Agent, Task, Process
 
 # Assemble your crew with memory capabilities
-my_crew: Crew = Crew(
-    agents = [...],
-    tasks = [...],
-    process = Process.sequential,
-    memory = True,
-    # Long-term memory for persistent storage across sessions
-    long_term_memory = LongTermMemory(
+my_crew = Crew(
+    agents=[...],
+    tasks=[...],
+    process="Process.sequential",
+    memory=True,
+    long_term_memory=EnhanceLongTermMemory(
         storage=LTMSQLiteStorage(
-            db_path="/my_crew1/long_term_memory_storage.db"
+            db_path="/my_data_dir/my_crew1/long_term_memory_storage.db"
         )
     ),
-    # Short-term memory for current context using RAG
-    short_term_memory = ShortTermMemory(
-        storage = RAGStorage(
-                embedder_config={
-                    "provider": "openai",
-                    "config": {
-                        "model": 'text-embedding-3-small'
-                    }
-                },
-                type="short_term",
-                path="/my_crew1/"
-            )
+    short_term_memory=EnhanceShortTermMemory(
+        storage=CustomRAGStorage(
+            crew_name="my_crew",
+            storage_type="short_term",
+            data_dir="//my_data_dir",
+            model=embedder["model"],
+            dimension=embedder["dimension"],
         ),
     ),
-    # Entity memory for tracking key information about entities
-    entity_memory = EntityMemory(
-        storage=RAGStorage(
-            embedder_config={
-                "provider": "openai",
-                "config": {
-                    "model": 'text-embedding-3-small'
-                }
-            },
-            type="short_term",
-            path="/my_crew1/"
-        )
+    entity_memory=EnhanceEntityMemory(
+        storage=CustomRAGStorage(
+            crew_name="my_crew",
+            storage_type="entities",
+            data_dir="//my_data_dir",
+            model=embedder["model"],
+            dimension=embedder["dimension"],
+        ),
     ),
     verbose=True,
 )
 ```
 
-## Security Considerations
-
-When configuring memory storage:
-- Use environment variables for storage paths (e.g., `CREWAI_STORAGE_DIR`)
-- Never hardcode sensitive information like database credentials
-- Consider access permissions for storage directories
-- Use relative paths when possible to maintain portability
-
-Example using environment variables:
-```python
-import os
-from crewai import Crew
-from crewai.memory import LongTermMemory
-from crewai.memory.storage import LTMSQLiteStorage
-
-# Configure storage path using environment variable
-storage_path = os.getenv("CREWAI_STORAGE_DIR", "./storage")
-crew = Crew(
-    memory=True,
-    long_term_memory=LongTermMemory(
-        storage=LTMSQLiteStorage(
-            db_path="{storage_path}/memory.db".format(storage_path=storage_path)
-        )
-    )
-)
-```
-
-## Configuration Examples
-
-### Basic Memory Configuration
-```python
-from crewai import Crew
-from crewai.memory import LongTermMemory
-
-# Simple memory configuration
-crew = Crew(memory=True)  # Uses default storage locations
-```
-
-### Custom Storage Configuration
-```python
-from crewai import Crew
-from crewai.memory import LongTermMemory
-from crewai.memory.storage import LTMSQLiteStorage
-
-# Configure custom storage paths
-crew = Crew(
-    memory=True,
-    long_term_memory=LongTermMemory(
-        storage=LTMSQLiteStorage(db_path="./memory.db")
-    )
-)
-```
-
 ## Integrating Mem0 for Enhanced User Memory
 
 [Mem0](https://mem0.ai/) is a self-improving memory layer for LLM applications, enabling personalized AI experiences. 
@@ -251,12 +185,7 @@ my_crew = Crew(
     process=Process.sequential,
     memory=True,
     verbose=True,
-    embedder={
-        "provider": "openai",
-        "config": {
-            "model": 'text-embedding-3-small'
-        }
-    }
+    embedder=OpenAIEmbeddingFunction(api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"),
 )
 ```
 
@@ -295,7 +224,7 @@ my_crew = Crew(
         "provider": "google",
         "config": {
             "api_key": "<YOUR_API_KEY>",
-            "model": "<model_name>"
+            "model_name": "<model_name>"
         }
     }
 )
@@ -313,15 +242,13 @@ my_crew = Crew(
     process=Process.sequential,
     memory=True,
     verbose=True,
-    embedder={
-        "provider": "openai",
-        "config": {
-            "api_key": "YOUR_API_KEY",
-            "api_base": "YOUR_API_BASE_PATH",
-            "api_version": "YOUR_API_VERSION",
-            "model_name": 'text-embedding-3-small'
-        }
-    }
+    embedder=OpenAIEmbeddingFunction(
+        api_key="YOUR_API_KEY",
+        api_base="YOUR_API_BASE_PATH",
+        api_type="azure",
+        api_version="YOUR_API_VERSION",
+        model_name="text-embedding-3-small"
+    )
 )
 ```
 
@@ -337,15 +264,12 @@ my_crew = Crew(
     process=Process.sequential,
     memory=True,
     verbose=True,
-    embedder={
-        "provider": "vertexai",
-        "config": {
-            "project_id"="YOUR_PROJECT_ID",
-            "region"="YOUR_REGION",
-            "api_key"="YOUR_API_KEY",
-            "model_name"="textembedding-gecko"
-        }
-    }
+    embedder=GoogleVertexEmbeddingFunction(
+        project_id="YOUR_PROJECT_ID",
+        region="YOUR_REGION",
+        api_key="YOUR_API_KEY",
+        model_name="textembedding-gecko"
+    )
 )
 ```
 
@@ -364,27 +288,7 @@ my_crew = Crew(
         "provider": "cohere",
         "config": {
             "api_key": "YOUR_API_KEY",
-            "model": "<model_name>"
-        }
-    }
-)
-```
-### 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": "<model_name>"
+            "model_name": "<model_name>"
         }
     }
 )
@@ -434,33 +338,6 @@ my_crew = Crew(
 )
 ```
 
-### Adding Custom Embedding Function
-
-```python Code
-from crewai import Crew, Agent, Task, Process
-from chromadb import Documents, EmbeddingFunction, Embeddings
-
-# Create a custom embedding function
-class CustomEmbedder(EmbeddingFunction):
-    def __call__(self, input: Documents) -> Embeddings:
-        # generate embeddings
-        return [1, 2, 3] # this is a dummy embedding
-
-my_crew = Crew(
-    agents=[...],
-    tasks=[...],
-    process=Process.sequential,
-    memory=True,
-    verbose=True,
-    embedder={
-        "provider": "custom",
-        "config": {
-            "embedder": CustomEmbedder()
-        }
-    }
-)
-```
-
 ### Resetting Memory
 
 ```shell

docs/concepts/planning.mdx

@@ -31,7 +31,7 @@ From this point on, your crew will have planning enabled, and the tasks will be
 
 #### Planning LLM
 
-Now you can define the LLM that will be used to plan the tasks. 
+Now you can define the LLM that will be used to plan the tasks. You can use any ChatOpenAI LLM model available.
 
 When running the base case example, you will see something like the output below, which represents the output of the `AgentPlanner` 
 responsible for creating the step-by-step logic to add to the Agents' tasks.
@@ -39,6 +39,7 @@ responsible for creating the step-by-step logic to add to the Agents' tasks.
 <CodeGroup>
 ```python Code
 from crewai import Crew, Agent, Task, Process
+from langchain_openai import ChatOpenAI
 
 # Assemble your crew with planning capabilities and custom LLM
 my_crew = Crew(
@@ -46,7 +47,7 @@ my_crew = Crew(
     tasks=self.tasks,
     process=Process.sequential,
     planning=True,
-    planning_llm="gpt-4o"
+    planning_llm=ChatOpenAI(model="gpt-4o")
 )
 
 # Run the crew
@@ -81,8 +82,8 @@ my_crew.kickoff()
 
 3. **Collect Data:**
 
-   - Search for the latest papers, articles, and reports published in 2024 and early 2025.
-   - Use keywords like "Large Language Models 2025", "AI LLM advancements", "AI ethics 2025", etc.
+   - Search for the latest papers, articles, and reports published in 2023 and early 2024.
+   - Use keywords like "Large Language Models 2024", "AI LLM advancements", "AI ethics 2024", etc.
 
 4. **Analyze Findings:**
 

docs/concepts/processes.mdx

@@ -23,7 +23,9 @@ Processes enable individual agents to operate as a cohesive unit, streamlining t
 To assign a process to a crew, specify the process type upon crew creation to set the execution strategy. For a hierarchical process, ensure to define `manager_llm` or `manager_agent` for the manager agent.
 
 ```python
-from crewai import Crew, Process
+from crewai import Crew
+from crewai.process import Process
+from langchain_openai import ChatOpenAI
 
 # Example: Creating a crew with a sequential process
 crew = Crew(
@@ -38,7 +40,7 @@ crew = Crew(
     agents=my_agents,
     tasks=my_tasks,
     process=Process.hierarchical,
-    manager_llm="gpt-4o"
+    manager_llm=ChatOpenAI(model="gpt-4")
     # or
     # manager_agent=my_manager_agent
 )

docs/concepts/tasks.mdx

@@ -33,12 +33,11 @@ crew = Crew(
 | :------------------------------- | :---------------- | :---------------------------- | :------------------------------------------------------------------------------------------------------------------- |
 | **Description**                  | `description`     | `str`                         | A clear, concise statement of what the task entails.                                                                 |
 | **Expected Output**              | `expected_output` | `str`                         | A detailed description of what the task's completion looks like.                                                     |
-| **Name** _(optional)_            | `name`            | `Optional[str]`               | A name identifier for the task.                                                                                      |
-| **Agent** _(optional)_           | `agent`           | `Optional[BaseAgent]`         | The agent responsible for executing the task.                                                                        |
-| **Tools** _(optional)_           | `tools`           | `List[BaseTool]`              | The tools/resources the agent is limited to use for this task.                                                       |
+| **Name** _(optional)_           | `name`           | `Optional[str]`               | A name identifier for the task.                                                                                      |
+| **Agent** _(optional)_          | `agent`           | `Optional[BaseAgent]`         | The agent responsible for executing the task.                                                                        |
+| **Tools** _(optional)_          | `tools`           | `List[BaseTool]`             | The tools/resources the agent is limited to use for this task.                                                       |
 | **Context** _(optional)_         | `context`         | `Optional[List["Task"]]`      | Other tasks whose outputs will be used as context for this task.                                                     |
 | **Async Execution** _(optional)_ | `async_execution` | `Optional[bool]`              | Whether the task should be executed asynchronously. Defaults to False.                                               |
-| **Human Input** _(optional)_     | `human_input`     | `Optional[bool]`              | Whether the task should have a human review the final answer of the agent. Defaults to False.                        |
 | **Config** _(optional)_          | `config`          | `Optional[Dict[str, Any]]`    | Task-specific configuration parameters.                                                                              |
 | **Output File** _(optional)_     | `output_file`     | `Optional[str]`               | File path for storing the task output.                                                                               |
 | **Output JSON** _(optional)_     | `output_json`     | `Optional[Type[BaseModel]]`   | A Pydantic model to structure the JSON output.                                                                       |
@@ -69,7 +68,7 @@ research_task:
   description: >
     Conduct a thorough research about {topic}
     Make sure you find any interesting and relevant information given
-    the current year is 2025.
+    the current year is 2024.
   expected_output: >
     A list with 10 bullet points of the most relevant information about {topic}
   agent: researcher
@@ -155,7 +154,7 @@ research_task = Task(
     description="""
         Conduct a thorough research about AI Agents.
         Make sure you find any interesting and relevant information given
-        the current year is 2025.
+        the current year is 2024.
     """,
     expected_output="""
         A list with 10 bullet points of the most relevant information about AI Agents

docs/concepts/tools.mdx

@@ -150,20 +150,15 @@ There are two main ways for one to create a CrewAI tool:
 
 ```python Code
 from crewai.tools import BaseTool
-from pydantic import BaseModel, Field
 
-class MyToolInput(BaseModel):
-    """Input schema for MyCustomTool."""
-    argument: str = Field(..., description="Description of the argument.")
 
 class MyCustomTool(BaseTool):
     name: str = "Name of my tool"
-    description: str = "What this tool does. It's vital for effective utilization."
-    args_schema: Type[BaseModel] = MyToolInput
+    description: str = "Clear description for what this tool is useful for, your agent will need this information to use it."
 
     def _run(self, argument: str) -> str:
-        # Your tool's logic here
-        return "Tool's result"
+        # Implementation goes here
+        return "Result from custom tool"
 ```
 
 ### Utilizing the `tool` Decorator

docs/how-to/custom-manager-agent.mdx

@@ -73,9 +73,9 @@ result = crew.kickoff()
 If you're using the hierarchical process and don't want to set a custom manager agent, you can specify the language model for the manager:
 
 ```python Code
-from crewai import LLM
+from langchain_openai import ChatOpenAI
 
-manager_llm = LLM(model="gpt-4o")
+manager_llm = ChatOpenAI(model_name="gpt-4")
 
 crew = Crew(
     agents=[researcher, writer],

docs/how-to/human-input-on-execution.mdx

@@ -60,12 +60,12 @@ writer = Agent(
 # Create tasks for your agents
 task1 = Task(
     description=(
-        "Conduct a comprehensive analysis of the latest advancements in AI in 2025. "
+        "Conduct a comprehensive analysis of the latest advancements in AI in 2024. "
         "Identify key trends, breakthrough technologies, and potential industry impacts. "
         "Compile your findings in a detailed report. "
         "Make sure to check with a human if the draft is good before finalizing your answer."
     ),
-    expected_output='A comprehensive full report on the latest AI advancements in 2025, leave nothing out',
+    expected_output='A comprehensive full report on the latest AI advancements in 2024, leave nothing out',
     agent=researcher,
     human_input=True
 )
@@ -76,7 +76,7 @@ task2 = Task(
         "Your post should be informative yet accessible, catering to a tech-savvy audience. "
         "Aim for a narrative that captures the essence of these breakthroughs and their implications for the future."
     ),
-    expected_output='A compelling 3 paragraphs blog post formatted as markdown about the latest AI advancements in 2025',
+    expected_output='A compelling 3 paragraphs blog post formatted as markdown about the latest AI advancements in 2024',
     agent=writer,
     human_input=True
 )

docs/how-to/llm-connections.mdx

@@ -23,7 +23,6 @@ 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/how-to/mlflow-observability.mdx

@@ -1,206 +0,0 @@
----
-title: Agent Monitoring with MLflow
-description: Quickly start monitoring your Agents with MLflow.
-icon: bars-staggered
----
-
-# MLflow Overview
-
-[MLflow](https://mlflow.org/) is an open-source platform to assist machine learning practitioners and teams in handling the complexities of the machine learning process.
-
-It provides a tracing feature that enhances LLM observability in your Generative AI applications by capturing detailed information about the execution of your application’s services. 
-Tracing provides a way to record the inputs, outputs, and metadata associated with each intermediate step of a request, enabling you to easily pinpoint the source of bugs and unexpected behaviors.
-
-![Overview of MLflow crewAI tracing usage](/images/mlflow-tracing.gif)
-
-### Features
-
-- **Tracing Dashboard**: Monitor activities of your crewAI agents with detailed dashboards that include inputs, outputs and metadata of spans.
-- **Automated Tracing**: A fully automated integration with crewAI, which can be enabled by running `mlflow.crewai.autolog()`. 
-- **Manual Trace Instrumentation with minor efforts**: Customize trace instrumentation through MLflow's high-level fluent APIs such as decorators, function wrappers and context managers.
-- **OpenTelemetry Compatibility**: MLflow Tracing supports exporting traces to an OpenTelemetry Collector, which can then be used to export traces to various backends such as Jaeger, Zipkin, and AWS X-Ray.
-- **Package and Deploy Agents**: Package and deploy your crewAI agents to an inference server with a variety of deployment targets.
-- **Securely Host LLMs**: Host multiple LLM from various providers in one unified endpoint through MFflow gateway.
-- **Evaluation**: Evaluate your crewAI agents with a wide range of metrics using a convenient API `mlflow.evaluate()`.
-
-## Setup Instructions
-
-<Steps>
-    <Step title="Install MLflow package">
-      ```shell
-      # The crewAI integration is available in mlflow>=2.19.0
-      pip install mlflow
-      ```
-    </Step>
-    <Step title="Start MFflow tracking server">
-      ```shell
-      # This process is optional, but it is recommended to use MLflow tracking server for better visualization and broader features.
-      mlflow server
-      ```
-    </Step>
-    <Step title="Initialize MLflow in Your Application">
-      Add the following two lines to your application code:
-
-      ```python
-      import mlflow
-
-      mlflow.crewai.autolog()
-
-      # Optional: Set a tracking URI and an experiment name if you have a tracking server
-      mlflow.set_tracking_uri("http://localhost:5000")
-      mlflow.set_experiment("CrewAI")
-      ```
-      
-      Example Usage for tracing CrewAI Agents:
-
-      ```python
-      from crewai import Agent, Crew, Task
-      from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
-      from crewai_tools import SerperDevTool, WebsiteSearchTool
-
-      from textwrap import dedent
-
-      content = "Users name is John. He is 30 years old and lives in San Francisco."
-      string_source = StringKnowledgeSource(
-          content=content, metadata={"preference": "personal"}
-      )
-
-      search_tool = WebsiteSearchTool()
-
-
-      class TripAgents:
-          def city_selection_agent(self):
-              return Agent(
-                  role="City Selection Expert",
-                  goal="Select the best city based on weather, season, and prices",
-                  backstory="An expert in analyzing travel data to pick ideal destinations",
-                  tools=[
-                      search_tool,
-                  ],
-                  verbose=True,
-              )
-
-          def local_expert(self):
-              return Agent(
-                  role="Local Expert at this city",
-                  goal="Provide the BEST insights about the selected city",
-                  backstory="""A knowledgeable local guide with extensive information
-              about the city, it's attractions and customs""",
-                  tools=[search_tool],
-                  verbose=True,
-              )
-
-
-      class TripTasks:
-          def identify_task(self, agent, origin, cities, interests, range):
-              return Task(
-                  description=dedent(
-                      f"""
-                      Analyze and select the best city for the trip based
-                      on specific criteria such as weather patterns, seasonal
-                      events, and travel costs. This task involves comparing
-                      multiple cities, considering factors like current weather
-                      conditions, upcoming cultural or seasonal events, and
-                      overall travel expenses.
-                      Your final answer must be a detailed
-                      report on the chosen city, and everything you found out
-                      about it, including the actual flight costs, weather
-                      forecast and attractions.
-
-                      Traveling from: {origin}
-                      City Options: {cities}
-                      Trip Date: {range}
-                      Traveler Interests: {interests}
-                  """
-                  ),
-                  agent=agent,
-                  expected_output="Detailed report on the chosen city including flight costs, weather forecast, and attractions",
-              )
-
-          def gather_task(self, agent, origin, interests, range):
-              return Task(
-                  description=dedent(
-                      f"""
-                      As a local expert on this city you must compile an
-                      in-depth guide for someone traveling there and wanting
-                      to have THE BEST trip ever!
-                      Gather information about key attractions, local customs,
-                      special events, and daily activity recommendations.
-                      Find the best spots to go to, the kind of place only a
-                      local would know.
-                      This guide should provide a thorough overview of what
-                      the city has to offer, including hidden gems, cultural
-                      hotspots, must-visit landmarks, weather forecasts, and
-                      high level costs.
-                      The final answer must be a comprehensive city guide,
-                      rich in cultural insights and practical tips,
-                      tailored to enhance the travel experience.
-
-                      Trip Date: {range}
-                      Traveling from: {origin}
-                      Traveler Interests: {interests}
-                  """
-                  ),
-                  agent=agent,
-                  expected_output="Comprehensive city guide including hidden gems, cultural hotspots, and practical travel tips",
-              )
-
-
-      class TripCrew:
-          def __init__(self, origin, cities, date_range, interests):
-              self.cities = cities
-              self.origin = origin
-              self.interests = interests
-              self.date_range = date_range
-
-          def run(self):
-              agents = TripAgents()
-              tasks = TripTasks()
-
-              city_selector_agent = agents.city_selection_agent()
-              local_expert_agent = agents.local_expert()
-
-              identify_task = tasks.identify_task(
-                  city_selector_agent,
-                  self.origin,
-                  self.cities,
-                  self.interests,
-                  self.date_range,
-              )
-              gather_task = tasks.gather_task(
-                  local_expert_agent, self.origin, self.interests, self.date_range
-              )
-
-              crew = Crew(
-                  agents=[city_selector_agent, local_expert_agent],
-                  tasks=[identify_task, gather_task],
-                  verbose=True,
-                  memory=True,
-                  knowledge={
-                      "sources": [string_source],
-                      "metadata": {"preference": "personal"},
-                  },
-              )
-
-              result = crew.kickoff()
-              return result
-
-
-      trip_crew = TripCrew("California", "Tokyo", "Dec 12 - Dec 20", "sports")
-      result = trip_crew.run()
-
-      print(result)
-      ```
-      Refer to [MLflow Tracing Documentation](https://mlflow.org/docs/latest/llms/tracing/index.html) for more configurations and use cases.
-    </Step>
-    <Step title="Visualize Activities of Agents">
-      Now traces for your crewAI agents are captured by MLflow. 
-      Let's visit MLflow tracking server to view the traces and get insights into your Agents.
-
-      Open `127.0.0.1:5000` on your browser to visit MLflow tracking server. 
-      <Frame caption="MLflow Tracing Dashboard">
-        <img src="/images/mlflow1.png" alt="MLflow tracing example with crewai" />
-      </Frame>
-    </Step>
-</Steps> 
-

docs/how-to/multimodal-agents.mdx

@@ -1,14 +1,14 @@
 ---
 title: Using Multimodal Agents
 description: Learn how to enable and use multimodal capabilities in your agents for processing images and other non-text content within the CrewAI framework.
-icon: video
+icon: image
 ---
 
-## Using Multimodal Agents
+# Using Multimodal Agents
 
 CrewAI supports multimodal agents that can process both text and non-text content like images. This guide will show you how to enable and use multimodal capabilities in your agents.
 
-### Enabling Multimodal Capabilities
+## Enabling Multimodal Capabilities
 
 To create a multimodal agent, simply set the `multimodal` parameter to `True` when initializing your agent:
 
@@ -25,7 +25,7 @@ agent = Agent(
 
 When you set `multimodal=True`, the agent is automatically configured with the necessary tools for handling non-text content, including the `AddImageTool`.
 
-### Working with Images
+## Working with Images
 
 The multimodal agent comes pre-configured with the `AddImageTool`, which allows it to process images. You don't need to manually add this tool - it's automatically included when you enable multimodal capabilities.
 
@@ -45,7 +45,6 @@ image_analyst = Agent(
 # Create a task for image analysis
 task = Task(
     description="Analyze the product image at https://example.com/product.jpg and provide a detailed description",
-    expected_output="A detailed description of the product image",
     agent=image_analyst
 )
 
@@ -82,7 +81,6 @@ inspection_task = Task(
     3. Compliance with standards
     Provide a detailed report highlighting any issues found.
     """,
-    expected_output="A detailed report highlighting any issues found",
     agent=expert_analyst
 )
 
@@ -110,7 +108,7 @@ The multimodal agent will automatically handle the image processing through its
 - Process image content with optional context or specific questions
 - Provide analysis and insights based on the visual information and task requirements
 
-### Best Practices
+## Best Practices
 
 When working with multimodal agents, keep these best practices in mind:
 

docs/installation.mdx

@@ -15,48 +15,10 @@ 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
 
-Now let's get you set up! 🚀
+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! 🚀
 
 <Steps>
     <Step title="Install CrewAI">
@@ -110,9 +72,9 @@ Now let's get you set up! 🚀
 
 # Creating a New Project
 
-<Tip>
+<Info>
   We recommend using the YAML Template scaffolding for a structured approach to defining agents and tasks.
-</Tip>
+</Info>
 
 <Steps>
   <Step title="Generate Project Structure">
@@ -142,18 +104,7 @@ Now let's get you set up! 🚀
                 └── tasks.yaml
     ```
     </Frame>
-  </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>   
 
   <Step title="Customize Your Project">
     Your project will contain these essential files:

docs/memory.md

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

docs/mint.json

@@ -91,7 +91,6 @@
         "how-to/custom-manager-agent",
         "how-to/llm-connections",
         "how-to/customizing-agents",
-        "how-to/multimodal-agents",
         "how-to/coding-agents",
         "how-to/force-tool-output-as-result",
         "how-to/human-input-on-execution",
@@ -101,7 +100,6 @@
         "how-to/conditional-tasks",
         "how-to/agentops-observability",
         "how-to/langtrace-observability",
-        "how-to/mlflow-observability",
         "how-to/openlit-observability",
         "how-to/portkey-observability"
       ]

docs/quickstart.mdx

@@ -58,7 +58,7 @@ Follow the steps below to get crewing! 🚣‍♂️
       description: >
         Conduct a thorough research about {topic}
         Make sure you find any interesting and relevant information given
-        the current year is 2025.
+        the current year is 2024.
       expected_output: >
         A list with 10 bullet points of the most relevant information about {topic}
       agent: researcher
@@ -195,10 +195,10 @@ Follow the steps below to get crewing! 🚣‍♂️
 
   <CodeGroup>
     ```markdown output/report.md
-    # Comprehensive Report on the Rise and Impact of AI Agents in 2025
+    # Comprehensive Report on the Rise and Impact of AI Agents in 2024
 
     ## 1. Introduction to AI Agents
-    In 2025, Artificial Intelligence (AI) agents are at the forefront of innovation across various industries. As intelligent systems that can perform tasks typically requiring human cognition, AI agents are paving the way for significant advancements in operational efficiency, decision-making, and overall productivity within sectors like Human Resources (HR) and Finance. This report aims to detail the rise of AI agents, their frameworks, applications, and potential implications on the workforce.
+    In 2024, Artificial Intelligence (AI) agents are at the forefront of innovation across various industries. As intelligent systems that can perform tasks typically requiring human cognition, AI agents are paving the way for significant advancements in operational efficiency, decision-making, and overall productivity within sectors like Human Resources (HR) and Finance. This report aims to detail the rise of AI agents, their frameworks, applications, and potential implications on the workforce.
 
     ## 2. Benefits of AI Agents
     AI agents bring numerous advantages that are transforming traditional work environments. Key benefits include:
@@ -252,7 +252,7 @@ Follow the steps below to get crewing! 🚣‍♂️
     To stay competitive and harness the full potential of AI agents, organizations must remain vigilant about latest developments in AI technology and consider continuous learning and adaptation in their strategic planning.
 
     ## 8. Conclusion
-    The emergence of AI agents is undeniably reshaping the workplace landscape in 5. With their ability to automate tasks, enhance efficiency, and improve decision-making, AI agents are critical in driving operational success. Organizations must embrace and adapt to AI developments to thrive in an increasingly digital business environment.
+    The emergence of AI agents is undeniably reshaping the workplace landscape in 2024. With their ability to automate tasks, enhance efficiency, and improve decision-making, AI agents are critical in driving operational success. Organizations must embrace and adapt to AI developments to thrive in an increasingly digital business environment.
     ```
   </CodeGroup>
   </Step>
@@ -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: openai/gpt-4o
+    llm: mixtal_llm
 ```
 
 <Tip>
@@ -301,166 +301,38 @@ Use the annotations to properly reference the agent and task in the `crew.py` fi
 
 ### Annotations include:
 
-Here are examples of how to use each annotation in your CrewAI project, and when you should use them:
+* `@agent`
+* `@task`
+* `@crew`
+* `@tool`
+* `@before_kickoff`
+* `@after_kickoff`
+* `@callback`
+* `@output_json`
+* `@output_pydantic`
+* `@cache_handler`
 
-#### @agent
-Used to define an agent in your crew. Use this when:
-- You need to create a specialized AI agent with a specific role
-- You want the agent to be automatically collected and managed by the crew
-- You need to reuse the same agent configuration across multiple tasks
-
-```python
+```python crew.py
+# ...
 @agent
-def research_agent(self) -> Agent:
+def email_summarizer(self) -> Agent:
     return Agent(
-        role="Research Analyst",
-        goal="Conduct thorough research on given topics",
-        backstory="Expert researcher with years of experience in data analysis",
-        tools=[SerperDevTool()],
-        verbose=True
+        config=self.agents_config["email_summarizer"],
     )
-```
 
-#### @task
-Used to define a task that can be executed by agents. Use this when:
-- You need to define a specific piece of work for an agent
-- You want tasks to be automatically sequenced and managed
-- You need to establish dependencies between different tasks
-
-```python
 @task
-def research_task(self) -> Task:
+def email_summarizer_task(self) -> Task:
     return Task(
-        description="Research the latest developments in AI technology",
-        expected_output="A comprehensive report on AI advancements",
-        agent=self.research_agent(),
-        output_file="output/research.md"
+        config=self.tasks_config["email_summarizer_task"],
     )
+# ...
 ```
 
-#### @crew
-Used to define your crew configuration. Use this when:
-- You want to automatically collect all @agent and @task definitions
-- You need to specify how tasks should be processed (sequential or hierarchical)
-- You want to set up crew-wide configurations
-
-```python
-@crew
-def research_crew(self) -> Crew:
-    return Crew(
-        agents=self.agents,  # Automatically collected from @agent methods
-        tasks=self.tasks,    # Automatically collected from @task methods
-        process=Process.sequential,
-        verbose=True
-    )
-```
-
-#### @tool
-Used to create custom tools for your agents. Use this when:
-- You need to give agents specific capabilities (like web search, data analysis)
-- You want to encapsulate external API calls or complex operations
-- You need to share functionality across multiple agents
-
-```python
-@tool
-def web_search_tool(query: str, max_results: int = 5) -> list[str]:
-    """
-    Search the web for information.
-
-    Args:
-        query: The search query
-        max_results: Maximum number of results to return
-
-    Returns:
-        List of search results
-    """
-    # Implement your search logic here
-    return [f"Result {i} for: {query}" for i in range(max_results)]
-```
-
-#### @before_kickoff
-Used to execute logic before the crew starts. Use this when:
-- You need to validate or preprocess input data
-- You want to set up resources or configurations before execution
-- You need to perform any initialization logic
-
-```python
-@before_kickoff
-def validate_inputs(self, inputs: Optional[Dict[str, Any]]) -> Optional[Dict[str, Any]]:
-    """Validate and preprocess inputs before the crew starts."""
-    if inputs is None:
-        return None
-        
-    if 'topic' not in inputs:
-        raise ValueError("Topic is required")
-    
-    # Add additional context
-    inputs['timestamp'] = datetime.now().isoformat()
-    inputs['topic'] = inputs['topic'].strip().lower()
-    return inputs
-```
-
-#### @after_kickoff
-Used to process results after the crew completes. Use this when:
-- You need to format or transform the final output
-- You want to perform cleanup operations
-- You need to save or log the results in a specific way
-
-```python
-@after_kickoff
-def process_results(self, result: CrewOutput) -> CrewOutput:
-    """Process and format the results after the crew completes."""
-    result.raw = result.raw.strip()
-    result.raw = f"""
-    # Research Results
-    Generated on: {datetime.now().isoformat()}
-    
-    {result.raw}
-    """
-    return result
-```
-
-#### @callback
-Used to handle events during crew execution. Use this when:
-- You need to monitor task progress
-- You want to log intermediate results
-- You need to implement custom progress tracking or metrics
-
-```python
-@callback
-def log_task_completion(self, task: Task, output: str):
-    """Log task completion details for monitoring."""
-    print(f"Task '{task.description}' completed")
-    print(f"Output length: {len(output)} characters")
-    print(f"Agent used: {task.agent.role}")
-    print("-" * 50)
-```
-
-#### @cache_handler
-Used to implement custom caching for task results. Use this when:
-- You want to avoid redundant expensive operations
-- You need to implement custom cache storage or expiration logic
-- You want to persist results between runs
-
-```python
-@cache_handler
-def custom_cache(self, key: str) -> Optional[str]:
-    """Custom cache implementation for storing task results."""
-    cache_file = f"cache/{key}.json"
-    
-    if os.path.exists(cache_file):
-        with open(cache_file, 'r') as f:
-            data = json.load(f)
-            # Check if cache is still valid (e.g., not expired)
-            if datetime.fromisoformat(data['timestamp']) > datetime.now() - timedelta(days=1):
-                return data['result']
-    return None
-```
-
-<Note>
-These decorators are part of the CrewAI framework and help organize your crew's structure by automatically collecting agents, tasks, and handling various lifecycle events. 
-They should be used within a class decorated with `@CrewBase`.
-</Note>
+<Tip>
+In addition to the [sequential process](../how-to/sequential-process), you can use the [hierarchical process](../how-to/hierarchical-process),
+which automatically assigns a manager to the defined crew to properly coordinate the planning and execution of tasks through delegation and validation of results.
+You can learn more about the core concepts [here](/concepts).
+</Tip>
 
 ### Replay Tasks from Latest Crew Kickoff
 

docs/tools/composiotool.mdx

@@ -1,118 +1,78 @@
 ---
 title: Composio Tool
-description: Composio provides 250+ production-ready tools for AI agents with flexible authentication management.
+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.
 icon: gear-code
 ---
 
-# `ComposioToolSet`
+# `ComposioTool`
 
 ## Description
-Composio is an integration platform that allows you to connect your AI agents to 250+ tools. Key features include:
 
-- **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
+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.
 
 ## Installation
 
-To incorporate Composio tools into your project, follow the instructions below:
+To incorporate this tool into your project, follow the installation instructions below:
 
 ```shell
-pip install composio-crewai
-pip install crewai
+pip install composio-core
+pip install 'crewai[tools]'
 ```
 
-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)
+after the installation is complete, either run `composio login` or export your composio API key as `COMPOSIO_API_KEY`.
 
 ## Example
 
 The following example demonstrates how to initialize the tool and execute a github action:
 
-1. Initialize Composio toolset
+1. Initialize Composio tools
 
 ```python Code
-from composio_crewai import ComposioToolSet, App, Action
-from crewai import Agent, Task, Crew
+from composio import App
+from crewai_tools import ComposioTool
+from crewai import Agent, Task
 
-toolset = ComposioToolSet()
+
+tools = [ComposioTool.from_action(action=Action.GITHUB_ACTIVITY_STAR_REPO_FOR_AUTHENTICATED_USER)]
 ```
 
-2. Connect your GitHub account
-<CodeGroup>
-```shell CLI
-composio add github
-```
+If you don't know what action you want to use, use `from_app` and `tags` filter to get relevant actions
+
 ```python Code
-request = toolset.initiate_connection(app=App.GITHUB)
-print(f"Open this URL to authenticate: {request.redirectUrl}")
+tools = ComposioTool.from_app(App.GITHUB, tags=["important"])
 ```
-</CodeGroup>
 
-3. Get Tools
+or use `use_case` to search relevant actions
 
-- Retrieving all the tools from an app (not recommended for production):
 ```python Code
-tools = toolset.get_tools(apps=[App.GITHUB])
+tools = ComposioTool.from_app(App.GITHUB, use_case="Star a github repository")
 ```
 
-- 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
+2. 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 behalf of users 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 users behalf. You need to take action on Github using Github APIs"
+    ),
     verbose=True,
     tools=tools,
-    llm= # pass an llm
 )
 ```
 
-5. Execute task
+3. 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="Status of the operation",
+    expected_output="if the star happened",
 )
 
-crew = Crew(agents=[crewai_agent], tasks=[task])
-
-crew.kickoff()
+task.execute()
 ```
 
-* 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)

docs/tools/filewritetool.mdx

@@ -8,9 +8,9 @@ icon: file-pen
 
 ## Description
 
-The `FileWriterTool` is a component of the crewai_tools package, designed to simplify the process of writing content to files with cross-platform compatibility (Windows, Linux, macOS). 
+The `FileWriterTool` is a component of the crewai_tools package, designed to simplify the process of writing content to files. 
 It is particularly useful in scenarios such as generating reports, saving logs, creating configuration files, and more. 
-This tool handles path differences across operating systems, supports UTF-8 encoding, and automatically creates directories if they don't exist, making it easier to organize your output reliably across different platforms.
+This tool supports creating new directories if they don't exist, making it easier to organize your output.
 
 ## Installation
 
@@ -43,8 +43,6 @@ print(result)
 
 ## Conclusion
 
-By integrating the `FileWriterTool` into your crews, the agents can reliably write content to files across different operating systems. 
-This tool is essential for tasks that require saving output data, creating structured file systems, and handling cross-platform file operations. 
-It's particularly recommended for Windows users who may encounter file writing issues with standard Python file operations.
-
-By adhering to the setup and usage guidelines provided, incorporating this tool into projects is straightforward and ensures consistent file writing behavior across all platforms.
+By integrating the `FileWriterTool` into your crews, the agents can execute the process of writing content to files and creating directories. 
+This tool is essential for tasks that require saving output data, creating structured file systems, and more. By adhering to the setup and usage guidelines provided, 
+incorporating this tool into projects is straightforward and efficient.

mkdocs.yml

@@ -152,7 +152,6 @@ nav:
     - Agent Monitoring with AgentOps: 'how-to/AgentOps-Observability.md'
     - Agent Monitoring with LangTrace: 'how-to/Langtrace-Observability.md'
     - Agent Monitoring with OpenLIT: 'how-to/openlit-Observability.md'
-    - Agent Monitoring with MLflow: 'how-to/mlflow-Observability.md'
   - Tools Docs:
     - Browserbase Web Loader: 'tools/BrowserbaseLoadTool.md'
     - Code Docs RAG Search: 'tools/CodeDocsSearchTool.md'

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "crewai"
-version = "0.100.1"
+version = "0.95.0"
 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"
@@ -11,22 +11,27 @@ dependencies = [
     # Core Dependencies
     "pydantic>=2.4.2",
     "openai>=1.13.3",
-    "litellm==1.60.2",
+    "litellm>=1.44.22",
     "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",
@@ -35,8 +40,7 @@ dependencies = [
     "uv>=0.4.25",
     "tomli-w>=1.1.0",
     "tomli>=2.0.2",
-    "blinker>=1.9.0",
-    "json5>=0.10.0",
+    "blinker>=1.9.0"
 ]
 
 [project.urls]
@@ -45,7 +49,7 @@ Documentation = "https://docs.crewai.com"
 Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
-tools = ["crewai-tools>=0.32.1"]
+tools = ["crewai-tools>=0.25.5"]
 embeddings = [
     "tiktoken~=0.7.0"
 ]

src/crewai/__init__.py

@@ -14,7 +14,7 @@ warnings.filterwarnings(
     category=UserWarning,
     module="pydantic.main",
 )
-__version__ = "0.100.1"
+__version__ = "0.95.0"
 __all__ = [
     "Agent",
     "Crew",

src/crewai/agent.py

@@ -21,6 +21,7 @@ from crewai.tools.base_tool import Tool
 from crewai.utilities import Converter, Prompts
 from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_FILE
 from crewai.utilities.converter import generate_model_description
+from crewai.utilities.llm_utils import create_llm
 from crewai.utilities.token_counter_callback import TokenCalcHandler
 from crewai.utilities.training_handler import CrewTrainingHandler
 
@@ -139,89 +140,9 @@ class Agent(BaseAgent):
     def post_init_setup(self):
         self._set_knowledge()
         self.agent_ops_agent_name = self.role
-        unaccepted_attributes = [
-            "AWS_ACCESS_KEY_ID",
-            "AWS_SECRET_ACCESS_KEY",
-            "AWS_REGION_NAME",
-        ]
 
-        # Handle different cases for self.llm
-        if isinstance(self.llm, str):
-            # If it's a string, create an LLM instance
-            self.llm = LLM(model=self.llm)
-        elif isinstance(self.llm, LLM):
-            # If it's already an LLM instance, keep it as is
-            pass
-        elif self.llm is None:
-            # Determine the model name from environment variables or use default
-            model_name = (
-                os.environ.get("OPENAI_MODEL_NAME")
-                or os.environ.get("MODEL")
-                or "gpt-4o-mini"
-            )
-            llm_params = {"model": model_name}
-
-            api_base = os.environ.get("OPENAI_API_BASE") or os.environ.get(
-                "OPENAI_BASE_URL"
-            )
-            if api_base:
-                llm_params["base_url"] = api_base
-
-            set_provider = model_name.split("/")[0] if "/" in model_name else "openai"
-
-            # Iterate over all environment variables to find matching API keys or use defaults
-            for provider, env_vars in ENV_VARS.items():
-                if provider == set_provider:
-                    for env_var in env_vars:
-                        # Check if the environment variable is set
-                        key_name = env_var.get("key_name")
-                        if key_name and key_name not in unaccepted_attributes:
-                            env_value = os.environ.get(key_name)
-                            if env_value:
-                                key_name = key_name.lower()
-                                for pattern in LITELLM_PARAMS:
-                                    if pattern in key_name:
-                                        key_name = pattern
-                                        break
-                                llm_params[key_name] = env_value
-                        # Check for default values if the environment variable is not set
-                        elif env_var.get("default", False):
-                            for key, value in env_var.items():
-                                if key not in ["prompt", "key_name", "default"]:
-                                    # Only add default if the key is already set in os.environ
-                                    if key in os.environ:
-                                        llm_params[key] = value
-
-            self.llm = LLM(**llm_params)
-        else:
-            # For any other type, attempt to extract relevant attributes
-            llm_params = {
-                "model": getattr(self.llm, "model_name", None)
-                or getattr(self.llm, "deployment_name", None)
-                or str(self.llm),
-                "temperature": getattr(self.llm, "temperature", None),
-                "max_tokens": getattr(self.llm, "max_tokens", None),
-                "logprobs": getattr(self.llm, "logprobs", None),
-                "timeout": getattr(self.llm, "timeout", None),
-                "max_retries": getattr(self.llm, "max_retries", None),
-                "api_key": getattr(self.llm, "api_key", None),
-                "base_url": getattr(self.llm, "base_url", None),
-                "organization": getattr(self.llm, "organization", None),
-            }
-            # Remove None values to avoid passing unnecessary parameters
-            llm_params = {k: v for k, v in llm_params.items() if v is not None}
-            self.llm = LLM(**llm_params)
-
-        # Similar handling for function_calling_llm
-        if self.function_calling_llm:
-            if isinstance(self.function_calling_llm, str):
-                self.function_calling_llm = LLM(model=self.function_calling_llm)
-            elif not isinstance(self.function_calling_llm, LLM):
-                self.function_calling_llm = LLM(
-                    model=getattr(self.function_calling_llm, "model_name", None)
-                    or getattr(self.function_calling_llm, "deployment_name", None)
-                    or str(self.function_calling_llm)
-                )
+        self.llm = create_llm(self.llm)
+        self.function_calling_llm = create_llm(self.function_calling_llm)
 
         if not self.agent_executor:
             self._setup_agent_executor()
@@ -243,15 +164,6 @@ class Agent(BaseAgent):
                 if isinstance(self.knowledge_sources, list) and all(
                     isinstance(k, BaseKnowledgeSource) for k in self.knowledge_sources
                 ):
-                    # Validate embedding configuration based on provider
-                    from crewai.utilities.constants import DEFAULT_EMBEDDING_PROVIDER
-                    provider = os.getenv("CREWAI_EMBEDDING_PROVIDER", DEFAULT_EMBEDDING_PROVIDER)
-                    
-                    if provider == "openai" and not os.getenv("OPENAI_API_KEY"):
-                        raise ValueError("Please provide an OpenAI API key via OPENAI_API_KEY environment variable")
-                    elif provider == "ollama" and not os.getenv("CREWAI_OLLAMA_URL", "http://localhost:11434/api/embeddings"):
-                        raise ValueError("Please provide Ollama URL via CREWAI_OLLAMA_URL environment variable")
-                    
                     self._knowledge = Knowledge(
                         sources=self.knowledge_sources,
                         embedder_config=self.embedder_config,
@@ -422,6 +334,7 @@ class Agent(BaseAgent):
 
     def get_multimodal_tools(self) -> List[Tool]:
         from crewai.tools.agent_tools.add_image_tool import AddImageTool
+
         return [AddImageTool()]
 
     def get_code_execution_tools(self):

src/crewai/agents/agent_builder/base_agent.py

@@ -18,13 +18,10 @@ 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
 from crewai.utilities.config import process_config
-from crewai.utilities.converter import Converter
 
 T = TypeVar("T", bound="BaseAgent")
 
@@ -43,7 +40,7 @@ class BaseAgent(ABC, BaseModel):
         max_rpm (Optional[int]): Maximum number of requests per minute for the agent execution.
         allow_delegation (bool): Allow delegation of tasks to agents.
         tools (Optional[List[Any]]): Tools at the agent's disposal.
-        max_iter (int): Maximum iterations for an agent to execute a task.
+        max_iter (Optional[int]): Maximum iterations for an agent to execute a task.
         agent_executor (InstanceOf): An instance of the CrewAgentExecutor class.
         llm (Any): Language model that will run the agent.
         crew (Any): Crew to which the agent belongs.
@@ -51,8 +48,6 @@ 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:
@@ -113,9 +108,9 @@ class BaseAgent(ABC, BaseModel):
         description="Enable agent to delegate and ask questions among each other.",
     )
     tools: Optional[List[Any]] = Field(
-        default_factory=lambda: [], description="Tools at agents' disposal"
+        default_factory=list, description="Tools at agents' disposal"
     )
-    max_iter: int = Field(
+    max_iter: Optional[int] = Field(
         default=25, description="Maximum iterations for an agent to execute a task"
     )
     agent_executor: InstanceOf = Field(
@@ -126,27 +121,15 @@ class BaseAgent(ABC, BaseModel):
     )
     crew: Any = Field(default=None, description="Crew to which the agent belongs.")
     i18n: I18N = Field(default=I18N(), description="Internationalization settings.")
-    cache_handler: Optional[InstanceOf[CacheHandler]] = Field(
+    cache_handler: InstanceOf[CacheHandler] = Field(
         default=None, description="An instance of the CacheHandler class."
     )
     tools_handler: InstanceOf[ToolsHandler] = Field(
-        default_factory=ToolsHandler,
-        description="An instance of the ToolsHandler class.",
+        default=None, description="An instance of the ToolsHandler class."
     )
     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
@@ -256,7 +239,7 @@ class BaseAgent(ABC, BaseModel):
     @abstractmethod
     def get_output_converter(
         self, llm: Any, text: str, model: type[BaseModel] | None, instructions: str
-    ) -> Converter:
+    ):
         """Get the converter class for the agent to create json/pydantic outputs."""
         pass
 
@@ -273,44 +256,13 @@ class BaseAgent(ABC, BaseModel):
             "tools_handler",
             "cache_handler",
             "llm",
-            "knowledge_sources",
-            "knowledge_storage",
-            "knowledge",
         }
 
-        # Copy llm
+        # Copy llm and clear callbacks
         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,
-            knowledge_sources=existing_knowledge_sources,
-            knowledge=copied_knowledge,
-            knowledge_storage=copied_knowledge_storage,
-        )
+        copied_agent = type(self)(**copied_data, llm=existing_llm, tools=self.tools)
 
         return copied_agent
 

src/crewai/agents/agent_builder/base_agent_executor_mixin.py

@@ -95,29 +95,18 @@ class CrewAgentExecutorMixin:
                 pass
 
     def _ask_human_input(self, final_answer: str) -> str:
-        """Prompt human input with mode-appropriate messaging."""
+        """Prompt human input for final decision making."""
         self._printer.print(
             content=f"\033[1m\033[95m ## Final Result:\033[00m \033[92m{final_answer}\033[00m"
         )
 
-        # Training mode prompt (single iteration)
-        if self.crew and getattr(self.crew, "_train", False):
-            prompt = (
+        self._printer.print(
+            content=(
                 "\n\n=====\n"
-                "## TRAINING MODE: Provide feedback to improve the agent's performance.\n"
-                "This will be used to train better versions of the agent.\n"
-                "Please provide detailed feedback about the result quality and reasoning process.\n"
+                "## Please provide feedback on the Final Result and the Agent's actions. "
+                "Respond with 'looks good' or a similar phrase when you're satisfied.\n"
                 "=====\n"
-            )
-        # Regular human-in-the-loop prompt (multiple iterations)
-        else:
-            prompt = (
-                "\n\n=====\n"
-                "## HUMAN FEEDBACK: Provide feedback on the Final Result and Agent's actions.\n"
-                "Respond with 'looks good' to accept or provide specific improvement requests.\n"
-                "You can provide multiple rounds of feedback until satisfied.\n"
-                "=====\n"
-            )
-
-        self._printer.print(content=prompt, color="bold_yellow")
+            ),
+            color="bold_yellow",
+        )
         return input()

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: int = Field(
+    max_attempts: Optional[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

@@ -2,26 +2,25 @@ from crewai.types.usage_metrics import UsageMetrics
 
 
 class TokenProcess:
-    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
+    total_tokens: int = 0
+    prompt_tokens: int = 0
+    cached_prompt_tokens: int = 0
+    completion_tokens: int = 0
+    successful_requests: int = 0
 
-    def sum_prompt_tokens(self, tokens: int) -> None:
-        self.prompt_tokens += tokens
-        self.total_tokens += tokens
+    def sum_prompt_tokens(self, tokens: int):
+        self.prompt_tokens = self.prompt_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_completion_tokens(self, tokens: int):
+        self.completion_tokens = self.completion_tokens + tokens
+        self.total_tokens = self.total_tokens + tokens
 
-    def sum_cached_prompt_tokens(self, tokens: int) -> None:
-        self.cached_prompt_tokens += tokens
+    def sum_cached_prompt_tokens(self, tokens: int):
+        self.cached_prompt_tokens = self.cached_prompt_tokens + tokens
 
-    def sum_successful_requests(self, requests: int) -> None:
-        self.successful_requests += requests
+    def sum_successful_requests(self, requests: int):
+        self.successful_requests = self.successful_requests + requests
 
     def get_summary(self) -> UsageMetrics:
         return UsageMetrics(

src/crewai/agents/crew_agent_executor.py

@@ -13,7 +13,6 @@ 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
@@ -55,7 +54,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         callbacks: List[Any] = [],
     ):
         self._i18n: I18N = I18N()
-        self.llm: LLM = llm
+        self.llm = llm
         self.task = task
         self.agent = agent
         self.crew = crew
@@ -81,8 +80,10 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self.tool_name_to_tool_map: Dict[str, BaseTool] = {
             tool.name: tool for tool in self.tools
         }
-        self.stop = stop_words
-        self.llm.stop = list(set(self.llm.stop + self.stop))
+        if self.llm.stop:
+            self.llm.stop = list(set(self.llm.stop + self.stop))
+        else:
+            self.llm.stop = self.stop
 
     def invoke(self, inputs: Dict[str, str]) -> Dict[str, Any]:
         if "system" in self.prompt:
@@ -97,22 +98,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self._show_start_logs()
 
         self.ask_for_human_input = bool(inputs.get("ask_for_human_input", False))
-
-        try:
-            formatted_answer = self._invoke_loop()
-        except AssertionError:
-            self._printer.print(
-                content="Agent failed to reach a final answer. This is likely a bug - please report it.",
-                color="red",
-            )
-            raise
-        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
+        formatted_answer = self._invoke_loop()
 
         if self.ask_for_human_input:
             formatted_answer = self._handle_human_feedback(formatted_answer)
@@ -121,7 +107,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self._create_long_term_memory(formatted_answer)
         return {"output": formatted_answer.output}
 
-    def _invoke_loop(self) -> AgentFinish:
+    def _invoke_loop(self):
         """
         Main loop to invoke the agent's thought process until it reaches a conclusion
         or the maximum number of iterations is reached.
@@ -138,6 +124,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                 self._enforce_rpm_limit()
 
                 answer = self._get_llm_response()
+
                 formatted_answer = self._process_llm_response(answer)
 
                 if isinstance(formatted_answer, AgentAction):
@@ -155,37 +142,15 @@ 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
 
-        # During the invoke loop, formatted_answer alternates between AgentAction
-        # (when the agent is using tools) and eventually becomes AgentFinish
-        # (when the agent reaches a final answer). This assertion confirms we've
-        # reached a final answer and helps type checking understand this transition.
-        assert isinstance(formatted_answer, AgentFinish)
         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
@@ -197,17 +162,10 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
 
     def _get_llm_response(self) -> str:
         """Call the LLM and return the response, handling any invalid responses."""
-        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
+        answer = self.llm.call(
+            self.messages,
+            callbacks=self.callbacks,
+        )
 
         if not answer:
             self._printer.print(
@@ -228,6 +186,7 @@ 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(
@@ -303,11 +262,8 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             self._printer.print(
                 content=f"\033[1m\033[95m# Agent:\033[00m \033[1m\033[92m{agent_role}\033[00m"
             )
-            description = (
-                getattr(self.task, "description") if self.task else "Not Found"
-            )
             self._printer.print(
-                content=f"\033[95m## Task:\033[00m \033[92m{description}\033[00m"
+                content=f"\033[95m## Task:\033[00m \033[92m{self.task.description}\033[00m"
             )
 
     def _show_logs(self, formatted_answer: Union[AgentAction, AgentFinish]):
@@ -360,7 +316,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             agent=self.agent,
             action=agent_action,
         )
-        tool_calling = tool_usage.parse_tool_calling(agent_action.text)
+        tool_calling = tool_usage.parse(agent_action.text)
 
         if isinstance(tool_calling, ToolUsageErrorException):
             tool_result = tool_calling.message
@@ -432,50 +388,58 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             )
 
     def _handle_crew_training_output(
-        self, result: AgentFinish, human_feedback: Optional[str] = None
+        self, result: AgentFinish, human_feedback: str | None = None
     ) -> None:
-        """Handle the process of saving training data."""
+        """Function to handle the process of the training data."""
         agent_id = str(self.agent.id)  # type: ignore
-        train_iteration = (
-            getattr(self.crew, "_train_iteration", None) if self.crew else None
-        )
-
-        if train_iteration is None or not isinstance(train_iteration, int):
-            self._printer.print(
-                content="Invalid or missing train iteration. Cannot save training data.",
-                color="red",
-            )
-            return
 
+        # Load training data
         training_handler = CrewTrainingHandler(TRAINING_DATA_FILE)
-        training_data = training_handler.load() or {}
+        training_data = training_handler.load()
 
-        # Initialize or retrieve agent's training data
-        agent_training_data = training_data.get(agent_id, {})
-
-        if human_feedback is not None:
-            # Save initial output and human feedback
-            agent_training_data[train_iteration] = {
-                "initial_output": result.output,
-                "human_feedback": human_feedback,
-            }
-        else:
-            # Save improved output
-            if train_iteration in agent_training_data:
-                agent_training_data[train_iteration]["improved_output"] = result.output
+        # Check if training data exists, human input is not requested, and self.crew is valid
+        if training_data and not self.ask_for_human_input:
+            if self.crew is not None and hasattr(self.crew, "_train_iteration"):
+                train_iteration = self.crew._train_iteration
+                if agent_id in training_data and isinstance(train_iteration, int):
+                    training_data[agent_id][train_iteration][
+                        "improved_output"
+                    ] = result.output
+                    training_handler.save(training_data)
+                else:
+                    self._printer.print(
+                        content="Invalid train iteration type or agent_id not in training data.",
+                        color="red",
+                    )
             else:
                 self._printer.print(
-                    content=(
-                        f"No existing training data for agent {agent_id} and iteration "
-                        f"{train_iteration}. Cannot save improved output."
-                    ),
+                    content="Crew is None or does not have _train_iteration attribute.",
                     color="red",
                 )
-                return
 
-        # Update the training data and save
-        training_data[agent_id] = agent_training_data
-        training_handler.save(training_data)
+        if self.ask_for_human_input and human_feedback is not None:
+            training_data = {
+                "initial_output": result.output,
+                "human_feedback": human_feedback,
+                "agent": agent_id,
+                "agent_role": self.agent.role,  # type: ignore
+            }
+            if self.crew is not None and hasattr(self.crew, "_train_iteration"):
+                train_iteration = self.crew._train_iteration
+                if isinstance(train_iteration, int):
+                    CrewTrainingHandler(TRAINING_DATA_FILE).append(
+                        train_iteration, agent_id, training_data
+                    )
+                else:
+                    self._printer.print(
+                        content="Invalid train iteration type. Expected int.",
+                        color="red",
+                    )
+            else:
+                self._printer.print(
+                    content="Crew is None or does not have _train_iteration attribute.",
+                    color="red",
+                )
 
     def _format_prompt(self, prompt: str, inputs: Dict[str, str]) -> str:
         prompt = prompt.replace("{input}", inputs["input"])
@@ -491,111 +455,82 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         return {"role": role, "content": prompt}
 
     def _handle_human_feedback(self, formatted_answer: AgentFinish) -> AgentFinish:
-        """Handle human feedback with different flows for training vs regular use.
+        """
+        Handles the human feedback loop, allowing the user to provide feedback
+        on the agent's output and determining if additional iterations are needed.
 
-        Args:
-            formatted_answer: The initial AgentFinish result to get feedback on
+        Parameters:
+            formatted_answer (AgentFinish): The initial output from the agent.
 
         Returns:
-            AgentFinish: The final answer after processing feedback
+            AgentFinish: The final output after incorporating human feedback.
         """
-        human_feedback = self._ask_human_input(formatted_answer.output)
-
-        if self._is_training_mode():
-            return self._handle_training_feedback(formatted_answer, human_feedback)
-
-        return self._handle_regular_feedback(formatted_answer, human_feedback)
-
-    def _is_training_mode(self) -> bool:
-        """Check if crew is in training mode."""
-        return bool(self.crew and self.crew._train)
-
-    def _handle_training_feedback(
-        self, initial_answer: AgentFinish, feedback: str
-    ) -> AgentFinish:
-        """Process feedback for training scenarios with single iteration."""
-        self._printer.print(
-            content="\nProcessing training feedback.\n",
-            color="yellow",
-        )
-        self._handle_crew_training_output(initial_answer, feedback)
-        self.messages.append(
-            self._format_msg(
-                self._i18n.slice("feedback_instructions").format(feedback=feedback)
-            )
-        )
-        improved_answer = self._invoke_loop()
-        self._handle_crew_training_output(improved_answer)
-        self.ask_for_human_input = False
-        return improved_answer
-
-    def _handle_regular_feedback(
-        self, current_answer: AgentFinish, initial_feedback: str
-    ) -> AgentFinish:
-        """Process feedback for regular use with potential multiple iterations."""
-        feedback = initial_feedback
-        answer = current_answer
-
         while self.ask_for_human_input:
-            response = self._get_llm_feedback_response(feedback)
+            human_feedback = self._ask_human_input(formatted_answer.output)
 
-            if not self._feedback_requires_changes(response):
+            if self.crew and self.crew._train:
+                self._handle_crew_training_output(formatted_answer, human_feedback)
+
+            # Make an LLM call to verify if additional changes are requested based on human feedback
+            additional_changes_prompt = self._i18n.slice(
+                "human_feedback_classification"
+            ).format(feedback=human_feedback)
+
+            retry_count = 0
+            llm_call_successful = False
+            additional_changes_response = None
+
+            while retry_count < MAX_LLM_RETRY and not llm_call_successful:
+                try:
+                    additional_changes_response = (
+                        self.llm.call(
+                            [
+                                self._format_msg(
+                                    additional_changes_prompt, role="system"
+                                )
+                            ],
+                            callbacks=self.callbacks,
+                        )
+                        .strip()
+                        .lower()
+                    )
+                    llm_call_successful = True
+                except Exception as e:
+                    retry_count += 1
+
+                    self._printer.print(
+                        content=f"Error during LLM call to classify human feedback: {e}. Retrying... ({retry_count}/{MAX_LLM_RETRY})",
+                        color="red",
+                    )
+
+            if not llm_call_successful:
+                self._printer.print(
+                    content="Error processing feedback after multiple attempts.",
+                    color="red",
+                )
                 self.ask_for_human_input = False
+                break
+
+            if additional_changes_response == "false":
+                self.ask_for_human_input = False
+            elif additional_changes_response == "true":
+                self.ask_for_human_input = True
+                # Add human feedback to messages
+                self.messages.append(self._format_msg(f"Feedback: {human_feedback}"))
+                # Invoke the loop again with updated messages
+                formatted_answer = self._invoke_loop()
+
+                if self.crew and self.crew._train:
+                    self._handle_crew_training_output(formatted_answer)
             else:
-                answer = self._process_feedback_iteration(feedback)
-                feedback = self._ask_human_input(answer.output)
+                # Unexpected response
+                self._printer.print(
+                    content=f"Unexpected response from LLM: '{additional_changes_response}'. Assuming no additional changes requested.",
+                    color="red",
+                )
+                self.ask_for_human_input = False
 
-        return answer
-
-    def _get_llm_feedback_response(self, feedback: str) -> Optional[str]:
-        """Get LLM classification of whether feedback requires changes."""
-        prompt = self._i18n.slice("human_feedback_classification").format(
-            feedback=feedback
-        )
-        message = self._format_msg(prompt, role="system")
-
-        for retry in range(MAX_LLM_RETRY):
-            try:
-                response = self.llm.call([message], callbacks=self.callbacks)
-                return response.strip().lower() if response else None
-            except Exception as error:
-                self._log_feedback_error(retry, error)
-
-        self._log_max_retries_exceeded()
-        return None
-
-    def _feedback_requires_changes(self, response: Optional[str]) -> bool:
-        """Determine if feedback response indicates need for changes."""
-        return response == "true" if response else False
-
-    def _process_feedback_iteration(self, feedback: str) -> AgentFinish:
-        """Process a single feedback iteration."""
-        self.messages.append(
-            self._format_msg(
-                self._i18n.slice("feedback_instructions").format(feedback=feedback)
-            )
-        )
-        return self._invoke_loop()
-
-    def _log_feedback_error(self, retry_count: int, error: Exception) -> None:
-        """Log feedback processing errors."""
-        self._printer.print(
-            content=(
-                f"Error processing feedback: {error}. "
-                f"Retrying... ({retry_count + 1}/{MAX_LLM_RETRY})"
-            ),
-            color="red",
-        )
-
-    def _log_max_retries_exceeded(self) -> None:
-        """Log when max retries for feedback processing are exceeded."""
-        self._printer.print(
-            content=(
-                f"Failed to process feedback after {MAX_LLM_RETRY} attempts. "
-                "Ending feedback loop."
-            ),
-            color="red",
-        )
+        return formatted_answer
 
     def _handle_max_iterations_exceeded(self, formatted_answer):
         """

src/crewai/cli/cli.py

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

src/crewai/cli/crew_chat.py

@@ -1,52 +1,17 @@
 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():
     """
@@ -54,47 +19,20 @@ 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
 
-    # 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",
+    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}]
     )
-
-    # 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")
+    click.secho(f"\nAssistant: {introductory_message}\n", fg="green")
 
     messages = [
         {"role": "system", "content": system_message},
@@ -105,17 +43,15 @@ 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:
@@ -149,7 +85,7 @@ 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}"
@@ -166,33 +102,25 @@ 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:
-            # Flush any pending input before accepting new input
-            flush_input()
+            user_input = click.prompt("You", type=str)
+            if user_input.strip().lower() in ["exit", "quit"]:
+                click.echo("Exiting chat. Goodbye!")
+                break
 
-            user_input = get_user_input()
-            handle_user_input(
-                user_input, chat_llm, messages, crew_tool_schema, available_functions
+            messages.append({"role": "user", "content": user_input})
+            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")
+
         except KeyboardInterrupt:
             click.echo("\nExiting chat. Goodbye!")
             break
@@ -201,55 +129,6 @@ 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.
@@ -444,10 +323,10 @@ 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 or ""
+                lambda m: m.group(1), task.description
             )
             expected_output = placeholder_pattern.sub(
-                lambda m: m.group(1), task.expected_output or ""
+                lambda m: m.group(1), task.expected_output
             )
             context_texts.append(f"Task Description: {task_description}")
             context_texts.append(f"Expected Output: {expected_output}")
@@ -458,10 +337,10 @@ 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 or "")
-            agent_goal = placeholder_pattern.sub(lambda m: m.group(1), agent.goal or "")
+            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 or ""
+                lambda m: m.group(1), agent.backstory
             )
             context_texts.append(f"Agent Role: {agent_role}")
             context_texts.append(f"Agent Goal: {agent_goal}")
@@ -502,20 +381,18 @@ 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 or ""
+            lambda m: m.group(1), task.description
         )
         expected_output = placeholder_pattern.sub(
-            lambda m: m.group(1), task.expected_output or ""
+            lambda m: m.group(1), task.expected_output
         )
         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 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 ""
-        )
+        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)
         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/reset_memories_command.py

@@ -2,7 +2,11 @@ import subprocess
 
 import click
 
-from crewai.cli.utils import get_crew
+from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
+from crewai.memory.entity.entity_memory import EntityMemory
+from crewai.memory.long_term.long_term_memory import LongTermMemory
+from crewai.memory.short_term.short_term_memory import ShortTermMemory
+from crewai.utilities.task_output_storage_handler import TaskOutputStorageHandler
 
 
 def reset_memories_command(
@@ -26,35 +30,30 @@ def reset_memories_command(
     """
 
     try:
-        crew = get_crew()
-        if not crew:
-            raise ValueError("No crew found.")
         if all:
-            crew.reset_memories(command_type="all")
+            ShortTermMemory().reset()
+            EntityMemory().reset()
+            LongTermMemory().reset()
+            TaskOutputStorageHandler().reset()
+            KnowledgeStorage().reset()
             click.echo("All memories have been reset.")
-            return
+        else:
+            if long:
+                LongTermMemory().reset()
+                click.echo("Long term memory has been reset.")
 
-        if not any([long, short, entity, kickoff_outputs, knowledge]):
-            click.echo(
-                "No memory type specified. Please specify at least one type to reset."
-            )
-            return
-
-        if long:
-            crew.reset_memories(command_type="long")
-            click.echo("Long term memory has been reset.")
-        if short:
-            crew.reset_memories(command_type="short")
-            click.echo("Short term memory has been reset.")
-        if entity:
-            crew.reset_memories(command_type="entity")
-            click.echo("Entity memory has been reset.")
-        if kickoff_outputs:
-            crew.reset_memories(command_type="kickoff_outputs")
-            click.echo("Latest Kickoff outputs stored has been reset.")
-        if knowledge:
-            crew.reset_memories(command_type="knowledge")
-            click.echo("Knowledge has been reset.")
+            if short:
+                ShortTermMemory().reset()
+                click.echo("Short term memory has been reset.")
+            if entity:
+                EntityMemory().reset()
+                click.echo("Entity memory has been reset.")
+            if kickoff_outputs:
+                TaskOutputStorageHandler().reset()
+                click.echo("Latest Kickoff outputs stored has been reset.")
+            if knowledge:
+                KnowledgeStorage().reset()
+                click.echo("Knowledge has been reset.")
 
     except subprocess.CalledProcessError as e:
         click.echo(f"An error occurred while resetting the memories: {e}", err=True)

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

@@ -1,3 +1,2 @@
 .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]>=0.100.1,<1.0.0"
+    "crewai[tools]>=0.95.0,<1.0.0"
 ]
 
 [project.scripts]

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

@@ -1,4 +1,3 @@
 .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 import Flow, listen, start
+from crewai.flow.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]>=0.100.1,<1.0.0",
+    "crewai[tools]>=0.95.0,<1.0.0",
 ]
 
 [project.scripts]

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

@@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.100.1"
+    "crewai[tools]>=0.95.0"
 ]
 
 [tool.crewai]

src/crewai/cli/utils.py

@@ -9,7 +9,6 @@ import tomli
 from rich.console import Console
 
 from crewai.cli.constants import ENV_VARS
-from crewai.crew import Crew
 
 if sys.version_info >= (3, 11):
     import tomllib
@@ -248,64 +247,3 @@ def write_env_file(folder_path, env_vars):
     with open(env_file_path, "w") as file:
         for key, value in env_vars.items():
             file.write(f"{key}={value}\n")
-
-
-def get_crew(crew_path: str = "crew.py", require: bool = False) -> Crew | None:
-    """Get the crew instance from the crew.py file."""
-    try:
-        import importlib.util
-        import os
-
-        for root, _, files in os.walk("."):
-            if "crew.py" in files:
-                crew_path = os.path.join(root, "crew.py")
-                try:
-                    spec = importlib.util.spec_from_file_location(
-                        "crew_module", crew_path
-                    )
-                    if not spec or not spec.loader:
-                        continue
-                    module = importlib.util.module_from_spec(spec)
-                    try:
-                        sys.modules[spec.name] = module
-                        spec.loader.exec_module(module)
-
-                        for attr_name in dir(module):
-                            attr = getattr(module, attr_name)
-                            try:
-                                if callable(attr) and hasattr(attr, "crew"):
-                                    crew_instance = attr().crew()
-                                    return crew_instance
-
-                            except Exception as e:
-                                print(f"Error processing attribute {attr_name}: {e}")
-                                continue
-
-                    except Exception as exec_error:
-                        print(f"Error executing module: {exec_error}")
-                        import traceback
-
-                        print(f"Traceback: {traceback.format_exc()}")
-
-                except (ImportError, AttributeError) as e:
-                    if require:
-                        console.print(
-                            f"Error importing crew from {crew_path}: {str(e)}",
-                            style="bold red",
-                        )
-                        continue
-
-                break
-
-        if require:
-            console.print("No valid Crew instance found in crew.py", style="bold red")
-            raise SystemExit
-        return None
-
-    except Exception as e:
-        if require:
-            console.print(
-                f"Unexpected error while loading crew: {str(e)}", style="bold red"
-            )
-            raise SystemExit
-        return None

src/crewai/crew.py

@@ -1,10 +1,11 @@
 import asyncio
 import json
+import re
 import uuid
 import warnings
 from concurrent.futures import Future
 from hashlib import md5
-from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
+from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union
 
 from pydantic import (
     UUID4,
@@ -16,6 +17,7 @@ from pydantic import (
     field_validator,
     model_validator,
 )
+from pydantic_core import PydanticCustomError
 
 from crewai.agent import Agent
 from crewai.agents.agent_builder.base_agent import BaseAgent
@@ -35,6 +37,7 @@ 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
@@ -44,7 +47,6 @@ from crewai.utilities.formatter import (
     aggregate_raw_outputs_from_task_outputs,
     aggregate_raw_outputs_from_tasks,
 )
-from crewai.utilities.llm_utils import create_llm
 from crewai.utilities.planning_handler import CrewPlanner
 from crewai.utilities.task_output_storage_handler import TaskOutputStorageHandler
 from crewai.utilities.training_handler import CrewTrainingHandler
@@ -81,7 +83,6 @@ 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
@@ -148,7 +149,7 @@ class Crew(BaseModel):
     manager_agent: Optional[BaseAgent] = Field(
         description="Custom agent that will be used as manager.", default=None
     )
-    function_calling_llm: Optional[Union[str, InstanceOf[LLM], Any]] = Field(
+    function_calling_llm: Optional[Any] = Field(
         description="Language model that will run the agent.", default=None
     )
     config: Optional[Union[Json, Dict[str, Any]]] = Field(default=None)
@@ -180,9 +181,9 @@ class Crew(BaseModel):
         default=None,
         description="Path to the prompt json file to be used for the crew.",
     )
-    output_log_file: Optional[Union[bool, str]] = Field(
+    output_log_file: Optional[str] = Field(
         default=None,
-        description="Path to the log file to be saved",
+        description="output_log_file",
     )
     planning: Optional[bool] = Field(
         default=False,
@@ -208,9 +209,8 @@ class Crew(BaseModel):
         default=None,
         description="LLM used to handle chatting with the crew.",
     )
-    knowledge: Optional[Knowledge] = Field(
+    _knowledge: Optional[Knowledge] = PrivateAttr(
         default=None,
-        description="Knowledge for the crew.",
     )
 
     @field_validator("id", mode="before")
@@ -245,9 +245,15 @@ class Crew(BaseModel):
         if self.output_log_file:
             self._file_handler = FileHandler(self.output_log_file)
         self._rpm_controller = RPMController(max_rpm=self.max_rpm, logger=self._logger)
-        if self.function_calling_llm and not isinstance(self.function_calling_llm, LLM):
-            self.function_calling_llm = create_llm(self.function_calling_llm)
-
+        if self.function_calling_llm:
+            if isinstance(self.function_calling_llm, str):
+                self.function_calling_llm = LLM(model=self.function_calling_llm)
+            elif not isinstance(self.function_calling_llm, LLM):
+                self.function_calling_llm = LLM(
+                    model=getattr(self.function_calling_llm, "model_name", None)
+                    or getattr(self.function_calling_llm, "deployment_name", None)
+                    or str(self.function_calling_llm)
+                )
         self._telemetry = Telemetry()
         self._telemetry.set_tracer()
         return self
@@ -288,9 +294,9 @@ 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=self.embedder,
+                        embedder_config=self.embedder,
                         collection_name="crew",
                     )
 
@@ -377,22 +383,6 @@ class Crew(BaseModel):
 
         return self
 
-    @model_validator(mode="after")
-    def validate_must_have_non_conditional_task(self) -> "Crew":
-        """Ensure that a crew has at least one non-conditional task."""
-        if not self.tasks:
-            return self
-        non_conditional_count = sum(
-            1 for task in self.tasks if not isinstance(task, ConditionalTask)
-        )
-        if non_conditional_count == 0:
-            raise PydanticCustomError(
-                "only_conditional_tasks",
-                "Crew must include at least one non-conditional task",
-                {},
-            )
-        return self
-
     @model_validator(mode="after")
     def validate_first_task(self) -> "Crew":
         """Ensure the first task is not a ConditionalTask."""
@@ -452,8 +442,6 @@ class Crew(BaseModel):
                         )
         return self
 
-
-
     @property
     def key(self) -> str:
         source = [agent.key for agent in self.agents] + [
@@ -509,34 +497,27 @@ class Crew(BaseModel):
         train_crew = self.copy()
         train_crew._setup_for_training(filename)
 
-        try:
-            for n_iteration in range(n_iterations):
-                train_crew._train_iteration = n_iteration
-                train_crew.kickoff(inputs=inputs)
+        for n_iteration in range(n_iterations):
+            train_crew._train_iteration = n_iteration
+            train_crew.kickoff(inputs=inputs)
 
-            training_data = CrewTrainingHandler(TRAINING_DATA_FILE).load()
+        training_data = CrewTrainingHandler(TRAINING_DATA_FILE).load()
 
-            for agent in train_crew.agents:
-                if training_data.get(str(agent.id)):
-                    result = TaskEvaluator(agent).evaluate_training_data(
-                        training_data=training_data, agent_id=str(agent.id)
-                    )
-                    CrewTrainingHandler(filename).save_trained_data(
-                        agent_id=str(agent.role), trained_data=result.model_dump()
-                    )
-        except Exception as e:
-            self._logger.log("error", f"Training failed: {e}", color="red")
-            CrewTrainingHandler(TRAINING_DATA_FILE).clear()
-            CrewTrainingHandler(filename).clear()
-            raise
+        for agent in train_crew.agents:
+            if training_data.get(str(agent.id)):
+                result = TaskEvaluator(agent).evaluate_training_data(
+                    training_data=training_data, agent_id=str(agent.id)
+                )
+
+                CrewTrainingHandler(filename).save_trained_data(
+                    agent_id=str(agent.role), trained_data=result.model_dump()
+                )
 
     def kickoff(
         self,
         inputs: Optional[Dict[str, Any]] = None,
     ) -> CrewOutput:
         for before_callback in self.before_kickoff_callbacks:
-            if inputs is None:
-                inputs = {}
             inputs = before_callback(inputs)
 
         """Starts the crew to work on its assigned tasks."""
@@ -696,7 +677,11 @@ class Crew(BaseModel):
                 manager.tools = []
                 raise Exception("Manager agent should not have tools")
         else:
-            self.manager_llm = create_llm(self.manager_llm)
+            self.manager_llm = (
+                getattr(self.manager_llm, "model_name", None)
+                or getattr(self.manager_llm, "deployment_name", None)
+                or self.manager_llm
+            )
             manager = Agent(
                 role=i18n.retrieve("hierarchical_manager_agent", "role"),
                 goal=i18n.retrieve("hierarchical_manager_agent", "goal"),
@@ -756,7 +741,6 @@ class Crew(BaseModel):
                     task, task_outputs, futures, task_index, was_replayed
                 )
                 if skipped_task_output:
-                    task_outputs.append(skipped_task_output)
                     continue
 
             if task.async_execution:
@@ -780,7 +764,7 @@ class Crew(BaseModel):
                     context=context,
                     tools=tools_for_task,
                 )
-                task_outputs.append(task_output)
+                task_outputs = [task_output]
                 self._process_task_result(task, task_output)
                 self._store_execution_log(task, task_output, task_index, was_replayed)
 
@@ -801,7 +785,7 @@ class Crew(BaseModel):
             task_outputs = self._process_async_tasks(futures, was_replayed)
             futures.clear()
 
-        previous_output = task_outputs[-1] if task_outputs else None
+        previous_output = task_outputs[task_index - 1] if task_outputs else None
         if previous_output is not None and not task.should_execute(previous_output):
             self._logger.log(
                 "debug",
@@ -923,15 +907,11 @@ class Crew(BaseModel):
             )
 
     def _create_crew_output(self, task_outputs: List[TaskOutput]) -> CrewOutput:
-        if not task_outputs:
-            raise ValueError("No task outputs available to create crew output.")
-            
-        # Filter out empty outputs and get the last valid one as the main output
-        valid_outputs = [t for t in task_outputs if t.raw]
-        if not valid_outputs:
-            raise ValueError("No valid task outputs available to create crew output.")
-        final_task_output = valid_outputs[-1]
-            
+        if len(task_outputs) != 1:
+            raise ValueError(
+                "Something went wrong. Kickoff should return only one task output."
+            )
+        final_task_output = task_outputs[0]
         final_string_output = final_task_output.raw
         self._finish_execution(final_string_output)
         token_usage = self.calculate_usage_metrics()
@@ -940,7 +920,7 @@ class Crew(BaseModel):
             raw=final_task_output.raw,
             pydantic=final_task_output.pydantic,
             json_dict=final_task_output.json_dict,
-            tasks_output=task_outputs,
+            tasks_output=[task.output for task in self.tasks if task.output],
             token_usage=token_usage,
         )
 
@@ -1013,8 +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]:
@@ -1058,8 +1038,6 @@ class Crew(BaseModel):
             "_telemetry",
             "agents",
             "tasks",
-            "knowledge_sources",
-            "knowledge",
         }
 
         cloned_agents = [agent.copy() for agent in self.agents]
@@ -1067,9 +1045,6 @@ 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)
@@ -1089,13 +1064,7 @@ class Crew(BaseModel):
         copied_data.pop("agents", None)
         copied_data.pop("tasks", None)
 
-        copied_crew = Crew(
-            **copied_data,
-            agents=cloned_agents,
-            tasks=cloned_tasks,
-            knowledge_sources=existing_knowledge_sources,
-            knowledge=existing_knowledge,
-        )
+        copied_crew = Crew(**copied_data, agents=cloned_agents, tasks=cloned_tasks)
 
         return copied_crew
 
@@ -1167,80 +1136,3 @@ class Crew(BaseModel):
 
     def __repr__(self):
         return f"Crew(id={self.id}, process={self.process}, number_of_agents={len(self.agents)}, number_of_tasks={len(self.tasks)})"
-
-    def reset_memories(self, command_type: str) -> None:
-        """Reset specific or all memories for the crew.
-
-        Args:
-            command_type: Type of memory to reset.
-                Valid options: 'long', 'short', 'entity', 'knowledge',
-                'kickoff_outputs', or 'all'
-
-        Raises:
-            ValueError: If an invalid command type is provided.
-            RuntimeError: If memory reset operation fails.
-        """
-        VALID_TYPES = frozenset(
-            ["long", "short", "entity", "knowledge", "kickoff_outputs", "all"]
-        )
-
-        if command_type not in VALID_TYPES:
-            raise ValueError(
-                f"Invalid command type. Must be one of: {', '.join(sorted(VALID_TYPES))}"
-            )
-
-        try:
-            if command_type == "all":
-                self._reset_all_memories()
-            else:
-                self._reset_specific_memory(command_type)
-
-            self._logger.log("info", f"{command_type} memory has been reset")
-
-        except Exception as e:
-            error_msg = f"Failed to reset {command_type} memory: {str(e)}"
-            self._logger.log("error", error_msg)
-            raise RuntimeError(error_msg) from e
-
-    def _reset_all_memories(self) -> None:
-        """Reset all available memory systems."""
-        memory_systems = [
-            ("short term", self._short_term_memory),
-            ("entity", self._entity_memory),
-            ("long term", self._long_term_memory),
-            ("task output", self._task_output_handler),
-            ("knowledge", self.knowledge),
-        ]
-
-        for name, system in memory_systems:
-            if system is not None:
-                try:
-                    system.reset()
-                except Exception as e:
-                    raise RuntimeError(f"Failed to reset {name} memory") from e
-
-    def _reset_specific_memory(self, memory_type: str) -> None:
-        """Reset a specific memory system.
-
-        Args:
-            memory_type: Type of memory to reset
-
-        Raises:
-            RuntimeError: If the specified memory system fails to reset
-        """
-        reset_functions = {
-            "long": (self._long_term_memory, "long term"),
-            "short": (self._short_term_memory, "short term"),
-            "entity": (self._entity_memory, "entity"),
-            "knowledge": (self.knowledge, "knowledge"),
-            "kickoff_outputs": (self._task_output_handler, "task output"),
-        }
-
-        memory_system, name = reset_functions[memory_type]
-        if memory_system is None:
-            raise RuntimeError(f"{name} memory system is not initialized")
-
-        try:
-            memory_system.reset()
-        except Exception as e:
-            raise RuntimeError(f"Failed to reset {name} memory") from e

src/crewai/crews/crew_output.py

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

src/crewai/flow/__init__.py

@@ -1,5 +1,3 @@
-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"]
+from crewai.flow.flow import Flow
 
+__all__ = ["Flow"]

src/crewai/flow/flow.py

@@ -1,6 +1,5 @@
 import asyncio
 import inspect
-import logging
 from typing import (
     Any,
     Callable,
@@ -14,10 +13,9 @@ from typing import (
     Union,
     cast,
 )
-from uuid import uuid4
 
 from blinker import Signal
-from pydantic import BaseModel, Field, ValidationError
+from pydantic import BaseModel, ValidationError
 
 from crewai.flow.flow_events import (
     FlowFinishedEvent,
@@ -26,70 +24,10 @@ 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",
-    )
-
-
-# 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}")
+T = TypeVar("T", bound=Union[BaseModel, Dict[str, Any]])
 
 
 def start(condition: Optional[Union[str, dict, Callable]] = None) -> Callable:
@@ -133,7 +71,6 @@ 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:
@@ -158,7 +95,6 @@ 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.
@@ -195,7 +131,6 @@ def listen(condition: Union[str, dict, Callable]) -> Callable:
     >>> def handle_completion(self):
     ...     pass
     """
-
     def decorator(func):
         if isinstance(condition, str):
             func.__trigger_methods__ = [condition]
@@ -260,7 +195,6 @@ def router(condition: Union[str, dict, Callable]) -> Callable:
     ...         return CONTINUE
     ...     return STOP
     """
-
     def decorator(func):
         func.__is_router__ = True
         if isinstance(condition, str):
@@ -284,7 +218,6 @@ 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.
@@ -387,32 +320,21 @@ class FlowMeta(type):
         routers = set()
 
         for attr_name, attr_value in dct.items():
-            # Check for any flow-related attributes
-            if (
-                hasattr(attr_value, "__is_flow_method__")
-                or hasattr(attr_value, "__is_start_method__")
-                or hasattr(attr_value, "__trigger_methods__")
-                or hasattr(attr_value, "__is_router__")
-            ):
-
-                # Register start methods
-                if hasattr(attr_value, "__is_start_method__"):
-                    start_methods.append(attr_name)
-
-                # Register listeners and routers
+            if hasattr(attr_value, "__is_start_method__"):
+                start_methods.append(attr_name)
                 if hasattr(attr_value, "__trigger_methods__"):
                     methods = attr_value.__trigger_methods__
                     condition_type = getattr(attr_value, "__condition_type__", "OR")
                     listeners[attr_name] = (condition_type, methods)
-
-                    if (
-                        hasattr(attr_value, "__is_router__")
-                        and attr_value.__is_router__
-                    ):
-                        routers.add(attr_name)
-                        possible_returns = get_possible_return_constants(attr_value)
-                        if possible_returns:
-                            router_paths[attr_name] = possible_returns
+            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
 
         setattr(cls, "_start_methods", start_methods)
         setattr(cls, "_listeners", listeners)
@@ -423,12 +345,7 @@ 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]]] = {}
@@ -444,130 +361,30 @@ class Flow(Generic[T], metaclass=FlowMeta):
         _FlowGeneric.__name__ = f"{cls.__name__}[{item.__name__}]"
         return _FlowGeneric
 
-    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
+    def __init__(self) -> None:
         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 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
+            if callable(getattr(self, method_name)) and not method_name.startswith(
+                "__"
+            ):
+                self._methods[method_name] = getattr(self, method_name)
 
     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):
-                    # 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
-
-                    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
+            return self._initial_state_T()  # type: ignore
         if self.initial_state is None:
-            return cast(T, {"id": str(uuid4())})
-
-        # Handle case where initial_state is a type (class)
-        if isinstance(self.initial_state, type):
-            if issubclass(self.initial_state, FlowState):
-                return cast(T, self.initial_state())  # Uses model defaults
-            elif issubclass(self.initial_state, BaseModel):
-                # Validate that the model has an id field
-                model_fields = getattr(self.initial_state, "model_fields", None)
-                if not model_fields or "id" not in model_fields:
-                    raise ValueError("Flow state model must have an 'id' field")
-                return cast(T, self.initial_state())  # Uses model defaults
-            elif self.initial_state is dict:
-                return cast(T, {"id": str(uuid4())})
-
-        # Handle dictionary instance case
-        if isinstance(self.initial_state, dict):
-            new_state = dict(self.initial_state)  # Copy to avoid mutations
-            if "id" not in new_state:
-                new_state["id"] = str(uuid4())
-            return cast(T, new_state)
-
-        # Handle BaseModel instance case
-        if isinstance(self.initial_state, BaseModel):
-            model = cast(BaseModel, self.initial_state)
-            if not hasattr(model, "id"):
-                raise ValueError("Flow state model must have an 'id' field")
-
-            # Create new instance with same values to avoid mutations
-            if hasattr(model, "model_dump"):
-                # Pydantic v2
-                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)}"
-        )
+            return {}  # type: ignore
+        elif isinstance(self.initial_state, type):
+            return self.initial_state()
+        else:
+            return self.initial_state
 
     @property
     def state(self) -> T:
@@ -578,163 +395,34 @@ 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):
-            # For dict states, preserve existing fields unless overridden
-            current_id = self._state.get("id")
-            # 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):
-            # For BaseModel states, preserve existing fields unless overridden
+        if isinstance(self._state, BaseModel):
+            # Structured state
             try:
-                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("_")
-                    }
 
-                # Create new state with preserved fields and updates
-                new_state = {**current_state, **inputs}
+                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"
 
-                # 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))
+                    return ModelWithExtraForbid
+
+                ModelWithExtraForbid = create_model_with_extra_forbid(
+                    self._state.__class__
+                )
+                self._state = cast(
+                    T, ModelWithExtraForbid(**{**self._state.model_dump(), **inputs})
+                )
             except ValidationError as e:
                 raise ValueError(f"Invalid inputs for structured state: {e}") from e
+        elif isinstance(self._state, dict):
+            self._state.update(inputs)
         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(
@@ -742,13 +430,9 @@ 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 and "id" not in inputs:
+        if inputs is not None:
             self._initialize_state(inputs)
-
         return asyncio.run(self.kickoff_async())
 
     async def kickoff_async(self, inputs: Optional[Dict[str, Any]] = None) -> Any:
@@ -991,32 +675,6 @@ 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

@@ -1,18 +0,0 @@
-"""
-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

@@ -1,53 +0,0 @@
-"""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

@@ -1,252 +0,0 @@
-"""
-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

@@ -1,123 +0,0 @@
-"""
-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/embedder/fastembed.py

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

src/crewai/knowledge/knowledge.py

@@ -15,20 +15,20 @@ class Knowledge(BaseModel):
     Args:
         sources: List[BaseKnowledgeSource] = Field(default_factory=list)
         storage: Optional[KnowledgeStorage] = Field(default=None)
-        embedder: Optional[Dict[str, Any]] = None
+        embedder_config: Optional[Dict[str, Any]] = None
     """
 
     sources: List[BaseKnowledgeSource] = Field(default_factory=list)
     model_config = ConfigDict(arbitrary_types_allowed=True)
     storage: Optional[KnowledgeStorage] = Field(default=None)
-    embedder: Optional[Dict[str, Any]] = None
+    embedder_config: Optional[Dict[str, Any]] = None
     collection_name: Optional[str] = None
 
     def __init__(
         self,
         collection_name: str,
         sources: List[BaseKnowledgeSource],
-        embedder: Optional[Dict[str, Any]] = None,
+        embedder_config: Optional[Dict[str, Any]] = None,
         storage: Optional[KnowledgeStorage] = None,
         **data,
     ):
@@ -37,23 +37,25 @@ class Knowledge(BaseModel):
             self.storage = storage
         else:
             self.storage = KnowledgeStorage(
-                embedder=embedder, collection_name=collection_name
+                embedder_config=embedder_config, collection_name=collection_name
             )
         self.sources = sources
         self.storage.initialize_knowledge_storage()
-        self._add_sources()
+        for source in sources:
+            source.storage = self.storage
+            source.add()
 
     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,
@@ -61,15 +63,6 @@ class Knowledge(BaseModel):
         return results
 
     def _add_sources(self):
-        try:
-            for source in self.sources:
-                source.storage = self.storage
-                source.add()
-        except Exception as e:
-            raise e
-
-    def reset(self) -> None:
-        if self.storage:
-            self.storage.reset()
-        else:
-            raise ValueError("Storage is not initialized.")
+        for source in self.sources:
+            source.storage = self.storage
+            source.add()

src/crewai/knowledge/source/base_file_knowledge_source.py

@@ -29,13 +29,7 @@ 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

@@ -8,7 +8,6 @@ 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
@@ -39,8 +38,8 @@ 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,
@@ -66,7 +65,7 @@ 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:
@@ -88,11 +87,11 @@ 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: Optional[Dict[str, Any]] = None,
+        embedder_config: Optional[Dict[str, Any]] = None,
         collection_name: Optional[str] = None,
     ):
         self.collection_name = collection_name
-        self._set_embedder_config(embedder)
+        self._set_embedder_config(embedder_config)
 
     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
+                    name=collection_name, embedding_function=self.embedder_config
                 )
             else:
                 raise Exception("Vector Database Client not initialized")
@@ -154,15 +154,9 @@ class KnowledgeStorage(BaseKnowledgeStorage):
                 filtered_ids.append(doc_id)
 
             # If we have no metadata at all, set it to None
-            final_metadata: Optional[List[Dict[str, Union[str, int, float, bool]]]] = None
-            if not all(m is None for m in filtered_metadata):
-                final_metadata = []
-                for m in filtered_metadata:
-                    if m is not None:
-                        filtered_m = {k: v for k, v in m.items() if isinstance(v, (str, int, float, bool))}
-                        final_metadata.append(filtered_m)
-                    else:
-                        final_metadata.append({"empty": True})
+            final_metadata: Optional[OneOrMany[chromadb.Metadata]] = (
+                None if all(m is None for m in filtered_metadata) else filtered_metadata
+            )
 
             self.collection.upsert(
                 documents=filtered_docs,
@@ -193,15 +187,17 @@ class KnowledgeStorage(BaseKnowledgeStorage):
             api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"
         )
 
-    def _set_embedder_config(self, embedder: Optional[Dict[str, Any]] = None) -> None:
+    def _set_embedder_config(
+        self, embedder_config: 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 = (
-            EmbeddingConfigurator().configure_embedder(embedder)
-            if embedder
+        self.embedder_config = (
+            EmbeddingConfigurator().configure_embedder(embedder_config)
+            if embedder_config
             else self._create_default_embedding_function()
         )

src/crewai/llm.py

@@ -5,17 +5,15 @@ import sys
 import threading
 import warnings
 from contextlib import contextmanager
-from typing import Any, Dict, List, Literal, Optional, Type, Union, cast
+from typing import Any, Dict, List, Optional, Union, cast
 
 from dotenv import load_dotenv
-from pydantic import BaseModel
 
 with warnings.catch_warnings():
     warnings.simplefilter("ignore", UserWarning)
     import litellm
     from litellm import Choices, get_supported_openai_params
     from litellm.types.utils import ModelResponse
-    from litellm.utils import supports_response_schema
 
 
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
@@ -130,23 +128,21 @@ class LLM:
         presence_penalty: Optional[float] = None,
         frequency_penalty: Optional[float] = None,
         logit_bias: Optional[Dict[int, float]] = None,
-        response_format: Optional[Type[BaseModel]] = None,
+        response_format: Optional[Dict[str, Any]] = None,
         seed: Optional[int] = None,
         logprobs: Optional[int] = None,
         top_logprobs: Optional[int] = None,
         base_url: Optional[str] = None,
-        api_base: Optional[str] = None,
         api_version: Optional[str] = None,
         api_key: Optional[str] = None,
         callbacks: List[Any] = [],
-        reasoning_effort: Optional[Literal["none", "low", "medium", "high"]] = None,
-        **kwargs,
     ):
         self.model = model
         self.timeout = timeout
         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
@@ -157,110 +153,47 @@ class LLM:
         self.logprobs = logprobs
         self.top_logprobs = top_logprobs
         self.base_url = base_url
-        self.api_base = api_base
         self.api_version = api_version
         self.api_key = api_key
         self.callbacks = callbacks
         self.context_window_size = 0
-        self.reasoning_effort = reasoning_effort
-        self.additional_params = kwargs
-        self.is_anthropic = self._is_anthropic_model(model)
 
         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 _is_anthropic_model(self, model: str) -> bool:
-        """Determine if the model is from Anthropic provider.
-        
-        Args:
-            model: The model identifier string.
-            
-        Returns:
-            bool: True if the model is from Anthropic, False otherwise.
-        """
-        ANTHROPIC_PREFIXES = ('anthropic/', 'claude-', 'claude/')
-        return any(prefix in model.lower() for prefix in ANTHROPIC_PREFIXES)
-
     def call(
         self,
-        messages: Union[str, List[Dict[str, str]]],
+        messages: List[Dict[str, str]],
         tools: Optional[List[dict]] = None,
         callbacks: Optional[List[Any]] = None,
         available_functions: Optional[Dict[str, Any]] = None,
-    ) -> Union[str, Any]:
-        """High-level LLM call method.
-        
-        Args:
-            messages: Input messages for the LLM.
-                     Can be a string or list of message dictionaries.
-                     If string, it will be converted to a single user message.
-                     If list, each dict must have 'role' and 'content' keys.
-            tools: Optional list of tool schemas for function calling.
-                  Each tool should define its name, description, and parameters.
-            callbacks: Optional list of callback functions to be executed
-                      during and after the LLM call.
-            available_functions: Optional dict mapping function names to callables
-                               that can be invoked by the LLM.
-        
-        Returns:
-            Union[str, Any]: Either a text response from the LLM (str) or
-                           the result of a tool function call (Any).
-        
-        Raises:
-            TypeError: If messages format is invalid
-            ValueError: If response format is not supported
-            LLMContextLengthExceededException: If input exceeds model's context limit
-        
-        Examples:
-            # Example 1: Simple string input
-            >>> response = llm.call("Return the name of a random city.")
-            >>> print(response)
-            "Paris"
-            
-            # Example 2: Message list with system and user messages
-            >>> messages = [
-            ...     {"role": "system", "content": "You are a geography expert"},
-            ...     {"role": "user", "content": "What is France's capital?"}
-            ... ]
-            >>> response = llm.call(messages)
-            >>> print(response)
-            "The capital of France is Paris."
+    ) -> str:
         """
-        # Validate parameters before proceeding with the call.
-        self._validate_call_params()
-
-        if isinstance(messages, str):
-            messages = [{"role": "user", "content": messages}]
-
-        # For O1 models, system messages are not supported.
-        # Convert any system messages into assistant messages.
-        if "o1" in self.model.lower():
-            for message in messages:
-                if message.get("role") == "system":
-                    message["role"] = "assistant"
+        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
 
+        :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
+        """
         with suppress_warnings():
             if callbacks and len(callbacks) > 0:
                 self.set_callbacks(callbacks)
 
             try:
-                # --- 1) Format messages according to provider requirements
-                formatted_messages = self._format_messages_for_provider(messages)
-
-                # --- 2) Prepare the parameters for the completion call
+                # --- 1) Make the completion call
                 params = {
                     "model": self.model,
-                    "messages": formatted_messages,
+                    "messages": messages,
                     "timeout": self.timeout,
                     "temperature": self.temperature,
                     "top_p": self.top_p,
@@ -274,20 +207,15 @@ class LLM:
                     "seed": self.seed,
                     "logprobs": self.logprobs,
                     "top_logprobs": self.top_logprobs,
-                    "api_base": self.api_base,
-                    "base_url": self.base_url,
+                    "api_base": self.base_url,
                     "api_version": self.api_version,
                     "api_key": self.api_key,
                     "stream": False,
-                    "tools": tools,
-                    "reasoning_effort": self.reasoning_effort,
-                    **self.additional_params,
+                    "tools": tools,  # pass the tool schema
                 }
 
-                # 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
@@ -295,24 +223,11 @@ class LLM:
                 text_response = response_message.content or ""
                 tool_calls = getattr(response_message, "tool_calls", [])
 
-                # --- 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
+                # --- 2) If no tool calls, return the text response
                 if not tool_calls or not available_functions:
                     return text_response
 
-                # --- 5) Handle the tool call
+                # --- 3) Handle the tool call
                 tool_call = tool_calls[0]
                 function_name = tool_call.function.name
 
@@ -327,6 +242,7 @@ class LLM:
                     try:
                         # Call the actual tool function
                         result = fn(**function_args)
+
                         return result
 
                     except Exception as e:
@@ -348,68 +264,6 @@ class LLM:
                     logging.error(f"LiteLLM call failed: {str(e)}")
                 raise
 
-    def _format_messages_for_provider(self, messages: List[Dict[str, str]]) -> List[Dict[str, str]]:
-        """Format messages according to provider requirements.
-        
-        Args:
-            messages: List of message dictionaries with 'role' and 'content' keys.
-                     Can be empty or None.
-        
-        Returns:
-            List of formatted messages according to provider requirements.
-            For Anthropic models, ensures first message has 'user' role.
-        
-        Raises:
-            TypeError: If messages is None or contains invalid message format.
-        """
-        if messages is None:
-            raise TypeError("Messages cannot be None")
-            
-        # Validate message format first
-        for msg in messages:
-            if not isinstance(msg, dict) or "role" not in msg or "content" not in msg:
-                raise TypeError("Invalid message format. Each message must be a dict with 'role' and 'content' keys")
-            
-        if not self.is_anthropic:
-            return messages
-            
-        # Anthropic requires messages to start with 'user' role
-        if not messages or messages[0]["role"] == "system":
-            # If first message is system or empty, add a placeholder user message
-            return [{"role": "user", "content": "."}, *messages]
-                
-        return messages
-
-    def _get_custom_llm_provider(self) -> str:
-        """
-        Derives the custom_llm_provider from the model string.
-        - For example, if the model is "openrouter/deepseek/deepseek-chat", returns "openrouter".
-        - If the model is "gemini/gemini-1.5-pro", returns "gemini".
-        - If there is no '/', defaults to "openai".
-        """
-        if "/" in self.model:
-            return self.model.split("/")[0]
-        return "openai"
-
-    def _validate_call_params(self) -> None:
-        """
-        Validate parameters before making a call. Currently this only checks if
-        a response_format is provided and whether the model supports it.
-        The custom_llm_provider is dynamically determined from the model:
-          - E.g., "openrouter/deepseek/deepseek-chat" yields "openrouter"
-          - "gemini/gemini-1.5-pro" yields "gemini"
-          - If no slash is present, "openai" is assumed.
-        """
-        provider = self._get_custom_llm_provider()
-        if self.response_format is not None and not supports_response_schema(
-            model=self.model,
-            custom_llm_provider=provider,
-        ):
-            raise ValueError(
-                f"The model {self.model} does not support response_format for provider '{provider}'. "
-                "Please remove response_format or use a supported model."
-            )
-
     def supports_function_calling(self) -> bool:
         try:
             params = get_supported_openai_params(model=self.model)

src/crewai/memory/entity/entity_memory.py

@@ -1,7 +1,3 @@
-from typing import Optional
-
-from pydantic import PrivateAttr
-
 from crewai.memory.entity.entity_memory_item import EntityMemoryItem
 from crewai.memory.memory import Memory
 from crewai.memory.storage.rag_storage import RAGStorage
@@ -14,15 +10,13 @@ class EntityMemory(Memory):
     Inherits from the Memory class.
     """
 
-    _memory_provider: Optional[str] = PrivateAttr()
-
     def __init__(self, crew=None, embedder_config=None, storage=None, path=None):
-        if crew and hasattr(crew, "memory_config") and crew.memory_config is not None:
-            memory_provider = crew.memory_config.get("provider")
+        if hasattr(crew, "memory_config") and crew.memory_config is not None:
+            self.memory_provider = crew.memory_config.get("provider")
         else:
-            memory_provider = None
+            self.memory_provider = None
 
-        if memory_provider == "mem0":
+        if self.memory_provider == "mem0":
             try:
                 from crewai.memory.storage.mem0_storage import Mem0Storage
             except ImportError:
@@ -42,13 +36,11 @@ class EntityMemory(Memory):
                     path=path,
                 )
             )
-
-        super().__init__(storage=storage)
-        self._memory_provider = memory_provider
+        super().__init__(storage)
 
     def save(self, item: EntityMemoryItem) -> None:  # type: ignore # BUG?: Signature of "save" incompatible with supertype "Memory"
         """Saves an entity item into the SQLite storage."""
-        if self._memory_provider == "mem0":
+        if self.memory_provider == "mem0":
             data = f"""
             Remember details about the following entity:
             Name: {item.name}

src/crewai/memory/long_term/long_term_memory.py

@@ -17,7 +17,7 @@ class LongTermMemory(Memory):
     def __init__(self, storage=None, path=None):
         if not storage:
             storage = LTMSQLiteStorage(db_path=path) if path else LTMSQLiteStorage()
-        super().__init__(storage=storage)
+        super().__init__(storage)
 
     def save(self, item: LongTermMemoryItem) -> None:  # type: ignore # BUG?: Signature of "save" incompatible with supertype "Memory"
         metadata = item.metadata

src/crewai/memory/memory.py

@@ -1,19 +1,15 @@
 from typing import Any, Dict, List, Optional
 
-from pydantic import BaseModel
+from crewai.memory.storage.rag_storage import RAGStorage
 
 
-class Memory(BaseModel):
+class Memory:
     """
     Base class for memory, now supporting agent tags and generic metadata.
     """
 
-    embedder_config: Optional[Dict[str, Any]] = None
-
-    storage: Any
-
-    def __init__(self, storage: Any, **data: Any):
-        super().__init__(storage=storage, **data)
+    def __init__(self, storage: RAGStorage):
+        self.storage = storage
 
     def save(
         self,

src/crewai/memory/short_term/short_term_memory.py

@@ -1,7 +1,5 @@
 from typing import Any, Dict, Optional
 
-from pydantic import PrivateAttr
-
 from crewai.memory.memory import Memory
 from crewai.memory.short_term.short_term_memory_item import ShortTermMemoryItem
 from crewai.memory.storage.rag_storage import RAGStorage
@@ -16,15 +14,13 @@ class ShortTermMemory(Memory):
     MemoryItem instances.
     """
 
-    _memory_provider: Optional[str] = PrivateAttr()
-
     def __init__(self, crew=None, embedder_config=None, storage=None, path=None):
-        if crew and hasattr(crew, "memory_config") and crew.memory_config is not None:
-            memory_provider = crew.memory_config.get("provider")
+        if hasattr(crew, "memory_config") and crew.memory_config is not None:
+            self.memory_provider = crew.memory_config.get("provider")
         else:
-            memory_provider = None
+            self.memory_provider = None
 
-        if memory_provider == "mem0":
+        if self.memory_provider == "mem0":
             try:
                 from crewai.memory.storage.mem0_storage import Mem0Storage
             except ImportError:
@@ -43,8 +39,7 @@ class ShortTermMemory(Memory):
                     path=path,
                 )
             )
-        super().__init__(storage=storage)
-        self._memory_provider = memory_provider
+        super().__init__(storage)
 
     def save(
         self,
@@ -53,7 +48,7 @@ class ShortTermMemory(Memory):
         agent: Optional[str] = None,
     ) -> None:
         item = ShortTermMemoryItem(data=value, metadata=metadata, agent=agent)
-        if self._memory_provider == "mem0":
+        if self.memory_provider == "mem0":
             item.data = f"Remember the following insights from Agent run: {item.data}"
 
         super().save(value=item.data, metadata=item.metadata, agent=item.agent)

src/crewai/memory/storage/base_rag_storage.py

@@ -13,7 +13,7 @@ class BaseRAGStorage(ABC):
         self,
         type: str,
         allow_reset: bool = True,
-        embedder_config: Optional[Dict[str, Any]] = None,
+        embedder_config: Optional[Any] = None,
         crew: Any = None,
     ):
         self.type = type

src/crewai/memory/storage/kickoff_task_outputs_storage.py

@@ -1,17 +1,12 @@
 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:
     """
@@ -19,24 +14,15 @@ class KickoffTaskOutputsSQLiteStorage:
     """
 
     def __init__(
-        self, db_path: Optional[str] = None
+        self, db_path: str = f"{db_storage_path()}/latest_kickoff_task_outputs.db"
     ) -> 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) -> 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.
+    def _initialize_db(self):
+        """
+        Initializes the SQLite database and creates LTM table
         """
         try:
             with sqlite3.connect(self.db_path) as conn:
@@ -57,9 +43,10 @@ class KickoffTaskOutputsSQLiteStorage:
 
                 conn.commit()
         except sqlite3.Error as e:
-            error_msg = DatabaseError.format_error(DatabaseError.INIT_ERROR, e)
-            logger.error(error_msg)
-            raise DatabaseOperationError(error_msg, e)
+            self._printer.print(
+                content=f"SAVING KICKOFF TASK OUTPUTS ERROR: An error occurred during database initialization: {e}",
+                color="red",
+            )
 
     def add(
         self,
@@ -68,22 +55,9 @@ 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(
                     """
@@ -102,31 +76,21 @@ class KickoffTaskOutputsSQLiteStorage:
                 )
                 conn.commit()
         except sqlite3.Error as e:
-            error_msg = DatabaseError.format_error(DatabaseError.SAVE_ERROR, e)
-            logger.error(error_msg)
-            raise DatabaseOperationError(error_msg, e)
+            self._printer.print(
+                content=f"SAVING KICKOFF TASK OUTPUTS ERROR: An error occurred during database initialization: {e}",
+                color="red",
+            )
 
     def update(
         self,
         task_index: int,
-        **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.
+        **kwargs,
+    ):
+        """
+        Updates an existing row in the latest_kickoff_task_outputs table based on task_index.
         """
         try:
             with sqlite3.connect(self.db_path) as conn:
-                conn.execute("BEGIN TRANSACTION")
                 cursor = conn.cursor()
 
                 fields = []
@@ -146,23 +110,14 @@ class KickoffTaskOutputsSQLiteStorage:
                 conn.commit()
 
                 if cursor.rowcount == 0:
-                    logger.warning(f"No row found with task_index {task_index}. No update performed.")
+                    self._printer.print(
+                        f"No row found with task_index {task_index}. No update performed.",
+                        color="red",
+                    )
         except sqlite3.Error as e:
-            error_msg = DatabaseError.format_error(DatabaseError.UPDATE_ERROR, e)
-            logger.error(error_msg)
-            raise DatabaseOperationError(error_msg, e)
+            self._printer.print(f"UPDATE KICKOFF TASK OUTPUTS ERROR: {e}", color="red")
 
-    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.
-        """
+    def load(self) -> Optional[List[Dict[str, Any]]]:
         try:
             with sqlite3.connect(self.db_path) as conn:
                 cursor = conn.cursor()
@@ -189,26 +144,23 @@ class KickoffTaskOutputsSQLiteStorage:
                 return results
 
         except sqlite3.Error as e:
-            error_msg = DatabaseError.format_error(DatabaseError.LOAD_ERROR, e)
-            logger.error(error_msg)
-            raise DatabaseOperationError(error_msg, e)
+            self._printer.print(
+                content=f"LOADING KICKOFF TASK OUTPUTS ERROR: An error occurred while querying kickoff task outputs: {e}",
+                color="red",
+            )
+            return None
 
-    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.
+    def delete_all(self):
+        """
+        Deletes all rows from the latest_kickoff_task_outputs table.
         """
         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:
-            error_msg = DatabaseError.format_error(DatabaseError.DELETE_ERROR, e)
-            logger.error(error_msg)
-            raise DatabaseOperationError(error_msg, e)
+            self._printer.print(
+                content=f"ERROR: Failed to delete all kickoff task outputs: {e}",
+                color="red",
+            )

src/crewai/memory/storage/ltm_sqlite_storage.py

@@ -1,6 +1,5 @@
 import json
 import sqlite3
-from pathlib import Path
 from typing import Any, Dict, List, Optional, Union
 
 from crewai.utilities import Printer
@@ -13,15 +12,10 @@ class LTMSQLiteStorage:
     """
 
     def __init__(
-        self, db_path: Optional[str] = None
+        self, db_path: str = f"{db_storage_path()}/long_term_memory_storage.db"
     ) -> 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/memory/storage/rag_storage.py

@@ -6,17 +6,12 @@ import shutil
 import uuid
 from typing import Any, Dict, List, Optional
 
-from chromadb.api import ClientAPI, Collection
-from chromadb.api.types import Documents, Embeddings, Metadatas
+from chromadb.api import ClientAPI
 
 from crewai.memory.storage.base_rag_storage import BaseRAGStorage
 from crewai.utilities import EmbeddingConfigurator
 from crewai.utilities.constants import MAX_FILE_NAME_LENGTH
 from crewai.utilities.paths import db_storage_path
-from crewai.utilities.exceptions.embedding_exceptions import (
-    EmbeddingConfigurationError,
-    EmbeddingInitializationError
-)
 
 
 @contextlib.contextmanager
@@ -37,24 +32,15 @@ def suppress_logging(
 
 
 class RAGStorage(BaseRAGStorage):
-    """RAG-based Storage implementation using ChromaDB for vector storage and retrieval.
-
-    This class extends BaseRAGStorage to handle embeddings for memory entries,
-    improving search efficiency through vector similarity.
-
-    Attributes:
-        app: ChromaDB client instance
-        collection: ChromaDB collection for storing embeddings
-        type: Type of memory storage
-        allow_reset: Whether memory reset is allowed
-        path: Custom storage path for the database
+    """
+    Extends Storage to handle embeddings for memory entries, improving
+    search efficiency.
     """
 
     app: ClientAPI | None = None
-    collection: Any = None
 
     def __init__(
-        self, type: str, allow_reset: bool = True, embedder_config: Dict[str, Any] | None = None, crew: Any = None, path: str | None = None
+        self, type, allow_reset=True, embedder_config=None, crew=None, path=None
     ):
         super().__init__(type, allow_reset, embedder_config, crew)
         agents = crew.agents if crew else []
@@ -64,6 +50,7 @@ class RAGStorage(BaseRAGStorage):
         self.storage_file_name = self._build_storage_file_name(type, agents)
 
         self.type = type
+
         self.allow_reset = allow_reset
         self.path = path
         self._initialize_app()
@@ -72,36 +59,26 @@ class RAGStorage(BaseRAGStorage):
         configurator = EmbeddingConfigurator()
         self.embedder_config = configurator.configure_embedder(self.embedder_config)
 
-    def _initialize_app(self) -> None:
-        """Initialize the ChromaDB client and collection.
-
-        Raises:
-            RuntimeError: If ChromaDB client initialization fails
-            EmbeddingConfigurationError: If embedding configuration is invalid
-            EmbeddingInitializationError: If embedding function fails to initialize
-        """
+    def _initialize_app(self):
         import chromadb
         from chromadb.config import Settings
 
         self._set_embedder_config()
-        try:
-            self.app = chromadb.PersistentClient(
-                path=self.path if self.path else self.storage_file_name,
-                settings=Settings(allow_reset=self.allow_reset),
-            )
-            if not self.app:
-                raise RuntimeError("Failed to initialize ChromaDB client")
+        chroma_client = chromadb.PersistentClient(
+            path=self.path if self.path else self.storage_file_name,
+            settings=Settings(allow_reset=self.allow_reset),
+        )
 
-            try:
-                self.collection = self.app.get_collection(
-                    name=self.type, embedding_function=self.embedder_config
-                )
-            except Exception:
-                self.collection = self.app.create_collection(
-                    name=self.type, embedding_function=self.embedder_config
-                )
-        except Exception as e:
-            raise RuntimeError(f"Failed to initialize ChromaDB: {str(e)}")
+        self.app = chroma_client
+
+        try:
+            self.collection = self.app.get_collection(
+                name=self.type, embedding_function=self.embedder_config
+            )
+        except Exception:
+            self.collection = self.app.create_collection(
+                name=self.type, embedding_function=self.embedder_config
+            )
 
     def _sanitize_role(self, role: str) -> str:
         """
@@ -124,21 +101,12 @@ class RAGStorage(BaseRAGStorage):
         return f"{base_path}/{file_name}"
 
     def save(self, value: Any, metadata: Dict[str, Any]) -> None:
-        """Save a value with metadata to the memory storage.
-
-        Args:
-            value: The text content to store
-            metadata: Additional metadata for the stored content
-
-        Raises:
-            EmbeddingInitializationError: If embedding generation fails
-        """
         if not hasattr(self, "app") or not hasattr(self, "collection"):
             self._initialize_app()
         try:
             self._generate_embedding(value, metadata)
         except Exception as e:
-            raise EmbeddingInitializationError(self.type, str(e))
+            logging.error(f"Error during {self.type} save: {str(e)}")
 
     def search(
         self,
@@ -146,18 +114,7 @@ class RAGStorage(BaseRAGStorage):
         limit: int = 3,
         filter: Optional[dict] = None,
         score_threshold: float = 0.35,
-    ) -> List[Dict[str, Any]]:
-        """Search for similar content in memory.
-
-        Args:
-            query: The search query text
-            limit: Maximum number of results to return
-            filter: Optional filter criteria
-            score_threshold: Minimum similarity score threshold
-
-        Returns:
-            List of matching results with metadata and scores
-        """
+    ) -> List[Any]:
         if not hasattr(self, "app"):
             self._initialize_app()
 
@@ -181,50 +138,37 @@ class RAGStorage(BaseRAGStorage):
             logging.error(f"Error during {self.type} search: {str(e)}")
             return []
 
-    def _generate_embedding(self, text: str, metadata: Optional[Dict[str, Any]] = None) -> Any:
-        """Generate and store embeddings for the given text.
-
-        Args:
-            text: The text to generate embeddings for
-            metadata: Optional additional metadata to store with the embeddings
-
-        Returns:
-            Any: The generated embedding or None if only storing
-        """
+    def _generate_embedding(self, text: str, metadata: Dict[str, Any]) -> None:  # type: ignore
         if not hasattr(self, "app") or not hasattr(self, "collection"):
             self._initialize_app()
 
-        try:
-            self.collection.add(
-                documents=[text],
-                metadatas=[metadata or {}],
-                ids=[str(uuid.uuid4())],
-            )
-            return None
-        except Exception as e:
-            raise EmbeddingInitializationError(self.type, f"Failed to generate embedding: {str(e)}")
+        self.collection.add(
+            documents=[text],
+            metadatas=[metadata or {}],
+            ids=[str(uuid.uuid4())],
+        )
 
     def reset(self) -> None:
-        """Reset the memory storage by clearing the database and removing files.
-
-        Raises:
-            RuntimeError: If memory reset fails and allow_reset is False
-            EmbeddingConfigurationError: If embedding configuration is invalid during reinitialization
-        """
         try:
             if self.app:
                 self.app.reset()
-                storage_path = self.path if self.path else db_storage_path()
-                db_dir = os.path.join(storage_path, self.type)
-                if os.path.exists(db_dir):
-                    shutil.rmtree(db_dir)
+                shutil.rmtree(f"{db_storage_path()}/{self.type}")
                 self.app = None
                 self.collection = None
         except Exception as e:
             if "attempt to write a readonly database" in str(e):
-                # Ignore this specific error as it's expected in some environments
+                # Ignore this specific error
                 pass
             else:
-                if not self.allow_reset:
-                    raise RuntimeError(f"Failed to reset {self.type} memory: {str(e)}")
-                logging.error(f"Error during {self.type} memory reset: {str(e)}")
+                raise Exception(
+                    f"An error occurred while resetting the {self.type} memory: {e}"
+                )
+
+    def _create_default_embedding_function(self):
+        from chromadb.utils.embedding_functions.openai_embedding_function import (
+            OpenAIEmbeddingFunction,
+        )
+
+        return OpenAIEmbeddingFunction(
+            api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"
+        )

src/crewai/project/crew_base.py

@@ -1,5 +1,4 @@
 import inspect
-import logging
 from pathlib import Path
 from typing import Any, Callable, Dict, TypeVar, cast
 
@@ -8,16 +7,12 @@ from dotenv import load_dotenv
 
 load_dotenv()
 
-logging.basicConfig(level=logging.WARNING)
-
 T = TypeVar("T", bound=type)
 
 """Base decorator for creating crew classes with configuration and function management."""
 
-
 def CrewBase(cls: T) -> T:
     """Wraps a class with crew functionality and configuration management."""
-
     class WrappedClass(cls):  # type: ignore
         is_crew_class: bool = True  # type: ignore
 
@@ -31,9 +26,16 @@ def CrewBase(cls: T) -> T:
 
         def __init__(self, *args, **kwargs):
             super().__init__(*args, **kwargs)
-            self.load_configurations()
+
+            agents_config_path = self.base_directory / self.original_agents_config_path
+            tasks_config_path = self.base_directory / self.original_tasks_config_path
+
+            self.agents_config = self.load_yaml(agents_config_path)
+            self.tasks_config = self.load_yaml(tasks_config_path)
+
             self.map_all_agent_variables()
             self.map_all_task_variables()
+
             # Preserve all decorated functions
             self._original_functions = {
                 name: method
@@ -49,6 +51,7 @@ def CrewBase(cls: T) -> T:
                     ]
                 )
             }
+
             # Store specific function types
             self._original_tasks = self._filter_functions(
                 self._original_functions, "is_task"
@@ -66,44 +69,6 @@ def CrewBase(cls: T) -> T:
                 self._original_functions, "is_kickoff"
             )
 
-        def load_configurations(self):
-            """Load agent and task configurations from YAML files."""
-            if isinstance(self.original_agents_config_path, str):
-                agents_config_path = (
-                    self.base_directory / self.original_agents_config_path
-                )
-                try:
-                    self.agents_config = self.load_yaml(agents_config_path)
-                except FileNotFoundError:
-                    logging.warning(
-                        f"Agent config file not found at {agents_config_path}. "
-                        "Proceeding with empty agent configurations."
-                    )
-                    self.agents_config = {}
-            else:
-                logging.warning(
-                    "No agent configuration path provided. Proceeding with empty agent configurations."
-                )
-                self.agents_config = {}
-
-            if isinstance(self.original_tasks_config_path, str):
-                tasks_config_path = (
-                    self.base_directory / self.original_tasks_config_path
-                )
-                try:
-                    self.tasks_config = self.load_yaml(tasks_config_path)
-                except FileNotFoundError:
-                    logging.warning(
-                        f"Task config file not found at {tasks_config_path}. "
-                        "Proceeding with empty task configurations."
-                    )
-                    self.tasks_config = {}
-            else:
-                logging.warning(
-                    "No task configuration path provided. Proceeding with empty task configurations."
-                )
-                self.tasks_config = {}
-
         @staticmethod
         def load_yaml(config_path: Path):
             try:

src/crewai/task.py

@@ -9,13 +9,11 @@ from copy import copy
 from hashlib import md5
 from pathlib import Path
 from typing import (
-    AbstractSet,
     Any,
     Callable,
     ClassVar,
     Dict,
     List,
-    Mapping,
     Optional,
     Set,
     Tuple,
@@ -111,7 +109,7 @@ class Task(BaseModel):
         description="Task output, it's final result after being executed", default=None
     )
     tools: Optional[List[BaseTool]] = Field(
-        default_factory=lambda: [],
+        default_factory=list,
         description="Tools the agent is limited to use for this task.",
     )
     id: UUID4 = Field(
@@ -127,7 +125,7 @@ class Task(BaseModel):
         description="A converter class used to export structured output",
         default=None,
     )
-    processed_by_agents: Set[str] = Field(default_factory=lambda: set())
+    processed_by_agents: Set[str] = Field(default_factory=set)
     guardrail: Optional[Callable[[TaskOutput], Tuple[bool, Any]]] = Field(
         default=None,
         description="Function to validate task output before proceeding to next task",
@@ -425,10 +423,6 @@ class Task(BaseModel):
         if self.callback:
             self.callback(self.output)
 
-        crew = self.agent.crew  # type: ignore[union-attr]
-        if crew and crew.task_callback and crew.task_callback != self.callback:
-            crew.task_callback(self.output)
-
         if self._execution_span:
             self._telemetry.task_ended(self._execution_span, self, agent.crew)
             self._execution_span = None
@@ -437,9 +431,7 @@ class Task(BaseModel):
             content = (
                 json_output
                 if json_output
-                else pydantic_output.model_dump_json()
-                if pydantic_output
-                else result
+                else pydantic_output.model_dump_json() if pydantic_output else result
             )
             self._save_file(content)
 
@@ -460,7 +452,7 @@ class Task(BaseModel):
         return "\n".join(tasks_slices)
 
     def interpolate_inputs_and_add_conversation_history(
-        self, inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]]
+        self, inputs: Dict[str, Union[str, int, float]]
     ) -> None:
         """Interpolate inputs into the task description, expected output, and output file path.
            Add conversation history if present.
@@ -532,9 +524,7 @@ class Task(BaseModel):
             )
 
     def interpolate_only(
-        self,
-        input_string: Optional[str],
-        inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]],
+        self, input_string: Optional[str], inputs: Dict[str, Union[str, int, float]]
     ) -> str:
         """Interpolate placeholders (e.g., {key}) in a string while leaving JSON untouched.
 
@@ -542,39 +532,17 @@ class Task(BaseModel):
             input_string: The string containing template variables to interpolate.
                          Can be None or empty, in which case an empty string is returned.
             inputs: Dictionary mapping template variables to their values.
-                   Supported value types are strings, integers, floats, and dicts/lists
-                   containing only these types and other nested dicts/lists.
+                   Supported value types are strings, integers, and floats.
+                   If input_string is empty or has no placeholders, inputs can be empty.
 
         Returns:
             The interpolated string with all template variables replaced with their values.
             Empty string if input_string is None or empty.
 
         Raises:
-            ValueError: If a value contains unsupported types
+            ValueError: If a required template variable is missing from inputs.
+            KeyError: If a template variable is not found in the inputs dictionary.
         """
-
-        # Validation function for recursive type checking
-        def validate_type(value: Any) -> None:
-            if value is None:
-                return
-            if isinstance(value, (str, int, float, bool)):
-                return
-            if isinstance(value, (dict, list)):
-                for item in value.values() if isinstance(value, dict) else value:
-                    validate_type(item)
-                return
-            raise ValueError(
-                f"Unsupported type {type(value).__name__} in inputs. "
-                "Only str, int, float, bool, dict, and list are allowed."
-            )
-
-        # Validate all input values
-        for key, value in inputs.items():
-            try:
-                validate_type(value)
-            except ValueError as e:
-                raise ValueError(f"Invalid value for key '{key}': {str(e)}") from e
-
         if input_string is None or not input_string:
             return ""
         if "{" not in input_string and "}" not in input_string:
@@ -583,7 +551,15 @@ class Task(BaseModel):
             raise ValueError(
                 "Inputs dictionary cannot be empty when interpolating variables"
             )
+
         try:
+            # Validate input types
+            for key, value in inputs.items():
+                if not isinstance(value, (str, int, float)):
+                    raise ValueError(
+                        f"Value for key '{key}' must be a string, integer, or float, got {type(value).__name__}"
+                    )
+
             escaped_string = input_string.replace("{", "{{").replace("}", "}}")
 
             for key in inputs.keys():
@@ -608,56 +584,37 @@ class Task(BaseModel):
         self.delegations += 1
 
     def copy(
-        self,
-        *,
-        include: Optional[AbstractSet[int] | AbstractSet[str] | Mapping[int, Any] | Mapping[str, Any]] = None,
-        exclude: Optional[AbstractSet[int] | AbstractSet[str] | Mapping[int, Any] | Mapping[str, Any]] = None,
-        update: Optional[Dict[str, Any]] = None,
-        deep: bool = False,
-    ) -> "Task":
-        """Create a copy of the Task."""
-        exclude_set = {"id", "agent", "context", "tools"}
-        if exclude:
-            if isinstance(exclude, (AbstractSet, set)):
-                exclude_set.update(str(x) for x in exclude)
-            elif isinstance(exclude, Mapping):
-                exclude_set.update(str(x) for x in exclude.keys())
-        
-        copied_task = super().copy(
-            include=include,
-            exclude=exclude_set,
-            update=update,
-            deep=deep,
-        )
-        
-        copied_task.id = uuid.uuid4()
-        copied_task.agent = None
-        copied_task.context = None
-        copied_task.tools = []
-        
-        return copied_task
-
-    def copy_with_agents(
         self, agents: List["BaseAgent"], task_mapping: Dict[str, "Task"]
     ) -> "Task":
-        """Create a copy of the Task with agent references."""
-        copied_task = self.copy()
+        """Create a deep copy of the Task."""
+        exclude = {
+            "id",
+            "agent",
+            "context",
+            "tools",
+        }
+
+        copied_data = self.model_dump(exclude=exclude)
+        copied_data = {k: v for k, v in copied_data.items() if v is not None}
+
+        cloned_context = (
+            [task_mapping[context_task.key] for context_task in self.context]
+            if self.context
+            else None
+        )
 
         def get_agent_by_role(role: str) -> Union["BaseAgent", None]:
             return next((agent for agent in agents if agent.role == role), None)
 
-        if self.agent:
-            copied_task.agent = get_agent_by_role(self.agent.role)
+        cloned_agent = get_agent_by_role(self.agent.role) if self.agent else None
+        cloned_tools = copy(self.tools) if self.tools else []
 
-        if self.context:
-            copied_task.context = [
-                task_mapping[context_task.key]
-                for context_task in self.context
-                if context_task.key in task_mapping
-            ]
-
-        if self.tools:
-            copied_task.tools = copy(self.tools)
+        copied_task = Task(
+            **copied_data,
+            context=cloned_context,
+            agent=cloned_agent,
+            tools=cloned_tools,
+        )
 
         return copied_task
 
@@ -695,32 +652,19 @@ class Task(BaseModel):
             return OutputFormat.PYDANTIC
         return OutputFormat.RAW
 
-    def _save_file(self, result: Union[Dict, str, Any]) -> None:
+    def _save_file(self, result: Any) -> None:
         """Save task output to a file.
 
-        Note:
-            For cross-platform file writing, especially on Windows, consider using FileWriterTool
-            from the crewai_tools package:
-                pip install 'crewai[tools]'
-                from crewai_tools import FileWriterTool
-
         Args:
             result: The result to save to the file. Can be a dict or any stringifiable object.
 
         Raises:
             ValueError: If output_file is not set
-            RuntimeError: If there is an error writing to the file. For cross-platform
-                compatibility, especially on Windows, use FileWriterTool from crewai_tools
-                package.
+            RuntimeError: If there is an error writing to the file
         """
         if self.output_file is None:
             raise ValueError("output_file is not set.")
 
-        FILEWRITER_RECOMMENDATION = (
-            "For cross-platform file writing, especially on Windows, "
-            "use FileWriterTool from crewai_tools package."
-        )
-
         try:
             resolved_path = Path(self.output_file).expanduser().resolve()
             directory = resolved_path.parent
@@ -736,12 +680,7 @@ class Task(BaseModel):
                 else:
                     file.write(str(result))
         except (OSError, IOError) as e:
-            raise RuntimeError(
-                "\n".join([
-                    f"Failed to save output file: {e}",
-                    FILEWRITER_RECOMMENDATION
-                ])
-            )
+            raise RuntimeError(f"Failed to save output file: {e}")
         return None
 
     def __repr__(self):

src/crewai/tasks/task_output.py

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

src/crewai/tools/agent_tools/add_image_tool.py

@@ -7,11 +7,11 @@ from crewai.utilities import I18N
 
 i18n = I18N()
 
-
 class AddImageToolSchema(BaseModel):
     image_url: str = Field(..., description="The URL or path of the image to add")
     action: Optional[str] = Field(
-        default=None, description="Optional context or question about the image"
+        default=None,
+        description="Optional context or question about the image"
     )
 
 
@@ -36,7 +36,10 @@ class AddImageTool(BaseTool):
                 "image_url": {
                     "url": image_url,
                 },
-            },
+            }
         ]
 
-        return {"role": "user", "content": content}
+        return {
+            "role": "user",
+            "content": content
+        }

src/crewai/tools/agent_tools/base_agent_tools.py

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

src/crewai/tools/base_tool.py

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

src/crewai/tools/structured_tool.py

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

src/crewai/tools/tool_usage.py

@@ -1,14 +1,9 @@
 import ast
 import datetime
-import json
 import time
 from difflib import SequenceMatcher
-from json import JSONDecodeError
 from textwrap import dedent
-from typing import Any, Dict, List, Optional, Union
-
-import json5
-from json_repair import repair_json
+from typing import Any, List, Union
 
 import crewai.utilities.events as events
 from crewai.agents.tools_handler import ToolsHandler
@@ -24,15 +19,7 @@ try:
     import agentops  # type: ignore
 except ImportError:
     agentops = None
-OPENAI_BIGGER_MODELS = [
-    "gpt-4",
-    "gpt-4o",
-    "o1-preview",
-    "o1-mini",
-    "o1",
-    "o3",
-    "o3-mini",
-]
+OPENAI_BIGGER_MODELS = ["gpt-4", "gpt-4o", "o1-preview", "o1-mini", "o1", "o3", "o3-mini"]
 
 
 class ToolUsageErrorException(Exception):
@@ -93,7 +80,7 @@ class ToolUsage:
             self._max_parsing_attempts = 2
             self._remember_format_after_usages = 4
 
-    def parse_tool_calling(self, tool_string: str):
+    def parse(self, tool_string: str):
         """Parse the tool string and return the tool calling."""
         return self._tool_calling(tool_string)
 
@@ -107,6 +94,7 @@ class ToolUsage:
             self.task.increment_tools_errors()
             return error
 
+        # BUG? The code below seems to be unreachable
         try:
             tool = self._select_tool(calling.tool_name)
         except Exception as e:
@@ -128,7 +116,7 @@ class ToolUsage:
                     self._printer.print(content=f"\n\n{error}\n", color="red")
                 return error
 
-        return f"{self._use(tool_string=tool_string, tool=tool, calling=calling)}"
+        return f"{self._use(tool_string=tool_string, tool=tool, calling=calling)}"  # type: ignore # BUG?: "_use" of "ToolUsage" does not return a value (it only ever returns None)
 
     def _use(
         self,
@@ -361,13 +349,13 @@ class ToolUsage:
         tool_name = self.action.tool
         tool = self._select_tool(tool_name)
         try:
-            arguments = self._validate_tool_input(self.action.tool_input)
-
+            tool_input = self._validate_tool_input(self.action.tool_input)
+            arguments = ast.literal_eval(tool_input)
         except Exception:
             if raise_error:
                 raise
             else:
-                return ToolUsageErrorException(
+                return ToolUsageErrorException(  # type: ignore # Incompatible return value type (got "ToolUsageErrorException", expected "ToolCalling | InstructorToolCalling")
                     f'{self._i18n.errors("tool_arguments_error")}'
                 )
 
@@ -375,14 +363,14 @@ class ToolUsage:
             if raise_error:
                 raise
             else:
-                return ToolUsageErrorException(
+                return ToolUsageErrorException(  # type: ignore # Incompatible return value type (got "ToolUsageErrorException", expected "ToolCalling | InstructorToolCalling")
                     f'{self._i18n.errors("tool_arguments_error")}'
                 )
 
         return ToolCalling(
             tool_name=tool.name,
             arguments=arguments,
-            log=tool_string,
+            log=tool_string,  # type: ignore
         )
 
     def _tool_calling(
@@ -408,55 +396,57 @@ class ToolUsage:
                 )
             return self._tool_calling(tool_string)
 
-    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
+    def _validate_tool_input(self, tool_input: str) -> str:
         try:
-            arguments = json.loads(tool_input)
-            if isinstance(arguments, dict):
-                return arguments
-        except (JSONDecodeError, TypeError):
-            pass  # Continue to the next parsing attempt
+            ast.literal_eval(tool_input)
+            return tool_input
+        except Exception:
+            # Clean and ensure the string is properly enclosed in braces
+            tool_input = tool_input.strip()
+            if not tool_input.startswith("{"):
+                tool_input = "{" + tool_input
+            if not tool_input.endswith("}"):
+                tool_input += "}"
 
-        # 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
+            # Manually split the input into key-value pairs
+            entries = tool_input.strip("{} ").split(",")
+            formatted_entries = []
 
-        # 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
+            for entry in entries:
+                if ":" not in entry:
+                    continue  # Skip malformed entries
+                key, value = entry.split(":", 1)
 
-        # 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")
+                # Remove extraneous white spaces and quotes, replace single quotes
+                key = key.strip().strip('"').replace("'", '"')
+                value = value.strip()
 
-        # If all parsing attempts fail, raise an error
-        raise Exception(
-            "Tool input must be a valid dictionary in JSON or Python literal format"
-        )
+                # Handle replacement of single quotes at the start and end of the value string
+                if value.startswith("'") and value.endswith("'"):
+                    value = value[1:-1]  # Remove single quotes
+                    value = (
+                        '"' + value.replace('"', '\\"') + '"'
+                    )  # Re-encapsulate with double quotes
+                elif value.isdigit():  # Check if value is a digit, hence integer
+                    value = value
+                elif value.lower() in [
+                    "true",
+                    "false",
+                ]:  # Check for boolean and null values
+                    value = value.lower().capitalize()
+                elif value.lower() == "null":
+                    value = "None"
+                else:
+                    # Assume the value is a string and needs quotes
+                    value = '"' + value.replace('"', '\\"') + '"'
+
+                # Rebuild the entry with proper quoting
+                formatted_entry = f'"{key}": {value}'
+                formatted_entries.append(formatted_entry)
+
+            # Reconstruct the JSON string
+            new_json_string = "{" + ", ".join(formatted_entries) + "}"
+            return new_json_string
 
     def on_tool_error(self, tool: Any, tool_calling: ToolCalling, e: Exception) -> None:
         event_data = self._prepare_event_data(tool, tool_calling)

src/crewai/translations/en.json

@@ -9,13 +9,13 @@
     "task": "\nCurrent Task: {input}\n\nBegin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it!\n\nThought:",
     "memory": "\n\n# Useful context: \n{memory}",
     "role_playing": "You are {role}. {backstory}\nYour personal goal is: {goal}",
-    "tools": "\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\n{tools}\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: you should always think about what to do\nAction: the action to take, only one name of [{tool_names}], just the name, exactly as it's written.\nAction Input: the input to the action, just a simple JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce all necessary information is gathered, return the following format:\n\n```\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n```",
-    "no_tools": "\nTo give my best complete final answer to the task respond using the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!",
-    "format": "I MUST either use a tool (use one at time) OR give my best final answer not both at the same time. When responding, I must use the following format:\n\n```\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action, dictionary enclosed in curly braces\nObservation: the result of the action\n```\nThis Thought/Action/Action Input/Result can repeat N times. Once I know the final answer, I must return the following format:\n\n```\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described\n\n```",
-    "final_answer_format": "If you don't need to use any more tools, you must give your best complete final answer, make sure it satisfies the expected criteria, use the EXACT format below:\n\n```\nThought: I now can give a great answer\nFinal Answer: my best complete final answer to the task.\n\n```",
-    "format_without_tools": "\nSorry, I didn't use the right format. I MUST either use a tool (among the available ones), OR give my best final answer.\nHere is the expected format I must follow:\n\n```\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n```\n This Thought/Action/Action Input/Result process can repeat N times. Once I know the final answer, I must return the following format:\n\n```\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described\n\n```",
+    "tools": "\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\n{tools}\n\nUse the following format:\n\nThought: you should always think about what to do\nAction: the action to take, only one name of [{tool_names}], just the name, exactly as it's written.\nAction Input: the input to the action, just a simple python dictionary, enclosed in curly braces, using \" to wrap keys and values.\nObservation: the result of the action\n\nOnce all necessary information is gathered:\n\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n",
+    "no_tools": "\nTo give my best complete final answer to the task use the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!",
+    "format": "I MUST either use a tool (use one at time) OR give my best final answer not both at the same time. To Use the following format:\n\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action, dictionary enclosed in curly braces\nObservation: the result of the action\n... (this Thought/Action/Action Input/Result can repeat N times)\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described\n\n",
+    "final_answer_format": "If you don't need to use any more tools, you must give your best complete final answer, make sure it satisfies the expected criteria, use the EXACT format below:\n\nThought: I now can give a great answer\nFinal Answer: my best complete final answer to the task.\n\n",
+    "format_without_tools": "\nSorry, I didn't use the right format. I MUST either use a tool (among the available ones), OR give my best final answer.\nI just remembered the expected format I must follow:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Result can repeat N times)\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described\n\n",
     "task_with_context": "{task}\n\nThis is the context you're working with:\n{context}",
-    "expected_output": "\nThis is the expected criteria for your final answer: {expected_output}\nyou MUST return the actual complete content as the final answer, not a summary.",
+    "expected_output": "\nThis is the expect criteria for your final answer: {expected_output}\nyou MUST return the actual complete content as the final answer, not a summary.",
     "human_feedback": "You got human feedback on your work, re-evaluate it and give a new Final Answer when ready.\n {human_feedback}",
     "getting_input": "This is the agent's final answer: {final_answer}\n\n",
     "summarizer_system_message": "You are a helpful assistant that summarizes text.",
@@ -24,8 +24,7 @@
     "manager_request": "Your best answer to your coworker asking you this, accounting for the context shared.",
     "formatted_task_instructions": "Ensure your final answer contains only the content in the following format: {output_format}\n\nEnsure the final output does not include any code block markers like ```json or ```python.",
     "human_feedback_classification": "Determine if the following feedback indicates that the user is satisfied or if further changes are needed. Respond with 'True' if further changes are needed, or 'False' if the user is satisfied. **Important** Do not include any additional commentary outside of your 'True' or 'False' response.\n\nFeedback: \"{feedback}\"",
-    "conversation_history_instruction": "You are a member of a crew collaborating to achieve a common goal. Your task is a specific action that contributes to this larger objective. For additional context, please review the conversation history between you and the user that led to the initiation of this crew. Use any relevant information or feedback from the conversation to inform your task execution and ensure your response aligns with both the immediate task and the crew's overall goals.",
-    "feedback_instructions": "User feedback: {feedback}\nInstructions: Use this feedback to enhance the next output iteration.\nNote: Do not respond or add commentary."
+    "conversation_history_instruction": "You are a member of a crew collaborating to achieve a common goal. Your task is a specific action that contributes to this larger objective. For additional context, please review the conversation history between you and the user that led to the initiation of this crew. Use any relevant information or feedback from the conversation to inform your task execution and ensure your response aligns with both the immediate task and the crew's overall goals."
   },
   "errors": {
     "force_final_answer_error": "You can't keep going, here is the best final answer you generated:\n\n {formatted_answer}",
@@ -44,7 +43,7 @@
     "ask_question": "Ask a specific question to one of the following coworkers: {coworkers}\nThe input to this tool should be the coworker, the question you have for them, and ALL necessary context to ask the question properly, they know nothing about the question, so share absolute everything you know, don't reference things but instead explain them.",
     "add_image": {
       "name": "Add image to content",
-      "description": "See image to understand its content, you can optionally ask a question about the image",
+      "description": "See image to understand it's content, you can optionally ask a question about the image",
       "default_action": "Please provide a detailed description of this image, including all visual elements, context, and any notable details you can observe."
     }
   }

src/crewai/utilities/constants.py

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

src/crewai/utilities/converter.py

@@ -26,24 +26,17 @@ class Converter(OutputConverter):
             if self.llm.supports_function_calling():
                 return self._create_instructor().to_pydantic()
             else:
-                response = self.llm.call(
+                return 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)
-            raise ConverterError(
-                f"Failed to convert text into a Pydantic model due to the following error: {e}"
+            return ConverterError(
+                f"Failed to convert text into a pydantic model due to the following error: {e}"
             )
 
     def to_json(self, current_attempt=1):
@@ -73,6 +66,7 @@ class Converter(OutputConverter):
             llm=self.llm,
             model=self.model,
             content=self.text,
+            instructions=self.instructions,
         )
         return inst
 
@@ -193,15 +187,10 @@ def convert_with_instructions(
 
 
 def get_conversion_instructions(model: Type[BaseModel], llm: Any) -> str:
-    instructions = "Please convert the following text into valid JSON."
+    instructions = "I'm gonna convert this raw text into valid JSON."
     if llm.supports_function_calling():
         model_schema = PydanticSchemaParser(model=model).get_schema()
-        instructions += (
-            f"\n\nThe JSON should follow this schema:\n```json\n{model_schema}\n```"
-        )
-    else:
-        model_description = generate_model_description(model)
-        instructions += f"\n\nThe JSON should follow this format:\n{model_description}"
+        instructions = f"{instructions}\n\nThe json should have the following structure, with the following keys:\n{model_schema}"
     return instructions
 
 
@@ -241,13 +230,9 @@ def generate_model_description(model: Type[BaseModel]) -> str:
         origin = get_origin(field_type)
         args = get_args(field_type)
 
-        if origin is Union or (origin is None and len(args) > 0):
-            # Handle both Union and the new '|' syntax
+        if origin is Union and type(None) in args:
             non_none_args = [arg for arg in args if arg is not type(None)]
-            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)}]]"
+            return f"Optional[{describe_field(non_none_args[0])}]"
         elif origin is list:
             return f"List[{describe_field(args[0])}]"
         elif origin is dict:
@@ -256,10 +241,8 @@ 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)
-        elif hasattr(field_type, "__name__"):
-            return field_type.__name__
         else:
-            return str(field_type)
+            return field_type.__name__
 
     fields = model.__annotations__
     field_descriptions = [

src/crewai/utilities/embedding_configurator.py

@@ -1,15 +1,9 @@
 import os
-from typing import Any, Dict, List, Optional, cast
+from typing import Any, Dict, cast
 
 from chromadb import Documents, EmbeddingFunction, Embeddings
 from chromadb.api.types import validate_embedding_function
 
-from crewai.utilities.exceptions.embedding_exceptions import (
-    EmbeddingConfigurationError,
-    EmbeddingProviderError,
-    EmbeddingInitializationError
-)
-
 
 class EmbeddingConfigurator:
     def __init__(self):
@@ -27,7 +21,7 @@ class EmbeddingConfigurator:
 
     def configure_embedder(
         self,
-        embedder_config: Optional[Dict[str, Any]] = None,
+        embedder_config: Dict[str, Any] | None = None,
     ) -> EmbeddingFunction:
         """Configures and returns an embedding function based on the provided config."""
         if embedder_config is None:
@@ -42,47 +36,42 @@ class EmbeddingConfigurator:
                 validate_embedding_function(provider)
                 return provider
             except Exception as e:
-                raise EmbeddingConfigurationError(f"Invalid custom embedding function: {str(e)}")
+                raise ValueError(f"Invalid custom embedding function: {str(e)}")
 
-        if not provider or provider not in self.embedding_functions:
-            raise EmbeddingProviderError(str(provider), list(self.embedding_functions.keys()))
+        if provider not in self.embedding_functions:
+            raise Exception(
+                f"Unsupported embedding provider: {provider}, supported providers: {list(self.embedding_functions.keys())}"
+            )
 
-        try:
-            return self.embedding_functions[str(provider)](config, model_name)
-        except Exception as e:
-            raise EmbeddingInitializationError(str(provider), str(e))
+        return self.embedding_functions[provider](config, model_name)
 
     @staticmethod
-    def _create_default_embedding_function() -> EmbeddingFunction:
-        from crewai.utilities.constants import DEFAULT_EMBEDDING_PROVIDER, DEFAULT_EMBEDDING_MODEL
-        
-        provider = os.getenv("CREWAI_EMBEDDING_PROVIDER", DEFAULT_EMBEDDING_PROVIDER)
-        model = os.getenv("CREWAI_EMBEDDING_MODEL", DEFAULT_EMBEDDING_MODEL)
-        
-        if provider == "openai":
-            api_key = os.getenv("OPENAI_API_KEY")
-            if not api_key:
-                raise EmbeddingConfigurationError("OpenAI API key is required but not provided")
-            from chromadb.utils.embedding_functions.openai_embedding_function import OpenAIEmbeddingFunction
-            return OpenAIEmbeddingFunction(api_key=api_key, model_name=model)
-        elif provider == "ollama":
-            from chromadb.utils.embedding_functions.ollama_embedding_function import OllamaEmbeddingFunction
-            url = os.getenv("CREWAI_OLLAMA_URL", "http://localhost:11434/api/embeddings")
-            return OllamaEmbeddingFunction(url=url, model_name=model)
-        else:
-            raise EmbeddingProviderError(provider, ["openai", "ollama"])
+    def _create_default_embedding_function():
+        from chromadb.utils.embedding_functions.openai_embedding_function import (
+            OpenAIEmbeddingFunction,
+        )
+
+        return OpenAIEmbeddingFunction(
+            api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"
+        )
 
     @staticmethod
-    def _configure_openai(config: Dict[str, Any], model_name: str) -> EmbeddingFunction:
-        from chromadb.utils.embedding_functions.openai_embedding_function import OpenAIEmbeddingFunction
+    def _configure_openai(config, model_name):
+        from chromadb.utils.embedding_functions.openai_embedding_function import (
+            OpenAIEmbeddingFunction,
+        )
+
         return OpenAIEmbeddingFunction(
             api_key=config.get("api_key") or os.getenv("OPENAI_API_KEY"),
             model_name=model_name,
         )
 
     @staticmethod
-    def _configure_azure(config: Dict[str, Any], model_name: str) -> EmbeddingFunction:
-        from chromadb.utils.embedding_functions.openai_embedding_function import OpenAIEmbeddingFunction
+    def _configure_azure(config, model_name):
+        from chromadb.utils.embedding_functions.openai_embedding_function import (
+            OpenAIEmbeddingFunction,
+        )
+
         return OpenAIEmbeddingFunction(
             api_key=config.get("api_key"),
             api_base=config.get("api_base"),
@@ -92,62 +81,79 @@ class EmbeddingConfigurator:
         )
 
     @staticmethod
-    def _configure_ollama(config: Dict[str, Any], model_name: str) -> EmbeddingFunction:
-        from chromadb.utils.embedding_functions.ollama_embedding_function import OllamaEmbeddingFunction
+    def _configure_ollama(config, model_name):
+        from chromadb.utils.embedding_functions.ollama_embedding_function import (
+            OllamaEmbeddingFunction,
+        )
+
         return OllamaEmbeddingFunction(
             url=config.get("url", "http://localhost:11434/api/embeddings"),
             model_name=model_name,
         )
 
     @staticmethod
-    def _configure_vertexai(config: Dict[str, Any], model_name: str) -> EmbeddingFunction:
-        from chromadb.utils.embedding_functions.google_embedding_function import GoogleVertexEmbeddingFunction
+    def _configure_vertexai(config, model_name):
+        from chromadb.utils.embedding_functions.google_embedding_function import (
+            GoogleVertexEmbeddingFunction,
+        )
+
         return GoogleVertexEmbeddingFunction(
             model_name=model_name,
             api_key=config.get("api_key"),
         )
 
     @staticmethod
-    def _configure_google(config: Dict[str, Any], model_name: str) -> EmbeddingFunction:
-        from chromadb.utils.embedding_functions.google_embedding_function import GoogleGenerativeAiEmbeddingFunction
+    def _configure_google(config, model_name):
+        from chromadb.utils.embedding_functions.google_embedding_function import (
+            GoogleGenerativeAiEmbeddingFunction,
+        )
+
         return GoogleGenerativeAiEmbeddingFunction(
             model_name=model_name,
             api_key=config.get("api_key"),
         )
 
     @staticmethod
-    def _configure_cohere(config: Dict[str, Any], model_name: str) -> EmbeddingFunction:
-        from chromadb.utils.embedding_functions.cohere_embedding_function import CohereEmbeddingFunction
+    def _configure_cohere(config, model_name):
+        from chromadb.utils.embedding_functions.cohere_embedding_function import (
+            CohereEmbeddingFunction,
+        )
+
         return CohereEmbeddingFunction(
             model_name=model_name,
             api_key=config.get("api_key"),
         )
 
     @staticmethod
-    def _configure_bedrock(config: Dict[str, Any], model_name: str) -> EmbeddingFunction:
-        from chromadb.utils.embedding_functions.amazon_bedrock_embedding_function import AmazonBedrockEmbeddingFunction
+    def _configure_bedrock(config, model_name):
+        from chromadb.utils.embedding_functions.amazon_bedrock_embedding_function import (
+            AmazonBedrockEmbeddingFunction,
+        )
+
         return AmazonBedrockEmbeddingFunction(
             session=config.get("session"),
         )
 
     @staticmethod
-    def _configure_huggingface(config: Dict[str, Any], model_name: str) -> EmbeddingFunction:
-        from chromadb.utils.embedding_functions.huggingface_embedding_function import HuggingFaceEmbeddingServer
+    def _configure_huggingface(config, model_name):
+        from chromadb.utils.embedding_functions.huggingface_embedding_function import (
+            HuggingFaceEmbeddingServer,
+        )
+
         return HuggingFaceEmbeddingServer(
             url=config.get("api_url"),
         )
 
     @staticmethod
-    def _configure_watson(config: Dict[str, Any], model_name: str) -> EmbeddingFunction:
+    def _configure_watson(config, model_name):
         try:
             import ibm_watsonx_ai.foundation_models as watson_models
             from ibm_watsonx_ai import Credentials
             from ibm_watsonx_ai.metanames import EmbedTextParamsMetaNames as EmbedParams
         except ImportError as e:
-            raise EmbeddingConfigurationError(
-                "IBM Watson dependencies are not installed. Please install them to use Watson embedding.",
-                provider="watson"
-            )
+            raise ImportError(
+                "IBM Watson dependencies are not installed. Please install them to use Watson embedding."
+            ) from e
 
         class WatsonEmbeddingFunction(EmbeddingFunction):
             def __call__(self, input: Documents) -> Embeddings:
@@ -172,6 +178,7 @@ class EmbeddingConfigurator:
                     embeddings = embedding.embed_documents(input)
                     return cast(Embeddings, embeddings)
                 except Exception as e:
-                    raise EmbeddingInitializationError("watson", str(e))
+                    print("Error during Watson embedding:", e)
+                    raise e
 
         return WatsonEmbeddingFunction()

src/crewai/utilities/errors.py

@@ -1,39 +0,0 @@
-"""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

@@ -92,34 +92,13 @@ class TaskEvaluator:
         """
 
         output_training_data = training_data[agent_id]
+
         final_aggregated_data = ""
-
-        for iteration, data in output_training_data.items():
-            improved_output = data.get("improved_output")
-            initial_output = data.get("initial_output")
-            human_feedback = data.get("human_feedback")
-
-            if not all([improved_output, initial_output, human_feedback]):
-                missing_fields = [
-                    field
-                    for field in ["improved_output", "initial_output", "human_feedback"]
-                    if not data.get(field)
-                ]
-                error_msg = (
-                    f"Critical training data error: Missing fields ({', '.join(missing_fields)}) "
-                    f"for agent {agent_id} in iteration {iteration}.\n"
-                    "This indicates a broken training process. "
-                    "Cannot proceed with evaluation.\n"
-                    "Please check your training implementation."
-                )
-                raise ValueError(error_msg)
-
+        for _, data in output_training_data.items():
             final_aggregated_data += (
-                f"Iteration: {iteration}\n"
-                f"Initial Output:\n{initial_output}\n\n"
-                f"Human Feedback:\n{human_feedback}\n\n"
-                f"Improved Output:\n{improved_output}\n\n"
-                "------------------------------------------------\n\n"
+                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"
             )
 
         evaluation_query = (

src/crewai/utilities/exceptions/embedding_exceptions.py

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

src/crewai/utilities/file_handler.py

@@ -1,64 +1,30 @@
-import json
 import os
 import pickle
 from datetime import datetime
-from typing import Union
 
 
 class FileHandler:
-    """Handler for file operations supporting both JSON and text-based logging.
-    
-    Args:
-        file_path (Union[bool, str]): Path to the log file or boolean flag
-    """
+    """take care of file operations, currently it only logs messages to a file"""
 
-    def __init__(self, file_path: Union[bool, str]):
-        self._initialize_path(file_path)
-        
-    def _initialize_path(self, file_path: Union[bool, str]):
-        if file_path is True:  # File path is boolean True
+    def __init__(self, file_path):
+        if isinstance(file_path, bool):
             self._path = os.path.join(os.curdir, "logs.txt")
-        
-        elif isinstance(file_path, str):  # File path is a string
-            if file_path.endswith((".json", ".txt")):
-                self._path = file_path  # No modification if the file ends with .json or .txt
-            else:
-                self._path = file_path + ".txt"  # Append .txt if the file doesn't end with .json or .txt
-        
+        elif isinstance(file_path, str):
+            self._path = file_path
         else:
-            raise ValueError("file_path must be a string or boolean.")  # Handle the case where file_path isn't valid
-        
+            raise ValueError("file_path must be either a boolean or a string.")
+
     def log(self, **kwargs):
-        try:
-            now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-            log_entry = {"timestamp": now, **kwargs}
+        now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+        message = (
+            f"{now}: "
+            + ", ".join([f'{key}="{value}"' for key, value in kwargs.items()])
+            + "\n"
+        )
+        with open(self._path, "a", encoding="utf-8") as file:
+            file.write(message + "\n")
 
-            if self._path.endswith(".json"):
-                # Append log in JSON format
-                with open(self._path, "a", encoding="utf-8") as file:
-                    # If the file is empty, start with a list; else, append to it
-                    try:
-                        # Try reading existing content to avoid overwriting
-                        with open(self._path, "r", encoding="utf-8") as read_file:
-                            existing_data = json.load(read_file)
-                            existing_data.append(log_entry)
-                    except (json.JSONDecodeError, FileNotFoundError):
-                        # If no valid JSON or file doesn't exist, start with an empty list
-                        existing_data = [log_entry]
-                    
-                    with open(self._path, "w", encoding="utf-8") as write_file:
-                        json.dump(existing_data, write_file, indent=4)
-                        write_file.write("\n")
-            
-            else:
-                # Append log in plain text format
-                message = f"{now}: " + ", ".join([f"{key}=\"{value}\"" for key, value in kwargs.items()]) + "\n"
-                with open(self._path, "a", encoding="utf-8") as file:
-                    file.write(message)
 
-        except Exception as e:
-            raise ValueError(f"Failed to log message: {str(e)}")
-        
 class PickleHandler:
     def __init__(self, file_name: str) -> None:
         """

src/crewai/utilities/internal_instructor.py

@@ -11,10 +11,12 @@ 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()
@@ -29,7 +31,10 @@ class InternalInstructor:
             import instructor
             from litellm import completion
 
-            self._client = instructor.from_litellm(completion)
+            self._client = instructor.from_litellm(
+                completion,
+                mode=instructor.Mode.TOOLS,
+            )
 
     def to_json(self):
         model = self.to_pydantic()
@@ -37,6 +42,8 @@ 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/llm_utils.py

@@ -1,7 +1,11 @@
 import os
 from typing import Any, Dict, List, Optional, Union
 
+from packaging import version
+
 from crewai.cli.constants import DEFAULT_LLM_MODEL, ENV_VARS, LITELLM_PARAMS
+from crewai.cli.utils import read_toml
+from crewai.cli.version import get_crewai_version
 from crewai.llm import LLM
 
 
@@ -53,7 +57,6 @@ def create_llm(
         timeout: Optional[float] = getattr(llm_value, "timeout", None)
         api_key: Optional[str] = getattr(llm_value, "api_key", None)
         base_url: Optional[str] = getattr(llm_value, "base_url", None)
-        api_base: Optional[str] = getattr(llm_value, "api_base", None)
 
         created_llm = LLM(
             model=model,
@@ -63,7 +66,6 @@ def create_llm(
             timeout=timeout,
             api_key=api_key,
             base_url=base_url,
-            api_base=api_base,
         )
         return created_llm
     except Exception as e:
@@ -103,18 +105,8 @@ def _llm_via_environment_or_fallback() -> Optional[LLM]:
     callbacks: List[Any] = []
 
     # Optional base URL from env
-    base_url = (
-        os.environ.get("BASE_URL")
-        or os.environ.get("OPENAI_API_BASE")
-        or os.environ.get("OPENAI_BASE_URL")
-    )
-
-    api_base = os.environ.get("API_BASE") or os.environ.get("AZURE_API_BASE")
-
-    # Synchronize base_url and api_base if one is populated and the other is not
-    if base_url and not api_base:
-        api_base = base_url
-    elif api_base and not base_url:
+    api_base = os.environ.get("OPENAI_API_BASE") or os.environ.get("OPENAI_BASE_URL")
+    if api_base:
         base_url = api_base
 
     # Initialize llm_params dictionary
@@ -127,7 +119,6 @@ def _llm_via_environment_or_fallback() -> Optional[LLM]:
         "timeout": timeout,
         "api_key": api_key,
         "base_url": base_url,
-        "api_base": api_base,
         "api_version": api_version,
         "presence_penalty": presence_penalty,
         "frequency_penalty": frequency_penalty,

src/crewai/utilities/paths.py

@@ -5,18 +5,14 @@ import appdirs
 
 """Path management utilities for CrewAI storage and configuration."""
 
-def db_storage_path() -> str:
-    """Returns the path for SQLite database storage.
-
-    Returns:
-        str: Full path to the SQLite database file
-    """
+def db_storage_path():
+    """Returns the path for database storage."""
     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 str(data_dir)
+    return data_dir
 
 
 def get_project_directory_name():
@@ -28,4 +24,4 @@ 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

@@ -21,16 +21,6 @@ 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)
 
@@ -54,18 +44,3 @@ 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 Dict, List, Type, Union, get_args, get_origin
+from typing import Type, Union, get_args, get_origin
 
 from pydantic import BaseModel
 
@@ -10,83 +10,40 @@ 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 "{\n" + self._get_model_schema(self.model) + "\n}"
+        return self._get_model_schema(self.model)
 
-    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_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_field_type(self, field, depth: int) -> str:
+    def _get_field_type(self, field, depth) -> str:
         field_type = field.annotation
-        origin = get_origin(field_type)
-
-        if origin in {list, List}:
+        if get_origin(field_type) is list:
             list_item_type = get_args(field_type)[0]
-            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}]"
+            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}]"
             else:
-                # 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}]]"
+                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)
         else:
-            # 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__
+            return getattr(field_type, "__name__", str(field_type))

src/crewai/utilities/token_counter_callback.py

@@ -23,15 +23,11 @@ class TokenCalcHandler(CustomLogger):
 
         with warnings.catch_warnings():
             warnings.simplefilter("ignore", UserWarning)
-            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
-                        )
+            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
+                )

src/crewai/utilities/training_handler.py

@@ -1,5 +1,3 @@
-import os
-
 from crewai.utilities.file_handler import PickleHandler
 
 
@@ -31,8 +29,3 @@ class CrewTrainingHandler(PickleHandler):
             data[agent_id] = {train_iteration: new_data}
 
         self.save(data)
-
-    def clear(self) -> None:
-        """Clear the training data by removing the file or resetting its contents."""
-        if os.path.exists(self.file_path):
-            self.save({})

tests/agent_test.py

@@ -10,7 +10,6 @@ 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
@@ -115,6 +114,35 @@ 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(
@@ -1183,7 +1211,7 @@ def test_agent_max_retry_limit():
             [
                 mock.call(
                     {
-                        "input": "Say the word: Hi\n\nThis is the expected criteria for your final answer: The word: Hi\nyou MUST return the actual complete content as the final answer, not a summary.",
+                        "input": "Say the word: Hi\n\nThis is the expect criteria for your final answer: The word: Hi\nyou MUST return the actual complete content as the final answer, not a summary.",
                         "tool_names": "",
                         "tools": "",
                         "ask_for_human_input": True,
@@ -1191,7 +1219,7 @@ def test_agent_max_retry_limit():
                 ),
                 mock.call(
                     {
-                        "input": "Say the word: Hi\n\nThis is the expected criteria for your final answer: The word: Hi\nyou MUST return the actual complete content as the final answer, not a summary.",
+                        "input": "Say the word: Hi\n\nThis is the expect criteria for your final answer: The word: Hi\nyou MUST return the actual complete content as the final answer, not a summary.",
                         "tool_names": "",
                         "tools": "",
                         "ask_for_human_input": True,
@@ -1467,7 +1495,7 @@ def test_agent_execute_task_basic():
         role="test role",
         goal="test goal",
         backstory="test backstory",
-        llm="gpt-4o-mini",
+        llm=LLM(model="gpt-3.5-turbo"),
     )
 
     task = Task(
@@ -1601,181 +1629,3 @@ 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()

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